diff --git a/Readme.md b/Readme.md index d3ef29473c800..2770c49a58ca8 100644 --- a/Readme.md +++ b/Readme.md @@ -47,7 +47,7 @@ IoTDB's features are as following: 7. Intense integration with Open Source Ecosystem. IoTDB supports Hadoop, Spark, etc. analysis ecosystems and Grafana visualization tool. -For the latest information about IoTDB, please visit our [IoTDB official website](http://tsfile.org/index) (will transfer to iotdb.apache.org in the future). +For the latest information about IoTDB, please visit our [IoTDB official website](http://iotdb.apache.org/#/). # Prerequisites @@ -58,38 +58,81 @@ To use IoTDB, you need to have: If you want to use Hadoop or Spark to analyze IoTDB data file (called as TsFile), you need to compile the hadoop and spark modules. - # Quick Start -This short guide will walk you through the basic process of using IoTDB. For a more-complete guide, please visit our website's [Document Part](http://tsfile.org/document). +This short guide will walk you through the basic process of using IoTDB. For a more-complete guide, please visit our website's [Document Part](http://iotdb.apache.org/#/Documents/Quick%20Start). -## Build +### Installation from source code + +Use git to get IoTDB source code: -If you are not the first time that building IoTDB, remember deleting the following files: ``` -rm -rf iotdb/iotdb/data/ -rm -rf iotdb/iotdb/lib/ +Shell > git clone https://github.com/apache/incubator-iotdb.git ``` -Then you can build IoTDB using Maven in current folder: +Or: ``` -mvn clean package -Dmaven.test.skip=true +Shell > git clone git@github.com:apache/incubator-iotdb.git +``` + +Now suppose your directory is like this: + ``` +> pwd +/User/workspace/incubator-iotdb -(__NOTICE:__ Remember that you have to use -Dmaven.test.skip=true before you run tests, it is because some IT (integration test) requires jars in iotdb-cli/cli/lib/, and the folder is empty before you run `mvn package`. After that, you can run `mvn test` as long as you do not run `mvn clean`. For more details, see: [How to test IoTDB -](https://github.com/thulab/iotdb/wiki/How-to-test-IoTDB) ) +> ls -l +incubator-iotdb/ <-- root path +| ++- iotdb/ +| ++- jdbc/ +| ++- tsile/ +| +... +| ++- pom.xml +``` + +Let $IOTDB_HOME = /User/workspace/incubator-iotdb/iotdb/iotdb/ + +If you are not the first time that building IoTDB, remember deleting the following files: + +``` +> rm -rf $IOTDB_HOME/data/ +> rm -rf $IOTDB_HOME/lib/ +``` + +Then under the root path of incubator-iotdb, you can build IoTDB using Maven: + +``` +> pwd +/User/workspace/incubator-iotdb + +> mvn clean package -pl iotdb -am -Dmaven.test.skip=true +``` If successful, you will see the the following text in the terminal: ``` +[INFO] ------------------------------------------------------------------------ +[INFO] Reactor Summary: +[INFO] +[INFO] IoTDB Root ......................................... SUCCESS [ 7.020 s] +[INFO] TsFile ............................................. SUCCESS [ 10.486 s] +[INFO] Service-rpc ........................................ SUCCESS [ 3.717 s] +[INFO] IoTDB Jdbc ......................................... SUCCESS [ 3.076 s] +[INFO] IoTDB .............................................. SUCCESS [ 8.258 s] +[INFO] ------------------------------------------------------------------------ [INFO] BUILD SUCCESS +[INFO] ------------------------------------------------------------------------ ``` Otherwise, you may need to check the error statements and fix the problems. After build, the IoTDB project will be at the folder "iotdb/iotdb". The folder will include the following contents: - ``` iotdb/iotdb/ <-- root path | @@ -98,11 +141,9 @@ iotdb/iotdb/ <-- root path +- conf/ <-- configuration files | +- lib/ <-- project dependencies -| -+- LICENSE <-- LICENSE ``` -> NOTE: We also provide already built JARs and project at [http://tsfile.org/download](http://tsfile.org/download) instead of build the jar package yourself. + ## Configure @@ -110,7 +151,7 @@ Before starting to use IoTDB, you need to config the configuration files first. In total, we provide users three kinds of configurations module: environment config module (iotdb-env.bat, iotdb-env.sh), system config module (tsfile-format.properties, iotdb-engine.properties) and log config module (logback.xml). All of these kinds of configuration files are put in iotdb/config folder. -For more, you are advised to check our website [document page](http://tsfile.org/document). The forth chapter in User Guide Document will give you the details. +For more, you are advised to check our website [document page](http://iotdb.apache.org/#/Documents/Quick%20Start). The forth chapter in User Guide Document will give you the details. ## Start @@ -120,10 +161,10 @@ After that we start the server. Running the startup script: ``` # Unix/OS X -> ./bin/start-server.sh +> $IOTDB_HOME/bin/start-server.sh # Windows -> bin\start-server.bat +> $IOTDB_HOME\bin\start-server.bat ``` ### Stop Server @@ -132,10 +173,10 @@ The server can be stopped with ctrl-C or the following script: ``` # Unix/ OS X -> ./bin/stop-server.sh +> $IOTDB_HOME/bin/stop-server.sh # Windows -> bin\stop-server.bat +> $IOTDB_HOME\bin\stop-server.bat ``` ### Start Client @@ -146,10 +187,10 @@ Now let's trying to read and write some data from IoTDB using our Client. To sta cd cli/cli # Unix/OS X -> ./bin/start-client.sh -h -p -u -pw +> $IOTDB_HOME/bin/start-client.sh -h -p -u # Windows -> bin\start-client.bat -h -p -u -pw +> $IOTDB_HOME\bin\start-client.bat -h -p -u ``` > NOTE: In the system, we set a default user in IoTDB named 'root'. The default password for 'root' is 'root'. You can use this default user if you are making the first try or you didn't create users by yourself. @@ -162,7 +203,7 @@ The command line client is interactive so if everything is ready you should see | | .--.|_/ | | \_| | | `. \ | |_) | | | / .'`\ \ | | | | | | | __'. _| |_| \__. | _| |_ _| |_.' /_| |__) | -|_____|'.__.' |_____| |______.'|_______/ version 0.7.0 +|_____|'.__.' |_____| |______.'|_______/ version x.x.x IoTDB> login successfully @@ -219,7 +260,7 @@ execute successfully. If your session looks similar to what's above, congrats, your IoTDB is operational! -For more on what commands are supported by IoTDB SQL, see our website [document page](http://tsfile.org/document). The eighth chapter in User Guide Document will give you help. +For more on what commands are supported by IoTDB SQL, see our website [document page](http://iotdb.apache.org/#/Documents/Quick%20Start). The eighth chapter in User Guide Document will give you help. # Usage of import-csv.sh @@ -238,6 +279,8 @@ CREATE TIMESERIES root.fit.p.s1 WITH DATATYPE=INT32,ENCODING=RLE; ### Run import shell ``` +cd cli/cli + # Unix/OS X > ./bin/import-csv.sh -h -p -u -pw -f @@ -253,6 +296,8 @@ csvInsertError.error ### Run export shell ``` +cd cli/cli + # Unix/OS X > ./bin/export-csv.sh -h -p -u -pw -td [-tf ] diff --git a/docs/Community-History&Vision.md b/docs/Community-History&Vision.md new file mode 100644 index 0000000000000..46c3cc907d834 --- /dev/null +++ b/docs/Community-History&Vision.md @@ -0,0 +1,3 @@ +# History&Vision + +Comming Soon. diff --git a/docs/Community-Powered By.md b/docs/Community-Powered By.md new file mode 100644 index 0000000000000..f29d0c70e7a76 --- /dev/null +++ b/docs/Community-Powered By.md @@ -0,0 +1,21 @@ + + +- [Powered By](#powered-by) + - [Project and Product names using "IoTDB"](#project-and-product-names-using-iotdb) + - [Companies and Organizations](#companies-and-organizations) + + +## Powered By + +### Project and Product names using "IoTDB" + +Organizations creating products and projects using Apache IoTDB, along with associated marketing materials, should take care to respect the trademark in “Apache IoTDB” and its logo. Please refer to [ASF Trademarks Guidance](https://www.apache.org/foundation/marks/) and associated [FAQ](https://www.apache.org/foundation/marks/faq/) for comprehensive and authoritative guidance on proper usage of ASF trademarks. +It is recommended to not include “IoTDB” in any names to prevent potential trademark issue with the IoTDB project. +As an example, names like “IoTDB BigDataProduct” should not be used, as the name include “IoTDB” in general. The above links, however, describe some exceptions, like for names such as “BigDataProduct, powered by Apache IoTDB” or “BigDataProduct for Apache IoTDB”. In summary, any names contain "Apache IoTDB" as a whole are acceptable. +A common practice you can take is to create software identifiers (Maven coordinates, module names, etc.) like “iotdb-tool”. These are permitted. Nominative use of trademarks in descriptions is also allowed, as in “BigDataProduct is a subproduct for Apache IoTDB”. + +### Companies and Organizations +To add yourself to the list, please email dev@iotdb.apache.org with your organization name, URL, a list of IoTDB components you are using, and a short description of your use case. + +- School of Software (Tsinghua University), and National Engineering Laboratery for Big Data Software that initially launched IoTDB + - We have both graduate students and a team of professional software engineers working on the stack diff --git a/docs/Community-Project Committers.md b/docs/Community-Project Committers.md new file mode 100644 index 0000000000000..70b40ef46f138 --- /dev/null +++ b/docs/Community-Project Committers.md @@ -0,0 +1,22 @@ +# Project Committers + +| Name | Organization | +| :----------- | -------------: | +| Jianmin Wang | Tsinghua University | +|Xiangdong Huang | Tsinghua University | +| Jun Yuan | Tsinghua University | +| Chen Wang | Tsinghua University | +| Jialin Qiao | Tsinghua University | +| Jinrui Zhang | Tsinghua University | +| Rong Kang | Tsinghua University | +| Tian Jiang | Tsinghua University | +| Shuo Zhang | K2Data Company | +| Lei Rui | Tsinghua University | +| Rui Liu | Tsinghua University | +| Gaofei Cao | Tsinghua University | +| Xinyi Zhao | Tsinghua University | +| Yi Xu | Tsinghua University | +| Dongfang Mao | Tsinghua University | +| Tianan li | Tsinghua University | +| Yue Su | Tsinghua University | +| Hui Da | Lenovo | diff --git a/docs/Development.md b/docs/Development.md new file mode 100644 index 0000000000000..cd6fec33fe201 --- /dev/null +++ b/docs/Development.md @@ -0,0 +1,176 @@ + + +- [Have Questions](#have-questions) + - [Mailing Lists](#mailing-lists) + - [JIRA issues](#jira-issues) +- [How to contribute](#how-to-contribute) + - [Becoming a committer](#becoming-a-committer) + - [Contributing by Helping Other Users](#contributing-by-helping-other-users) + - [Contributing by Testing Releases](#contributing-by-testing-releases) + - [Contributing by Reviewing Changes](#contributing-by-reviewing-changes) + - [Contributing by Documentation Changes](#contributing-by-documentation-changes) + - [Contributing Bug Reports](#contributing-bug-reports) + - [Contributing Code Changes](#contributing-code-changes) + - [Cloning source code](#cloning-source-code) + - [JIRA](#jira) + - [Pull Request](#pull-request) + - [The Review Process](#the-review-process) + - [Closing Your Pull Request / JIRA](#closing-your-pull-request--jira) + - [Code Style](#code-style) + + + +# Have Questions + +## Mailing Lists + +It is recommended to use our mailing lists to ask for help, report issues or contribute to the project. + +* dev@iotdb.apache.org is for anyone who want to contribute codes to IoTDB or have usage questions for IoTDB. + +Some quick tips when using email: +* For error logs or long code examples, please use GitHub gist and include only few lines of the pertinent code / log within the email. +* No jobs, sales, or solicitation is permitted on the Apache IoTDB mailing lists. + +PS. To subscribe our mail list, you can send an email to dev-subscribe@iotdb.incubator.apache.org and you will receive a "confirm subscribe to dev@iotdb.apache.org" email, following the steps to confirm your subscription. + +## JIRA issues + +The project tracks issues and new features on [JIRA issues](https://issues.apache.org/jira/projects/IOTDB/issues). You can create a new issue to report a bug, request a new feature or provide your custmon issue. + +# How to contribute + +## Becoming a committer + +To become a committer, you should first be active in our community so that most of our existing committers recognize you. Pushing codes and creating pull requests is just one of committer's rights. Moreover, it is committer's duty to help new uesrs on mail list, test new releases and improve documentation. + +### Contributing by Helping Other Users + +Since Apache IoTDB always attracts new users, it would be great if you can help them by answering questions on the dev@iotdb.apache.org mail list. We regard it as a valuable contribution. Also, the more questions you answer, the more poeple know you. Popularity is one of the necessary conditions to be a committer. + +Contributors should subscribe to our mailing list to catch up the latest progress. + +### Contributing by Testing Releases + +IoTDB's new release is visible to everyone, members of the community can vote to accept these releases on the dev@iotdb.apache.org mailing list. Users of IoTDB will be invited to try out on their workloads and provide feedback on any performance or correctness issues found in the newer release. + +### Contributing by Reviewing Changes + +Changes to IoTDB source codes are made through Github pull request. Anyone can review and comment on these changes. Reviewing others' pull requests can help you comprehend how a bug is fixed or a new feature is added. Besides, Learning directly from source code will give you a deeper understanding of how IoTDB system works and where its bottlenecks lie. You can help by reviewing the changes, asking questions and pointing out issues. + +### Contributing by Documentation Changes + +To propose a change to release documentation (that is, docs that appear under ), edit the Markdown source files in Iotdb’s docs/ directory(`documentation-EN` branch). The process to propose a doc change is otherwise the same as the process for proposing code changes below. + +### Contributing Bug Reports + +If you encounter a problem, try to search the mailing list and JIRA to check whether other people have faced the same situation. If it is not reported before, please report an issue. + +Once you are sure it is a bug, it may be reported by creating a JIRA without creating a pull request. In the bug report, you should provide enough information to understand, isolate and ideally reproduce the bug. Unreproducible bugs, or simple error reports, may be closed. + +It’s very helpful if the bug report has a description about how the bug was introduced, by which commit, so that reviewers can easily understand the bug. It also helps committers to decide how far the bug fix should be backported, when the pull request is merged. The pull request to fix the bug should narrow down the problem to the root cause. + +Performance regression is also one kind of bug. The pull request to fix a performance regression must provide a benchmark to prove the problem is indeed fixed. + +Note that, data correctness/loss bugs are our first priority to solve. Please make sure the corresponding bug-reporting JIRA ticket is labeled as correctness or data-loss. If the bug report doesn’t gain enough attention, please include it and send an email to dev@iotdb.apache.org. + +### Contributing Code Changes + +> When you contribute code, you affirm that the contribution is your original work and that you license the work to the project under the project’s open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project’s open source license and warrant that you have the legal authority to do so. Any new files contributed should be under Apache 2.0 License with a header on top of it. + +#### Cloning source code + +``` +$ git clone git@github.com:apache/incubator-iotdb.git +``` +Following README.md to test, run or build IoTDB. + +#### JIRA + +Generally, IoTDB uses JIRA to track logical issues, including bugs and improvements, and uses Github pull requests to manage the review and merge specific code changes. That is, JIRAs are used to describe what should be fixed or changed, proposing high-level approaches. Pull requests describe how to implement that change in the project’s source code. For example, major design decisions discussed in JIRA. + +1. Find the existing IoTDB JIRA that the change pertains to. + 1. o not create a new JIRA if you send a PR to address an existing issue labeled in JIRA; add it to the existing discussion. + 2. Look for existing pull requests that are linked from the JIRA, to understand if someone is already working on the JIRA +2. If the change is new, then it usually needs a new JIRA. However, trivial changes, such as changes are self-explained do not require a JIRA. Example: Fix spelling error in JavaDoc +3. If required, create a new JIRA: + 1. Provide a descriptive Title. “Problem in XXXManager” is not sufficient. “IoTDB failed to start on jdk11 because jdk11 does not support -XX:+PrintGCDetail” is good. + 2. Write a detailed Description. For bug reports, this should ideally include a short reproduction of the problem. For new features, it may include a design document. + 3. Set required fields: + 1. Issue Type. Generally, Bug, Improvement and New Feature are the only types used in IoTDB. + 2. Priority. Set to Major or below; higher priorities are generally reserved for committers to set. The main exception is correctness or data-loss issues, which can be flagged as Blockers. JIRA tends to unfortunately conflate “size” and “importance” in its Priority field values. Their meaning is roughly: + 1. Blocker: pointless to release without this change as the release would be unusable to a large minority of users. Correctness and data loss issues should be considered Blockers. + 2. Critical: a large minority of users are missing important functionality without this, and/or a workaround is difficult + 3. Major: a small minority of users are missing important functionality without this, and there is a workaround + 4. Minor: a niche use case is missing some support, but it does not affect usage or is easily worked around + 5. Trivial: a nice-to-have change but unlikely to be any problem in practice otherwise + 3. Affected Version. For Bugs, assign at least one version that is known to reproduce the issue or need to be changed + 4. Label. Not widely used, except for the following: + * correctness: a correctness issue + * data-loss: a data loss issue + * release-notes: the change’s effects need mention in release notes. The JIRA or pull request should include detail suitable for inclusion in release notes – see “Docs Text” below. + * starter: small, simple change suitable for new contributors + 5. Docs Text: For issues that require an entry in the release notes, this should contain the information that the release manager should include. Issues should include a short summary of what behavior is impacted, and detail on what behavior changed. It can be provisionally filled out when the JIRA is opened, but will likely need to be updated with final details when the issue is resolved. + 4. Do not set the following fields: + 1. Fix Version. This is assigned by committers only when resolved. + 2. Target Version. This is assigned by committers to indicate a PR has been accepted for possible fix by the target version. + 5. Do not include a patch file; pull requests are used to propose the actual change. +4. If the change is a large change, consider raising a discussion on it at dev@iotdb.apache.org first before proceeding to implement the change. Currently, we use https://cwiki.apache.org/confluence to store design proposals and release process. Users can also send them there. + + +#### Pull Request + +1. Fork the Github repository at https://github.com/apache/incubator-iotdb if you haven’t done already. +2. Clone your fork, create a new branch, push commits to the branch. +3. Please add documentation and tests to explain/cover your changes. +Run all tests with [How to test](https://github.com/thulab/iotdb/wiki/How-to-test-IoTDB) to verify your change. +4. Open a pull request against the master branch of IoTDB. (Only in special cases would the PR be opened against other branches.) + 1. The PR title should be in the form of "IoTDB-xxxx", where xxxx is the relevant JIRA number. + 2. If the pull request is still under work in progress stage but needs to be pushed to Github to request for review, please add "WIP" after the PR title. + 3. Consider identifying committers or other contributors who have worked on the code being changed. Find the file(s) in Github and click “Blame” to see a line-by-line annotation of who changed the code last. You can add @username in the PR description to ping them immediately. + 4. Please state that the contribution is your original work and that you license the work to the project under the project’s open source license. +5. The related JIRA, if any, will be marked as “In Progress” and your pull request will automatically be linked to it. There is no need to be the Assignee of the JIRA to work on it, though you are welcome to comment that you have begun work. +6. The Jenkins automatic pull request builder will test your changes + 1. If it is your first contribution, Jenkins will wait for confirmation before building your code and post “Can one of the admins verify this patch?” + 2. A committer can authorize testing with a comment like “ok to test” + 3. A committer can automatically allow future pull requests from a contributor to be tested with a comment like “Jenkins, add to whitelist” +7. Watch for the results, and investigate and fix failures promptly + 1. Fixes can simply be pushed to the same branch from which you opened your pull request + 2. Jenkins will automatically re-test when new commits are pushed + 3. If the tests failed for reasons unrelated to the change (e.g. Jenkins outage), then a committer can request a re-test with “Jenkins, retest this please”. Ask if you need a test restarted. If you were added by “Jenkins, add to whitelist” from a committer before, you can also request the re-test. + +#### The Review Process + +* Other reviewers, including committers, may comment on the changes and suggest modifications. Changes can be added by simply pushing more commits to the same branch. +* Lively, polite, rapid technical debate is encouraged from everyone in the community. The outcome may be a rejection of the entire change. +* Keep in mind that changes to more critical parts of IoTDB, like its read/write data from/to disk, will be subjected to more review, and may require more testing and proof of its correctness than other changes. +* Reviewers can indicate that a change looks suitable for merging with a comment such as: “I think this patch looks good” or "LGTM". If you comment LGTM you will be expected to help with bugs or follow-up issues on the patch. Consistent, judicious use of LGTMs is a great way to gain credibility as a reviewer with the broader community. +* Sometimes, other changes will be merged which conflict with your pull request’s changes. The PR can’t be merged until the conflict is resolved. This can be resolved by, for example, adding a remote to keep up with upstream changes by + +```shell +git remote add upstream git@github.com:apache/incubator-iotdb.git +git fetch upstream +git rebase upstream/master +# or you can use `git pull --rebase upstream master` to replace the above two commands +# resolve your conflicts +# push codes to your branch +``` + +* Try to be responsive to the discussion rather than let days pass between replies + +#### Closing Your Pull Request / JIRA +* If a change is accepted, it will be merged and the pull request will automatically be closed, along with the associated JIRA if any + * Note that in the rare case you are asked to open a pull request against a branch beside master, you actually have to close the pull request manually + * The JIRA will be Assigned to the primary contributor to the change as a way of giving credit. If the JIRA isn’t closed and/or Assigned promptly, comment on the JIRA. +* If your pull request is ultimately rejected, please close it promptly + * … because committers can’t close PRs directly + * Pull requests will be automatically closed by an automated process at Apache after about a week if a committer has made a comment like “mind closing this PR?” This means that the committer is specifically requesting that it be closed. +* If a pull request has gotten little or no attention, consider improving the description or the change itself and ping likely reviewers again after a few days. Consider proposing a change that’s easier to include, like a smaller and/or less invasive change. +* If it has been reviewed but not taken up after weeks, after soliciting review from the most relevant reviewers, or, has met with neutral reactions, the outcome may be considered a “soft no”. It is helpful to withdraw and close the PR in this case. +* If a pull request is closed because it is deemed not the right approach to resolve a JIRA, then leave the JIRA open. However if the review makes it clear that the issue identified in the JIRA is not going to be resolved by any pull request (not a problem, won’t fix) then also resolve the JIRA + +#### Code Style + +For Java code, Apache IoTDB follows Google’s Java Style Guide. + + diff --git a/docs/Documentation/Frequently asked questions.md b/docs/Documentation/Frequently asked questions.md new file mode 100644 index 0000000000000..dc9a4bed73e29 --- /dev/null +++ b/docs/Documentation/Frequently asked questions.md @@ -0,0 +1,105 @@ + + +- [Frequently Asked Questions](#frequently-asked-questions) + - [How can I identify my version of IoTDB?](#how-can-i-identify-my-version-of-iotdb) + - [Where can I find IoTDB logs?](#where-can-i-find-iotdb-logs) + - [Where can I find IoTDB data files?](#where-can-i-find-iotdb-data-files) + - [How do I know how many time series are stored in IoTDB?](#how-do-i-know-how-many-time-series-are-stored-in-iotdb) + - [Can I use Hadoop and Spark to read TsFile in IoTDB?](#can-i-use-hadoop-and-spark-to-read-tsfile-in-iotdb) + - [How does IoTDB handle duplicate points?](#how-does-iotdb-handle-duplicate-points) + - [How can I tell what type of the specific timeseries?](#how-can-i-tell-what-type-of-the-specific-timeseries) + - [How can I change IoTDB's CLI time display format?](#how-can-i-change-iotdbs-cli-time-display-format) + + +# Frequently Asked Questions + +## How can I identify my version of IoTDB? + +There are several ways to identify the version of IoTDB that you are using: + +* Launch IoTDB's Command Line Interface: + +``` +> ./start-client.sh -p 6667 -pw root -u root -h localhost + _____ _________ ______ ______ +|_ _| | _ _ ||_ _ `.|_ _ \ + | | .--.|_/ | | \_| | | `. \ | |_) | + | | / .'`\ \ | | | | | | | __'. + _| |_| \__. | _| |_ _| |_.' /_| |__) | +|_____|'.__.' |_____| |______.'|_______/ version 0.7.0 +``` + +* Check pom.xml file: + +``` +0.7.0 +``` + +* Use JDBC API: + +``` +String iotdbVersion = tsfileDatabaseMetadata.getDatabaseProductVersion(); +``` + +## Where can I find IoTDB logs? + +By default settings, the logs are stored under ```IOTDB_HOME/iotdb/logs```. You can change log level and storage path by configuring ```logback.xml``` under ```IOTDB_HOME/iotdb/conf```. ```IOTDB_HOME``` is the root path of IoTDB project. + +## Where can I find IoTDB data files? + +By default settings, the data files (including tsfile, metadata, and WAL files) are stored under ```IOTDB_HOME/iotdb/data```. + +## How do I know how many time series are stored in IoTDB? + +Use IoTDB's Command Line Interface: + +``` +IoTDB> show timeseries root.* +``` + +In the result, there will be a statement shows `Total timeseries number`, this number is the timeseries number in IoTDB. + +If you are using Linux, you can use the following shell command: + +``` +> grep "0,root" IOTDB_HOME/iotdb/data/system/schema/mlog.txt | wc -l +> 6 +``` + +## Can I use Hadoop and Spark to read TsFile in IoTDB? + +Yes. IoTDB has intense integration with Open Source Ecosystem. IoTDB supports [Hadoop](https://github.com/apache/incubator-iotdb/tree/master/hadoop), [Spark](https://github.com/apache/incubator-iotdb/tree/master/spark) and [Grafana](https://github.com/apache/incubator-iotdb/tree/master/grafana) visualization tool. + +## How does IoTDB handle duplicate points? + +A data point is uniquely identified by a full time series path (e.g. ```root.vehicle.d0.s0```) and timestamp. If you submit a new point with the same path and timestamp as an existing point, + +## How can I tell what type of the specific timeseries? + +Use ```SHOW TIMESERIES ``` SQL in IoTDB's Command Line Interface: + +For example, if you want to know the type of all timeseries, the \ should be `root`. The statement will be: + +``` +IoTDB> show timeseries root +``` + +If you want to query specific sensor, you can replace the \ with the sensor name. For example: + +``` +IoTDB> show timeseries root.fit.d1.s1 +``` + +Otherwise, you can also use wildcard in timeseries path: + +``` +IoTDB> show timeseries root.fit.d1.