You are viewing a plain text version of this content. The canonical link for it is here.
Posted to reviews@spark.apache.org by yuany <gi...@git.apache.org> on 2015/09/15 14:27:31 UTC
[GitHub] spark pull request: Master
GitHub user yuany opened a pull request:
https://github.com/apache/spark/pull/8767
Master
Small typo in the example for `LabelledPoint` in the MLLib docs.
Author: Sean Paradiso <se...@gmail.com>
Closes #8680 from sparadiso/docs_mllib_smalltypo.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/apache/spark master
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/8767.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #8767
----
commit c6df5f66d9a8b9760f2cd46fcd930f977650c9c5
Author: zsxwing <zs...@gmail.com>
Date: 2015-08-24T00:41:49Z
[SPARK-10148] [STREAMING] Display active and inactive receiver numbers in Streaming page
Added the active and inactive receiver numbers in the summary section of Streaming page.
<img width="1074" alt="screen shot 2015-08-21 at 2 08 54 pm" src="https://cloud.githubusercontent.com/assets/1000778/9402437/ff2806a2-480f-11e5-8f8e-efdf8e5d514d.png">
Author: zsxwing <zs...@gmail.com>
Closes #8351 from zsxwing/receiver-number.
commit b963c19a803c5a26c9b65655d40ca6621acf8bd4
Author: Joseph K. Bradley <jo...@databricks.com>
Date: 2015-08-24T01:34:07Z
[SPARK-10164] [MLLIB] Fixed GMM distributed decomposition bug
GaussianMixture now distributes matrix decompositions for certain problem sizes. Distributed computation actually fails, but this was not tested in unit tests.
This PR adds a unit test which checks this. It failed previously but works with this fix.
CC: mengxr
Author: Joseph K. Bradley <jo...@databricks.com>
Closes #8370 from jkbradley/gmm-fix.
commit 053d94fcf32268369b5a40837271f15d6af41aa4
Author: Tathagata Das <ta...@gmail.com>
Date: 2015-08-24T02:24:32Z
[SPARK-10142] [STREAMING] Made python checkpoint recovery handle non-local checkpoint paths and existing SparkContexts
The current code only checks checkpoint files in local filesystem, and always tries to create a new Python SparkContext (even if one already exists). The solution is to do the following:
1. Use the same code path as Java to check whether a valid checkpoint exists
2. Create a new Python SparkContext only if there no active one.
There is not test for the path as its hard to test with distributed filesystem paths in a local unit test. I am going to test it with a distributed file system manually to verify that this patch works.
Author: Tathagata Das <ta...@gmail.com>
Closes #8366 from tdas/SPARK-10142 and squashes the following commits:
3afa666 [Tathagata Das] Added tests
2dd4ae5 [Tathagata Das] Added the check to not create a context if one already exists
9bf151b [Tathagata Das] Made python checkpoint recovery use java to find the checkpoint files
commit 4e0395ddb764d092b5b38447af49e196e590e0f0
Author: zsxwing <zs...@gmail.com>
Date: 2015-08-24T19:38:01Z
[SPARK-10168] [STREAMING] Fix the issue that maven publishes wrong artifact jars
This PR removed the `outputFile` configuration from pom.xml and updated `tests.py` to search jars for both sbt build and maven build.
I ran ` mvn -Pkinesis-asl -DskipTests clean install` locally, and verified the jars in my local repository were correct. I also checked Python tests for maven build, and it passed all tests.
Author: zsxwing <zs...@gmail.com>
Closes #8373 from zsxwing/SPARK-10168 and squashes the following commits:
e0b5818 [zsxwing] Fix the sbt build
c697627 [zsxwing] Add the jar pathes to the exception message
be1d8a5 [zsxwing] Fix the issue that maven publishes wrong artifact jars
commit 7478c8b66d6a2b1179f20c38b49e27e37b0caec3
Author: Tathagata Das <ta...@gmail.com>
Date: 2015-08-24T19:40:09Z
[SPARK-9791] [PACKAGE] Change private class to private class to prevent unnecessary classes from showing up in the docs
In addition, some random cleanup of import ordering
Author: Tathagata Das <ta...@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
commit 9ce0c7ad333f4a3c01207e5e9ed42bcafb99d894
Author: Burak Yavuz <br...@gmail.com>
Date: 2015-08-24T20:48:01Z
[SPARK-7710] [SPARK-7998] [DOCS] Docs for DataFrameStatFunctions
This PR contains examples on how to use some of the Stat Functions available for DataFrames under `df.stat`.
rxin
Author: Burak Yavuz <br...@gmail.com>
Closes #8378 from brkyvz/update-sql-docs.
