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…nt unnecessary classes from showing up in the docs In addition, some random cleanup of import ordering Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #8387 from tdas/SPARK-9791 and squashes the following commits: 67f3ee9 [Tathagata Das] Change private class to private[package] class to prevent them from showing up in the docs
This PR contains examples on how to use some of the Stat Functions available for DataFrames under `df.stat`. rxin Author: Burak Yavuz <brkyvz@gmail.com> Closes #8378 from brkyvz/update-sql-docs.
The peak execution memory metric was introduced in SPARK-8735. That was before Tungsten was enabled by default, so it assumed that `spark.sql.unsafe.enabled` must be explicitly set to true. The result is that the memory is not displayed by default. Author: Andrew Or <andrew@databricks.com> Closes #8345 from andrewor14/show-memory-default.
…ore test cases This PR refactors `ParquetHiveCompatibilitySuite` so that it's easier to add new test cases. Hit two bugs, SPARK-10177 and HIVE-11625, while working on this, added test cases for them and marked as ignored for now. SPARK-10177 will be addressed in a separate PR. Author: Cheng Lian <lian@databricks.com> Closes #8392 from liancheng/spark-8580/parquet-hive-compat-tests.
…kage? Move `test.org.apache.spark.sql.hive` package tests to apparent intended `org.apache.spark.sql.hive` as they don't intend to test behavior from outside org.apache.spark.* Alternate take, per discussion at #8051 I think this is what vanzin and I had in mind but also CC rxin to cross-check, as this does indeed depend on whether these tests were accidentally in this package or not. Testing from a `test.org.apache.spark` package is legitimate but didn't seem to be the intent here. Author: Sean Owen <sowen@cloudera.com> Closes #8307 from srowen/SPARK-9758.
User guide for spark.ml GBTs and Random Forests. The examples are copied from the decision tree guide and modified to run. I caught some issues I had somehow missed in the tree guide as well. I have run all examples, including Java ones. (Of course, I thought I had previously as well...) CC: mengxr manishamde yanboliang Author: Joseph K. Bradley <joseph@databricks.com> Closes #8369 from jkbradley/ml-ensemble-docs.
…rter This adds a missing null check to the Decimal `toScala` converter in `CatalystTypeConverters`, fixing an NPE. Author: Josh Rosen <joshrosen@databricks.com> Closes #8401 from JoshRosen/SPARK-10190.
Currently, we eagerly attempt to resolve functions, even before their children are resolved. However, this is not valid in cases where we need to know the types of the input arguments (i.e. when resolving Hive UDFs). As a fix, this PR delays function resolution until the functions children are resolved. This change also necessitates a change to the way we resolve aggregate expressions that are not in aggregate operators (e.g., in `HAVING` or `ORDER BY` clauses). Specifically, we can't assume that these misplaced functions will be resolved, allowing us to differentiate aggregate functions from normal functions. To compensate for this change we now attempt to resolve these unresolved expressions in the context of the aggregate operator, before checking to see if any aggregate expressions are present. Author: Michael Armbrust <michael@databricks.com> Closes #8371 from marmbrus/hiveUDFResolution.
cc: shivaram ## Summary - Modify `tdname` of expression functions. i.e. `ascii`: `rdname functions` => `rdname ascii` - Replace the dynamical function definitions to the static ones because of thir documentations. ## Generated PDF File https://drive.google.com/file/d/0B9biIZIU47lLX2t6ZjRoRnBTSEU/view?usp=sharing ## JIRA [[SPARK-10118] Improve SparkR API docs for 1.5 release - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10118) Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8386 from yu-iskw/SPARK-10118.
…Products * Makes `SQLImplicits.rddToDataFrameHolder` scaladoc consistent with `SQLContext.createDataFrame[A <: Product](rdd: RDD[A])` since the former is essentially a wrapper for the latter * Clarifies `createDataFrame[A <: Product]` scaladoc to apply for any `RDD[Product]`, not just case classes Author: Feynman Liang <fliang@databricks.com> Closes #8406 from feynmanliang/sql-doc-fixes.
…provided by the conf of executionHive's state https://issues.apache.org/jira/browse/SPARK-10121 Looks like the problem is that if we add a jar through another thread, the thread handling the JDBC session will not get the latest classloader. Author: Yin Huai <yhuai@databricks.com> Closes #8368 from yhuai/SPARK-10121.