* +``` + +## How can I change IoTDB's CLI time display format? + +The default IoTDB's CLI time display format is human readable (e.g. ```1970-01-01T08:00:00.001```), if you want to display time in timestamp type or other readable format, add parameter ```-disableIS08601``` in start command: + +``` +>sh IOTDB_HOME/iotdb/bin/start-client.sh -h 10.129.187.21 -p 6667 -u root -pw root -disableIS08601 +``` \ No newline at end of file diff --git a/docs/Documentation/OtherMaterial-Examples.md b/docs/Documentation/OtherMaterial-Examples.md new file mode 100644 index 0000000000000..a1563278d44b7 --- /dev/null +++ b/docs/Documentation/OtherMaterial-Examples.md @@ -0,0 +1,63 @@ +# IoTDB Examples + +These examples give a quick overview of the IoTDB JDBC. IoTDB offers standard JDBC for users to interact with IoTDB, other language versions are comming soon. + +To use IoTDB, you need to set storage group (detail concept about storage group, please view our documentations) for your timeseries. Then you need to create specific timeseries((detail concept about storage group, please view our documentations)) according to its data type, name, etc. After that, inserting and query data is allowed. In this page, we will show an basic example using IoTDB JDBC. + +## IoTDB Hello World + +``` JAVA +/** + * The class is to show how to write and read date from IoTDB through JDBC + */ +package com.tsinghua.iotdb.demo; + +import java.sql.*; +import java.text.SimpleDateFormat; +import java.util.Date; + +public class IotdbHelloWorld { + + public static void main(String[] args) throws SQLException, ClassNotFoundException { + Connection connection = null; + Statement statement = null; + try { + + // 1. load JDBC driver of IoTDB + Class.forName("org.apache.iotdb.iotdb.jdbc.IoTDBDriver"); + // 2. DriverManager connect to IoTDB + connection = DriverManager.getConnection("jdbc:iotdb://localhost:6667/", "root", "root"); + // 3. Create statement + statement = connection.createStatement(); + // 4. Set storage group + statement.execute("set storage group to root.vehicle.sensor"); + // 5. Create timeseries + statement.execute("CREATE TIMESERIES root.vehicle.sensor.sensor0 WITH DATATYPE=DOUBLE, ENCODING=PLAIN"); + // 6. Insert data to IoTDB + statement.execute("INSERT INTO root.vehicle.sensor(timestamp, sensor0) VALUES (2018/10/24 19:33:00, 142)"); + // 7. Query data + String sql = "select * from root.vehicle.sensor"; + String path = "root.vehicle.sensor.sensor0"; + boolean hasResultSet = statement.execute(sql); + SimpleDateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS"); + if (hasResultSet) { + ResultSet res = statement.getResultSet(); + System.out.println(" Time" + "|" + path); + while (res.next()) { + long time = Long.parseLong(res.getString("Time")); + String dateTime = dateFormat.format(new Date(time)); + System.out.println(dateTime + " | " + res.getString(path)); + } + res.close(); + } + } + finally { + // 8. Close + if (statement != null) statement.close(); + if (connection != null) connection.close(); + } + } +} + + +``` diff --git a/docs/Documentation/OtherMaterial-Reference.md b/docs/Documentation/OtherMaterial-Reference.md new file mode 100644 index 0000000000000..86ae366c21bec --- /dev/null +++ b/docs/Documentation/OtherMaterial-Reference.md @@ -0,0 +1,9 @@ +# Research Papers + +Apache IoTDB started at Tsinghua University, School of Software. IoTDB is a database for managing large amount of time series data with columnar storage, data encoding, pre-computation, and index techniques. It has SQL-like interface to write millions of data points per second per node and is optimized to get query results in few seconds over trillions of data points. It can also be easily integrated with Apache Hadoop MapReduce and Apache Spark for analytics. + +The research papers related are in the following: + +* [PISA: An Index for Aggregating Big Time Series Data](https://dl.acm.org/citation.cfm?id=2983775&dl=ACM&coll=DL), Xiangdong Huang and Jianmin Wang and Raymond K. Wong and Jinrui Zhang and Chen Wang. CIKM 2016. +* [Matching Consecutive Subpatterns over Streaming Time Series](https://link.springer.com/chapter/10.1007/978-3-319-96893-3_8), Rong Kang and Chen Wang and Peng Wang and Yuting Ding and Jianmin Wang. APWeb/WAIM 2018. +* [KV-match: A Subsequence Matching Approach Supporting Normalization and Time Warping](https://www.semanticscholar.org/paper/KV-match%3A-A-Subsequence-Matching-Approach-and-Time-Wu-Wang/9ed84cb15b7e5052028fc5b4d667248713ac8592), Jiaye Wu and Peng Wang and Chen Wang and Wei Wang and Jianmin Wang. ICDE 2019. diff --git a/docs/Documentation/OtherMaterial-ReleaseNotesV0.7.0.md b/docs/Documentation/OtherMaterial-ReleaseNotesV0.7.0.md new file mode 100644 index 0000000000000..03b55ba2a66af --- /dev/null +++ b/docs/Documentation/OtherMaterial-ReleaseNotesV0.7.0.md @@ -0,0 +1,38 @@ + + +- [v0.7.0 Release Notes](#v070-release-notes) + - [Features](#features) + - [IoTDB](#iotdb) + - [IoTDB-Transfer-Tool](#iotdb-transfer-tool) + - [Bugfixes](#bugfixes) + - [IoTDB](#iotdb-1) + - [System Organization](#system-organization) + + +### v0.7.0 Release Notes + +Add postback tools, multi-path data storage mechanism, ```SHOW TIMESERIES / STORAGE GROUP``` SQL extended expressions, and other new features. Fix several issues in version 0.6.0. Improve system stability. + +#### Features + +##### IoTDB + +* Add ```Show Storage Group``` SQL statement, support for displaying storage groups. +* Enhance ```Show Timeseries``` SQL, support for displaying time series information under different prefix paths. +* Add multi-path data storage mechanism for distributed storage, allowing different data files (write ahead logs, metadata files, etc.) to be stored in different paths. +* Add data directory configuration which allows data files to be stored in different paths, facilitating the use of multiple disks to store data files. + +##### IoTDB-Transfer-Tool + +* Added IoTDB data postback module to provide users with end-to-cloud timed data file postback function. + +#### Bugfixes +##### IoTDB +* Fix the problem that the IoTDB shutdown script does not work +* Fix the problem that the IoTDB installation path does not support spaces in Windows environment. +* Fix the problem that the system cannot be restarted after merging Overflow data +* Fix the problem that the permission module missing the ALL keyword +* Fixed an issue that quotation marks in strings were not supported when querying TEXT type data + +#### System Organization +* Further improve system stability \ No newline at end of file diff --git a/docs/Documentation/OtherMaterial-Sample Data.md b/docs/Documentation/OtherMaterial-Sample Data.md new file mode 100644 index 0000000000000..2040fab4786e0 --- /dev/null +++ b/docs/Documentation/OtherMaterial-Sample Data.md @@ -0,0 +1,46 @@ + + +- [Material: Sample Data](#material-sample-data) + - [Scenario Description](#scenario-description) + - [Sample Data](#sample-data) + + +# Material: Sample Data + +### Scenario Description + +A power department needs to monitor the operation of various power plants under its jurisdiction. By collecting real-time monitoring data sent by various types of sensors deployed by various power plants, the power department can monitor the real-time operation of the power plants and understand the trend of data changes, etc. IoTDB has the characteristics of high write throughput and rich query functions, which can provide effective support for the needs of the power department. + +The real-time data needed to be monitored involves multiple attribute layers: + +* **Power Generation Group**: The data belongs to nearly ten power generation groups, and the name codes are ln, sgcc, etc. + +* **Power Plant**: The power generation group has more than 10 kinds of electric fields, such as wind farm, hydropower plant and photovoltaic power plant, numbered as wf01, wf02, wf03 and so on. + +* **Device**: Each power plant has about 5,000 kinds of power generation devices such as wind turbines and photovoltaic panels, numbered as wt01, wt02 and so on. + +* **Sensor**: For different devices, there are 10 to 1000 sensors monitoring different states of the devices , such as power supply status sensor (named status), temperature sensor (named temperature), hardware version sensor (named hardware), etc. + +It is worth noting that prior to the use of IoTDB by the power sector, some historical monitoring data of various power plants needs to be imported into the IoTDB system (we will introduce the import method in [Import Historical Data](需要连接到具体的网页链接Chapter3ImportHistoricalData)). Simutaneouly, the real-time monitoring data is continuously flowing into the IoTDB system (we will introduce the import method in Section 3.3.2 of this chapter). + +### Sample Data +Based on the description of the above sample scenarios, we provide you with a simplified sample data. The data download address is http://tsfile.org/download. + +The basic information of the data is shown in Table below. + +
**Table: The basic information of the data** + +|Name |Data Type| Coding | Meaning | +|:---|:---|:---|:---| +|root.ln.wf01.wt01.status| Boolean|PLAIN| the power supply status of ln group wf01 plant wt01 device | +|root.ln.wf01.wt01.temperature |Float|RLE| the temperature of ln group wf01 plant wt01 device| +|root.ln.wf02.wt02.hardware |Text|PLAIN| the hardware version of ln group wf02 plant wt02 device| +|root.ln.wf02.wt02.status |Boolean|PLAIN| the power supply status of ln group wf02 plant wt02 device| +|root.sgcc.wf03.wt01.status|Boolean|PLAIN| the power supply status of sgcc group wf03 plant wt01 device| +|root.sgcc.wf03.wt01.temperature |Float|RLE| the temperature of sgcc group wf03 plant wt01 device| + +
+ +The time span of this data is from 10:00 on November 1, 2017 to 12:00 on November 2, 2017. The frequency at which data is generated is two minutes each. + +In [Data Model Selection and Creation](Chapter3DataModelSelectionandCreation), we will show how to apply IoTDB's data model rules to construct the data model shown above. In [Import Historical Data](Chapter3ImportHistoricalData), we will introduce you to the method of importing historical data, and in [Import Real-time Data](Chapter3ImportReal-timeData), we will introduce you to the method of accessing real-time data. In [Data Query](Chapter3DataQuery), we will introduce you to three typical data query patterns using IoTDB. In [Data Maintenance](Chapter3DataMaintenance), we will show you how to update and delete data using IoTDB. \ No newline at end of file diff --git a/docs/Documentation/OtherMaterial-Sample Data.txt b/docs/Documentation/OtherMaterial-Sample Data.txt new file mode 100644 index 0000000000000..3ac8b0c8b8279 --- /dev/null +++ b/docs/Documentation/OtherMaterial-Sample Data.txt @@ -0,0 +1,60488 @@ +set storage group to root.ln +create timeseries root.ln.wf01.wt01.status with datatype=BOOLEAN,encoding=PLAIN +insert into root.ln.wf01.wt01(timestamp,status) values(1509465600000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509465660000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509465720000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509465780000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509465840000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509465900000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509465960000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466020000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466080000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466140000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466200000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466260000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466320000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466380000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466440000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466500000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466560000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466620000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466680000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466740000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466800000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466860000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466920000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509466980000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467040000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467100000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467160000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467220000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467280000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467340000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467400000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467460000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467520000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467580000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467640000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467700000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467760000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467820000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467880000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509467940000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468000000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468060000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468120000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468180000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468240000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468300000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468360000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468420000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468480000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468540000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468600000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468660000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468720000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468780000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468840000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468900000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509468960000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469020000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469080000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469140000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469200000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469260000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469320000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469380000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469440000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469500000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469560000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469620000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469680000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469740000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469800000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469860000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469920000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509469980000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470040000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470100000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470160000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470220000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470280000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470340000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470400000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470460000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470520000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470580000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470640000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470700000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470760000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470820000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470880000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509470940000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471000000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471060000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471120000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471180000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471240000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471300000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471360000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471420000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471480000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471540000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471600000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471660000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471720000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471780000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471840000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471900000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509471960000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472020000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472080000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472140000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472200000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472260000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472320000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472380000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472440000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472500000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472560000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472620000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472680000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472740000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472800000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472860000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472920000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509472980000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473040000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473100000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473160000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473220000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473280000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473340000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473400000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473460000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473520000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473580000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473640000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473700000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473760000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473820000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473880000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509473940000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509474000000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509474060000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509474120000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509474180000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509474240000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509474300000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509474360000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1509474420000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1509474480000,false) +insert into 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root.ln.wf01.wt01(timestamp,status) values(1510065780000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510065840000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510065900000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510065960000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066020000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066080000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066140000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066200000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066260000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066320000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066380000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066440000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066500000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066560000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066620000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066680000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066740000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066800000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066860000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066920000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510066980000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067040000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067100000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067160000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067220000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067280000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067340000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067400000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067460000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067520000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067580000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067640000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067700000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067760000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067820000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067880000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510067940000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068000000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068060000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068120000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068180000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068240000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068300000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068360000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068420000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068480000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068540000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068600000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068660000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068720000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068780000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068840000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068900000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510068960000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069020000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069080000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069140000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069200000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069260000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069320000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069380000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069440000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069500000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069560000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069620000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069680000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069740000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069800000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069860000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069920000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510069980000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510070040000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510070100000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510070160000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510070220000,true) +insert into root.ln.wf01.wt01(timestamp,status) values(1510070280000,false) +insert into root.ln.wf01.wt01(timestamp,status) values(1510070340000,false) +create timeseries root.ln.wf01.wt01.temperature with datatype=FLOAT,encoding=RLE +insert into 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root.ln.wf01.wt01(timestamp,temperature) values(1509468360000,24.657654) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509468420000,23.39864) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509468480000,20.783094) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509468540000,25.184992) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509468600000,25.27486) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509468660000,23.209702) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509468720000,21.950687) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509468780000,20.352585) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509468840000,23.73704) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509468900000,22.276535) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509468960000,21.017519) +insert into root.ln.wf01.wt01(timestamp,temperature) 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root.ln.wf01.wt01(timestamp,temperature) values(1509469740000,24.92198) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509469800000,20.104885) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509469860000,20.84593) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509469920000,25.385426) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509469980000,21.18261) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509470040000,21.272478) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509470100000,21.463137) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509470160000,23.196428) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509470220000,21.598326) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509470280000,21.734905) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1509470340000,23.468197) +insert into root.ln.wf01.wt01(timestamp,temperature) 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root.ln.wf01.wt01(timestamp,temperature) values(1510070040000,22.51532) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1510070100000,25.899776) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1510070160000,24.43927) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1510070220000,24.39154) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1510070280000,22.931036) +insert into root.ln.wf01.wt01(timestamp,temperature) values(1510070340000,21.067368) +create timeseries root.ln.wf02.wt02.hardware with datatype=TEXT,encoding=PLAIN +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509465600000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509465660000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509465720000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509465780000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509465840000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509465900000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509465960000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466020000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466080000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466140000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466200000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466260000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466320000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466380000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466440000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466500000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466560000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509466620000,"v1") 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values(1509467400000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509467460000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509467520000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509467580000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509467640000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509467700000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509467760000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509467820000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509467880000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509467940000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468000000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468060000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468120000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468180000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468240000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468300000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468360000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468420000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468480000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468540000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468600000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468660000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468720000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468780000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468840000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509468900000,"v1") 