commit 662bb9667669cb07cf6d2ccee0d8e76bb561cd89
Author: Andrew Or <an...@databricks.com>
Date: 2015-08-24T21:10:50Z
[SPARK-10144] [UI] Actually show peak execution memory by default
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 <an...@databricks.com>
Closes #8345 from andrewor14/show-memory-default.
commit a2f4cdceba32aaa0df59df335ca0ce1ac73fc6c2
Author: Cheng Lian <li...@databricks.com>
Date: 2015-08-24T21:11:19Z
[SPARK-8580] [SQL] Refactors ParquetHiveCompatibilitySuite and adds more 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 <li...@databricks.com>
Closes #8392 from liancheng/spark-8580/parquet-hive-compat-tests.
commit cb2d2e15844d7ae34b5dd7028b55e11586ed93fa
Author: Sean Owen <so...@cloudera.com>
Date: 2015-08-24T21:35:21Z
[SPARK-9758] [TEST] [SQL] Compilation issue for hive test / wrong package?
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 https://github.com/apache/spark/pull/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 <so...@cloudera.com>
Closes #8307 from srowen/SPARK-9758.
commit 13db11cb08eb90eb0ea3402c9fe0270aa282f247
Author: Joseph K. Bradley <jo...@databricks.com>
Date: 2015-08-24T22:38:54Z
[SPARK-10061] [DOC] ML ensemble docs
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 <jo...@databricks.com>
Closes #8369 from jkbradley/ml-ensemble-docs.
commit d7b4c095271c36fcc7f9ded267ecf5ec66fac803
Author: Josh Rosen <jo...@databricks.com>
Date: 2015-08-24T23:17:45Z
[SPARK-10190] Fix NPE in CatalystTypeConverters Decimal toScala converter
This adds a missing null check to the Decimal `toScala` converter in `CatalystTypeConverters`, fixing an NPE.
Author: Josh Rosen <jo...@databricks.com>
Closes #8401 from JoshRosen/SPARK-10190.
commit 2bf338c626e9d97ccc033cfadae8b36a82c66fd1
Author: Michael Armbrust <mi...@databricks.com>
Date: 2015-08-25T01:10:51Z
[SPARK-10165] [SQL] Await child resolution in ResolveFunctions
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 <mi...@databricks.com>
Closes #8371 from marmbrus/hiveUDFResolution.
commit 6511bf559b736d8e23ae398901c8d78938e66869
Author: Yu ISHIKAWA <yu...@gmail.com>
Date: 2015-08-25T01:17:51Z
[SPARK-10118] [SPARKR] [DOCS] Improve SparkR API docs for 1.5 release
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 <yu...@gmail.com>
Author: Yuu ISHIKAWA <yu...@gmail.com>
Closes #8386 from yu-iskw/SPARK-10118.
commit 642c43c81c835139e3f35dfd6a215d668a474203
Author: Feynman Liang <fl...@databricks.com>
Date: 2015-08-25T02:45:41Z
[SQL] [MINOR] [DOC] Clarify docs for inferring DataFrame from RDD of 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 <fl...@databricks.com>
Closes #8406 from feynmanliang/sql-doc-fixes.
commit a0c0aae1defe5e1e57704065631d201f8e3f6bac
Author: Yin Huai <yh...@databricks.com>
Date: 2015-08-25T04:49:50Z
[SPARK-10121] [SQL] Thrift server always use the latest class loader 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 <yh...@databricks.com>
Closes #8368 from yhuai/SPARK-10121.
commit 5175ca0c85b10045d12c3fb57b1e52278a413ecf
Author: Michael Armbrust <mi...@databricks.com>
Date: 2015-08-25T06:15:27Z
[SPARK-10178] [SQL] HiveComparisionTest should print out dependent tables
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 <mi...@databricks.com>
Closes #8388 from marmbrus/generatedTables.
commit d9c25dec87e6da7d66a47ff94e7eefa008081b9d
Author: cody koeninger <co...@koeninger.org>
Date: 2015-08-25T06:26:14Z
[SPARK-9786] [STREAMING] [KAFKA] fix backpressure so it works with defa…
…ult maxRatePerPartition setting of 0
Author: cody koeninger <co...@koeninger.org>
Closes #8413 from koeninger/backpressure-testing-master.
commit f023aa2fcc1d1dbb82aee568be0a8f2457c309ae
Author: zsxwing <zs...@gmail.com>
Date: 2015-08-25T06:34:50Z
[SPARK-10137] [STREAMING] Avoid to restart receivers if scheduleReceivers 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 <zs...@gmail.com>
Closes #8340 from zsxwing/fix-receiver-scheduling.