…bles In `HiveComparisionTest`s it is possible to fail a query of the form `SELECT * FROM dest1`, where `dest1` is the query that is actually computing the incorrect results. To aid debugging this patch improves the harness to also print these query plans and their results. Author: Michael Armbrust <michael@databricks.com> Closes #8388 from marmbrus/generatedTables.
…ult maxRatePerPartition setting of 0 Author: cody koeninger <cody@koeninger.org> Closes #8413 from koeninger/backpressure-testing-master.
…vers returns balanced results This PR fixes the following cases for `ReceiverSchedulingPolicy`. 1) Assume there are 4 executors: host1, host2, host3, host4, and 5 receivers: r1, r2, r3, r4, r5. Then `ReceiverSchedulingPolicy.scheduleReceivers` will return (r1 -> host1, r2 -> host2, r3 -> host3, r4 -> host4, r5 -> host1). Let's assume r1 starts at first on `host1` as `scheduleReceivers` suggested, and try to register with ReceiverTracker. But the previous `ReceiverSchedulingPolicy.rescheduleReceiver` will return (host2, host3, host4) according to the current executor weights (host1 -> 1.0, host2 -> 0.5, host3 -> 0.5, host4 -> 0.5), so ReceiverTracker will reject `r1`. This is unexpected since r1 is starting exactly where `scheduleReceivers` suggested. This case can be fixed by ignoring the information of the receiver that is rescheduling in `receiverTrackingInfoMap`. 2) Assume there are 3 executors (host1, host2, host3) and each executors has 3 cores, and 3 receivers: r1, r2, r3. Assume r1 is running on host1. Now r2 is restarting, the previous `ReceiverSchedulingPolicy.rescheduleReceiver` will always return (host1, host2, host3). So it's possible that r2 will be scheduled to host1 by TaskScheduler. r3 is similar. Then at last, it's possible that there are 3 receivers running on host1, while host2 and host3 are idle. This issue can be fixed by returning only executors that have the minimum wight rather than returning at least 3 executors. Author: zsxwing <zsxwing@gmail.com> Closes #8340 from zsxwing/fix-receiver-scheduling.
https://issues.apache.org/jira/browse/SPARK-10196 Author: Yin Huai <yhuai@databricks.com> Closes #8408 from yhuai/DecimalJsonSPARK-10196.
PR #8341 is a valid fix for SPARK-10136, but it didn't catch the real root cause. The real problem can be rather tricky to explain, and requires audiences to be pretty familiar with parquet-format spec, especially details of `LIST` backwards-compatibility rules. Let me have a try to give an explanation here. The structure of the problematic Parquet schema generated by parquet-avro is something like this: ``` message m { <repetition> group f (LIST) { // Level 1 repeated group array (LIST) { // Level 2 repeated <primitive-type> array; // Level 3 } } } ``` (The schema generated by parquet-thrift is structurally similar, just replace the `array` at level 2 with `f_tuple`, and the other one at level 3 with `f_tuple_tuple`.) This structure consists of two nested legacy 2-level `LIST`-like structures: 1. The repeated group type at level 2 is the element type of the outer array defined at level 1 This group should map to an `CatalystArrayConverter.ElementConverter` when building converters. 2. The repeated primitive type at level 3 is the element type of the inner array defined at level 2 This group should also map to an `CatalystArrayConverter.ElementConverter`. The root cause of SPARK-10136 is that, the group at level 2 isn't properly recognized as the element type of level 1. Thus, according to parquet-format spec, the repeated primitive at level 3 is left as a so called "unannotated repeated primitive type", and is recognized as a required list of required primitive type, thus a `RepeatedPrimitiveConverter` instead of a `CatalystArrayConverter.ElementConverter` is created for it. According to parquet-format spec, unannotated repeated type shouldn't appear in a `LIST`- or `MAP`-annotated group. PR #8341 fixed this issue by allowing such unannotated repeated type appear in `LIST`-annotated groups, which is a non-standard, hacky, but valid fix. (I didn't realize this when authoring #8341 though.) As for the reason why level 2 isn't recognized as a list element type, it's because of the following `LIST` backwards-compatibility rule defined in the parquet-format spec: > If the repeated field is a group with one field and is named either `array` or uses the `LIST`-annotated group's name with `_tuple` appended then the repeated type is the element type and elements are required. (The `array` part is for parquet-avro compatibility, while the `_tuple` part is for parquet-thrift.) This rule is implemented in [`CatalystSchemaConverter.isElementType`] [1], but neglected in [`CatalystRowConverter.isElementType`] [2]. This PR delivers a more robust fix by adding this rule in the latter method. Note that parquet-avro 1.7.0 also suffers from this issue. Details can be found at [PARQUET-364] [3]. [1]: https://github.com/apache/spark/blob/85f9a61357994da5023b08b0a8a2eb09388ce7f8/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala#L259-L305 [2]: https://github.com/apache/spark/blob/85f9a61357994da5023b08b0a8a2eb09388ce7f8/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala#L456-L463 [3]: https://issues.apache.org/jira/browse/PARQUET-364 Author: Cheng Lian <lian@databricks.com> Closes #8361 from liancheng/spark-10136/proper-version.