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values(1509469680000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509469740000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509469800000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509469860000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509469920000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509469980000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509470040000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509470100000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509470160000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509470220000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509470280000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509470340000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509470400000,"v1") +insert into 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root.ln.wf02.wt02(timestamp,hardware) values(1509472740000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509472800000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509472860000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509472920000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509472980000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509473040000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509473100000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509473160000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509473220000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509473280000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509473340000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509473400000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1509473460000,"v1") 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values(1510067040000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067100000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067160000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067220000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067280000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067340000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067400000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067460000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067520000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067580000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067640000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067700000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067760000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067820000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067880000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510067940000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510068000000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510068060000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510068120000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510068180000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510068240000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510068300000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510068360000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510068420000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510068480000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510068540000,"v1") 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values(1510069320000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069380000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069440000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069500000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069560000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069620000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069680000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069740000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069800000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069860000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069920000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510069980000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510070040000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510070100000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510070160000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510070220000,"v2") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510070280000,"v1") +insert into root.ln.wf02.wt02(timestamp,hardware) values(1510070340000,"v1") +create timeseries root.ln.wf02.wt02.status with datatype=BOOLEAN,encoding=PLAIN +insert into root.ln.wf02.wt02(timestamp,status) values(1509465600000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1509465660000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1509465720000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1509465780000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1509465840000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1509465900000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1509465960000,false) 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root.ln.wf02.wt02(timestamp,status) values(1510056480000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510056540000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510056600000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510056660000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510056720000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510056780000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510056840000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510056900000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510056960000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057020000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057080000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057140000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057200000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057260000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057320000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057380000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057440000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057500000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057560000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057620000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057680000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057740000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057800000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057860000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057920000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510057980000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058040000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058100000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058160000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058220000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058280000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058340000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058400000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058460000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058520000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058580000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058640000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058700000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058760000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058820000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058880000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510058940000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059000000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059060000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059120000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059180000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059240000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059300000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059360000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059420000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059480000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059540000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059600000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059660000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059720000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059780000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059840000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059900000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510059960000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060020000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060080000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060140000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060200000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060260000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060320000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060380000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060440000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060500000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060560000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060620000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060680000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060740000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060800000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060860000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060920000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510060980000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061040000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061100000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061160000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061220000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061280000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061340000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061400000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061460000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061520000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061580000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061640000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061700000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061760000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061820000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061880000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510061940000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062000000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062060000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062120000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062180000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062240000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062300000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062360000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062420000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062480000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062540000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062600000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062660000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062720000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062780000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062840000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062900000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510062960000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063020000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063080000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063140000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063200000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063260000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063320000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063380000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063440000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063500000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063560000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063620000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063680000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063740000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063800000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063860000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063920000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510063980000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064040000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064100000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064160000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064220000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064280000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064340000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064400000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064460000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064520000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064580000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064640000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064700000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064760000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064820000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064880000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510064940000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065000000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065060000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065120000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065180000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065240000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065300000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065360000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065420000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065480000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065540000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065600000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065660000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065720000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065780000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065840000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065900000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510065960000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066020000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066080000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066140000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066200000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066260000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066320000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066380000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066440000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066500000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066560000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066620000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066680000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066740000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066800000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066860000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066920000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510066980000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067040000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067100000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067160000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067220000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067280000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067340000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067400000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067460000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067520000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067580000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067640000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067700000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067760000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067820000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067880000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510067940000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068000000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068060000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068120000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068180000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068240000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068300000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068360000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068420000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068480000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068540000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068600000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068660000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068720000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068780000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068840000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068900000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510068960000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069020000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069080000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069140000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069200000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069260000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069320000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069380000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069440000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069500000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069560000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069620000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069680000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069740000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069800000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069860000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069920000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510069980000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510070040000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510070100000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510070160000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510070220000,true) +insert into root.ln.wf02.wt02(timestamp,status) values(1510070280000,false) +insert into root.ln.wf02.wt02(timestamp,status) values(1510070340000,false) +set storage group to root.sgcc +create timeseries root.sgcc.wf03.wt01.status with datatype=BOOLEAN,encoding=PLAIN +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509465600000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509465660000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509465720000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509465780000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509465840000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509465900000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509465960000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466020000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466080000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466140000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466200000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466260000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466320000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466380000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466440000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466500000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466560000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466620000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466680000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466740000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466800000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466860000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466920000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509466980000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467040000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467100000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467160000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467220000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467280000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467340000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467400000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467460000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467520000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467580000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467640000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467700000,false) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467760000,true) +insert into root.sgcc.wf03.wt01(timestamp,status) values(1509467820000,false) +insert into 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root.sgcc.wf03.wt01(timestamp,temperature) values(1510069200000,21.481789) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069260000,20.085176) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069320000,20.826221) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069380000,22.559513) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069440000,20.548622) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069500000,22.702442) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069560000,24.637224) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069620000,23.17672) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069680000,21.716215) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069740000,25.51346) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069800000,23.704056) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069860000,22.243551) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069920000,20.178394) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510069980000,24.580292) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510070040000,22.51532) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510070100000,25.899776) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510070160000,24.43927) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510070220000,24.39154) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510070280000,22.931036) +insert into root.sgcc.wf03.wt01(timestamp,temperature) values(1510070340000,21.067368) diff --git a/docs/Documentation/QuickStart.md b/docs/Documentation/QuickStart.md new file mode 100755 index 0000000000000..e2556f1ad1b49 --- /dev/null +++ b/docs/Documentation/QuickStart.md @@ -0,0 +1,324 @@ + + +- [Quick Start](#quick-start) + - [Prerequisites](#prerequisites) + - [Installation](#installation) + - [Installation from source code](#installation-from-source-code) + - [Configure](#configure) + - [Start](#start) + - [Start Server](#start-server) + - [Start Client](#start-client) + - [Have a try](#have-a-try) + - [Stop Server](#stop-server) + + +# Quick Start + +This short guide will walk you through the basic process of using IoTDB. For a more-complete guide, please visit our website’s documents. + +## Prerequisites + +To use IoTDB, you need to have: + +1. Java >= 1.8 (Please make sure the environment path has been set) +2. Maven >= 3.0 (If you want to compile and install IoTDB from source code) +3. TsFile >= 0.7.0 (TsFile Github page: [https://github.com/apache/incubator-iotdb/tree/master/tsfile](https://github.com/apache/incubator-iotdb/tree/master/tsfile)) +4. IoTDB-JDBC >= 0.7.0 (IoTDB-JDBC Github page: [https://github.com/apache/incubator-iotdb/tree/master/jdbc](https://github.com/apache/incubator-iotdb/tree/master/jdbc)) + +TODO: TsFile and IoTDB-JDBC dependencies will be removed after the project reconstruct. + +## Installation + +IoTDB provides you two installation methods, you can refer to the following suggestions, choose one of them: + + +* Installation from source code. If you need to modify the code yourself, you can use this method. +* Installation from binary files. Download the binary files from the official website. This is the recommended method, in which you will get a binary released package which is out-of-the-box.(Comming Soon...) + +Here in the Quick Start part, we give a brief introduction of using source code to install IoTDB. For further information, please refer to Chapter 5 of this document. + +### Installation from source code + +Use git to get IoTDB source code: + +``` +Shell > git clone https://github.com/apache/incubator-iotdb.git +``` + +Or: + +``` +Shell > git clone git@github.com:apache/incubator-iotdb.git +``` + +Now suppose your directory is like this: + +``` +> pwd +/User/workspace/incubator-iotdb + +> ls -l +incubator-iotdb/ <-- root path +| ++- iotdb/ +| ++- jdbc/ +| ++- tsile/ +| +... +| ++- pom.xml +``` + +Let $IOTDB_HOME = /User/workspace/incubator-iotdb/iotdb/iotdb/ + +If you are not the first time that building IoTDB, remember deleting the following files: + +``` +> rm -rf $IOTDB_HOME/data/ +> rm -rf $IOTDB_HOME/lib/ +``` + +Then under the root path of incubator-iotdb, you can build IoTDB using Maven: + +``` +> pwd +/User/workspace/incubator-iotdb + +> mvn clean package -pl iotdb -am -Dmaven.test.skip=true +``` + +If successful, you will see the the following text in the terminal: + +``` +[INFO] ------------------------------------------------------------------------ +[INFO] Reactor Summary: +[INFO] +[INFO] IoTDB Root ......................................... SUCCESS [ 7.020 s] +[INFO] TsFile ............................................. SUCCESS [ 10.486 s] +[INFO] Service-rpc ........................................ SUCCESS [ 3.717 s] +[INFO] IoTDB Jdbc ......................................... SUCCESS [ 3.076 s] +[INFO] IoTDB .............................................. SUCCESS [ 8.258 s] +[INFO] ------------------------------------------------------------------------ +[INFO] BUILD SUCCESS +[INFO] ------------------------------------------------------------------------ +``` + +Otherwise, you may need to check the error statements and fix the problems. + +After building, the IoTDB project will be at the subfolder named iotdb. The folder will include the following contents: + +``` +$IOTDB_HOME/ +| ++- bin/ <-- script files +| ++- conf/ <-- configuration files +| ++- lib/ <-- project dependencies +``` + + + +## Configure + +Before starting to use IoTDB, you need to config the configuration files first. For your convenience, we have already set the default config in the files. + +In total, we provide users three kinds of configurations module: + +* environment config module (iotdb-env.`sh`(Linux or OSX), iotdb-env.`bat`(Windows)) +* system config module (tsfile-format.properties, iotdb-engine.properties) +* log config module (logback.xml) + +The configuration files of the three configuration items are located in the IoTDB installation directory: $IOTDB_HOME/conf folder. For more, you are advised to check Chapter 5 to give you the details. + +## Start + +### Start Server + +After that we start the server. Running the startup script: + +``` +# Unix/OS X +> $IOTDB_HOME/bin/start-server.sh + +# Windows +> $IOTDB_HOME\bin\start-server.bat +``` + +### Start Client + +Now let's trying to read and write some data from IoTDB using our Client. To start the client, you need to explicit the server's IP and PORT as well as the USER_NAME and PASSWORD. + +``` +# Unix/OS X +> $IOTDB_HOME/bin/start-client.sh -h -p -u + +# Windows +> $IOTDB_HOME\bin\start-client.bat -h -p -u +``` + +> NOTE: In the system, we set a default user in IoTDB named 'root'. The default password for 'root' is 'root'. You can use this default user if you are making the first try or you didn't create users by yourself. + +The command line client is interactive so if everything is ready you should see the welcome logo and statements: + +``` + _____ _________ ______ ______ +|_ _| | _ _ ||_ _ `.|_ _ \ + | | .--.|_/ | | \_| | | `. \ | |_) | + | | / .'`\ \ | | | | | | | __'. + _| |_| \__. | _| |_ _| |_.' /_| |__) | +|_____|'.__.' |_____| |______.'|_______/ version x.x.x + + +IoTDB> login successfully +IoTDB> +``` + + +### Have a try + +Now, you can use IoTDB SQL to operate IoTDB, and when you’ve had enough fun, you can input 'quit' or 'exit' command to leave the client. + +But lets try something slightly more interesting: + +``` +IoTDB> SET STORAGE GROUP TO root.ln +execute successfully. +IoTDB> CREATE TIMESERIES root.ln.wf01.wt01.status WITH DATATYPE=BOOLEAN, ENCODING=PLAIN +execute successfully. +``` +Till now, we have already create a table called root.vehicle and add a colomn called d0.s0 in the table. Let's take a look at what we have done by 'SHOW TIMESERIES' command. + +``` +IoTDB> SHOW TIMESERIES +=== Timeseries Tree === + +root:{ + ln:{ + wf01:{ + wt01:{ + status:{ + DataType: BOOLEAN, + Encoding: PLAIN, + args: {}, + StorageGroup: root.ln + } + } + } + } +} +``` + +For a further try, create a timeseries again and use SHOW TIMESERIES to check result. + +``` +IoTDB> CREATE TIMESERIES root.ln.wf01.wt01.temperature WITH DATATYPE=FLOAT, ENCODING=RLE +IoTDB> SHOW TIMESERIES +=== Timeseries Tree === + +root:{ + ln:{ + wf01:{ + wt01:{ + status:{ + DataType: BOOLEAN, + Encoding: PLAIN, + args: {}, + StorageGroup: root.ln + }, + temperature:{ + DataType: FLOAT, + Encoding: RLE, + args: {}, + StorageGroup: root.ln + } + } + } + } +} +``` +Now, for your conveniect, SHOW TIMESERIES clause also supports extention syntax, the pattern is (for further details, check Chapter x): + +``` +SHOW TIMESERIES +``` + +Here is the example: + +``` +IoTDB> SHOW TIMESERIES root.ln.wf01.wt01 ++------------------------------+--------------+--------+--------+ +| Timeseries| Storage Group|DataType|Encoding| ++------------------------------+--------------+--------+--------+ +| root.ln.wf01.wt01.status| root.ln| BOOLEAN| PLAIN| +| root.ln.wf01.wt01.temperature| root.ln| FLOAT| RLE| ++------------------------------+--------------+--------+--------+ +Total timeseries number = 2 +Execute successfully. +``` +We can also use SHOW STORAGE GROUP to check created storage group: + +``` +IoTDB> show storage group ++-----------------------------------+ +| Storage Group| ++-----------------------------------+ +| root.ln| ++-----------------------------------+ +Total storage group number = 1 +Execute successfully. +It costs 0.006s +``` + + +Insert timeseries data is the basic operation of IoTDB, you can use 'INSERT' command to finish this: + +``` +IoTDB> INSERT INTO root.ln.wf01.wt01(timestamp,status) values(100,true); +execute successfully. +IoTDB> INSERT INTO root.ln.wf01.wt01(timestamp,status,temperature) values(200,false,20.71)
