Release v1.0

@adityagp adityagp released this Jan 6, 2017 · 31 commits to master since this release

OrpheusDB: Bolt-On Versioning for Relational Databases

orpheusdb101cropped

OrpheusDB is a hosted system that supports relational dataset version management. OrpheusDB is built on top of standard relational databases, thus it inherits much of the same benefits of relational databases, while also compactly storing, tracking, and recreating versions on demand, all very efficiently.

OrpheusDB is built using PostgreSQL and Click, a command line tool written in Python. Our current version supports advanced querying capabilities, using both the git-style version control commands, as well as SQL queries on one or more dataset versions. The paper describing the design, functionality, optimization, and performance evaluation can be found at this link.

OrpheusDB is a multi-year project, supported by the National Science Foundation via award number 1513407. It shares the vision of the DataHub project in supporting collaborative data analytics.

Version

The current version is v1.0 (Released January 1, 2017).

Key Design Innovations

  • OrpheusDB is built on top of a traditional relational database, thus it inherits all of the standard benefits of relational database systems "for free"
  • OrpheusDB supports advanced querying and versioning capabilities, via both SQL queries and git-style version control commands.
  • OrpheusDB uses a sophisticated data model, coupled with partition optimization algorithms1, to provide efficient version control performance over large-scale datasets.

Dataset Version Control in OrpheusDB

The fundamental unit of storage within OrpheusDB is a collaborative versioned dataset (CVD) to which one or more users can contribute,
representing a collection of versions of a single relational dataset, with a fixed schema. There is a many-to-many relationship between records in the relation and versions that are captured within the CVD: each record can belong to many versions, and each version can contain many records.

Users can operate on CVDs much like they would with source code version control. The checkout command allows users to materialize one or more specific versions of a CVD as a newly created regular table within a relational database or as a csv file; the commit command allows users to add a new version to a CVD by making the local changes made by the user on their materialized table or on their exported csv file visible to others. Other git-style commands we support include init, create_user, config, whoami, ls, drop, and optimize.

Users can also execute SQL queries on one or more relational dataset versions within a CVD via the command line using the run command, without requiring the corresponding dataset versions to be materialized. Beyond executing queries on a small number of versions, users can also apply aggregation grouped by version ids, or identify versions that satisfy some property.

Data Model

Each CVD in OrpheusDB corresponds to three underlying relational tables: the data table, the index table, and the version table. To capture dataset versions, we represent the records of a dataset in the data table and mapping between versions and records in the index table. Finally, we store version-level provenance information in the version table, including attributes such as author, num_records, parent, children, create_time, commit_time, and commit_msg.

Our experimental evaluation demonstrates that, compared to other alternative data models, our data model, coupled with the partition optimizer results in 10x less storage consumption, 1000x less time for commit and comparable query performance for the checkout command. In other words, OrpheusDB acheives an efficient balance between storage consumption and query latencies.

System Requirement

OrpheusDB requires the following software to be installed successfully prior to setup:

  • Python 2.7.x
  • PostgreSQL >= 9.5

Installation Instructions

OrpheusDB comes with a standard setup.py script for installation. The required python dependency packages include

  • click >= 6.6
  • psycopy2 >= 2.6.2
  • pandas >= 0.19.0
  • pyyaml >= 3.12
  • pyparsing >=2.1.1
  • sqlparse >= 0.2.2

Users are able to install any of missing dependencies themselves via pip. Alternatively, an easier way to install all the requisite dependencies is via pip install . (If you encounter permission errors, install via sudo -H pip install .)

After installation, users can use dh --help to list all the available commands in OrpheusDB. By default, dh is the alias for OrpheusDB user interface.

Configuration

To start with, users need to install PostgresSQL successfully. (A tutorial of installing PostgresSQL on Mac OSX can be found here.) After installing, and then starting PostgresSQL (e.g., via pg_ctl), users can call createdb to create a new database with a new username and password, all under the current user login. Remember the username and password, the parameters of the new database, and other details of the PostgreSQL setup. Once the configuration is complete, edit the appropriate entries in the file config.yaml.