…re only performed on tables with equal numbers of columns This patch adds an analyzer rule to ensure that set operations (union, intersect, and except) are only applied to tables with the same number of columns. Without this rule, there are scenarios where invalid queries can return incorrect results instead of failing with error messages; SPARK-9813 provides one example of this problem. In other cases, the invalid query can crash at runtime with extremely confusing exceptions. I also performed a bit of cleanup to refactor some of those logical operators' code into a common `SetOperation` base class. Author: Josh Rosen <joshrosen@databricks.com> Closes #7631 from JoshRosen/SPARK-9293.
cc: shivaram
## Summary
- Add name tags to each methods in DataFrame.R and column.R
- Replace `rdname column` with `rdname {each_func}`. i.e. alias method : `rdname column` => `rdname alias`
## Generated PDF File
https://drive.google.com/file/d/0B9biIZIU47lLNHN2aFpnQXlSeGs/view?usp=sharing
## JIRA
[[SPARK-10214] Improve SparkR Column, DataFrame API docs - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10214)
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes #8414 from yu-iskw/SPARK-10214.
Follow up to #7047 pwendell mentioned that MapR should use `hadoop-provided` now, and indeed the new build script does not produce `mapr3`/`mapr4` artifacts anymore. Hence the action seems to be to remove the profiles, which are now not used. CC trystanleftwich Author: Sean Owen <sowen@cloudera.com> Closes #8338 from srowen/SPARK-6196.
…ing BlockRDD When write ahead log is not enabled, a recovered streaming driver still tries to run jobs using pre-failure block ids, and fails as the block do not exists in-memory any more (and cannot be recovered as receiver WAL is not enabled). This occurs because the driver-side WAL of ReceivedBlockTracker is recovers that past block information, and ReceiveInputDStream creates BlockRDDs even if those blocks do not exist. The solution in this PR is to filter out block ids that do not exist before creating the BlockRDD. In addition, it adds unit tests to verify other logic in ReceiverInputDStream. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #8405 from tdas/SPARK-10210.
We misunderstood the Julian days and nanoseconds of the day in parquet (as TimestampType) from Hive/Impala, they are overlapped, so can't be added together directly. In order to avoid the confusing rounding when do the converting, we use `2440588` as the Julian Day of epoch of unix timestamp (which should be 2440587.5). Author: Davies Liu <davies@databricks.com> Author: Cheng Lian <lian@databricks.com> Closes #8400 from davies/timestamp_parquet.
Spark SQL's data sources API exposes Catalyst's internal types through its Filter interfaces. This is a problem because types like UTF8String are not stable developer APIs and should not be exposed to third-parties. This issue caused incompatibilities when upgrading our `spark-redshift` library to work against Spark 1.5.0. To avoid these issues in the future we should only expose public types through these Filter objects. This patch accomplishes this by using CatalystTypeConverters to add the appropriate conversions. Author: Josh Rosen <joshrosen@databricks.com> Closes #8403 from JoshRosen/datasources-internal-vs-external-types.
…rs). https://issues.apache.org/jira/browse/SPARK-10197 Author: Yin Huai <yhuai@databricks.com> Closes #8407 from yhuai/ORCSPARK-10197.
Author: Zhang, Liye <liye.zhang@intel.com> Closes #8412 from liyezhang556520/minorDoc.
Author: ehnalis <zoltan.zvara@gmail.com> Closes #8308 from ehnalis/master.
…ng uses to JavaConverters Replace `JavaConversions` implicits with `JavaConverters` Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet. Author: Sean Owen <sowen@cloudera.com> Closes #8033 from srowen/SPARK-9613.