execute successfully. +``` +The data we've just inserted displays like this: + +``` +IoTDB> SELECT status FROM root.ln.wf01.wt01 ++-----------------------+------------------------+ +| Time|root.ln.wf01.wt01.status| ++-----------------------+------------------------+ +|1970-01-01T08:00:00.100| true| +|1970-01-01T08:00:00.200| false| ++-----------------------+------------------------+ +record number = 1 +execute successfully. +``` + +We can also query several timeseries data at once like this: + +``` +IoTDB> SELECT * FROM root.ln.wf01.wt01 ++-----------------------+--------------------------+-----------------------------+ +| Time| root.ln.wf01.wt01.status|root.ln.wf01.wt01.temperature| ++-----------------------+--------------------------+-----------------------------+ +|1970-01-01T08:00:00.100| true| null| +|1970-01-01T08:00:00.200| false| 20.71| ++-----------------------+--------------------------+-----------------------------+ +``` + +If your session looks similar to what’s above, congrats, your IoTDB is operational! + +For more on what commands are supported by IoTDB SQL, see Chapter xx. It will give you help. + +### Stop Server + +The server can be stopped with ctrl-C or the following script: + +``` +# Unix/ OS X +> $IOTDB_HOME/bin/stop-server.sh + +# Windows +> $IOTDB_HOME\bin\stop-server.bat +``` + diff --git a/docs/Documentation/UserGuideV0.7.0/1-Overview.md b/docs/Documentation/UserGuideV0.7.0/1-Overview.md new file mode 100644 index 0000000000000..1c203c559b16b --- /dev/null +++ b/docs/Documentation/UserGuideV0.7.0/1-Overview.md @@ -0,0 +1,99 @@ + + +- [Chapter 1: Overview](#chapter-1-overview) + - [What is IoTDB](#what-is-iotdb) + - [Architecture](#architecture) + - [Scenario](#scenario) + - [Scenario 1](#scenario-1) + - [Scenario 2](#scenario-2) + - [Scenario 3](#scenario-3) + - [Scenario 4](#scenario-4) + - [Features](#features) + + +# Chapter 1: Overview + +## What is IoTDB + +IoTDB(Internet of Things Database) is an integrated data management engine designed for timeseries data, which can provide users specific services for data collection, storage and analysis. Due to its light weight structure, high performance and usable features together with its intense integration with Hadoop and Spark ecology, IoTDB meets the requirements of massive dataset storage, high-speed data input and complex data analysis in the IoT industrial field. + +## Architecture + +Besides IoTDB engine, we also developed several components to provide better IoT service. All components are referred to below as the IoTDB suite, and IoTDB refers specifically to the IoTDB engine. + +IoTDB suite can provide a series of functions in the real situation such as data collection, data writing, data storage, data query, data visualization and data analysis. Figure 1.1 shows the overall application architecture brought by all the components of the IoTDB suite. + + + +As shown in Figure 1.1, users can use JDBC to import timeseries data collected by sensor on the device to local/remote IoTDB. These timeseries data may be system state data (such as server load and CPU memory, etc.), message queue data, timeseries data from applications, or other timeseries data in the database. Users can also write the data directly to the TsFile (local or on HDFS). + +For the data written to IoTDB and local TsFile, users can use TsFileSync tool to synchronize the TsFile to the HDFS, thereby implementing data processing tasks such as abnormality detection and machine learning on the Hadoop or Spark data processing platform. The results of the analysis can be write back to TsFile in the same way. + +Also, IoTDB and TsFile provide client tools to meet the various needs of users in writing and viewing data in SQL form, script form and graphical form. + +## Scenario + +### Scenario 1 + +A company uses surface mount technology (SMT) to produce chips: it is necessary to first print solder paste on the joints of the chip, then place the components on the solder paste, and then melt the solder paste by heating and cool it. Finally, the components are soldered to the chip. + +The above process uses an automated production line. In order to ensure the quality of the product, after printing the solder paste, the quality of the solder paste printing needs to be evaluated by optical equipment. The volume (v), height (h), area (a), horizontal offset (px), and vertical offset (py) of the solder paste on each joint are measured by a three-dimensional solder paste printing (SPI) device. + +In order to improve the quality of the printing, it is necessary for the company to store the metrics of the solder joints on each chip for subsequent analysis based on these data. + +At this point, the data can be stored using TsFile component, TsFileSync tool, and Hadoop/Spark integration component in the IoTDB suite.That is, each time a new chip is printed, a data is written on the SPI device using the SDK, which ultimately forms a TsFile. Through the TsFileSync tool, the generated TsFile will be synchronized to the data center according to certain rules (such as daily) and analyzed by data analysts tools. + + + +In this scenario, only TsFile and TsFileSync are required to be deployed on a PC, and a Hadoop/Spark cluster is required. The schematic diagram is shown in Figure 1.2. Figure 1.3 shows the architecture at this time. + + + +### Scenario 2 + +A company has several wind turbines which are installed hundreds of sensors on each generator to collect information such as the working status of the generator and the wind speed in the working environment. + +In order to ensure the normal operation of the turbines and timely monitoring and analysis of the turbines, the company needs to collect these sensor data, perform partial calculation and analysis in the turbines working environment, and upload the original data collected to the data center. + + + +In this situation, IoTDB, TsFileSync tools, and Hadoop/Spark integration components in the IoTDB suite can be used. A PC needs to be deployed with IoTDB and TsFileSync tools installed to support reading and writing data, local computing and analysis, and uploading data to the data center. In addition, Hadoop/Spark clusters need to be deployed for data storage and analysis on the data center side. As shown in Figure 1.4. Figure 1.5 shows the architecture at this time. + + + +### Scenario 3 + +A factory has a variety of robotic equipment within the plant area. These robotic equipment have limited hardware and are difficult to carry complex applications. + +A variety of sensors are installed on each robotic device to monitor the robot's operating status, temperature, and other information. Due to the network environment of the factory, the robots inside the factory are all within the LAN of the factory and cannot connect to the external network. But there will be several servers in the factory that can connect directly to the external public network. + +In order to ensure that the data of the robot can be monitored and analyzed in time, the company needs to collect the information of these robot sensors, send them to the server that can connect to the external network, and then upload the original data information to the data center for complex calculation and analysis. + + + +At this point, IoTDB, IoTDB-CLI tools, TsFileSync tools, and Hadoop/Spark integration components in the IoTDB suite can be used. IoTDB-CLI tool is installed on the robot and each of them is connected to the LAN of the factory. When sensors generate real-time data, the data will be uploaded to the server in the factory. The IoTDB server and TsFileSync is installed on the server connected to the external network. Once triggered, the data on the server will be upload to the data center. In addition, Hadoop/Spark clusters need to be deployed for data storage and analysis on the data center side. As shown in Figure 1.6. Figure 1.7 shows the architecture at this time. + + + +### Scenario 4 + +A car company installed sensors on its cars to collect monitoring information such as the driving status of the vehicle. These automotive devices have limited hardware configurations and are difficult to carry complex applications. Cars with sensors can be connected to each other or send data via narrow-band IoT. + +In order to receive the IoT data collected by the car sensor in real time, the company needs to send the sensor data to the data center in real time through the narrowband IoT while the vehicle is running. Thus, they can perform complex calculations and analysis on the server in the data center. + +At this point, IoTDB, IoTDB-CLI, and Hadoop/Spark integration components in the IoTDB suite can be used. IoTDB-CLI tool is installed on each car and use IoTDB-JDBC tool to send data directly back to the server in the data center. + +In addition, Hadoop/Spark clusters need to be deployed for data storage and analysis on the data center side. As shown in Figure 1.8. + + + +## Features + + +* Flexible deployment. IoTDB provides users one-click installation tool on the cloud, once-decompressed-used terminal tool and the bridge tool between cloud platform and terminal tool (Data Synchronization Tool). +* Low cost on hardware. IoTDB can reach a high compression ratio of disk storage (For one billion data storage, hard drive cost less than $0.23) +* Efficient directory structure. IoTDB supports efficient oganization for complex timeseries data structure from intelligent networking devices, oganization for timeseries data from devices of the same type, fuzzy searching strategy for massive and complex directory of timeseries data. +* High-throughput read and write. IoTDB supports millions of low-power devices' strong connection data access, high-speed data read and write for intelligent networking devices and mixed devices mentioned above. +* Rich query semantics. IoTDB supports time alignment for timeseries data accross devices and sensors, computation in timeseries field (frequency domain transformation) and rich aggregation function support in time dimension. +* Easy to get start. IoTDB supports SQL-Like language, JDBC standard API and import/export tools which is easy to use. +* Intense integration with Open Source Ecosystem. IoTDB supports Hadoop, Spark, etc. analysis ecosystems and Grafana visualization tool. diff --git a/docs/Documentation/UserGuideV0.7.0/2-Concept.md b/docs/Documentation/UserGuideV0.7.0/2-Concept.md new file mode 100644 index 0000000000000..bc2f0a6877dd5 --- /dev/null +++ b/docs/Documentation/UserGuideV0.7.0/2-Concept.md @@ -0,0 +1,226 @@ + + +- [Chapter 2: Concept](#chapter-2-concept) + - [Key Concepts and Terminology](#key-concepts-and-terminology) + - [Data Type](#data-type) + - [Encoding](#encoding) + - [Compression](#compression) + + +# Chapter 2: Concept +## Key Concepts and Terminology + +The following basic concepts are involved in IoTDB: + +* Device + +A devices is an installation equipped with sensors in real scenarios. In IoTDB, all sensors should have their corresponding devices. + +* Sensor + +A sensor is a detection equipment in an actual scene, which can sense the information to be measured, and can transform the sensed information into an electrical signal or other desired form of information output and send it to IoTDB. In IoTDB, all data and paths stored are organized in units of sensors. + +* Storage Group + +Storage groups are used to let users define how to organize and isolate different time series data on disk. Time series belonging to the same storage group will be continuously written to the same file in the corresponding folder. The file may be closed due to user commands or system policies, and hence the data coming next from these sensors will be stored in a new file in the same folder. Time series belonging to different storage groups are stored in different folders. + +Users can set any prefix path as a storage group. Provided that there are four time series `root.vehicle.d1.s1`, `root.vehicle.d1.s2`, `root.vehicle.d2.s1`, `root.vehicle.d2.s2`, two devices `d1` and `d2` under the path `root.vehicle` may belong to the same owner or the same manufacturer, so d1 and d2 are closely related. At this point, the prefix path root.vehicle can be designated as a storage group, which will enable IoTDB to store all devices under it in the same folder. Newly added devices under `root.vehicle` will also belong to this storage group. + +> Note: A full path (`root.vehicle.d1.s1` as in the above example) is not allowed to be set as a storage group. + +Setting a reasonable number of storage groups can lead to performance gains: there is neither the slowdown of the system due to frequent switching of IO (which will also take up a lot of memory and result in frequent memory-file switching) caused by too many storage files (or folders), nor the block of write commands caused by too few storage files (or folders) (which reduces concurrency). + +Users should balance the storage group settings of storage files according to their own data size and usage scenarios to achieve better system performance. (There will be officially provided storage group scale and performance test reports in the future). + +> Note: The prefix of a time series must belong to a storage group. Before creating a time series, the user must set which storage group the series belongs to. Only the time series whose storage group is set can be persisted to disk. + +Once a prefix path is set as a storage group, the storage group settings cannot be changed. + +After a storage group is set, all parent and child layers of the corresponding prefix path are not allowed to be set up again (for example, after `root.ln` is set as the storage group, the root layer and `root.ln.wf01` are not allowed to be set as storage groups). + +* Path + +In IoTDB, a path is an expression that conforms to the following constraints: + +``` +path: LayerName (DOT LayerName)+ +LayerName: Identifier | STAR +``` + +Among them, STAR is "*" and DOT is ".". + +We call the middle part of a path between two "." as a layer, and thus `root.A.B.C` is a path with four layers. + +It is worth noting that in the path, root is a reserved character, which is only allowed to appear at the beginning of the time series mentioned below. If root appears in other layers, it cannot be parsed and an error is reported. + +* Timeseries Path + +The timeseries path is the core concept in IoTDB. A timeseries path can be thought of as the complete path of a sensor that produces the time series data. All timeseries paths in IoTDB must start with root and end with the sensor. A timeseries path can also be called a full path. + +For example, if device1 of the vehicle type has a sensor named sensor1, its timeseries path can be expressed as: `root.vehicle.device1.sensor1`. + +> Note: The layer of timeseries paths supported by the current IoTDB must be greater than or equal to four (it will be changed to two in the future). + +* Prefix Path + +The prefix path refers to the path where the prefix of a timeseries path is located. A prefix path contains all timeseries paths prefixed by the path. For example, suppose that we have three sensors: `root.vehicle.device1.sensor1`, `root.vehicle.device1.sensor2`, `root.vehicle.device2.sensor1`, the prefix path `root.vehicle.device1` contains two timeseries paths `root.vehicle.device1.sensor1` and `root.vehicle.device1.sensor2` while `root.vehicle.device2.sensor1` is excluded. + +* Path With Star + +In order to make it easier and faster to express multiple timeseries paths or prefix paths, IoTDB provides users with the path pith star. `*` can appear in any layer of the path. According to the position where `*` appears, the path with star can be divided into two types: + +`*` appears at the end of the path; + +`*` appears in the middle of the path; + +When `*` appears at the end of the path, it represents (`*`)+, which is one or more layers of `*`. For example, `root.vehicle.device1.*` represents all paths prefixed by `root.vehicle.device1` with layers greater than or equal to 4, like `root.vehicle.device1.*`, `root.vehicle.device1.*.*`, `root.vehicle.device1.*.*.*`, etc. + +When `*` appears in the middle of the path, it represents `*` itself, i.e., a layer. For example, `root.vehicle.*.sensor1` represents a 4-layer path which is prefixed with `root.vehicle` and suffixed with `sensor1`. + +> Note1: `*` cannot be placed at the beginning of the path. + +> Note2: A path with `*` at the end has the same meaning as a prefix path, e.g., `root.vehicle.*` and `root.vehicle` is the same. + +* Timestamp + +The timestamp is the time point at which a data arrives. IoTDB timestamps are divided into two types: LONG and DATETIME (including DATETIME-INPUT and DATETIME-DISPLAY). When a user enters a timestamp, he can use a LONG type timestamp or a DATETIME-INPUT type timestamp, where the support format of the DATETIME-INPUT type timestamp is shown in Table 2-1. + +
**Table 2-1 Support format of DATETIME-INPUT type timestamp** + +|format| +|:---:| +|yyyy-MM-dd HH:mm:ss| +|yyyy/MM/dd HH:mm:ss| +|yyyy.MM.dd HH:mm:ss| +|yyyy-MM-dd'T'HH:mm:ss| +|yyyy/MM/dd'T'HH:mm:ss| +|yyyy.MM.dd'T'HH:mm:ss| +|yyyy-MM-dd HH:mm:ssZZ| +|yyyy/MM/dd HH:mm:ssZZ| +|yyyy.MM.dd HH:mm:ssZZ| +|yyyy-MM-dd'T'HH:mm:ssZZ| +|yyyy/MM/dd'T'HH:mm:ssZZ| +|yyyy.MM.dd'T'HH:mm:ssZZ| +|yyyy/MM/dd HH:mm:ss.SSS| +|yyyy-MM-dd HH:mm:ss.SSS| +|yyyy.MM.dd HH:mm:ss.SSS| +|yyyy/MM/dd'T'HH:mm:ss.SSS| +|yyyy-MM-dd'T'HH:mm:ss.SSS| +|yyyy.MM.dd'T'HH:mm:ss.SSS| +|yyyy-MM-dd HH:mm:ss.SSSZZ| +|yyyy/MM/dd HH:mm:ss.SSSZZ| +|yyyy.MM.dd HH:mm:ss.SSSZZ| +|yyyy-MM-dd'T'HH:mm:ss.SSSZZ| +|yyyy/MM/dd'T'HH:mm:ss.SSSZZ| +|yyyy.MM.dd'T'HH:mm:ss.SSSZZ| +|ISO8601 standard time format| + +
+ +IoTDB can support LONG types and DATETIME-DISPLAY types when displaying timestamps. The DATETIME-DISPLAY type can support user-defined time formats. The syntax of the custom time format is shown in Table 2-2. + +
**Table 2-2 The syntax of the custom time format** + +|Symbol|Meaning|Presentation|Examples| +|:---:|:---:|:---:|:---:| +|G|era|era|era| +|C|century of era (>=0)| number| 20| +| Y |year of era (>=0)| year| 1996| +||||| +| x |weekyear| year| 1996| +| w |week of weekyear| number |27| +| e |day of week |number| 2| +| E |day of week |text |Tuesday; Tue| +||||| +| y| year| year| 1996| +| D |day of year |number| 189| +| M |month of year |month| July; Jul; 07| +| d |day of month |number| 10| +||||| +| a |halfday of day |text |PM| +| K |hour of halfday (0~11) |number| 0| +| h |clockhour of halfday (1~12) |number| 12| +||||| +| H |hour of day (0~23)| number| 0| +| k |clockhour of day (1~24) |number| 24| +| m |minute of hour| number| 30| +| s |second of minute| number| 55| +| S |fraction of second |millis| 978| +||||| +| z |time zone |text |Pacific Standard Time; PST| +| Z |time zone offset/id| zone| -0800; -08:00; America/Los_Angeles| +||||| +| '| escape for text |delimiter| 　| +| ''| single quote| literal |'| + +
+ +* Value + +The value of a time series is actually the value sent by a sensor to IoTDB. This value can be stored by IoTDB according to the data type. At the same time, users can select the compression mode and the corresponding encoding mode according to the data type of this value. See [Data Type](需要连接到具体的网页链接Chapter2) and [Encoding](需要连接到具体的网页链接Chapter2) of this document for details on data type and corresponding encoding. + +* Point + +A data point is made up of a timestamp value pair (timestamp, value). + +* Column + +A column of data contains all values belonging to a time series and the timestamps corresponding to these values. When there are multiple columns of data, IoTDB merges the timestamps into multiple < timestamp-value > pairs (timestamp, value, value,...). + +## Data Type +IoTDB supports six data types in total: BOOLEAN (Boolean), INT32 (Integer), INT64 (Long Integer), FLOAT (Single Precision Floating Point), DOUBLE (Double Precision Floating Point), TEXT (String). + +The time series of FLOAT and DOUBLE type can specify (MAX\_POINT\_NUMBER, see [this page](需要连接到具体的网页链接Chapter5CreateTimeseriesStatement) for more information on how to specify), which is the number of digits after the decimal point of the floating point number, if the encoding method is [RLE](需要连接到具体的网页链接Chapter2RLE) or [TS\_2DIFF](需要连接到具体的网页链接Chapter2TS2DIFF) (Refer to [Create Timeseries Statement](需要连接到具体的网页链接) for more information on how to specify). If MAX\_POINT\_NUMBER is not specified, the system will use [float\_precision](需要连接到具体的网页链接chapter4float\_precision) in the configuration file "tsfile-format.properties" for configuration for the configuration method. + +When the data type of data input by the user in the system does not correspond to the data type of the time series, the system will report type errors. As shown below, the second-order difference encoding does not support the Boolean type: + +``` +IoTDB> create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODING=TS_2DIFF +error: encoding TS_2DIFF does not support BOOLEAN +``` + +## Encoding +In order to improve the efficiency of data storage, it is necessary to encode data during data writing, thereby reducing the amount of disk space used. In the process of writing and reading data, the amount of data involved in the I/O operations can be reduced to improve performance. IoTDB supports four encoding methods for different types of data: + +* PLAIN + +PLAIN encoding, the default encoding mode, i.e, no encoding, supports multiple data types. It has high compression and decompression efficiency while suffering from low space storage efficiency. + +* TS_2DIFF + +Second-order differential encoding is more suitable for encoding monotonically increasing or decreasing sequence data, and is not recommended for sequence data with large fluctuations. + +Second-order differential encoding can also be used to encode floating-point numbers, but it is necessary to specify reserved decimal digits (MAX\_POINT\_NUMBER, see [this page](需要连接到具体的网页链接Chapter5CreateTimeseriesStatement) for more information on how to specify) when creating time series. It is more suitable for storing sequence data where floating-point values appear continuously, monotonously increase or decrease, and it is not suitable for storing sequence data with high precision requirements after the decimal point or with large fluctuations. + +* RLE + +Run-length encoding is more suitable for storing sequence with continuous integer values, and is not recommended for sequence data with most of the time different values. + +Run-length encoding can also be used to encode floating-point numbers, but it is necessary to specify reserved decimal digits (MAX\_POINT\_NUMBER, see [this page](需要连接到具体的网页链接Chapter5CreateTimeseriesStatement) for more information on how to specify) when creating time series. It is more suitable for storing sequence data where floating-point values appear continuously, monotonously increase or decrease, and it is not suitable for storing sequence data with high precision requirements after the decimal point or with large fluctuations. + +* GORILLA + +GORILLA encoding is more suitable for floating-point sequence with similar values and is not recommended for sequence data with large fluctuations. + +* Correspondence between data type and encoding + +The four encodings described in the previous sections are applicable to different data types. If the correspondence is wrong, the time series cannot be created correctly. The correspondence between the data type and its supported encodings is summarized in Table 2-3. + +