commit df7041d02d3fd44b08a859f5d77bf6fb726895f0
Author: Yin Huai <yh...@databricks.com>
Date: 2015-08-25T06:38:32Z
[SPARK-10196] [SQL] Correctly saving decimals in internal rows to JSON.
https://issues.apache.org/jira/browse/SPARK-10196
Author: Yin Huai <yh...@databricks.com>
Closes #8408 from yhuai/DecimalJsonSPARK-10196.
commit bf03fe68d62f33dda70dff45c3bda1f57b032dfc
Author: Cheng Lian <li...@databricks.com>
Date: 2015-08-25T06:58:42Z
[SPARK-10136] [SQL] A more robust fix for SPARK-10136
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 <li...@databricks.com>
Closes #8361 from liancheng/spark-10136/proper-version.
commit 82268f07abfa658869df2354ae72f8d6ddd119e8
Author: Josh Rosen <jo...@databricks.com>
Date: 2015-08-25T07:04:10Z
[SPARK-9293] [SPARK-9813] Analysis should check that set operations are 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 <jo...@databricks.com>
Closes #7631 from JoshRosen/SPARK-9293.
commit d4549fe58fa0d781e0e891bceff893420cb1d598
Author: Yu ISHIKAWA <yu...@gmail.com>
Date: 2015-08-25T07:28:51Z
[SPARK-10214] [SPARKR] [DOCS] Improve SparkR Column, DataFrame API docs
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 <yu...@gmail.com>
Closes #8414 from yu-iskw/SPARK-10214.
commit 57b960bf3706728513f9e089455a533f0244312e
Author: Sean Owen <so...@cloudera.com>
Date: 2015-08-25T07:32:20Z
[SPARK-6196] [BUILD] Remove MapR profiles in favor of hadoop-provided
Follow up to https://github.com/apache/spark/pull/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 <so...@cloudera.com>
Closes #8338 from srowen/SPARK-6196.
commit 1fc37581a52530bac5d555dbf14927a5780c3b75
Author: Tathagata Das <ta...@gmail.com>
Date: 2015-08-25T07:35:51Z
[SPARK-10210] [STREAMING] Filter out non-existent blocks before creating 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 <ta...@gmail.com>
Closes #8405 from tdas/SPARK-10210.
commit 2f493f7e3924b769160a16f73cccbebf21973b91
Author: Davies Liu <da...@databricks.com>
Date: 2015-08-25T08:00:44Z
[SPARK-10177] [SQL] fix reading Timestamp in parquet from Hive
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 <da...@databricks.com>
Author: Cheng Lian <li...@databricks.com>
Closes #8400 from davies/timestamp_parquet.
commit 7bc9a8c6249300ded31ea931c463d0a8f798e193
Author: Josh Rosen <jo...@databricks.com>
Date: 2015-08-25T08:06:36Z
[SPARK-10195] [SQL] Data sources Filter should not expose internal types
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 <jo...@databricks.com>
Closes #8403 from JoshRosen/datasources-internal-vs-external-types.
commit 0e6368ffaec1965d0c7f89420e04a974675c7f6e
Author: Yin Huai <yh...@databricks.com>
Date: 2015-08-25T08:19:34Z
[SPARK-10197] [SQL] Add null check in wrapperFor (inside HiveInspectors).
https://issues.apache.org/jira/browse/SPARK-10197
Author: Yin Huai <yh...@databricks.com>
Closes #8407 from yhuai/ORCSPARK-10197.
commit 5c14890159a5711072bf395f662b2433a389edf9
Author: Zhang, Liye <li...@intel.com>
Date: 2015-08-25T10:48:55Z
[DOC] add missing parameters in SparkContext.scala for scala doc
Author: Zhang, Liye <li...@intel.com>
Closes #8412 from liyezhang556520/minorDoc.
commit 7f1e507bf7e82bff323c5dec3c1ee044687c4173
Author: ehnalis <zo...@gmail.com>
Date: 2015-08-25T11:30:06Z
Fixed a typo in DAGScheduler.
Author: ehnalis <zo...@gmail.com>
Closes #8308 from ehnalis/master.
commit 69c9c177160e32a2fbc9b36ecc52156077fca6fc
Author: Sean Owen <so...@cloudera.com>
Date: 2015-08-25T11:33:13Z
[SPARK-9613] [CORE] Ban use of JavaConversions and migrate all existing 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 <so...@cloudera.com>
Closes #8033 from srowen/SPARK-9613.
----
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org
[GitHub] spark pull request: Master
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/8767#issuecomment-140372637
Do you mind closing this PR? It looks like a mistake.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org
[GitHub] spark pull request: Master
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/8767#issuecomment-140373255
Can one of the admins verify this patch?
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org
[GitHub] spark pull request: Master
Posted by asfgit <gi...@git.apache.org>.
Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/8767
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org