Author: Michael Armbrust <michael@databricks.com> Closes #8404 from marmbrus/turnOffPartitionVerification.
jira: https://issues.apache.org/jira/browse/SPARK-8531 Update ML user guide for MinMaxScaler Author: Yuhao Yang <hhbyyh@gmail.com> Author: unknown <yuhaoyan@yuhaoyan-MOBL1.ccr.corp.intel.com> Closes #7211 from hhbyyh/minmaxdoc.
See [discussion](#8254 (comment)) CC jkbradley Author: Feynman Liang <fliang@databricks.com> Closes #8422 from feynmanliang/SPARK-10230.
Update `Since` annotation in `mllib.classification`: 1. add version to classes, objects, constructors, and public variables declared in constructors 2. correct some versions 3. remove `Since` on `toString` MechCoder dbtsai Author: Xiangrui Meng <meng@databricks.com> Closes #8421 from mengxr/SPARK-10231 and squashes the following commits: b2dce80 [Xiangrui Meng] update @SInCE annotation for mllib.classification
…obContext methods This is a followup to #8499 which adds a Scalastyle rule to mandate the use of SparkHadoopUtil's JobContext accessor methods and fixes the existing violations. Author: Josh Rosen <joshrosen@databricks.com> Closes #8521 from JoshRosen/SPARK-10330-part2.
…r of GraphX Finish deprecating Bagel; remove reference to nonexistent example Author: Sean Owen <sowen@cloudera.com> Closes #8731 from srowen/SPARK-10222.
A few Identifiable types did override their toString method but without using the parent implementation. As a consequence, the uid was not present anymore in the toString result. It is the default behaviour. This patch is a quick fix. The question of enforcement is still up. No tests have been written to verify the toString method behaviour. That would be long to do because all types should be tested and not only those which have a regression now. It is possible to enforce the condition using the compiler by making the toString method final but that would introduce unwanted potential API breaking changes (see jira). Author: Bertrand Dechoux <BertrandDechoux@users.noreply.github.com> Closes #8062 from BertrandDechoux/SPARK-9720.
…lation enabled This is a follow-up of #8317. When speculation is enabled, there may be multiply tasks writing to the same path. Generally it's OK as we will write to a temporary directory first and only one task can commit the temporary directory to target path. However, when we use direct output committer, tasks will write data to target path directly without temporary directory. This causes problems like corrupted data. Please see [PR comment](#8191 (comment)) for more details. Unfortunately, we don't have a simple flag to tell if a output committer will write to temporary directory or not, so for safety, we have to disable any customized output committer when `speculation` is true. Author: Wenchen Fan <cloud0fan@outlook.com> Closes #8687 from cloud-fan/direct-committer.
…e.version is wrong.
The default value of hive metastore version is 1.2.1 but the documentation says the value of `spark.sql.hive.metastore.version` is 0.13.1.
Also, we cannot get the default value by `sqlContext.getConf("spark.sql.hive.metastore.version")`.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes #8739 from sarutak/SPARK-10584.
…rameter in Python [SPARK-3382](https://issues.apache.org/jira/browse/SPARK-3382) added a ```convergenceTol``` parameter for GradientDescent-based methods in Scala. We need that parameter in Python; otherwise, Python users will not be able to adjust that behavior (or even reproduce behavior from previous releases since the default changed). Author: Yanbo Liang <ybliang8@gmail.com> Closes #8457 from yanboliang/spark-10194.
Fixes bug where IndexToString output schema was DoubleType. Correct me if I'm wrong, but it doesn't seem like the output needs to have any "ML Attribute" metadata. Author: Nick Pritchard <nicholas.pritchard@falkonry.com> Closes #8751 from pnpritchard/SPARK-10573.
…itive Or Hive can't read it back correctly. Thanks vanzin for report this. Author: Davies Liu <davies@databricks.com> Closes #8674 from davies/positive_nano.
…LContext Alternative to PR #6122; in this case the refactored out classes are replaced by inner classes with the same name for backwards binary compatibility * process in a lighter-weight, backwards-compatible way Author: Edoardo Vacchi <uncommonnonsense@gmail.com> Closes #6356 from evacchi/sqlctx-refactoring-lite.
…rties-file `ApplicationMaster` no longer has the `--num-executors` flag, and had an undocumented `--properties-file` configuration option. cc srowen Author: Erick Tryzelaar <erick.tryzelaar@gmail.com> Closes #8754 from erickt/master.
Move .java files in `src/main/scala` to `src/main/java` root, except for `package-info.java` (to stay next to package.scala) Author: Sean Owen <sowen@cloudera.com> Closes #8736 from srowen/SPARK-10576.