**Table 2-3 The correspondence between the data type and its supported encodings** + +|Data Type |Supported Encoding| +|:---:|:---:| +|BOOLEAN| PLAIN, RLE| +|INT32 |PLAIN, RLE, TS_2DIFF| +|INT64 |PLAIN, RLE, TS_2DIFF| +|FLOAT |PLAIN, RLE, TS_2DIFF, GORILLA| +|DOUBLE |PLAIN, RLE, TS_2DIFF, GORILLA| +|TEXT |PLAIN| + +
+ +## Compression + +When the time series is written and encoded as binary data according to the specified type, IoTDB compresses the data using compression technology to further improve space storage efficiency. Although both encoding and compression are designed to improve storage efficiency, encoding techniques are usually only available for specific data types (e.g., second-order differential encoding is only suitable for INT32 or INT64 data type, and storing floating-point numbers requires multiplying them by 10m to convert to integers), after which the data is converted to a binary stream. The compression method (SNAPPY) compresses the binary stream, so the use of the compression method is no longer limited by the data type. + +IoTDB allows you to specify the compression method of the column when creating a time series. IoTDB now supports two kinds of compression: UNCOMPRESSOR (no compression) and SNAPPY compression. The specified syntax for compression is detailed in [Create Timeseries Statement](需要连接到具体的网页链接Chapter5). \ No newline at end of file diff --git a/docs/Documentation/UserGuideV0.7.0/3-Operation Manual.md b/docs/Documentation/UserGuideV0.7.0/3-Operation Manual.md new file mode 100644 index 0000000000000..03a38714d890a --- /dev/null +++ b/docs/Documentation/UserGuideV0.7.0/3-Operation Manual.md @@ -0,0 +1,854 @@ + + +- [Chapter 3: Operation Manual](#chapter-3-operation-manual) + - [Sample Data](#sample-data) + - [Data Model Selection](#data-model-selection) + - [Storage Model Selection](#storage-model-selection) + - [Storage Group Creation](#storage-group-creation) + - [Show Storage Group](#show-storage-group) + - [Timeseries Creation](#timeseries-creation) + - [Show Timeseries](#show-timeseries) + - [Precautions](#precautions) + - [Data Import](#data-import) + - [Import Historical Data](#import-historical-data) + - [Import Real-time Data](#import-real-time-data) + - [Use of INSERT Statements](#use-of-insert-statements) + - [Error Handling of INSERT Statements](#error-handling-of-insert-statements) + - [Data Query](#data-query) + - [Time Slice Query](#time-slice-query) + - [Select a Column of Data Based on a Time Interval](#select-a-column-of-data-based-on-a-time-interval) + - [Select Multiple Columns of Data Based on a Time Interval](#select-multiple-columns-of-data-based-on-a-time-interval) + - [Select Multiple Columns of Data for the Same Device According to Multiple Time Intervals](#select-multiple-columns-of-data-for-the-same-device-according-to-multiple-time-intervals) + - [Choose Multiple Columns of Data for Different Devices According to Multiple Time Intervals](#choose-multiple-columns-of-data-for-different-devices-according-to-multiple-time-intervals) + - [Down-Frequency Aggregate Query](#down-frequency-aggregate-query) + - [Down-Frequency Aggregate Query without Specifying the Time Axis Origin Position](#down-frequency-aggregate-query-without-specifying-the-time-axis-origin-position) + - [Down-Frequency Aggregate Query Specifying the Time Axis Origin Position](#down-frequency-aggregate-query-specifying-the-time-axis-origin-position) + - [Down-Frequency Aggregate Query Specifying the Time Filtering Conditions](#down-frequency-aggregate-query-specifying-the-time-filtering-conditions) + - [Automated Fill](#automated-fill) + - [Fill Function](#fill-function) + - [Correspondence between Data Type and Fill Method](#correspondence-between-data-type-and-fill-method) + - [Row and Column Control over Query Results](#row-and-column-control-over-query-results) + - [Row Control over Query Results](#row-control-over-query-results) + - [Column Control over Query Results](#column-control-over-query-results) + - [Row and Column Control over Query Results](#row-and-column-control-over-query-results-1) + - [Error Handling](#error-handling) + - [Data Maintenance](#data-maintenance) + - [Data Update](#data-update) + - [Update Single Timeseries](#update-single-timeseries) + - [Data Deletion](#data-deletion) + - [Delete Single Timeseries](#delete-single-timeseries) + - [Delete Multiple Timeseries](#delete-multiple-timeseries) + - [Priviledge Management](#priviledge-management) + - [Basic Concepts](#basic-concepts) + - [User](#user) + - [Priviledge](#priviledge) + - [Role](#role) + - [Default User](#default-user) + - [Priviledge Management Operation Examples](#priviledge-management-operation-examples) + - [Create User](#create-user) + - [Grant User Priviledge](#grant-user-priviledge) + - [Other Instructions](#other-instructions) + - [The Relationship among Users, Priviledges and Roles](#the-relationship-among-users-priviledges-and-roles) + - [List of Priviledges Included in the System](#list-of-priviledges-included-in-the-system) + - [Username Restrictions](#username-restrictions) + - [Password Restrictions](#password-restrictions) + - [Role Name Restrictions](#role-name-restrictions) + + +# Chapter 3: Operation Manual + +## Sample Data + +To make this manual more practical, we will use a specific scenario example to illustrate how to operate IoTDB databases at all stages of use. See [this page](Material-SampleData) for a look. For your convenience, we also provide you with a sample data file in real scenario to import into the IoTDB system for trial and operation. + +Download file: [IoTDB-SampleData.txt](sampledata文件下载链接). + +## Data Model Selection + +Before importing data to IoTDB, we first select the appropriate data storage model according to the [sample data](Material-SampleData), and then create the storage group and timeseries using [SET STORAGE GROUP](Chapter5,setstoragegroup) statement and [CREATE TIMESERIES](Chapter5,createtimeseries) statement respectively. + +### Storage Model Selection +According to the data attribute layers described in [sample data](Material-SampleData), we can express it as an attribute hierarchy structure based on the coverage of attributes and the subordinate relationship between them, as shown in Figure 3.1 below. Its hierarchical relationship is: power group layer - power plant layer - device layer - sensor layer. ROOT is the root node, and each node of sensor layer is called a leaf node. In the process of using IoTDB, you can directly connect the attributes on the path from ROOT node to each leaf node with ".", thus forming the name of a timeseries in IoTDB. For example, The left-most path in Figure 3.1 can generate a timeseries named `ROOT.ln.wf01.wt01.status`. + +
+ +**Figure 3.1 Attribute hierarchy structure**
+ +After getting the name of the timeseries, we need to set up the storage group according to the actual scenario and scale of the data. Because in the scenario of this chapter data is usually arrived in the unit of groups (i.e., data may be across electric fields and devices), in order to avoid frequent switching of IO when writing data, and to meet the user's requirement of physical isolation of data in the unit of groups, we set the storage group at the group layer. + +### Storage Group Creation +After selecting the storage model, according to which we can set up the corresponding storage group. The SQL statements for creating storage groups are as follows: + +``` +IoTDB > set storage group to root.ln +IoTDB > set storage group to root.sgcc +``` + +We can thus create two storage groups using the above two SQL statements. + +It is worth noting that when the path itself or the parent/child layer of the path is already set as a storage group, the path is then not allowed to be set as a storage group. For example, it is not feasible to set `root.ln.wf01` as a storage group when there exist two storage groups `root.ln` and `root.sgcc`. The system will give the corresponding error prompt as shown below: + +``` +IoTDB> set storage group to root.ln.wf01 +error: The prefix of root.ln.wf01 has been set to the storage group. +``` + +### Show Storage Group +After the storage group is created, we can use the [SHOW STORAGE GROUP](Chapter5,showstoragegroup) statement to view all the storage groups. The SQL statement is as follows: + +``` +IoTDB> show storage group +``` + +The result is as follows: +
+ +### Timeseries Creation +According to the storage model selected before, we can create corresponding timeseries in the two storage groups respectively. The SQL statements for creating timeseries are as follows: + +``` +IoTDB > create timeseries root.ln.wf01.wt01.status with datatype=BOOLEAN,encoding=PLAIN +IoTDB > create timeseries root.ln.wf01.wt01.temperature with datatype=FLOAT,encoding=RLE +IoTDB > create timeseries root.ln.wf02.wt02.hardware with datatype=TEXT,encoding=PLAIN +IoTDB > create timeseries root.ln.wf02.wt02.status with datatype=BOOLEAN,encoding=PLAIN +IoTDB > create timeseries root.sgcc.wf03.wt01.status with datatype=BOOLEAN,encoding=PLAIN +IoTDB > create timeseries root.sgcc.wf03.wt01.temperature with datatype=FLOAT,encoding=RLE +``` + +It is worth noting that when in the CRATE TIMESERIES statement the encoding method conflicts with the data type, the system will give the corresponding error prompt as shown below: + +``` +IoTDB> create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODING=TS_2DIFF +error: encoding TS_2DIFF does not support BOOLEAN +``` + +Please refer to [Encoding](Chapter2,encoding) for correspondence between data type and encoding. + +### Show Timeseries + +Currently, IoTDB supports two ways of viewing timeseries: + +* SHOW TIMESERIES statement presents all timeseries information in JSON form +* SHOW TIMESERIES <`Path`> statement returns all timeseries information and the total number of timeseries under the given <`Path`> in tabular form. timeseries information includes: timeseries path, storage group it belongs to, data type, encoding type. <`Path`> needs to be a prefix path or a path with star or a timeseries path. SQL statements are as follows: + +``` +IoTDB> show timeseries root +IoTDB> show timeseries root.ln +``` + +The results are shown below respectly: + +
+
+ +It is worth noting that when the path queries does not exist, the system will give the corresponding error prompt as shown below: + +``` +IoTDB> show timeseries root.ln.wf03 +Msg: Failed to fetch timeseries root.ln.wf03's metadata because: Timeseries does not exist. +``` + +### Precautions + +Version 0.7.0 imposes some limitations on the scale of data that users can operate: + +Limit 1: Assuming that the JVM memory allocated to IoTDB at runtime is p and the user-defined size of data in memory written to disk ([group\_size\_in\_byte](Chap4group_size_in_byte)) is Q, then the number of storage groups should not exceed p/q. + +Limit 2: The number of timeseries should not exceed the ratio of JVM memory allocated to IoTDB at run time to 20KB. + +## Data Import +### Import Historical Data + +This feature is not supported in version 0.7.0. + +### Import Real-time Data + +IoTDB provides users with a variety of ways to insert real-time data, such as directly inputting [INSERT SQL statement](chapter5.InsertRecordStatement) in [Cli/Shell tools](cli-page), or using [Java JDBC](Java-api-page,commingsoon) to perform single or batch execution of [INSERT SQL statement](chapter5.InsertRecordStatement). + +This section mainly introduces the use of [INSERT SQL statement](chapter5.InsertRecordStatement) for real-time data import in the scenario. See Section 7.1.3.1 for a detailed syntax of [INSERT SQL statement](chapter5.InsertRecordStatement). + +#### Use of INSERT Statements +The [INSERT SQL statement](chapter5.InsertRecordStatement) statement can be used to insert data into one or more specified timeseries that have been created. For each point of data inserted, it consists of a [timestamp](chap2,timestamp) and a sensor acquisition value of a numerical type (see [Data Type](Chapter2datatype)). + +In the scenario of this section, take two timeseries `root.ln.wf02.wt02.status` and `root.ln.wf02.wt02.hardware` as an example, and their data types are BOOLEAN and TEXT, respectively. + +The sample code for single column data insertion is as follows: +``` +IoTDB > insert into root.ln.wf02.wt02(timestamp,status) values(1,true) +IoTDB > insert into root.ln.wf02.wt02(timestamp,hardware) values(1, "v1") +``` + +The above example code inserts the long integer timestamp and the value "true" into the timeseries `root.ln.wf02.wt02.status` and inserts the long integer timestamp and the value "v1" into the timeseries `root.ln.wf02.wt02.hardware`. When the execution is successful, a prompt "execute successfully" will appear to indicate that the data insertion has been completed. + +> Note: In IoTDB, TEXT type data can be represented by single and double quotation marks. The insertion statement above uses double quotation marks for TEXT type data. The following example will use single quotation marks for TEXT type data. + +The INSERT statement can also support the insertion of multi-column data at the same time point. The sample code of inserting the values of the two timeseries at the same time point '2' is as follows: + +``` +IoTDB > insert into root.ln.wf02.wt02(timestamp, status, hardware) VALUES (2, false, 'v2') +``` + +After inserting the data, we can simply query the inserted data using the SELECT statement: + +``` +IoTDB > select * from root.ln.wf02 where time < 3 +``` + +The result is shown below. From the query results, it can be seen that the insertion statements of single column and multi column data are performed correctly. + +
+ +### Error Handling of INSERT Statements +If the user inserts data into a non-existent timeseries, for example, execute the following commands: + +``` +IoTDB > insert into root.ln.wf02.wt02(timestamp, temperature) values(1,"v1") +``` + +Because `root.ln.wf02.wt02. temperature` does not exist, the system will return the following ERROR information: + +``` +error: Timeseries root.ln.wf02.wt02.temperature does not exist. +``` +If the data type inserted by the user is inconsistent with the corresponding data type of the timeseries, for example, execute the following command: + +``` +IoTDB > insert into root.ln.wf02.wt02(timestamp,hardware) values(1,100) +``` +The system will return the following ERROR information: + +``` +error: The TEXT data type should be covered by " or ' +``` + +## Data Query +### Time Slice Query + +This chapter mainly introduces the relevant examples of time slice query using IoTDB SELECT statements. Detailed SQL syntax and usage specifications can be found in [SQL Documentation](Chpater5). You can also use the [Java JDBC](Java-api-page,commingsoon) standard interface to execute related queries. + +#### Select a Column of Data Based on a Time Interval + +The SQL statement is: + +``` +select temperature from root.ln.wf01.wt01 where time < 2017-11-01T00:08:00.000 +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is the temperature sensor (temperature). The SQL statement requires that all temperature sensor values before the time point of "2017-11-01T00:08:00.000" be selected. + +The execution result of this SQL statement is as follows: + +
+ +#### Select Multiple Columns of Data Based on a Time Interval + +The SQL statement is: + +``` +select status, temperature from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000; +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is "status" and "temperature". The SQL statement requires that the status and temperature sensor values between the time point of "2017-11-01T00:05:00.000" and "2017-11-01T00:12:00.000" be selected. + +The execution result of this SQL statement is as follows: +
+ +#### Select Multiple Columns of Data for the Same Device According to Multiple Time Intervals +IoTDB supports specifying multiple time interval conditions in a query. Users can combine time interval conditions at will according to their needs. For example, the SQL statement is: + +``` +select status,temperature from root.ln.wf01.wt01 where (time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000) or (time >= 2017-11-01T16:35:00.000 and time <= 2017-11-01T16:37:00.000); +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is "status" and "temperature"; the statement specifies two different time intervals, namely "2017-11-01T00:05:00.000 to 2017-11-01T00:12:00.000" and "2017-11-01T16:35:00.000 to 2017-11-01T16:37:00.000". The SQL statement requires that the values of selected timeseries satisfying any time interval be selected. + +The execution result of this SQL statement is as follows: +
+ + +#### Choose Multiple Columns of Data for Different Devices According to Multiple Time Intervals +The system supports the selection of data in any column in a query, i.e., the selected columns can come from different devices. For example, the SQL statement is: + +``` +select wf01.wt01.status,wf02.wt02.hardware from root.ln where (time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000) or (time >= 2017-11-01T16:35:00.000 and time <= 2017-11-01T16:37:00.000); +``` +which means: + +The selected timeseries are "the power supply status of ln group wf01 plant wt01 device" and "the hardware version of ln group wf02 plant wt02 device"; the statement specifies two different time intervals, namely "2017-11-01T00:05:00.000 to 2017-11-01T00:12:00.000" and "2017-11-01T16:35:00.000 to 2017-11-01T16:37:00.000". The SQL statement requires that the values of selected timeseries satisfying any time interval be selected. + +The execution result of this SQL statement is as follows: +
+ +### Down-Frequency Aggregate Query +This section mainly introduces the related examples of down-frequency aggregation query, using the [GROUP BY clause](Chap5-groupbystatement), which is used to partition the result set according to the user's given partitioning conditions and aggregate the partitioned result set. IoTDB supports partitioning result sets according to time intervals, and by default results are sorted by time in ascending order. You can also use the [Java JDBC](Java-api-page,commingsoon) standard interface to execute related queries. + +The GROUP BY statement provides users with three types of specified parameters: + +* Parameter 1: Time interval for dividing the time axis +* Parameter 2: Time axis origin position (optional) +* Parameter 3: The display window(s) (one or more) on the time axis + +The actual meanings of the three types of parameters are shown in Figure 3.2 below. Among them, the paramter 2 is optional. Next we will give three typical examples of frequency reduction aggregation: parameter 2 specified, parameter 2 not specified, and time filtering conditions specified. + +
+ +**Figure 3.2 The actual meanings of the three types of parameters**
+ +#### Down-Frequency Aggregate Query without Specifying the Time Axis Origin Position +The SQL statement is: + +``` +select count(status), max_value(temperature) from root.ln.wf01.wt01 group by (1d, [2017-11-01T00:00:00, 2017-11-07T23:00:00]); +``` +which means: + +Since the user does not specify the time axis origin position, the GROUP BY statement will by default set the origin at 0 (+0 time zone) on January 1, 1970. + +The first parameter of the GROUP BY statement above is the time interval for dividing the time axis. Taking this parameter (1d) as time interval and the default origin as the dividing origin, the time axis is divided into several continuous intervals, which are [0,1d], [1d, 2d], [2d, 3d], etc. + +The second parameter of the GROUP BY statement above is the display window paramter, which determines the final display range is [2017-11-01T00:00:00, 2017-11-07T23:00:00]. + +Then the system will use the time and value filtering condition in the WHERE clause and the second parameter of the GROUP BY statement as the data filtering condition to obtain the data satisfying the filtering condition (which in this case is the data in the range of [2017-11-01T00:00:00, 2017-11-07 T23:00:00]), and map these data to the previously segmented time axis (in this case there are mapped data in every 1-day period from 2017-11-01T00:00:00 to 2017-11-07T23:00:00:00). + +Since there is data for each time period in the result range to be displayed, the execution result of the SQL statement is shown below: + +
+ +#### Down-Frequency Aggregate Query Specifying the Time Axis Origin Position +The SQL statement is: + +``` +select count(status), max_value(temperature) from root.ln.wf01.wt01 group by (1d, 2017-11-03 00:00:00, [2017-11-01 00:00:00, 2017-11-07 23:00:00]); +``` + +which means: + +Since the user specifies the time axis origin position parameter as 2017-11-03 00:00:00, the GROUP BY statement will set the origin at 0 (system default time zone) on November 3, 2017. + +The first parameter of the GROUP BY statement above is the time interval for dividing the time axis. Taking this parameter (1d) as time interval and the speicified origin as the dividing origin, the time axis is divided into several continuous intervals, which are [2017-11-02T00:00:00, 2017-11-03T00:00:00], [2017-11-03T00:00:00, 2017-11-04T00:00:00], etc. + +The third parameter of the GROUP BY statement above is the display window paramter, which determines the final display range is [2017-11-01T00:00:00, 2017-11-07T23:00:00]. + +hen the system will use the time and value filtering condition in the WHERE clause and the second parameter of the GROUP BY statement as the data filtering condition to obtain the data satisfying the filtering condition (which in this case is the data in the range of [2017-11-01T00:00:00, 2017-11-07T23:00:00]), and map these data to the previously segmented time axis (in this case there are mapped data in every 1-day period from 2017-11-01T00:00:00 to 2017-11-07T23:00:00:00). + +Since there is data for each time period in the result range to be displayed, the execution result of the SQL statement is shown below: + +
+ +#### Down-Frequency Aggregate Query Specifying the Time Filtering Conditions +The SQL statement is: + +``` +select count(status), max_value(temperature) from root.ln.wf01.wt01 where time > 2017-11-03T06:00:00 and temperature > 20 group by(1h, [2017-11-03T00:00:00, 2017-11-03T23:00:00]); +``` +which means: + +Since the user does not specify the time axis origin position, the GROUP BY statement will by default set the origin at 0 (+0 time zone) on January 1, 1970. + +The first parameter of the GROUP BY statement above is the time interval for dividing the time axis. Taking this parameter (1d) as time interval and the default origin as the dividing origin, the time axis is divided into several continuous intervals, which are [0,1d], [1d, 2d], [2d, 3d], etc. + +The second parameter of the GROUP BY statement above is the display window paramter, which determines the final display range is [2017-11-03T00:00:00, 2017-11-03T23:00:00]. + +Then the system will use the time and value filtering condition in the WHERE clause and the second parameter of the GROUP BY statement as the data filtering condition to obtain the data satisfying the filtering condition (which in this case is the data in the range of (2017-11-03T06:00:00, 2017-11-03T23:00:00] and satisfying root.ln.wf01.wt01.temperature > 20), and map these data to the previously segmented time axis (in this case there are mapped data in every 1-day period from 2017-11-03T00:06:00 to 2017-11-03T23:00:00). + +Since there is no data in the result range [2017-11-03T00:00:00, 2017-11-03T00:06:00], the aggregation results of this segment will be null. There is data in all other time periods in the result range to be displayed. The execution result of the SQL statement is shown below: + +
+ +It is worth noting that the path after SELECT in GROUP BY statement must be aggregate function, otherwise the system will give the corresponding error prompt, as shown below: + +
+ +### Automated Fill +In the actual use of IoTDB, when doing the query operation of timeseries, situations where the value is null at some time points may appear, which will obstruct the further analysis by users. In order to better reflect the degree of data change, users expect missing values to be automatically filled. Therefore, the IoTDB system introduces the function of Automated Fill. + +Automated fill function refers to filling empty values according to the user's specified method and effective time range when performing timeseries queries for single or multiple columns. If the queried point's value is not null, the fill function will not work. + +> Note: In the current version 0.7.0, IoTDB provides users with two methods: Previous and Linear. The previous method fills blanks with previous value. The linear method fills blanks through linear fitting. And the fill function can only be used when performing point-in-time queries. + +#### Fill Function +* Previous Function + +When the value of the queried timestamp is null, the value of the previous timestamp is used to fill the blank. The formalized previous method is as follows (see Section 7.1.3.6 for detailed syntax): + +``` +select from where time = fill([previous, ], …) +``` + +Detailed descriptions of all parameters are given in Table 3-4. + +
**Table 3-4 Previous fill paramter list** + +|Parameter name (case insensitive)|Interpretation| +|:---|:---| +|path, prefixPath|query path; mandatory field| +|T|query timestamp (only one can be specified); mandatory field| +|data\_type|the type of data used by the fill method. Optional values are int32, int64, float, double, boolean, text; optional field| +|before\_range|represents the valid time range of the previous method. The previous method works when there are values in the [T-before\_range, T] range. When before\_range is not specified, before\_range takes the default value T; optional field| +
+ +Here we give an example of filling null values using the previous method. The SQL statement is as follows: + +``` +select temperature from root.sgcc.wf03.wt01 where time = 2017-11-01T16:37:50.000 fill(float[previous, 1m]) +``` +which means: + +Because the timeseries root.sgcc.wf03.wt01.temperature is null at 2017-11-01T16:37:50.000, the system uses the previous timestamp of 2017-11-01T16:37:50.000 (and the timestamp is in the [2017-11-01T16:36:50.000, 2017-11-01T16:37:50.000] time range) for fill and display. + +On the [sample data](Material-SampleData), the execution result of this statement is shown below: +
+ +It is worth noting that if there is no value in the specified valid time range, the system will not fill the null value, as shown below: +
+ +* Linear Method + +When the value of the queried timestamp is null, the value of the previous and the next timestamp is used to fill the blank. The formalized linear method is as follows: + +``` +select from where time = fill([linear, , ]…) +``` +Detailed descriptions of all parameters are given in Table 3-5. + +
**Table 3-5 Linear fill paramter list** + +|Parameter name (case insensitive)|Interpretation| +|:---|:---| +|path, prefixPath|query path; mandatory field| +|T|query timestamp (only one can be specified); mandatory field| +|data_type|the type of data used by the fill method. Optional values are int32, int64, float, double, boolean, text; optional field| +|before\_range, after\_range|represents the valid time range of the linear method. The previous method works when there are values in the [T-before\_range, T+after\_range] range. When before\_range and after\_range are not explicitly specified, both before\_range and after\_range default to infinity; optional field| +
+ +Here we give an example of filling null values using the linear method. The SQL statement is as follows: + +``` +select temperature from root.sgcc.wf03.wt01 where time = 2017-11-01T16:37:50.000 fill(float [linear, 1m, 1m]) +``` +which means: + +Because the timeseries root.sgcc.wf03.wt01.temperature is null at 2017-11-01T16:37:50.000, the system uses the previous timestamp 2017-11-01T16:37:00.000 (and the timestamp is in the [2017-11-01T16:36:50.000, 2017-11-01T16:37:50.000] time range) and its value 21.927326, the next timestamp 2017-11-01T16:39:00.000 (and the timestamp is in the [2017-11-01T16:36:50.000, 2017-11-01T16:37:50.000] time range) and its value 25.311783 to perform linear fitting calculation: 21.927326 + (25.311783-21.927326)/60s*50s = 24.747707 + +On the [sample data](Material-SampleData), the execution result of this statement is shown below: +
+ +#### Correspondence between Data Type and Fill Method +Data types and the supported fill methods are shown in Table 3-6. + +
**Table 3-6 Data types and the supported fill methods** + +|Data Type|Supported Fill Methods| +|:---|:---| +|boolean|previous| +|int32|previous, linear| +|int64|previous, linear| +|float|previous, linear| +|double|previous, linear| +|text|previous| +
+ +It is worth noting that IoTDB will give error prompts for fill methods that are not supported by data types, as shown below: + +
+ +When the fill method is not specified, each data type bears its own default fill methods and parameters. The corresponding relationship is shown in Table 3-7. + +
**Table 3-7 Default fill methods and parameters for various data types** + +|Data Type|Default Fill Methods and Parameters| +|:---|:---| +|boolean|previous, 0| +|int32|linear, 0, 0| +|int64|linear, 0, 0| +|float|linear, 0, 0| +|double|linear, 0, 0| +|text|previous, 0| +
+ +> Note: In version 0.7.0, at least one fill method should be specified in the Fill statement. + +### Row and Column Control over Query Results + +IoTDB provides [LIMIT/SLIMIT](Chpater5,LimitStatement) clause and [OFFSET/SOFFSET](Chpater5,LimitStatement) clause in order to make users have more control over query results. The use of LIMIT and SLIMIT clauses allows users to control the number of rows and columns of query results, and the use of OFFSET and SOFSET clauses allows users to set the starting position of the results for display. + +This chapter mainly introduces related examples of row and column control of query results. You can also use the [Java JDBC](Java-api-page,commingsoon) standard interface to execute queries. + +#### Row Control over Query Results +By using LIMIT and OFFSET clauses, users can control the query results in a row-related manner. We will demonstrate how to use LIMIT and OFFSET clauses through the following examples. + +* Example 1: basic LIMIT clause + +The SQL statement is: + +``` +select status, temperature from root.ln.wf01.wt01 limit 10 +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is "status" and "temperature". The SQL statement requires the first 10 rows of the query result be returned. + +The result is shown below: + +
+ + +* Example 2: LIMIT clause with OFFSET + +The SQL statement is: + +``` +select status, temperature from root.ln.wf01.wt01 limit 5 offset 3 +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is "status" and "temperature". The SQL statement requires rows 3 to 7 of the query result be returned (with the first row numbered as row 0). + +The result is shown below: + +
+ +* Example 3: LIMIT clause combined with WHERE clause + +The SQL statement is: + +``` +select status,temperature from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time< 2017-11-01T00:12:00.000 limit 2 offset 3 +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is "status" and "temperature". The SQL statement requires rows 3 to 4 of the status and temperature sensor values between the time point of "2017-11-01T00:05:00.000" and "2017-11-01T00:12:00.000" be returned (with the first row numbered as row 0). + +The result is shown below: + +
+ +* Example 4: LIMIT clause combined with GROUP BY clause + +The SQL statement is: + +``` +select count(status), max_value(temperature) from root.ln.wf01.wt01 group by (1d,[2017-11-01T00:00:00, 2017-11-07T23:00:00]) limit 5 offset 3 +``` +which means: + +The SQL statement clause requires rows 3 to 7 of the query result be returned (with the first row numbered as row 0). + +The result is shown below: + +