User Tutorials

To start working on versioned datasets, users need to run dh config to set up OrpheusDB for the given user. To start off, use ths same username that was used during the PostgreSQL configuration -- this will initialize a OrpheusDB user with the same username. Following that, users can create new OrpheusDB usernames via the create_user command. Upon finishing, this new username will be pushed to the underlying data storage with a SUPERUSER privilege. Command config can also be used to login through created username and whoami is used to list the current username that is currently logged in.

Please note here that OrpheusDB provides the most basic implementation for user information, i.e. there is no password protection. However, this feature is subject to change in future versions.

db config
dh create_user
dh whoami

The init command provides a mechanism to load a csv file into OrpheusDB as a CVD, with all the records as its first version (i.e., vid = 1). To let OrpheusDB know what is the schema for this dataset, user can provide a sample schema file through option -s. Each line in the schema file has the format <attribute name>, <type of the attribute>. In the following example, data.csv file contains 3 attributes, namely age, employee_id and salary. The command below loads the data.csv file into OrpheusDB as a CVD named dataset1, whose schema is indicated in the file ``sample_schema.csv`.

dh init test/data.csv dataset1 -s test/sample_schema.csv

User can checkout one or more desired versions through the checkout command, to either a csv file or a structured table in RDBMS. In the following example, version 1 of CVD dataset1 is checked out as a csv file named checkout.csv.

dh checkout dataset1 -v 1 -f checkout.csv

After changes are made to the previous checkout versions, OrpheusDB can commit these changes to its corresponding CVD assuming that the schema is unchanged.

In the following example, we commit the modified checkout.csv back to CVD dataset1. Note here that since OrpheusDB internally logged the CVD name that checkout.csv file was checked out from, there is no need to specify the CVD name in the commit command.

Any changed or new records from commit file will be appended to the corresponding CVD, labeled with a new version id. A special case is the committing of a subset of a previously checked-out version. In such a setting, OrpheusDB will perform the commit as expected; the new version is added with the subset of the records.

dh commit -f checkout.csv -m 'first commit'

OrpheusDB also supports direct execution of queries on CVDs without materialization. This is done via the run command. The run command will prompt the user to provide the SQL command to be executed directly. If -f is specified, it will execute the SQL file specified.

dh run

OrpheusDB supports a rich syntax of SQL statements on versions and CVDs. During the execution of these steatements, OrpheusDB will detect keywords like CVD so it knows the query is against one or more CVDs. There are mainly the following two types of queries supported:

  1. Query against known version(s) of a particular dataset
  2. Query against unknown version(s) of a particular dataset

To query against known version(s), the version number needs to be specified. In the following example, OrpheusDB will select the employee_id and age columns from CVD dataset1 whose version id is equal to either 1 or 2.

SELECT employee_id, age FROM VERSION 1,2 OF CVD dataset1;

If version number is unknown, OrpheusDB supports queries where the desired version numbers are also identified. In the following examples, OrpheusDB will select all the version ids that have one or more records whose age is less than 25. It is worth noting that the GROUP BY clause is required to aggregate on version numbers.

SELECT vid FROM CVD dataset1 WHERE age < 25 GROUP BY vid;

Here are a couple other examples of SQL on versions:

(1). Find all versions in CVD dataset1 that have more than 5 records where salary is larger than 7400.

SELECT vid FROM CVD dataset1 WHERE salary > 7400 GROUP BY vid HAVING COUNT(employee_id) > 5;

(2). Find all versions in CVD dataset1 whose commit time is later than December 1st, 2016.

SELECT vid FROM CVD dataset1 WHERE commit_time >  '2016-12-01' GROUP BY vid;

Development Plan

We plan to release versions of OrpheusDB in a regular manner, adding on further
querying, partitioning, and query optimization capabilities, as well as regular bug-fixes.

License

MIT

1The partition optimization algorithms are not part of this release.

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