Make this lazy so that it can set the yarn mode before creating the securityManager. Author: Tom Graves <tgraves@yahoo-inc.com> Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com> Closes #8719 from tgravescs/SPARK-10549.
…k Basis
Read `PEAK_EXECUTION_MEMORY` using `update` to get per task partial value instead of cumulative value.
I tested with this workload:
```scala
val size = 1000
val repetitions = 10
val data = sc.parallelize(1 to size, 5).map(x => (util.Random.nextInt(size / repetitions),util.Random.nextDouble)).toDF("key", "value")
val res = data.toDF.groupBy("key").agg(sum("value")).count
```
Before:
After:
Tasks view:
cc andrewor14 I appreciate if you can give feedback on this since I think you introduced display of this metric.
Author: Forest Fang <forest.fang@outlook.com>
Closes #8726 from saurfang/stagepage.
This patch adds support for submitting map stages in a DAG individually so that we can make downstream decisions after seeing statistics about their output, as part of SPARK-9850. I also added more comments to many of the key classes in DAGScheduler. By itself, the patch is not super useful except maybe to switch between a shuffle and broadcast join, but with the other subtasks of SPARK-9850 we'll be able to do more interesting decisions. The main entry point is SparkContext.submitMapStage, which lets you run a map stage and see stats about the map output sizes. Other stats could also be collected through accumulators. See AdaptiveSchedulingSuite for a short example. Author: Matei Zaharia <matei@databricks.com> Closes #8180 from mateiz/spark-9851.
Author: Davies Liu <davies@databricks.com> Closes #8707 from davies/fix_namedtuple.
…ement __eq__ and __hash__ correctly PySpark DenseVector, SparseVector ```__eq__``` method should use semantics equality, and DenseVector can compared with SparseVector. Implement PySpark DenseVector, SparseVector ```__hash__``` method based on the first 16 entries. That will make PySpark Vector objects can be used in collections. Author: Yanbo Liang <ybliang8@gmail.com> Closes #8166 from yanboliang/spark-9793.
Duplicated the since decorator from pyspark.sql into pyspark (also tweaked to handle functions without docstrings). Added since to methods + "versionadded::" to classes (derived from the git file history in pyspark). Author: noelsmith <mail@noelsmith.com> Closes #8633 from noel-smith/SPARK-10273-since-mllib-feature.
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8666 from yu-iskw/SPARK-10275.
Links work now properly + consistent use of *Spark standalone cluster* (Spark uppercase + lowercase the rest -- seems agreed in the other places in the docs). Author: Jacek Laskowski <jacek.laskowski@deepsense.io> Closes #8759 from jaceklaskowski/docs-submitting-apps.
Comments preceding toMessage method state: "The edge partition is encoded in the lower * 30 bytes of the Int, and the position is encoded in the upper 2 bytes of the Int.". References to bytes should be changed to bits. This contribution is my original work and I license the work to the Spark project under it's open source license. Author: Robin East <robin.east@xense.co.uk> Closes #8756 from insidedctm/master.
Author: Reynold Xin <rxin@databricks.com> Closes #8350 from rxin/1.6.
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Do you mind closing this PR? It looks like a mistake. |
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Can one of the admins verify this patch? |
jira: https://issues.apache.org/jira/browse/SPARK-10491 We implemented dspr with sparse vector support in `RowMatrix`. This method is also used in WeightedLeastSquares and other places. It would be useful to move it to `linalg.BLAS`. Let me know if new UT needed. Author: Yuhao Yang <hhbyyh@gmail.com> Closes #8663 from hhbyyh/movedspr.
This change does two things: - tag a few tests and adds the mechanism in the build to be able to disable those tags, both in maven and sbt, for both junit and scalatest suites. - add some logic to run-tests.py to disable some tags depending on what files have changed; that's used to disable expensive tests when a module hasn't explicitly been changed, to speed up testing for changes that don't directly affect those modules. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #8437 from vanzin/test-tags.
…ib.random Missed this when reviewing `pyspark.mllib.random` for SPARK-10275. Author: noelsmith <mail@noelsmith.com> Closes #8773 from noel-smith/mllib-random-versionadded-fix.
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Small typo in the example for
LabelledPointin the MLLib docs.Author: Sean Paradiso seanparadiso@gmail.com
Closes #8680 from sparadiso/docs_mllib_smalltypo.