+ +It is worth noting that because the current FILL clause can only fill in the missing value of timeseries at a certain time point, that is to say, the execution result of FILL clause is exactly one line, so LIMIT and OFFSET are not expected to be used in combination with FILL clause, otherwise errors will be prompted. For example, executing the following SQL statement: + +``` +select temperature from root.sgcc.wf03.wt01 where time = 2017-11-01T16:37:50.000 fill(float[previous, 1m]) limit 10 +``` + +The SQL statement will not be executed and the corresponding error prompt is given as follows: + +
+ +#### Column Control over Query Results + +By using SLIMIT and SOFFSET clauses, users can control the query results in a column-related manner. We will demonstrate how to use SLIMIT and SOFFSET clauses through the following examples. + +* Example 1: basic SLIMIT clause + +The SQL statement is: + +``` +select * from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000 slimit 1 +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is the first column under this device, i.e., the power supply status. The SQL statement requires the status sensor values between the time point of "2017-11-01T00:05:00.000" and "2017-11-01T00:12:00.000" be selected. + +The result is shown below: + +
+ +* Example 2: SLIMIT clause with SOFFSET + +The SQL statement is: + +``` +select * from root.ln.wf01.wt01 +where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000 +slimit 1 soffset 1 +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is the second column under this device, i.e., the temperature. The SQL statement requires the temperature sensor values between the time point of "2017-11-01T00:05:00.000" and "2017-11-01T00:12:00.000" be selected. + +The result is shown below: + +
+ +* Example 3: SLIMIT clause combined with GROUP BY clause + +The SQL statement is: + +``` +select max_value(*) from root.ln.wf01.wt01 group by (1d, [2017-11-01T00:00:00, 2017-11-07T23:00:00]) slimit 1 soffset 1 +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is the second column under this device, i.e., the temperature. + +The result is shown below: + +
+ +* Example 4: SLIMIT clause combined with FILL clause + +The SQL statement is: + +``` +select * from root.sgcc.wf03.wt01 where time = 2017-11-01T16:37:50.000 fill(float[previous, 1m]) slimit 1 soffset 1 +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is the second column under this device, i.e., the temperature. + +The result is shown below: + +
+ +It is worth noting that SLIMIT clause is expected to be used in conjunction with star path or prefix path, and the system will prompt errors when SLIMIT clause is used in conjunction with complete path query. For example, executing the following SQL statement: + +``` +select status,temperature from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000 slimit 1 +``` + +The SQL statement will not be executed and the corresponding error prompt is given as follows: +
+ +#### Row and Column Control over Query Results + +In addition to row or column control over query results, IoTDB allows users to control both rows and columns of query results. Here is a complete example with both LIMIT clauses and SLIMIT clauses. + +The SQL statement is: + +``` +select * from root.ln.wf01.wt01 limit 10 offset 100 slimit 2 soffset 0 +``` +which means: + +The selected device is ln group wf01 plant wt01 device; the selected timeseries is columns 0 to 1 under this device (with the first column numbered as column 0). The SQL statement clause requires rows 100 to 109 of the query result be returned (with the first row numbered as row 0). + +The result is shown below: + +
+ +#### Error Handling + +When the parameter N/SN of LIMIT/SLIMIT exceeds the size of the result set, IoTDB will return all the results as expected. For example, the query result of the original SQL statement consists of six rows, and we select the first 100 rows through the LIMIT clause: + +``` +select status,temperature from root.ln.wf01.wt01 +where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000 +limit 100 +``` +The result is shown below: + +
+ +When the parameter N/SN of LIMIT/SLIMIT clause exceeds the allowable maximum value (N/SN is of type int32), the system will prompt errors. For example, executing the following SQL statement: + +``` +select status,temperature from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000 limit 1234567890123456789 +``` +The SQL statement will not be executed and the corresponding error prompt is given as follows: + +
+ +When the parameter N/SN of LIMIT/SLIMIT clause is not a positive intege, the system will prompt errors. For example, executing the following SQL statement: + +``` +select status,temperature from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000 limit 13.1 +``` + +The SQL statement will not be executed and the corresponding error prompt is given as follows: + +
+ +When the parameter OFFSET of LIMIT clause exceeds the size of the result set, IoTDB will return an empty result set. For example, executing the following SQL statement: + +``` +select status,temperature from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000 limit 2 offset 6 +``` +The result is shown below: +
+ +When the parameter SOFFSET of SLIMIT clause is not smaller than the number of available timeseries, the system will prompt errors. For example, executing the following SQL statement: + +``` +select * from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000 slimit 1 soffset 2 +``` +The SQL statement will not be executed and the corresponding error prompt is given as follows: +
+ +## Data Maintenance +### Data Update + +Users can use [UPDATE statements](Chap5,updatestatement) to update data over a period of time in a specified timeseries. When updating data, users can select a timeseries to be updated (version 0.7.0 does not support multiple timeseries updates) and specify a time point or period to be updated (version 0.7.0 must have time filtering conditions). + +In a JAVA programming environment, you can use the [Java JDBC](Java-api-page,commingsoon) to execute single or batch UPDATE statements. + +#### Update Single Timeseries +Taking the power supply status of ln group wf02 plant wt02 device as an example, there exists such a usage scenario: + +After data access and analysis, it is found that the power supply status from 2017-11-01 15:54:00 to 2017-11-01 16:00:00 is true, but the actual power supply status is abnormal. You need to update the status to false during this period. The SQL statement for this operation is: + +``` +update root.ln.wf02 SET wt02.status = false where time <=2017-11-01T16:00:00 and time >= 2017-11-01T15:54:00 +``` +It should be noted that when the updated data type does not match the actual data type, IoTDB will give the corresponding error prompt as shown below: + +``` +IoTDB> update root.ln.wf02 set wt02.status = 1205 where time < now() +error: The BOOLEAN data type should be true/TRUE or false/FALSE +``` +When the updated path does not exist, IoTDB will give the corresponding error prompt as shown below: + +``` +IoTDB> update root.ln.wf02 set wt02.sta = false where time < now() +error: do not select any existing path +``` +### Data Deletion + +Users can delete data that meet the deletion condition in the specified timeseries by using the [DELETE statement](Chap5,deletestatement). When deleting data, users can select one or more timeseries paths, prefix paths, or paths with star to delete data before a certain time (version 0.7.0 does not support the deletion of data within a closed time interval). + +In a JAVA programming environment, you can use the [Java JDBC](Java-api-page,commingsoon) to execute single or batch UPDATE statements. + +#### Delete Single Timeseries +Taking ln Group as an example, there exists such a usage scenario: + +The wf02 plant's wt02 device has many segments of errors in its power supply status before 2017-11-01 16:26:00, and the data cannot be analyzed correctly. The erroneous data affected the correlation analysis with other devices. At this point, the data before this time point needs to be deleted. The SQL statement for this operation is + +``` +delete from root.ln.wf02.wt02.status where time<=2017-11-01T16:26:00; +``` + +#### Delete Multiple Timeseries +When both the power supply status and hardware version of the ln group wf02 plant wt02 device before 2017-11-01 16:26:00 need to be deleted, the prefix path with broader meaning (see Section 3.1.6 of this manual) or the path with star can be used to delete the data. The SQL statement for this operation is: + +``` +delete from root.ln.wf02.wt02 where time <= 2017-11-01T16:26:00; +``` +or + +``` +delete from root.ln.wf02.wt02.* where time <= 2017-11-01T16:26:00; +``` +It should be noted that when the deleted path does not exist, IoTDB will give the corresponding error prompt as shown below: + +``` +IoTDB> delete from root.ln.wf03.wt02.status where time < now() +error: TimeSeries does not exist and cannot be delete data +``` + +## Priviledge Management +IoTDB provides users with priviledge management operations, so as to ensure data security. + +We will show you basic user priviledge management operations through the following specific examples. Detailed SQL syntax and usage details can be found in [Chapter5.SQL Documentation](chap5). At the same time, in the JAVA programming environment, you can use the [Java JDBC](Java-api-page,commingsoon) to execute priviledge management statements in a single or batch mode. + +### Basic Concepts +#### User +The user is the legal user of the database. A user corresponds to a unique username and has a password as a means of authentication. Before using a database, a person must first provide a legitimate username and password to make himself/herself a user. + +#### Priviledge +The database provides a variety of operations, and not all users can perform all operations. If a user can perform an operation, the user is said to have the priviledge to perform the operation. Priviledges can be divided into data management priviledge (such as adding, deleting and modifying data) and authority management priviledge (such as creation and deletion of users and roles, granting and revoking of priviledges, etc.). Data management priviledge often needs a path to limit its effective range, which is a subtree rooted at the path's corresponding node. + +#### Role +A role is a set of priviledges and has a unique role name as an identifier. A user usually corresponds to a real identity (such as a traffic dispatcher), while a real identity may correspond to multiple users. These users with the same real identity tend to have the same priviledges. Roles are abstractions that can unify the management of such priviledges. + +#### Default User +There is a default user in IoTDB after the initial installation: root, and the default password is root. This user is an administrator user, who cannot be deleted and has all the priviledges. Neither can new priviledges be granted to the root user nor can priviledges owned by the root user be deleted. + +### Priviledge Management Operation Examples +According to the [sample data](material-sampledata), the sample data of IoTDB may belong to different power generation groups such as ln, sgcc, etc. Different power generation groups do not want others to obtain their own database data, so we need to have data priviledge isolated at the group layer. + +#### Create User + +We can create two users for ln and sgcc groups, named ln\_write\_user and sgcc\_write\_user, with both passwords being write\_pwd. The SQL statement is: + +``` +CREATE USER ln_write_user write_pwd +CREATE USER sgcc_write_user write_pwd +``` +Then use the following SQL statement to show the user: + +``` +LIST USER +``` +As can be seen from the result shown below, the two users have been created: + +
+ +#### Grant User Priviledge +At this point, although two users have been created, they do not have any priviledges, so they can not operate on the database. For example, we use ln_write_user to write data in the database, the SQL statement is: + +``` +INSERT INTO root.ln.wf01.wt01(timestamp,status) values(1509465600000,true) +``` +The SQL statement will not be executed and the corresponding error prompt is given as follows: + +
+ +Now, we grant the two users write priviledges to the corresponding storage groups, and try to write data again. The SQL statement is: + +``` +GRANT USER ln_write_user PRIVILEGES 'INSERT_TIMESERIES' on root.ln +GRANT USER sgcc_write_user PRIVILEGES 'INSERT_TIMESERIES' on root.sgcc +INSERT INTO root.ln.wf01.wt01(timestamp, status) values(1509465600000, true) +``` +The execution result is as follows: +
+ +### Other Instructions +#### The Relationship among Users, Priviledges and Roles + +A Role is a set of priviledges, and priviledges and roles are both attributes of users. That is, a role can have several priviledges and a user can have several roles and priviledges (called the user's own priviledges). + +At present, there is no conflicting priviledge in IoTDB, so the real priviledges of a user is the union of the user's own priviledges and the priviledges of the user's roles. That is to say, to determine whether a user can perform an operation, it depends on whether one of the user's own priviledges or the priviledges of the user's roles permits the operation. The user's own priviledges and priviledges of the user's roles may overlap, but it does not matter. + +It should be noted that if users have a priviledge (corresponding to operation A) themselves and their roles contain the same priviledge, then revoking the priviledge from the users themselves alone can not prohibit the users from performing operation A, since it is necessary to revoke the priviledge from the role, or revoke the role from the user. Similarly, revoking the priviledge from the users's roles alone can not prohibit the users from performing operation A. + +At the same time, changes to roles are immediately reflected on all users who own the roles. For example, adding certain priviledges to roles will immediately give all users who own the roles corresponding priviledges, and deleting certain priviledges will also deprive the corresponding users of the priviledges (unless the users themselves have the priviledges). + +#### List of Priviledges Included in the System + +
**Table 3-8 List of Priviledges Included in the System** + +|Priviledge Name|Interpretation| +|:---|:---| +|SET\_STORAGE\_GROUP|create timeseries; set storage groups; path dependent| +|INSERT\_TIMESERIES|insert data; path dependent| +|UPDATE\_TIMESERIES|update data; path dependent| +|READ\_TIMESERIES|query data; path dependent| +|DELETE\_TIMESERIES|delete data or timeseries; path dependent| +|CREATE\_USER|create users; path independent| +|DELETE\_USER|delete users; path independent| +|MODIFY\_PASSWORD|modify passwords for all users; path independent; (Those who do not have this priviledge can still change their own asswords. )| +|LIST\_USER|list all users; list a user's priviledges; list a user's roles with three kinds of operation priviledges; path independent| +|GRANT\_USER\_PRIVILEGE|grant user priviledges; path independent| +|REVOKE\_USER\_PRIVILEGE|revoke user priviledges; path independent| +|GRANT\_USER\_ROLE|grant user roles; path independent| +|REVOKE\_USER\_ROLE|revoke user roles; path independent| +|CREATE\_ROLE|create roles; path independent| +|DELETE\_ROLE|delete roles; path independent| +|LIST\_ROLE|list all roles; list the priviledges of a role; list the three kinds of operation priviledges of all users owning a role; path independent| +|GRANT\_ROLE\_PRIVILEGE|grant role priviledges; path independent| +|REVOKE\_ROLE\_PRIVILEGE|revoke role priviledges; path independent| +
+ +#### Username Restrictions +IoTDB specifies that the character length of a username should not be less than 4, and the username cannot contain spaces. +#### Password Restrictions +IoTDB specifies that the character length of a password should not be less than 4, and the password cannot contain spaces. The password is encrypted with MD5. +#### Role Name Restrictions +IoTDB specifies that the character length of a role name should not be less than 4, and the role name cannot contain spaces. diff --git a/docs/Documentation/UserGuideV0.7.0/4-Deployment and Management.md b/docs/Documentation/UserGuideV0.7.0/4-Deployment and Management.md new file mode 100644 index 0000000000000..ad0217d5fcc17 --- /dev/null +++ b/docs/Documentation/UserGuideV0.7.0/4-Deployment and Management.md @@ -0,0 +1,749 @@ + + +- [Chapter4: Deployment and Management](#chapter4-deployment-and-management) + - [Deployment](#deployment) + - [Prerequisites](#prerequisites) + - [Installation from binary files](#installation-from--binary-files) + - [Installation from source code](#installation-from-source-code) + - [Configuration](#configuration) + - [IoTDB Environment Configuration File](#iotdb-environment-configuration-file) + - [IoTDB System Configuration File](#iotdb-system-configuration-file) + - [File Layer](#file-layer) + - [Engine Layer](#engine-layer) + - [System Monitor](#system-monitor) + - [System Status Monitoring](#system-status-monitoring) + - [Data Status Monitoring](#data-status-monitoring) + - [Writing Data Monitor](#writing-data-monitor) + - [Example](#example) + - [System log](#system-log) + - [Dynamic System Log Configuration](#dynamic-system-log-configuration) + - [Connect JMX](#connect-jmx) + - [Interface Instruction](#interface-instruction) + - [Data Management](#data-management) + - [Data Files](#data-files) + - [System Files](#system-files) + - [Pre-write Log Files](#pre-write-log-files) + - [Example of Setting Data storage Directory](#example-of-setting-data-storage-directory) + + +# Chapter4: Deployment and Management + +## Deployment + +IoTDB provides you two installation methods, you can refer to the following suggestions, choose one of them: + +* Installation from binary files. Download the binary files from the official website. This is the recommended method, in which you will get a binary released package which is out-of-the-box. +* Installation from source code. If you need to modify the code yourself, you can use this method. + +### Prerequisites + +To install and use IoTDB, you need to have: + +1. Java >= 1.8 (Please make sure the environment path has been set) +2. Maven >= 3.0 (If you want to compile and install IoTDB from source code) +3. TsFile >= 0.7.0 (TsFile Github page: [https://github.com/thulab/tsfile](https://github.com/thulab/tsfile)) +4. IoTDB-JDBC >= 0.7.0 (IoTDB-JDBC Github page: [https://github.com/thulab/iotdb-jdbc](https://github.com/thulab/iotdb-jdbc)) + +TODO: TsFile and IoTDB-JDBC dependencies will be removed after the project reconstruct. + +### Installation from binary files + +IoTDB provides you binary files which contains all the necessary components for the IoTDB system to run. You can get them on our website [http://tsfile.org/download](http://tsfile.org/download). + +``` +NOTE: +iotdb-.tar.gz # For Linux or MacOS +iotdb-.zip # For Windows +``` + +After downloading, you can extract the IoTDB tarball using the following operations: + +``` +Shell > uzip iotdb-.zip # For Windows +Shell > tar -zxf iotdb-.tar.gz # For Linux or MacOS +``` + +The IoTDB project will be at the subfolder named iotdb. The folder will include the following contents: + +``` +iotdb/ <-- root path +| ++- bin/ <-- script files +| ++- conf/ <-- configuration files +| ++- lib/ <-- project dependencies +| ++- LICENSE <-- LICENSE +``` + +### Installation from source code + +Use git to get IoTDB source code: + +``` +Shell > git clone https://github.com/thulab/iotdb.git +``` + +If you are not the first time that building IoTDB, remember deleting the following files: + +``` +rm -rf iotdb/data/ +rm -rf iotdb/lib/ +``` + +Then under the IoTDB path, you can build IoTDB using Maven: + +``` +mvn clean package -Dmaven.test.skip=true +``` + +If successful, you will see the the following text in the terminal: + +``` +[INFO] BUILD SUCCESS +``` +Otherwise, you may need to check the error statements and fix the problems. + +After build, the IoTDB project will be at the subfolder named iotdb. The folder will include the following contents: + + +``` +iotdb/ <-- root path +| ++- bin/ <-- script files +| ++- conf/ <-- configuration files +| ++- lib/ <-- project dependencies +| ++- LICENSE <-- LICENSE +``` + +## Configuration + + +Before starting to use IoTDB, you need to config the configuration files first. For your convenience, we have already set the default config in the files. + +In total, we provide users three kinds of configurations module: + +* environment configuration file (iotdb-env.bat, iotdb-env.sh). The default configuration file for the environment configuration item. Users can configure the relevant system configuration items of JAVA-JVM in the file. +* system configuration file (tsfile-format.properties, iotdb-engine.properties). + * tsfile-format.properties: The default configuration file for the IoTDB file layer configuration item. Users can configure the information about the TsFile, such as the data size written to the disk per time(group\_size\_in_byte). + * iotdb-engine.properties: The default configuration file for the IoTDB engine layer configuration item. Users can configure the IoTDB engine related parameters in the file, such as JDBC service listening port (rpc\_port), overflow data storage directory (overflow\_data\_dir), etc. +* log configuration file (logback.xml) + +The configuration files of the three configuration items are located in the IoTDB installation directory: $IOTDB_HOME/conf folder. + +### IoTDB Environment Configuration File + +The environment configuration file is mainly used to configure the Java environment related parameters when IoTDB Server is running, such as JVM related configuration. This part of the configuration is passed to the JVM when the IoTDB Server starts. Users can view the contents of the environment configuration file by viewing the iotdb-env.sh (or iotdb-env.bat) file. + +The detail of each variables are as follows: + +* JMX\_LOCAL + +|Name|JMX\_LOCAL| +|:---:|:---| +|Description|JMX monitoring mode, configured as yes to allow only local monitoring, no to allow remote monitoring| +|Type|Enum String: "yes", "no"| +|Default|yes| +|Effective|After restart system| + + +* JMX\_PORT + +|Name|JMX\_PORT| +|:---:|:---| +|Description|JMX listening port. Please confirm that the port is not a system reserved port and is not occupied| +|Type|Short Int: [0,65535]| +|Default|31999| +|Effective|After restart system| + +* MAX\_HEAP\_SIZE + +|Name|MAX\_HEAP\_SIZE| +|:---:|:---| +|Description|The maximum heap memory size that IoTDB can use at startup.| +|Type|String| +|Default| On Linux or MacOS, the default is one quarter of the memory. On Windows, the default value for 32-bit systems is 512M, and the default for 64-bit systems is 2G.| +|Effective|After restart system| + +* HEAP\_NEWSIZE + +|Name|HEAP\_NEWSIZE| +|:---:|:---| +|Description|The minimum heap memory size that IoTDB can use at startup.| +|Type|String| +|Default| On Linux or MacOS, the default is min{cores * 100M, one quarter of MAX\_HEAP\_SIZE}. On Windows, the default value for 32-bit systems is 512M, and the default for 64-bit systems is 2G.| +|Effective|After restart system| + +### IoTDB System Configuration File + +#### File Layer + +* compressor + +|Name|compressor| +|:---:|:---| +|Description|Data compression method| +|Type|Enum String : “UNCOMPRESSED”, “SNAPPY”| +|Default| UNCOMPRESSED | +|Effective|Immediately| + +* group\_size\_in\_byte + +|Name|group\_size\_in\_byte| +|:---:|:---| +|Description|The data size written to the disk per time| +|Type|Int32| +|Default| 134217728 | +|Effective|Immediately| + +* max\_number\_of\_points\_in\_page + +|Name| max\_number\_of\_points\_in\_page | +|:---:|:---| +|Description|The maximum number of data points (timestamps - valued groups) contained in a page| +|Type|Int32| +|Default| 1048576 | +|Effective|Immediately| + +* max\_string\_length + +|Name| max\_string\_length | +|:---:|:---| +|Description|The maximum length of a single string (number of character)| +|Type|Int32| +|Default| 128 | +|Effective|Immediately| + +* page\_size\_in\_byte + +|Name| page\_size\_in\_byte | +|:---:|:---| +|Description|The maximum size of a single page written in memory when each column in memory is written (in bytes)| +|Type|Int32| +|Default| 134217728 | +|Effective|Immediately| + +* time\_series\_data\_type + +|Name| time\_series\_data\_type | +|:---:|:---| +|Description|Timestamp data type| +|Type|Enum String: "INT32", "INT64"| +|Default| Int64 | +|Effective|Immediately| + +* time\_series\_encoder + +|Name| time\_series\_data\_type | +|:---:|:---| +|Description| TimeSeries encoding type| +|Type|Enum String: “TS_2DIFF”,“PLAIN”,“RLE”| +|Default| TS_2DIFF | +|Effective|Immediately| + +* float_precision + +|Name| float_precision | +|:---:|:---| +|Description| The precision of the floating point number.(The number of digits after the decimal point) | +|Type|Int32| +|Default| The default is 2 digits. Note: The 32-bit floating point number has a decimal precision of 7 bits, and the 64-bit floating point number has a decimal precision of 15 bits. If the setting is out of the range, it will have no practical significance. | +|Effective|Immediately| + +#### Engine Layer + +* back\_loop\_period + +|Name| back\_loop\_period | +|:---:|:---| +|Description| The frequency at which the system statistic module triggers(in seconds). | +|Type|Int32| +|Default| 10 | +|Effective|After restart system| + +* data\_dir + +|Name| data\_dir | +|:---:|:---| +|Description| The IoTDB data path.By default, it is stored in the data directory at the same level as the bin directory. It is recommended to use an absolute path. | +|Type|String| +|Default| data | +|Effective|After restart system| + +* enable_wal + +|Name| enable_wal | +|:---:|:---| +|Description| Whether to enable the pre-write log. The default value is true(enabled), and false means closed. | +|Type|Bool| +|Default| true | +|Effective|After restart system| + +* fetch_size + +|Name| fetch_size | +|:---:|:---| +|Description| The amount of data read each time in batches(the number of data strips, that is, the number of different time stamps.) | +|Type|Int32| +|Default| 10000 | +|Effective|After restart system| + +* flush\_wal\_period\_in\_ms + +|Name| flush\_wal\_period\_in\_ms | +|:---:|:---| +|Description| The period during which the log is periodically flushed to disk(in milliseconds) | +|Type|Int32| +|Default| 10 | +|Effective|After restart system| + +* flush\_wal\_threshold + +|Name| flush\_wal\_threshold | +|:---:|:---| +|Description| After the WAL reaches this value, it is flushed to disk, and it is possible to lose at most flush_wal_threshold operations. | +|Type|Int32| +|Default| 10000 | +|Effective|After restart system| + +* max\_opened\_folder + +|Name| max\_opened\_folder | +|:---:|:---| +|Description| The maximum number of folders opened at the same time. When the value becomes larger, the memory usage increases, the IO random read and write becomes less, and the file partition (ie, group) is more neat; the smaller the value, the less memory is occupied, the IO random read and write becomes more, and the file block size is insufficient. | +|Type|Int32| +|Default| 100 | +|Effective|After restart system| + +* merge\_concurrent\_threads + +|Name| merge\_concurrent\_threads | +|:---:|:---| +|Description| THe max threads which can be used when overflow data is merged. The larger it is, the more IO and CPU cost. The smaller the value, the more the disk is occupied when the overflow data is too large, the reading will be slower. | +|Type|Int32| +|Default| 10 | +|Effective|After restart system| + +* mult\_dir\_strategy + +|Name| mult\_dir\_strategy | +|:---:|:---| +|Description| IoTDB's strategy for selecting directories for TsFile in tsfile_dir. You can use a simple class name or a full name of the class. The system provides the following three strategies:
1. SequenceStrategy: IoTDB selects the directory from tsfile\_dir in order, traverses all the directories in tsfile\_dir in turn, and keeps counting;
2. MaxDiskUsableSpaceFirstStrategy: IoTDB first selects the directory with the largest free disk space in tsfile\_dir;
3. MinFolderOccupiedSpaceFirstStrategy: IoTDB prefers the directory with the least space used in tsfile\_dir;
4. (user-defined policy)
You can complete a user-defined policy in the following ways:
1. Inherit the cn.edu.tsinghua.iotdb.conf.directories.strategy.DirectoryStrategy class and implement its own Strategy method;
2. Fill in the configuration class with the full class name of the implemented class (package name plus class name, UserDfineStrategyPackage);
3. Add the jar file to the project. | +|Type|String| +|Default| MaxDiskUsableSpaceFirstStrategy | +|Effective|After restart system| + +* period\_time\_for\_flush\_in\_second + +|Name| period\_time\_for\_flush\_in\_second | +|:---:|:---| +|Description| The interval period IoTDB closes files(in seconds). At every set time, the system will automatically flush the data in the memory to the disk and seal all the files that are currently open.| +|Type|Int32| +|Default| 3600 | +|Effective|After restart system| + +* period\_time\_for\_merge\_in\_second + +|Name| period\_time\_for\_merge\_in\_second | +|:---:|:---| +|Description| IoTDB has two parts of data in memory at runtime: overflow and bufferwrite. The system will automatically merge the two parts of data at regular intervals. This is the merge interval(in seconds).| +|Type|Int32| +|Default| 7200 | +|Effective|After restart system| + +* rpc_port + +|Name| rpc_port | +|:---:|:---| +|Description| The jdbc service listens on the port. Please confirm that the port is not a system reserved port and is not occupied.| +|Type|Short Int : [0,65535]| +|Default| 6667 | +|Effective|After restart system| + +* tsfile_dir + +|Name| tsfile_dir | +|:---:|:---| +|Description| The storage path of TsFile. By default, it is stored in three folders under data directory(soldled1, settled2, and settled3). See the [mult\_dir\_strategy](chapter4,multdirstrategy) configuration item for data distribution strategy. The starting directory of the relative path is related to the operating system. It is recommended to use an absolute path. If the path does not exist, the system will automatically create it.| +|Type|String[]| +|Default| settled1, settled2, settled3 | +|Effective|After restart system| + +* wal\_cleanup\_threshold + +|Name| wal\_cleanup\_threshold | +|:---:|:---| +|Description| When the total number of logs in the file and in memory reaches this value, all logs are compressed and the useless log is removed. The default is 500000. If this value is too large, it will cause a short write pause. If it is too small, it will increase IO and CPU consumption. | +|Type|Int32| +|Default| 500000 | +|Effective|After restart system| + +* sys\_dir + +|Name| sys\_dir | +|:---:|:---| +|Description| IoTDB metadata storage path.(By default it is in the data directory at the same level as the bin directory. The starting directory of the relative path is related to the operating system. It is recommended to use an absolute path. | +|Type|String| +|Default| system | +|Effective|After restart system| + +* time_zone + +|Name| time_zone | +|:---:|:---| +|Description| The time zone in which the server is located, the default is Beijing time (+8) | +|Type|Time Zone String| +|Default| +08:00 | +|Effective|After restart system| + +* enable\_stat\_monitor + +|Name| enable\_stat\_monitor | +|:---:|:---| +|Description| Whether to enable background statistics| +|Type| Boolean | +|Default| true | +|Effective|After restart system| + + +* mem\_threshold\_warning + +|Name| mem\_threshold\_warning | +|:---:|:---| +|Description| A percentage value, which is multiplied by the maximum heap memory assigned by the IoTDB runtime to get a threshold. When the IoTDB uses memory beyond the threshold, it will trigger the operation of writing the current in-memory data to the disk and releasing the corresponding memory. By default, IoTDB runtime can use 80% of the maximum heap memory. If the value is configured to exceed 1, the configuration item will not take effect. If the value is less than or equal to 0, then the default value is used.| +|Type| Float | +|Default| 0.8 | +|Effective|After restart system| + + +* mem\_threshold\_dangerous + +|Name| mem\_threshold\_dangerous | +|:---:|:---| +|Description| A percentage value, which is multiplied by the maximum heap memory allocated by the IoTDB runtime to get a threshold. When the IoTDB uses memory beyond the threshold, it will trigger the operation of writing the current in-memory data to the disk and releasing the corresponding memory. At the same time, the write operation will be blocked. By default, the IoTDB runtime can use 90% of the maximum heap memory. If the value is configured to exceed 1, the configuration item will not take effect. If the value is less than or equal to 0, then the default value is used.| +|Type| Float | +|Default| 0.9 | +|Effective|After restart system| + +* mem\_monitor\_interval + +|Name| mem\_monitor\_interval | +|:---:|:---| +|Description| The time interval IoTDB system checks the current memory usage. If the threshold calculated according to mem_threshold_warning or mem_threshold_dangerous is exceeded, the corresponding operation will be triggered. The unit is milliseconds and the default is 1000 milliseconds.| +|Type| Int64 | +|Default| 1000 | +|Effective|After restart system| + +* bufferwrite\_meta\_size\_threshold + +|Name| bufferwrite\_meta\_size\_threshold | +|:---:|:---| +|Description| When the metadata size of the TsFile saved in the memory exceeds the threshold, the metadata is saved at the end of the TsFile, and then the file is closed and the memory space occupied by the metadata is released. The unit is byte and the default value is 200M.| +|Type| Int64 | +|Default| 209715200 | +|Effective|After restart system| + +* bufferwrite\_file\_size\_threshold + +|Name| bufferwrite\_meta\_size\_threshold | +|:---:|:---| +|Description| When a TsFile size on the disk exceeds this threshold, the TsFile is closed and open a new TsFile to accept data writes. The unit is byte and the default value is 2G.| +|Type| Int64 | +|Default| 2147483648 | +|Effective|After restart system| + + +* overflow\_meta\_size\_threshold + +|Name| overflow\_meta\_size\_threshold | +|:---:|:---| +|Description| When the size of the Overflow metadata stored in the memory exceeds the threshold, the metadata is saved at the end of the TsFile, then the file is closed and the memory space occupied by the metadata is released. The unit is byte and the default value is 200M.| +|Type| Int64 | +|Default| 209715200 | +|Effective|After restart system| + +* overflow\_file\_size\_threshold + +|Name| overflow\_file\_size\_threshold | +|:---:|:---| +|Description| When an Overflow file size on the disk exceeds this threshold, the Overflow file is closed. And open a new Overflow file to accept data writes. The unit is byte, the default value is 2G| +|Type| Int64 | +|Default| 2147483648 | +|Effective|After restart system| + +* concurrent\_flush\_thread + +|Name| concurrent\_flush\_thread | +|:---:|:---| +|Description| The thread number used to perform the operation when IoTDB writes data in memory to disk. If the value is less than or equal to 0, then the number of CPU cores installed on the machine is used. The default is 0.| +|Type| Int32 | +|Default| 0 | +|Effective|After restart system| + + +* stat\_monitor\_detect\_freq\_sec + +|Name| concurrent\_flush\_thread | +|:---:|:---| +|Description| The time interval which the system check whether the current record statistic time range exceeds stat_monitor_retain_interval every time (in seconds) and perform regular cleaning| +|Type| Int32 | +|Default|600 | +|Effective|After restart system| + + +* stat\_monitor\_retain\_interval\_sec + +|Name| stat\_monitor\_retain\_interval\_sec | +|:---:|:---| +|Description| The retention time of system statistics data(in seconds). Statistics data over the retention time range will be cleaned regularly.| +|Type| Int32 | +|Default|600 | +|Effective|After restart system| + +## System Monitor + +Currently, IoTDB provides users to use Java's JConsole tool to monitor system status or use IoTDB's open API to check data status. + +### System Status Monitoring + +After starting JConsole tool and connecting to IoTDB server, you will have a basic look at IoTDB system status(CPU Occupation, in-memory information, etc.). See [official documentation](https://docs.oracle.com/javase/7/docs/technotes/guides/management/jconsole.html) for more informations. + +### Data Status Monitoring + +This module is the statistical monitoring method provided by IoTDB for users to store data information. We will record the statistical data in the system and store it in the database. The current 0.7.0 version of IoTDB provides statistics for writing data. + +The user can choose to enable or disable the data statistics monitoring function (set the `enable_stat_monitor` item in the configuration file, see [Engine Layer](chapter4,enginelayer) for details). + +#### Writing Data Monitor + +The current statistics of writing data by the system can be divided into two major modules: **Global Writing Data Statistics** and **Storage Group Writing Data Statistics**. **Global Writing Data Statistics** records the point number written by the user and the number of requests. **Storage Group Writing Data Statistics** records data of a certain storage group. + +The system defaults to collect data every 5 seconds, and writes the statistics to the IoTDB and stores them in a system-specified locate. (If you need to change the statistic frequency, you can set The `back_loop_period_sec entry` in the configuration file, see Section [Engine Layer](chapter4,enginelayer) for details). After the system is refreshed or restarted, IoTDB does not recover the statistics, and the statistics data will restart from zero. + +In order to avoid the excessive use of statistical information, we add a mechanism to periodically clear invalid data for statistical information. The system will delete invalid data at regular intervals. The user can set the trigger frequency (`stat_monitor_retain_interval_sec`, default is 600s, see section [Engine Layer](chapter4,enginelayer) for details) to set the frequency of deleting data. By setting the valid data duration (`stat_monitor_detect_freq_sec entry`, the default is 600s, see section [Engine Layer](chapter4,enginelayer) for details) to set the time period of valid data, that is, the data within the time of the clear operation trigger time is stat_monitor_detect_freq_sec is valid data. In order to ensure the stability of the system, it is not allowed to delete the statistics frequently. Therefore, if the configuration parameter time is less than the default value (600s), the system will abort the configuration parameter and uses the default parameter. + +It's convenient for you to use `select` clause to get the writing data statistics the same as other timeseires. + +Here are the writing data statistics: + +* TOTAL_POINTS (GLOABAL) + +|Name| TOTAL\_POINTS | +|:---:|:---| +|Description| Calculate the global writing points number.| +|Type| Writing data statistics | +|Timeseries Name| root.stats.write.global.TOTAL\_POINTS | +|Reset After Restarting System| yes | +|Example| select TOTAL_POINTS from root.stats.write.global| + +* TOTAL\_REQ\_SUCCESS (GLOABAL) + +|Name| TOTAL\_REQ\_SUCCESS | +|:---:|:---| +|Description| Calculate the global successful requests number.| +|Type| Writing data statistics | +|Timeseries Name| root.stats.write.global.TOTAL\_REQ\_SUCCESS | +|Reset After Restarting System| yes | +|Example| select TOTAL\_REQ\_SUCCESS from root.stats.write.global| + +* TOTAL\_REQ\_FAIL (GLOABAL) + +|Name| TOTAL\_REQ\_FAIL | +|:---:|:---| +|Description| Calculate the global failed requests number.| +|Type| Writing data statistics | +|Timeseries Name| root.stats.write.global.TOTAL\_REQ\_FAIL | +|Reset After Restarting System| yes | +|Example| select TOTAL\_REQ\_FAIL from root.stats.write.global| + + +* TOTAL\_POINTS\_FAIL (GLOABAL) + +|Name| TOTAL\_POINTS\_FAIL | +|:---:|:---| +|Description| Calculate the global failed writing points number.| +|Type| Writing data statistics | +|Timeseries Name| root.stats.write.global.TOTAL\_POINTS\_FAIL | +|Reset After Restarting System| yes | +|Example| select TOTAL\_POINTS\_FAIL from root.stats.write.global| + + +* TOTAL\_POINTS\_SUCCESS (GLOABAL) + +|Name| TOTAL\_POINTS\_SUCCESS | +|:---:|:---| +|Description| Calculate the c.| +|Type| Writing data statistics | +|Timeseries Name| root.stats.write.global.TOTAL\_POINTS\_SUCCESS | +|Reset After Restarting System| yes | +|Example| select TOTAL\_POINTS\_SUCCESS from root.stats.write.global| + +* TOTAL\_REQ\_SUCCESS (STORAGE GROUP) + +|Name| TOTAL\_REQ\_SUCCESS | +|:---:|:---| +|Description| Calculate the successful requests number for specific storage group| +|Type| Writing data statistics | +|Timeseries Name| root.stats.write.\.TOTAL\_REQ\_SUCCESS | +|Reset After Restarting System| yes | +|Example| select TOTAL\_REQ\_SUCCESS from root.stats.write.\| + +* TOTAL\_REQ\_FAIL (STORAGE GROUP) + +|Name| TOTAL\_REQ\_FAIL | +|:---:|:---| +|Description| Calculate the fail requests number for specific storage group| +|Type| Writing data statistics | +|Timeseries Name| root.stats.write.\.TOTAL\_REQ\_FAIL | +|Reset After Restarting System| yes | +|Example| select TOTAL\_REQ\_FAIL from root.stats.write.\| + + +* TOTAL\_POINTS\_SUCCESS (STORAGE GROUP) + +|Name| TOTAL\_POINTS\_SUCCESS | +|:---:|:---| +|Description| Calculate the successful writing points number for specific storage group.| +|Type| Writing data statistics | +|Timeseries Name| root.stats.write.\.TOTAL\_POINTS\_SUCCESS | +|Reset After Restarting System| yes | +|Example| select TOTAL\_POINTS\_SUCCESS from root.stats.write.\| + + +* TOTAL\_POINTS\_FAIL (STORAGE GROUP) + +|Name| TOTAL\_POINTS\_FAIL | +|:---:|:---| +|Description| Calculate the fail writing points number for specific storage group.| +|Type| Writing data statistics | +|Timeseries Name| root.stats.write.\.TOTAL\_POINTS\_FAIL | +|Reset After Restarting System| yes | +|Example| select TOTAL\_POINTS\_FAIL from root.stats.write.\| + +> Note: +> +> \ should be replaced by real storage group name, and the '.' in storage group need to be replaced by '_'. For example, the storage group name is 'root.a.b', when using in the statistics, it will change to 'root\_a\_b' + +##### Example + +Here we give some example of using writing data statistics. + +If you want to know the global successful writing points number, you can use `select` clause to query it's value. The query statement is like this: + +``` +select TOTAL_POINTS_SUCCESS from root.stats.write.global +``` + +If you want to know the successfule writing points number of root.ln (storage group), here is the query statement: + +``` +select TOTAL_POINTS_SUCCESS from root.stats.write.root_ln +``` + +If you want to know the current timeseries point in the system, you can use `MAX_VALUE` function to query. Here is the query statement: + +``` +select MAX_VALUE(TOTAL_POINTS_SUCCESS) from root.stats.write.root_ln +``` + +## System log + +IoTDB allows users to configure IoTDB system logs (such as log output level) by modifying the log configuration file. The default location of the system log configuration file is in \$IOTDB_HOME/conf folder. + +The default log configuration file is named logback.xml. The user can modify the configuration of the system running log by adding or changing the xml tree node parameters. It should be noted that the configuration of the system log using the log configuration file does not take effect immediately after the modification, instead, it will take effect after restarting the system. The usage of logback.xml is just as usual. + +At the same time, in order to facilitate the debugging of the system by the developers and DBAs, we provide several JMX interface to dynamically modify the log configuration, and configure the Log module of the system in real time without restarting the system. For detailed usage, see [Dynamic System Log Configuration](Chap4dynamicsystemlog) section. + +### Dynamic System Log Configuration + +#### Connect JMX + +Here we use Jconsole to connect with JMX. + +Start the Jconsole, establish a new JMX connection with the IoTDB Server (you can select the local process or input the IP and PORT for remote connection, the default operation port of the IoTDB JMX service is 31999). Fig 4.1 shows the connection GUI of jconsole. + + + +After connected, click `MBean` and find `ch.qos.logback.classic.default.ch.qos.logback.classic.jmx.JMXConfigurator`(As shown in fig 4.2). + + +In the JMXConfigurator Window, there are 6 operation provided for you, as shown in fig 4.3. You can use there interface to perform operation. + + + +#### Interface Instruction + +* reloadDefaultConfiguration + +This method is to reload the default logback configuration file. The user can modify the default configuration file first, and then call this method to reload the modified configuration file into the system to take effect. + +* reloadByFileName + +This method loads a logback configuration file with the specified path and name, and then makes it take effect. This method accepts a parameter of type String named p1, which is the path to the configuration file that needs to be specified for loading. + +* getLoggerEffectiveLevel + +This method is to obtain the current log level of the specified Logger. This method accepts a String type parameter named p1, which is the name of the specified Logger. This method returns the log level currently in effect for the specified Logger. + +* getLoggerLevel + +This method is to obtain the log level of the specified Logger. This method accepts a String type parameter named p1, which is the name of the specified Logger. This method returns the log level of the specified Logger. +It should be noted that the difference between this method and the `getLoggerEffectiveLevel` method is that the method returns the log level that the specified Logger is set in the configuration file. If the user does not set the log level for the Logger. , then return empty. According to Logre's log-level inheritance mechanism, if a Logger is not displayed to set the log level, it will inherit the log level settings from its nearest ancestor. At this point, calling the `getLoggerEffectiveLevel` method will return the log level in which the Logger is in effect; calling the methods described in this section will return null. + +## Data Management + +In IoTDB, there are many kinds of data needed to be storage. In this section, we will introduce IoTDB's data storage strategy in order to give you an intuitive understanding of IoTDB's data management. + +The data that IoTDB stores is divided into three categories, namely data files, system files, and pre-write log files. + +### Data Files + +Data files store all the data that the user wrote to IoTDB, which contains TsFile and other files. TsFile storage directory can be configured with the `tsfile_dir` configuration item (see [file layer](Chap4filelayer) for details). Other files can be configured through [data_dir](chap4datadir) configuration item (see [Engine Layer](chapter4,enginelayer) for details). + +In order to better support users' storage requirements such as disk space expansion, IoTDB supports multiple file directorys storage methods for TsFile storage configuration. Users can set multiple storage paths as data storage locations( see [tsfile_dir](Chap4,tsfiledir) configuration item), and you can specify or customize the directory selection policy (see [mult_dir_strategy](chapter4,enginelayer) configuration item for details). + +### System Files + +System files include restore files and schema files, which store metadata information of data in IoTDB. It can be configured through the `sys_dir` configuration item (see [System Layer](chapter4,systemlayer) for details). + +### Pre-write Log Files + +Pre-write log files store WAL files. It can be configured through the `wal_dir` configuration item (see [System Layer](chapter4,systemlayer) for details). + +### Example of Setting Data storage Directory + +For a clearer understanding of configuring the data storage directory, we will give an excample in this section. + +All data directory paths involved in storage directory setting are: data_dir, tsfile_dir, mult_dir_strategy, sys_dir, and wal_dir, which refer to data files, stroage strategy, system files, and pre-write log files. You can choose to configure the items you'd like to change, otherwise, you can use the system default configuration item without any operation. + +Here we give an example of a user who configures all five configurations mentioned above. The configuration items are as follow: + +``` +data_dir = D:\\iotdb\\data\\data +tsfile_dir = E:\\iotdb\\data\\data1, data\\data2, F:\\data3 
mult_dir_strategy = MaxDiskUsableSpaceFirstStrategy
sys_dir = data\\system
wal_dir = data + +``` +After setting the configuration, the system will: + +* Save all data files except TsFile in D:\\iotdb\\data\\data +* Save TsFile in E:\\iotdb\\data\\data1, $IOTDB_HOME\\data\\data2 and F:\\data3. And the choosing strategy is `MaxDiskUsableSpaceFirstStrategy`, that is every time data writes to the disk, the system will automatically select a directory with the largest remaining disk space to write data. +* Save system data in $IOTDB_HOME\\data\\system +* Save WAL data in $IOTDB_HOME\\data + +> Note: +> +> If you change directory names in tsfile_dir, the newer name and the older name should be one-to-one correspondence. Also, the files in the older directory needs to be moved to the newer directory. +> +> If you add some directorys in tsfile_dir, IoTDB will add the path automatically. Nothing needs to do by your own. + +For example, modify the tsfile_dir to: + +``` +tsfile_dir = D:\\data4, E:\\data5, F:\\data6 +``` + +You need to move files in E:\iotdb\data\data1 to D:\data4, move files in %IOTDB_HOME%\data\data2 to E:\data5, move files in F:\data3 to F:\data6. In this way, the system will operation normally. diff --git a/docs/Documentation/UserGuideV0.7.0/5-SQL Documentation.md b/docs/Documentation/UserGuideV0.7.0/5-SQL Documentation.md new file mode 100644 index 0000000000000..b9fa6177887bb --- /dev/null +++ b/docs/Documentation/UserGuideV0.7.0/5-SQL Documentation.md @@ -0,0 +1,612 @@ + + +- [Chapter 5: IoTDB SQL Documentation](#chapter-5-iotdb-sql-documentation) + - [IoTDB Query Statement](#iotdb-query-statement) + - [Schema Statement](#schema-statement) + - [Data Management Statement](#data-management-statement) + - [Database Management Statement](#database-management-statement) + - [Functions](#functions) + - [Reference](#reference) + - [Keywords](#keywords) + - [Identifiers](#identifiers) + - [Literals](#literals) + + +# Chapter 5: IoTDB SQL Documentation + +In this part, we will introduce you IoTDB's Query Language. IoTDB offers you a SQL-like query language for interacting with IoTDB, the query language can be devided into 4 major parts: + +* Schema Statement: statements about schema management are all listed in this section. +* Data Management Statement: statements about data management (such as: data insertion, data query, etc.) are all listed in this section. +* Database Management Statement: statements about database management and authentication are all listed in this section. +* Functions: functions that IoTDB offers are all listed in this section. + +All of these statements are write in IoTDB's own syntax, for details about the syntax composition, please check the `Reference` section. + +## IoTDB Query Statement + + +### Schema Statement + +* Set Storage Group + +``` SQL +SET STORAGE GROUP TO +Eg: IoTDB > SET STORAGE GROUP TO root.ln.wf01.wt01 +Note: PrefixPath can not include `*` +``` +* Create Timeseries Statement + +``` +CREATE TIMESERIES WITH +AttributeClauses : DATATYPE= COMMA ENCODING= [COMMA ]* +DataTypeValue: BOOLEAN | DOUBLE | FLOAT | INT32 | INT64 | TEXT +EncodingValue: GORILLA | PLAIN | RLE | TS_2DIFF +ExtraAttributeClause: { + COMPRESSOR = + MAX_POINT_NUMBER = Integer +} +CompressorValue: UNCOMPRESSOR | SNAPPY +Eg: IoTDB > CREATE TIMESERIES root.ln.wf01.wt01.status WITH DATATYPE=BOOLEAN, ENCODING=PLAIN +Eg: IoTDB > CREATE TIMESERIES root.ln.wf01.wt01.temperature WITH DATATYPE=FLOAT, ENCODING=RLE +Eg: IoTDB > CREATE TIMESERIES root.ln.wf01.wt01.temperature WITH DATATYPE=FLOAT, ENCODING=RLE, COMPRESSOR=SNAPPY, MAX_POINT_NUMBER=3 +Note: Datatype and encoding type must be corresponding. Please check Chapter 3 Encoding Section for details. +``` + +* Delete Timeseries Statement + +``` +DELETE TIMESERIES [COMMA ]* +Eg: IoTDB > DELETE TIMESERIES root.ln.wf01.wt01.status +Eg: IoTDB > DELETE TIMESERIES root.ln.wf01.wt01.status, root.ln.wf01.wt01.temperature +Eg: IoTDB > DELETE TIMESERIES root.ln.wf01.wt01.* +``` + +* Show All Timeseries Statement + +``` +SHOW TIMESERIES +Eg: IoTDB > SHOW TIMESERIES +Note: This statement can only be used in IoTDB Client. If you need to show all timeseries in JDBC, please use `DataBaseMetadata` interface. +``` + +* Show Specific Timeseries Statement + +``` +SHOW TIMESERIES +Eg: IoTDB > SHOW TIMESERIES root +Eg: IoTDB > SHOW TIMESERIES root.ln +Eg: IoTDB > SHOW TIMESERIES root.ln.*.*.status +Eg: IoTDB > SHOW TIMESERIES root.ln.wf01.wt01.status +Note: The path can be prefix path, star path or timeseries path +Note: This statement can be used in IoTDB Client and JDBC. +``` + +* Show Storage Group Statement + +``` +SHOW STORAGE GROUP +Eg: IoTDB > SHOW STORAGE GROUP +Note: This statement can be used in IoTDB Client and JDBC. +``` + +### Data Management Statement + +* Insert Record Statement + +``` +INSERT INTO LPAREN TIMESTAMP COMMA [COMMA ]* RPAREN VALUES LPAREN , [COMMA ]* RPAREN +Sensor : Identifier +Eg: IoTDB > INSERT INTO root.ln.wf01.wt01(timestamp,status) values(1509465600000,true) +Eg: IoTDB > INSERT INTO root.ln.wf01.wt01(timestamp,status) VALUES(NOW(), false) +Eg: IoTDB > INSERT INTO root.ln.wf01.wt01(timestamp,temperature) VALUES(2017-11-01T00:17:00.000+08:00,24.22028) +Eg: IoTDB > INSERT INTO root.ln.wf01.wt01(timestamp, status, temperature) VALUES (1509466680000, false, 20.060787); +Note: the statement needs to satisfy this constraint: + = +Note: The order of Sensor and PointValue need one-to-one correspondence +``` + +* Update Record Statement + +``` +UPDATE SET WHERE +UpdateClause: +SetClause: +SetExpression: EQUAL +WhereClause : [(AND | OR) ]* +Condition : [(AND | OR) ]* +Expression : [NOT | !]? TIME PrecedenceEqualOperator +Eg: IoTDB > UPDATE root.ln.wf01.wt01 SET temperature = 23 WHERE time < NOW() and time > 2017-11-1T00:15:00+08:00 +Note: the statement needs to satisfy this constraint: + = +``` + +* Delete Record Statement + +``` +DELETE FROM [COMMA ]* WHERE TIME LESSTHAN +Eg: DELETE FROM root.ln.wf01.wt01.temperature WHERE time < 2017-11-1T00:05:00+08:00 +Eg: DELETE FROM root.ln.wf01.wt01.status, root.ln.wf01.wt01.temperature WHERE time < NOW() +Eg: DELETE FROM root.ln.wf01.wt01.* WHERE time < 1509466140000 +``` + +* Select Record Statement + +``` +SELECT FROM [WHERE ]? +SelectClause : (COMMA )* +SelectPath : LPAREN RPAREN | +FUNCTION : ‘COUNT’ , ‘MIN_TIME’, ‘MAX_TIME’, ‘MIN_VALUE’, ‘MAX_VALUE’ +FromClause : (COMMA )? +WhereClause : [(AND | OR) ]* +Condition : [(AND | OR) ]* +Expression : [NOT | !]? | [NOT | !]? +TimeExpr : TIME PrecedenceEqualOperator +SensorExpr : ( | ) PrecedenceEqualOperator +Eg: IoTDB > SELECT status, temperature FROM root.ln.wf01.wt01 WHERE temperature < 24 and time > 2017-11-1 0:13:00 +Eg. IoTDB > SELECT * FROM root +Eg. IoTDB > SELECT COUNT(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 25 +Eg. IoTDB > SELECT MIN_TIME(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 25 +Eg. IoTDB > SELECT MAX_TIME(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature > 24 +Eg. IoTDB > SELECT MIN_VALUE(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature > 23 +Eg. IoTDB > SELECT MAX_VALUE(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 25 +Note: the statement needs to satisfy this constraint: (SelectClause) + (FromClause) = +Note: If the (WhereClause) is started with and not with ROOT, the statement needs to satisfy this constraint: (FromClause) + (SensorExpr) = +Note: In Version 0.7.0, if includes `OR`, time filter can not be used. +``` + +* Group By Statement + +``` +SELECT FROM WHERE GROUP BY +SelectClause : [COMMA < Function >]* +Function : LPAREN RPAREN +FromClause : +WhereClause : [(AND | OR) ]* +Condition : [(AND | OR) ]* +Expression : [NOT | !]? | [NOT | !]? +TimeExpr : TIME PrecedenceEqualOperator +SensorExpr : ( | ) PrecedenceEqualOperator +GroupByClause : LPAREN (COMMA TimeValue)? COMMA (COMMA )* RPAREN +TimeUnit : Integer +DurationUnit : "ms" | "s" | "m" | "h" | "d" | "w" +TimeInterval: LBRACKET COMMA RBRACKET +Eg: SELECT COUNT(status), COUNT(temperature) FROM root.ln.wf01.wt01 where temperature < 24 GROUP BY(5m, [1509465720000, 1509466380000]) +Eg. SELECT COUNT (status), MAX_VALUE(temperature) FROM root.ln.wf01.wt01 WHERE time < 1509466500000 GROUP BY(5m, 1509465660000, [1509465720000, 1509466380000]) +Eg. SELECT MIN_TIME(status), MIN_VALUE(temperature) FROM root.ln.wf01.wt01 WHERE temperature < 25 and time < 1509466800000 GROUP BY (3m, 1509465600000, [1509466140000, 1509466380000], [1509466440000, 1509466620000]) +Note: the statement needs to satisfy this constraint: (SelectClause) + (FromClause) = +Note: If the (WhereClause) is started with and not with ROOT, the statement needs to satisfy this constraint: (FromClause) + (SensorExpr) = +Note: (TimeInterval) needs to be greater than 0 +Note: First (TimeInterval) in needs to be smaller than second (TimeInterval) +``` + +* Fill Statement + +``` +SELECT FROM WHERE FILL +SelectClause : [COMMA ]* +FromClause : < PrefixPath > [COMMA < PrefixPath >]* +WhereClause : +WhereExpression : TIME EQUAL +FillClause : LPAREN [COMMA ]* RPAREN +TypeClause : | | | | | +Int32Clause: INT32 LBRACKET ( | ) RBRACKET +Int64Clause: INT64 LBRACKET ( | ) RBRACKET +FloatClause: FLOAT LBRACKET ( | ) RBRACKET +DoubleClause: DOUBLE LBRACKET ( | ) RBRACKET +BoolClause: BOOLEAN LBRACKET ( | ) RBRACKET +TextClause: TEXT LBRACKET ( | ) RBRACKET +PreviousClause : PREVIOUS [COMMA ]? +LinearClause : LINEAR [COMMA COMMA ]? +ValidPreviousTime, ValidBehindTime: +TimeUnit : Integer +DurationUnit : "ms" | "s" | "m" | "h" | "d" | "w" +Eg: SELECT temperature FROM root.ln.wf01.wt01 WHERE time = 2017-11-01T16:37:50.000 FILL(float[previous, 1m]) +Eg: SELECT temperature,status FROM root.ln.wf01.wt01 WHERE time = 2017-11-01T16:37:50.000 FILL (float[linear, 1m, 1m], boolean[previous, 1m]) +Eg: SELECT temperature,status,hardware FROM root.ln.wf01.wt01 WHERE time = 2017-11-01T16:37:50.000 FILL (float[linear, 1m, 1m], boolean[previous, 1m], text[previous]) +Eg: SELECT temperature,status,hardware FROM root.ln.wf01.wt01 WHERE time = 2017-11-01T16:37:50.000 FILL (float[linear], boolean[previous, 1m], text[previous]) +Note: the statement needs to satisfy this constraint: (FromClause) + (SelectClause) = +Note: Integer in needs to be greater than 0 +``` + +* Limit Statement + +``` +SELECT FROM [WHERE ] [LIMIT ] [SLIMIT ] +SelectClause : [ | Function]+ +Function : LPAREN RPAREN +FromClause : +WhereClause : [(AND | OR) ]* +Condition : [(AND | OR) ]* +Expression: [NOT|!]? | [NOT|!]? +TimeExpr : TIME PrecedenceEqualOperator +SensorExpr : (|) PrecedenceEqualOperator +LIMITClause : [OFFSETClause]? +N : NonNegativeInteger +OFFSETClause : OFFSET +OFFSETValue : NonNegativeInteger +SLIMITClause : [SOFFSETClause]? +SN : NonNegativeInteger +SOFFSETClause : SOFFSET +SOFFSETValue : NonNegativeInteger +NonNegativeInteger:= ('+')? Digit+ +Eg: IoTDB > SELECT status, temperature FROM root.ln.wf01.wt01 WHERE temperature < 24 and time > 2017-11-1 0:13:00 LIMIT 3 OFFSET 2 +Eg. IoTDB > SELECT COUNT (status), MAX_VALUE(temperature) FROM root.ln.wf01.wt01 WHERE time < 1509466500000 GROUP BY(5m, 1509465660000, [1509465720000, 1509466380000]) LIMIT 3 +Note: The order of and does not affect the grammatical correctness. +Note: can only effect in Prefixpath and StarPath. +Note: can not use but not . +``` + +### Database Management Statement + +* Create User + +``` +CREATE USER ; +userName:=identifier +password:=identifier +Eg: IoTDB > CREATE USER thulab pwd; +``` + +* Delete User + +``` +DROP USER ; +userName:=identifier +Eg: IoTDB > DROP USER xiaoming; +``` + +* Create Role + +``` +CREATE ROLE ; +roleName:=identifie +Eg: IoTDB > CREATE ROLE admin; +``` + +* Delete Role + +``` +DROP ROLE ; +roleName:=identifier +Eg: IoTDB > DROP ROLE admin; +``` + +* Grant User Privileges + +``` +GRANT USER PRIVILEGES ON ; +userName:=identifier +nodeName:=identifier (DOT identifier)* +privileges:= string (COMMA string)* +Eg: IoTDB > GRANT USER tempuser PRIVILEGES 'DELETE_TIMESERIES' on root.ln; +``` + +* Grant Role Privileges + +``` +GRANT ROLE PRIVILEGES ON ; +privileges:= string (COMMA string)* +roleName:=identifier +nodeName:=identifier (DOT identifier)* +Eg: IoTDB > GRANT ROLE temprole PRIVILEGES 'DELETE_TIMESERIES' ON root.ln; +``` + +* Grant User Role + +``` +GRANT TO ; +roleName:=identifier +userName:=identifier +Eg: IoTDB > GRANT temprole TO tempuser; +``` + +* Revoke User Privileges + +``` +REVOKE USER PRIVILEGES ON ; +privileges:= string (COMMA string)* +userName:=identifier +nodeName:=identifier (DOT identifier)* +Eg: IoTDB > REVOKE USER tempuser PRIVILEGES 'DELETE_TIMESERIES' on root.ln; +``` + +* Revoke Role Privileges + +``` +REVOKE ROLE PRIVILEGES ON ; +privileges:= string (COMMA string)* +roleName:= identifier +nodeName:=identifier (DOT identifier)* +Eg: IoTDB > REVOKE ROLE temprole PRIVILEGES 'DELETE_TIMESERIES' ON root.ln; +``` + +* Revoke Role From User + +``` +REVOKE FROM ; +roleName:=identifier +userName:=identifier +Eg: IoTDB > REVOKE temproleFROM tempuser; +``` + +* List Users + +``` +LIST USER +Eg: IoTDB > LIST USER +``` + +* List Roles + +``` +LIST ROLE +Eg: IoTDB > LIST ROLE +``` + +* List Privileges + +``` +LIST PRIVILEGES USER ON ; +username:=identifier +path=‘root’ (DOT identifier)* +Eg: IoTDB > LIST PRIVIEGES USER sgcc_wirte_user ON root.sgcc; +``` + +* List Privileges of Roles(On Specific Path) + +``` +LIST PRIVILEGES ROLE ON ; +roleName:=identifier +path=‘root’ (DOT identifier)* +Eg: IoTDB > LIST PRIVIEGES ROLE wirte_role ON root.sgcc; +``` + +* List Privileges of Users + +``` +LIST USER PRIVILEGES ; +username:=identifier +Eg: IoTDB > LIST USER PRIVIEGES tempuser; +``` + +* List Privileges of Roles + +``` +LIST ROLE PRIVILEGES +roleName:=identifier +Eg: IoTDB > LIST ROLE PRIVIEGES actor; +``` + +* List Roles of Users + +``` +LIST ALL ROLE OF USER ; +username:=identifier +Eg: IoTDB > LIST ALL ROLE OF USER tempuser; +``` + +* List Users of Role + +``` +LIST ALL USER OF ROLE ; +roleName:=identifier +Eg: IoTDB > LIST ALL USER OF ROLE roleuser; +``` + +* Update Password + +``` +UPDATE USER SET PASSWORD ; +roleName:=identifier +password:=identifier +Eg: IoTDB > UPDATE USER tempuser SET PASSWORD newpwd; +``` + +### Functions + +* COUNT + +The COUNT function returns the value number of timeseries(one or more) non-null values selected by the SELECT statement. The result is a signed 64-bit integer. If there are no matching rows, COUNT () returns 0. + +``` +SELECT COUNT(Path) (COMMA COUNT(Path))* FROM [WHERE ]? +Eg. SELECT COUNT(status), COUNT(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 24 +Note: the statement needs to satisfy this constraint: + = +``` + +* FIRST + +The FIRST function returns the first point value of the choosen timeseries(oen or more). + +``` +SELECT FIRST (Path) (COMMA FIRST (Path))* FROM [WHERE ]? +Eg. SELECT FIRST (status), FIRST (temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 24 +Note: the statement needs to satisfy this constraint: + = +``` + +* MAX_TIME + +The MAX_TIME function returns the maximum timestamp of the the choosen timeseries(oen or more). The result is a signed 64-bit integer, greater than 0. + +``` +SELECT MAX_TIME (Path) (COMMA MAX_TIME (Path))* FROM [WHERE ]? +Eg. SELECT MAX_TIME(status), MAX_TIME(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 24 +Note: the statement needs to satisfy this constraint: + = +``` + +* MAX_VALUE + +The MAX_VALUE function returns the maximum value(lexicographically ordered) of the the choosen timeseries (oen or more). + +``` +SELECT MAX_VALUE (Path) (COMMA MAX_VALUE (Path))* FROM [WHERE ]? +Eg. SELECT MAX_VALUE(status), MAX_VALUE(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 24 +Note: the statement needs to satisfy this constraint: + = +``` + +* MEAN + +The MEAN function returns the arithmetic mean value of the choosen timeseries over a specified period of time. The timeseries must be int32, int64, float, double type, and the other types are not to be calculated. The result is a double type number. + +``` +SELECT MEAN (Path) (COMMA MEAN (Path))* FROM [WHERE ]? +Eg. SELECT MEAN (status), MEAN (temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 24 +Note: the statement needs to satisfy this constraint: + = +``` + +* MIN_TIME + +The MIN_TIME function returns the minimum timestamp of the the choosen timeseries(oen or more). The result is a signed 64-bit integer, greater than 0. + +``` +SELECT MIN_TIME (Path) (COMMA MIN_TIME (Path))*FROM [WHERE ]? +Eg. SELECT MIN_TIME(status), MIN_TIME(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 24 +Note: the statement needs to satisfy this constraint: + = +``` + +* MIN_VALUE + +The MIN_VALUE function returns the minimum value(lexicographically ordered) of the the choosen timeseries (oen or more). + +``` +MIN_VALUE函数返回的是该列按字典序排序的最小值。 +SELECT MIN_VALUE (Path) (COMMA MIN_VALUE (Path))* FROM [WHERE ]? +Eg. SELECT MIN_VALUE(status),MIN_VALUE(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 24 +Note: the statement needs to satisfy this constraint: + = +``` + +* NOW + +The NOW function returns the current timestamp. This function can be used in the data operation statement to represent time. The result is a signed 64-bit integer, greater than 0. + +``` +NOW() +Eg. INSERT INTO root.ln.wf01.wt01(timestamp,status) VALUES(NOW(), false) +Eg. UPDATE root.ln.wf01.wt01 SET temperature = 23 WHERE time < NOW() +Eg. DELETE FROM root.ln.wf01.wt01.status, root.ln.wf01.wt01.temperature WHERE time < NOW() +Eg. SELECT * FROM root WHERE time < NOW() +Eg. SELECT COUNT(temperature) FROM root.ln.wf01.wt01 WHERE time < NOW() +``` +* SUM + +The SUM function returns the sum of the the choosen timeseries (oen or more) over a specified period of time. The timeseries must be int32, int64, float, double type, and the other types are not to be calculated. The result is a double type number. + +``` +SELECT SUM(Path) (COMMA SUM(Path))* FROM [WHERE ]? +Eg. SELECT SUM(status), SUM(temperature) FROM root.ln.wf01.wt01 WHERE root.ln.wf01.wt01.temperature < 24 +Note: the statement needs to satisfy this constraint: + = +``` + +## Reference + +### Keywords + +``` +Keywords for IoTDB (case insensitive): +ADD, BY, COMPRESSOR, CREATE, DATATYPE, DELETE, DESCRIBE, DROP, ENCODING, EXIT, FROM, GRANT, GROUP, LABLE, LINK, INDEX, INSERT, INTO, LOAD, MAX_POINT_NUMBER, MERGE, METADATA, ON, ORDER, PASSWORD, PRIVILEGES, PROPERTY, QUIT, REVOKE, ROLE, ROOT, SELECT, SET, SHOW, STORAGE, TIME, TIMESERIES, TIMESTAMP, TO, UNLINK, UPDATE, USER, USING, VALUE, VALUES, WHERE, WITH + +Keywords with special meanings (case sensitive): +* Data Types: BOOLEAN, DOUBLE, FLOAT, INT32, INT64, TEXT (Only capitals is acceptable) +* Encoding Methods: BITMAP, DFT, GORILLA, PLAIN, RLE, TS_2DIFF (Only capitals is acceptable) +* Compression Methods: UNCOMPRESSOR, SNAPPY (Only capitals is acceptable) +* Logical symbol: AND, &, &&, OR, | , ||, NOT, !, TRUE, FALSE +``` + +### Identifiers + +``` +QUOTE := '\''; +DOT := '.'; +COLON : ':' ; +COMMA := ',' ; +SEMICOLON := ';' ; +LPAREN := '(' ; +RPAREN := ')' ; +LBRACKET := '['; +RBRACKET := ']'; +EQUAL := '=' | '=='; +NOTEQUAL := '<>' | '!='; +LESSTHANOREQUALTO := '<='; +LESSTHAN := '<'; +GREATERTHANOREQUALTO := '>='; +GREATERTHAN := '>'; +DIVIDE := '/'; +PLUS := '+'; +MINUS := '-'; +STAR := '*'; +Letter := 'a'..'z' | 'A'..'Z'; +HexDigit := 'a'..'f' | 'A'..'F'; +Digit := '0'..'9'; +Boolean := TRUE | FALSE | 0 | 1 (case insensitive) + +``` + +``` +StringLiteral := ( '\'' ( ~('\'') )* '\'' | '\"' ( ~('\"') )* '\"'); +eg. ‘abc’ +eg. “abc” +``` + +``` +Integer := ('-' | '+')? Digit+; +eg. 123 +eg. -222 +``` + +``` +Float := ('-' | '+')? Digit+ DOT Digit+ (('e' | 'E') ('-' | '+')? Digit+)?; +eg. 3.1415 +eg. 1.2E10 +eg. -1.33 +``` + +``` +Identifier := (Letter | '_') (Letter | Digit | '_' | MINUS)*; +eg. a123 +eg. _abc123 + +``` + +### Literals + + +``` +PointValue : Integer | Float | StringLiteral | Boolean +``` +``` +TimeValue : Integer | DateTime | ISO8601 | NOW() +Note: Integer means timestamp type. + +DateTime : +eg. 2016-11-16T16:22:33+08:00 +eg. 2016-11-16 16:22:33+08:00 +eg. 2016-11-16T16:22:33.000+08:00 +eg. 2016-11-16 16:22:33.000+08:00 +Note: DateTime Type can support several types, see Chapter 3 Datetime section for details. +``` +``` +PrecedenceEqualOperator : EQUAL | NOTEQUAL | LESSTHANOREQUALTO | LESSTHAN | GREATERTHANOREQUALTO | GREATERTHAN +``` +``` +Timeseries : ROOT [DOT ]* DOT +LayerName : Identifier +SensorName : Identifier +eg. root.ln.wf01.wt01.status +eg. root.sgcc.wf03.wt01.temperature +Note: Timeseries must be start with `root`(case insensitive) and end with sensor name. +``` + +``` +PrefixPath : ROOT (DOT )* +LayerName : Identifier | STAR +eg. root.sgcc +eg. root.* +``` +``` +Path: (ROOT | ) (DOT )* +LayerName: Identifier | STAR +eg. root.ln.wf01.wt01.status +eg. root.*.wf01.wt01.status +eg. root.ln.wf01.wt01.* +eg. *.wt01.* +eg. * +``` diff --git a/docs/Documentation/UserGuideV0.7.0/6-JDBC Documentation.md b/docs/Documentation/UserGuideV0.7.0/6-JDBC Documentation.md new file mode 100644 index 0000000000000..b003ba451205e --- /dev/null +++ b/docs/Documentation/UserGuideV0.7.0/6-JDBC Documentation.md @@ -0,0 +1,6 @@ + + +- [Chaper6: JDBC API](#chaper6-jdbc-api) + + +# Chaper6: JDBC API \ No newline at end of file diff --git a/docs/Documentation/UserGuideV0.7.0/7-Tools-Cli.md b/docs/Documentation/UserGuideV0.7.0/7-Tools-Cli.md new file mode 100644 index 0000000000000..5faec15a0c8f6 --- /dev/null +++ b/docs/Documentation/UserGuideV0.7.0/7-Tools-Cli.md @@ -0,0 +1,72 @@ + + +- [Cli/shell tool](#clishell-tool) + - [Running Cli/Shell](#running-clishell) + - [Cli/Shell Parameters](#clishell-parameters) + + +# Cli/shell tool +IoTDB provides Cli/shell tools for users to interact with IoTDB server in command lines. This document will show how Cli/shell tool works and what does it parameters mean. + +> Note: In this document, \$IOTDB\_HOME represents the path of the IoTDB installation directory. + +## Running Cli/Shell + +After installation, there is a default user in IoTDB: `root`, and the +default password is `root`. Users can use this username to try IoTDB Cli/Shell tool. The client startup script is the `start-client` file under the \$IOTDB\_HOME/bin folder. When starting the script, you need to specify the IP and PORT. (Make sure the IoTDB server is running properly when you use Cli/Shell tool to connect it.) + +Here is an example where the server is started locally and the user has not changed the running port. The default port is +6667. If you need to connect to the remote server or changes +the port number of the server running, set the specific IP and PORT at -h and -p. + +The Linux and MacOS system startup commands are as follows: + +``` + Shell > ./bin/start-client.sh -h 127.0.0.1 -p 6667 -u root -pw root +``` +The Windows system startup commands are as follows: + +``` + Shell > \bin\start-client.bat -h 127.0.0.1 -p 6667 -u root -pw root +``` +After using these commands, the client can be started successfully. The successful status will be as follows: + +``` + _____ _________ ______ ______ +|_ _| | _ _ ||_ _ `.|_ _ \ + | | .--.|_/ | | \_| | | `. \ | |_) | + | | / .'`\ \ | | | | | | | __'. + _| |_| \__. | _| |_ _| |_.' /_| |__) | +|_____|'.__.' |_____| |______.'|_______/ version + + +IoTDB> login successfully +IoTDB> +``` +Enter ```quit``` or `exit` can exit Client. The client will shows `quit normally` + +## Cli/Shell Parameters + +|Parameter name|Parameter type|Required| Description| Example | +|:---|:---|:---|:---|:---| +|-disableIS08601 |No parameters | No |If this parameter is set, IoTDB will print the timestamp in digital form|-disableIS08601| +|-h <`host`> |string, no quotation marks|Yes|The IP address of the IoTDB server|-h 10.129.187.21| +|-help|No parameters|No|Print help information for IoTDB|-help| +|-p <`port`>|int|Yes|The port number of the IoTDB server. IoTDB runs on port 6667 by default|-p 6667| +|-pw <`password`>|string, no quotation marks|No|The password used for IoTDB to connect to the server. If no password is entered, IoTDB will ask for password in Cli command|-pw root| +|-u <`username`>|string, no quotation marks|Yes|User name used for IoTDB to connect the server|-u root| +|-maxPRC <`maxPrintRowCount`>|int|No|Set the maximum number of rows that IoTDB returns|-maxPRC 10| + +Following is a client command which connects the host with IP +10.129.187.21, port 6667, username "root", password "root", and prints the timestamp in digital form. The maximum number of lines displayed on the IoTDB command line is 10. + +The Linux and MacOS system startup commands are as follows: + +``` + Shell >./bin/start-client.sh -h 10.129.187.21 -p 6667 -u root -pw root -disableIS08601 -maxPRC 10 +``` +The Windows system startup commands are as follows: + +``` + Shell > \bin\start-client.bat -h 10.129.187.21 -p 6667 -u root -pw root -disableIS08601 -maxPRC 10 +``` diff --git a/docs/Documentation/UserGuideV0.7.0/7-Tools-Grafana.md b/docs/Documentation/UserGuideV0.7.0/7-Tools-Grafana.md new file mode 100644 index 0000000000000..26d20fb9a8174 --- /dev/null +++ b/docs/Documentation/UserGuideV0.7.0/7-Tools-Grafana.md @@ -0,0 +1,117 @@ + + +- [IoTDB-Grafana](#iotdb-grafana) + - [Grafana installation](#grafana-installation) + - [Install Grafana](#install-grafana) + - [Install data source plugin](#install-data-source-plugin) + - [Start Grafana](#start-grafana) + - [IoTDB installation](#iotdb-installation) + - [IoTDB-Grafana installation](#iotdb-grafana-installation) + - [Start IoTDB-Grafana](#start-iotdb-grafana) + - [Explore in Grafana](#explore-in-grafana) + - [Add data source](#add-data-source) + - [Design in dashboard](#design-in-dashboard) + + +# IoTDB-Grafana + +This project provides a connector which reads data from IoTDB and sends to Grafana(https://grafana.com/). Before you use this tool, make sure Grafana and IoTDB are correctly installed and started. + +## Grafana installation + +### Install Grafana + +* Download url: https://grafana.com/grafana/download +* version >= 4.4.1 + +### Install data source plugin + +* plugin name: simple-json-datasource +* Download url: https://github.com/grafana/simple-json-datasource + +After downloading this plugin, you can use the grafana-cli tool to install SimpleJson from the commandline: + +``` +grafana-cli plugins install grafana-simple-json-datasource +``` + +Alternatively, you can manually download the .zip file and unpack it into your grafana plugins directory. + +* `{grafana-install-directory}/data/plugin/` (Windows) +* `/var/lib/grafana/plugins` (Linux) +* `/usr/local/var/lib/grafana/plugins`(Mac) + +### Start Grafana +If you use Unix, Grafana will auto start after installing, or you can run `sudo service grafana-server start` command. See more information [here](http://docs.grafana.org/installation/debian/). + +If you use Mac and `homebrew` to install Grafana, you can use `homebrew` to start Grafana. +First make sure homebrew/services is installed by running `brew tap homebrew/services`, then start Grafana using: `brew services start grafana`. +See more information [here](http://docs.grafana.org/installation/mac/). + +If you use Windows, start Grafana by executing grafana-server.exe, located in the bin directory, preferably from the command line. See more information [here](http://docs.grafana.org/installation/windows/). + +## IoTDB installation + +See https://github.com/apache/incubator-iotdb + +## IoTDB-Grafana installation + +```shell +git clone https://github.com/apache/incubator-iotdb.git +mvn clean package -pl grafana -am -Dmaven.test.skip=true +cd grafana +``` + +Copy `application.properties` from `conf/` directory to `target` directory. (Or just make sure that `application.properties` and `iotdb-grafana-{version}-SNAPSHOT.war` are in the same directory.) + +Edit `application.properties` + +``` +# ip and port of IoTDB +spring.datasource.url = jdbc:iotdb://127.0.0.1:6667/ +spring.datasource.username = root +spring.datasource.password = root +spring.datasource.driver-class-name=org.apache.iotdb.jdbc.IoTDBDriver +server.port = 8888 +``` + +### Start IoTDB-Grafana + +```shell +cd grafana/target/ +java -jar iotdb-grafana-{version}-SNAPSHOT.war +``` + +If you see the following output, iotdb-grafana connector is successfully activated. + +```shell +$ java -jar iotdb-grafana-{version}-SNAPSHOT.war + + . ____ _ __ _ _ + /\\ / ___'_ __ _ _(_)_ __ __ _ \ \ \ \ +( ( )\___ | '_ | '_| | '_ \/ _` | \ \ \ \ + \\/ ___)| |_)| | | | | || (_| | ) ) ) ) + ' |____| .__|_| |_|_| |_\__, | / / / / + =========|_|==============|___/=/_/_/_/ + :: Spring Boot :: (v1.5.4.RELEASE) +... +``` + +## Explore in Grafana + +The default port of Grafana is 3000, see http://localhost:3000 + +Username and password are both "admin" by default. + +### Add data source + +Select `Data Sources` and then `Add data source`, select `SimpleJson` in `Type` and `URL` is http://localhost:8888 + + + + +### Design in dashboard + +Add diagrams in dashboard and customize your query. See http://docs.grafana.org/guides/getting_started/ + + \ No newline at end of file diff --git a/docs/Documentation/UserGuideV0.7.0/7-Tools-Hadoop.md b/docs/Documentation/UserGuideV0.7.0/7-Tools-Hadoop.md new file mode 100644 index 0000000000000..88eb12459f176 --- /dev/null +++ b/docs/Documentation/UserGuideV0.7.0/7-Tools-Hadoop.md @@ -0,0 +1,8 @@ + + +- [TsFile-Hadoop-Connector User Guide](#tsfile-hadoop-connector-user-guide) + + +# TsFile-Hadoop-Connector User Guide + +Comming Soon. \ No newline at end of file diff --git a/docs/Documentation/UserGuideV0.7.0/7-Tools-spark.md b/docs/Documentation/UserGuideV0.7.0/7-Tools-spark.md new file mode 100644 index 0000000000000..afdfb6540b741 --- /dev/null +++ b/docs/Documentation/UserGuideV0.7.0/7-Tools-spark.md @@ -0,0 +1,270 @@ + + +- [TsFile-Spark-Connector User Guide](#tsfile-spark-connector-user-guide) + - [Dependencies & Version](#dependencies--version) + - [Quick Start](#quick-start) + - [Step1: Build TsFile-Spark-Connector](#step1-build-tsfile-spark-connector) + - [Step2: Import Connector Lib into Spark](#step2-import-connector-lib-into-spark) + - [Step3: Use Connector in Spark](#step3-use-connector-in-spark) + - [5.2.1.1 Local Mode](#5211-local-mode) + - [5.2.1.2 Distributed Mode](#5212-distributed-mode) + - [Detail: Conversion between TsFile and Spark](#detail-conversion-between-tsfile-and-spark) + - [TsFile Type <-> SparkSQL type](#tsfile-type---sparksql-type) + - [TsFile Schema <-> SparkSQL Table Structure](#tsfile-schema---sparksql-table-structure) + - [the default way](#the-default-way) + - [unfolding delta_object column](#unfolding-delta_object-column) + - [Example](#example) + - [5.1 Scala API](#51-scala-api) + + +# TsFile-Spark-Connector User Guide + +TsFile-Spark-Connector implements the support of Spark for external data sources of Tsfile type. This enables users to read, write and query Tsfile by Spark. + +## Dependencies & Version + +The versions required for Spark and Java are as follow: + +| Spark Version | Scala Version | Java Version | TsFile | +| ------------- | ------------- | ------------ |------- | +| `2.0+` | `2.11` | `1.8` | `0.7.0`| + +> Note: For more information about how to download and use TsFile, please see the following link: https://github.com/apache/incubator-iotdb/tree/master/tsfile. + +## Quick Start + +### Step1: Build TsFile-Spark-Connector + +To build TsFile-Spark-Connector, you can use the following command: + +``` +mvn clean scala:compile compile package +``` + +In addition, you can also choose to download the available lib package directly from our website. The download link will be comming soon. + +### Step2: Import Connector Lib into Spark + +* import tsfile-spark-connector.jar into spark lib +* replace libthrift-0.9.2.jar and libfb303-0.9.2.jar with libthrift-0.9.1.jar and libfb303-0.9.1.jar respectively. + +### Step3: Use Connector in Spark +#### 5.2.1.1 Local Mode + +Start Spark with TsFile-Spark-Connector in local mode: + +``` +./ --jars tsfile-.jar,tsfile-spark-connector-.jar +``` +Note: + +* \ is the real path of your spark-shell. +* \ is the tsfile version. +* \ is the TsFile-Spark-Connector version. Note that, the version of TsFile-Spark-Connector and TsFile should be correspondence. +* Multiple jar packages are separated by commas without any spaces. +* See https://github.com/apache/incubator-iotdb/tree/master/tsfile for how to get TsFile. + +#### 5.2.1.2 Distributed Mode + +Start Spark with TsFile-Spark-Connector in distributed mode (That is, the spark cluster is connected by spark-shell): + +``` +. / --jars tsfile-.jar,tsfile-spark-connector-.jar --master spark://ip:7077 +``` + +Note: + +* \ is the real path of your spark-shell. +* \ is the tsfile version. +* \ is the TsFile-Spark-Connector version. Note that, the version of TsFile-Spark-Connector and TsFile should be correspondence. +* Multiple jar packages are separated by commas without any spaces. +* See https://github.com/apache/incubator-iotdb/tree/master/tsfile for how to get TsFile. + +## Detail: Conversion between TsFile and Spark + +### TsFile Type <-> SparkSQL type + +This library uses the following mapping the data type from TsFile to SparkSQL: + +| TsFile | SparkSQL| +| --------------| -------------- | +| BOOLEAN | BooleanType | +| INT32 | IntegerType | +| INT64 | LongType | +| FLOAT | FloatType | +| DOUBLE | DoubleType | +| ENUMS | StringType | +| TEXT | StringType | + +### TsFile Schema <-> SparkSQL Table Structure + +The way to display TsFile is related to TsFile Schema. Take the following TsFile structure as an example: There are three Measurements in the Schema of TsFile: status, temperature, and hardware. The basic info of these three Measurements is as follows: + +
+ + + + + +
NameTypeEncode
statusBooleanPLAIN
temperatureFloatRLE
hardwareTextPLAIN
+Basic info of Measurements +
+ +The existing data in the file is as follows: + +
+ + + + + + + + +
delta\_object:root.ln.wf01.wt01delta\_object:root.ln.wf02.wt02delta\_object:root.sgcc.wf03.wt01
statustemperaturehardwarestatusstatustemperature
timevaluetimevaluetimevaluetimevaluetimevaluetimevalue
1True12.22"aaa"1True2True33.3
3True22.24"bbb"2False3True66.6
5 False 32.16"ccc"4True4True88.8
7 True 42.08"ddd"5False6True99.9
+A set of time-series data +
+ +There are two ways to show it out: + +#### the default way + +Two columns will be created to store the full path of the device: time(LongType) and delta_object(StringType). + +- `time` : Timestamp, LongType +- `delta_object` : Delta_object ID, StringType + +Next, a column is created for each Measurement to store the specific data. The SparkSQL table structure is as follows: + +
+ + + + + + + + + + + + + + + + + + + + + +
time(LongType) delta\_object(StringType)status(BooleanType)temperature(FloatType)hardware(StringType)
1 root.ln.wf01.wt01 True2.2null
1 root.ln.wf02.wt02 Truenullnull
2 root.ln.wf01.wt01 null2.2null
2 root.ln.wf02.wt02 Falsenull"aaa"
2 root.sgcc.wf03.wt01 Truenullnull
3 root.ln.wf01.wt01 True2.1null
3 root.sgcc.wf03.wt01 True3.3null
4 root.ln.wf01.wt01 null2.0null
4 root.ln.wf02.wt02 Truenull"bbb"
4 root.sgcc.wf03.wt01 Truenullnull
5 root.ln.wf01.wt01 Falsenullnull
5 root.ln.wf02.wt02 Falsenullnull
5 root.sgcc.wf03.wt01 Truenullnull
6 root.ln.wf02.wt02 nullnull"ccc"
6 root.sgcc.wf03.wt01 null6.6null
7 root.ln.wf01.wt01 Truenullnull
8 root.ln.wf02.wt02 nullnull"ddd"
8 root.sgcc.wf03.wt01 null8.8null
9 root.sgcc.wf03.wt01 null9.9null
+
+ + +#### unfolding delta_object column + +Expand the device column by "." into multiple columns, ignoring the root directory "root". Convenient for richer aggregation operations. If the user wants to use this display way, the parameter "delta\_object\_name" needs to be set in the table creation statement (refer to Example 5 in Section 5.1 of this manual), as in this example, parameter "delta\_object\_name" is set to "root.device.turbine". The number of path layers needs to be one-to-one. At this point, one column is created for each layer of the device path except the "root" layer. The column name is the name in the parameter and the value is the name of the corresponding layer of the device. Next, one column will be created for each Measurement to store the specific data. + +Then The SparkSQL Table Structure is as follow: + +
+ + + + + + + + + + + + + + + + + + + + + +
time(LongType) group(StringType) field(StringType) device(StringType)status(BooleanType)temperature(FloatType)hardware(StringType)
1 ln wf01 wt01 True2.2null
1 ln wf02 wt02 Truenullnull
2 ln wf01 wt01 null2.2null
2 ln wf02 wt02 Falsenull"aaa"
2 sgcc wf03 wt01 Truenullnull
3 ln wf01 wt01 True2.1null
3 sgcc wf03 wt01 True3.3null
4 ln wf01 wt01 null2.0null
4 ln wf02 wt02 Truenull"bbb"
4 sgcc wf03 wt01 Truenullnull
5 ln wf01 wt01 Falsenullnull
5 ln wf02 wt02 Falsenullnull
5 sgcc wf03 wt01 Truenullnull
6 ln wf02 wt02 nullnull"ccc"
6 sgcc wf03 wt01 null6.6null
7 ln wf01 wt01 Truenullnull
8 ln wf02 wt02 nullnull"ddd"
8 sgcc wf03 wt01 null8.8null
9 sgcc wf03 wt01 null9.9null
+ +
+ +TsFile-Spark-Connector can display one or more TsFiles as a table in SparkSQL By SparkSQL. It also allows users to specify a single directory or use wildcards to match multiple directories. If there are multiple TsFiles, the union of the measurements in all TsFiles will be retained in the table, and the measurement with the same name will have the same data type by default. Note that if there is a situation with the same name but different data types, TsFile-Spark-Connector will not guarantee the correctness of the results. + +The writing process is to write a DataFrame as one or more TsFiles. By default, two columns need to be included: time and delta_object. The rest of the columns are used as Measurement. If user wants to write the second table structure back to TsFile, user can set the "delta\_object\_name" parameter(refer to Section 5.1 of Section 5.1 of this manual). + +## Example + +The path of 'test.tsfile' used in the following examples is "data/test.tsfile". Please upload 'test.tsfile' to hdfs in advance and the directory is "/test.tsfile". + +### 5.1 Scala API + +* **Example 1** + + ```scala + import cn.edu.tsinghua.tsfile._ + + //read data in TsFile and create a table + val df = spark.read.tsfile("/test.tsfile") + df.createOrReplaceTempView("tsfile_table") + + //query with filter + val newDf = spark.sql("select * from tsfile_table where temperature > 1.2").cache() + + newDf.show() + ``` + +* **Example 2** + + ```scala + val df = spark.read + .format("cn.edu.tsinghua.tsfile") + .load("/test.tsfile ") + df.filter("time < 10").show() + ``` + +* **Example 3** + + ```scala + //create a table in SparkSQL and build relation with a TsFile + spark.sql("create temporary view tsfile_table using cn.edu.tsinghua.tsfile options(path = \"test.ts\")") + + spark.sql("select * from tsfile_table where temperature > 1.2").show() + ``` + +* **Example 4(using options to read)** + + ```scala + import cn.edu.tsinghua.tsfile._ + + val df = spark.read.option("delta_object_name", "root.group.field.device").tsfile("/test.tsfile") + + //create a table in SparkSQL and build relation with a TsFile + df.createOrReplaceTempView("tsfile_table") + + spark.sql("select * from tsfile_table where device = 'wt01' and field = 'wf01' and group = 'ln' and time < 10").show() + ``` + +* **Example 5(write)** + + ```scala + import cn.edu.tsinghua.tsfile._ + + val df = spark.read.tsfile("/test.tsfile").write.tsfile("/out") + ``` + +* **Example 6(using options to write)** + + ```scala + import cn.edu.tsinghua.tsfile._ + + val df = spark.read.option("delta_object_name", "root.group.field.device").tsfile("/test.tsfile") + + df.write.option("delta_object_name", "root.group.field.device").tsfile("/out") + ``` +