- Dec 22, 2015
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Josh Rosen authored
We should update to the latest version of Zinc in order to match our SBT version. Author: Josh Rosen <joshrosen@databricks.com> Closes #10426 from JoshRosen/update-zinc.
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hyukjinkwon authored
[SPARK-11677][SQL][FOLLOW-UP] Add tests for checking the ORC filter creation against pushed down filters. https://issues.apache.org/jira/browse/SPARK-11677 Although it checks correctly the filters by the number of results if ORC filter-push-down is enabled, the filters themselves are not being tested. So, this PR includes the test similarly with `ParquetFilterSuite`. Since the results are checked by `OrcQuerySuite`, this `OrcFilterSuite` only checks if the appropriate filters are created. One thing different with `ParquetFilterSuite` here is, it does not check the results because that is checked in `OrcQuerySuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #10341 from HyukjinKwon/SPARK-11677-followup.
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Cheng Lian authored
This PR adds a new expression `AssertNotNull` to ensure non-nullable fields of products and case classes don't receive null values at runtime. Author: Cheng Lian <lian@databricks.com> Closes #10331 from liancheng/dataset-nullability-check.
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Takeshi YAMAMURO authored
No tests done for JDBCRDD#compileFilter. Author: Takeshi YAMAMURO <linguin.m.s@gmail.com> Closes #10409 from maropu/AddTestsInJdbcRdd.
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Holden Karau authored
Some methods are missing, such as ways to access the std, mean, etc. This PR is for feature parity for pyspark.mllib.feature.StandardScaler & StandardScalerModel. Author: Holden Karau <holden@us.ibm.com> Closes #10298 from holdenk/SPARK-12296-feature-parity-pyspark-mllib-StandardScalerModel.
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Josh Rosen authored
This patch fixes a flaky "test jdbc cancel" test in HiveThriftBinaryServerSuite. This test is prone to a race-condition which causes it to block indefinitely with while waiting for an extremely slow query to complete, which caused many Jenkins builds to time out. For more background, see my comments on #6207 (the PR which introduced this test). Author: Josh Rosen <joshrosen@databricks.com> Closes #10425 from JoshRosen/SPARK-11823.
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Shixiong Zhu authored
Author: Shixiong Zhu <shixiong@databricks.com> Closes #10424 from zsxwing/typo.
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Reynold Xin authored
i.e. Hadoop 1 and Hadoop 2.0 Author: Reynold Xin <rxin@databricks.com> Closes #10404 from rxin/SPARK-11807.
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- Dec 21, 2015
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Davies Liu authored
According the benchmark [1], LZ4-java could be 80% (or 30%) faster than Snappy. After changing the compressor to LZ4, I saw 20% improvement on end-to-end time for a TPCDS query (Q4). [1] https://github.com/ning/jvm-compressor-benchmark/wiki cc rxin Author: Davies Liu <davies@databricks.com> Closes #10342 from davies/lz4.
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Andrew Or authored
``` [info] ReplayListenerSuite: [info] - Simple replay (58 milliseconds) java.lang.NullPointerException at org.apache.spark.deploy.master.Master$$anonfun$asyncRebuildSparkUI$1.applyOrElse(Master.scala:982) at org.apache.spark.deploy.master.Master$$anonfun$asyncRebuildSparkUI$1.applyOrElse(Master.scala:980) ``` https://amplab.cs.berkeley.edu/jenkins/view/Spark-QA-Test/job/Spark-Master-SBT/4316/AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.2,label=spark-test/consoleFull This was introduced in #10284. It's harmless because the NPE is caused by a race that occurs mainly in `local-cluster` tests (but don't actually fail the tests). Tested locally to verify that the NPE is gone. Author: Andrew Or <andrew@databricks.com> Closes #10417 from andrewor14/fix-harmless-npe.
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Reynold Xin authored
Author: Reynold Xin <rxin@databricks.com> Closes #10394 from rxin/SPARK-2331.
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Alex Bozarth authored
Updates made in SPARK-11206 missed an edge case which cause's a NullPointerException when a task is killed. In some cases when a task ends in failure taskMetrics is initialized as null (see JobProgressListener.onTaskEnd()). To address this a null check was added. Before the changes in SPARK-11206 this null check was called at the start of the updateTaskAccumulatorValues() function. Author: Alex Bozarth <ajbozart@us.ibm.com> Closes #10405 from ajbozarth/spark12339.
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pshearer authored
Author: pshearer <pshearer@massmutual.com> Closes #10414 from pshearer/patch-1.
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Takeshi YAMAMURO authored
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com> Closes #4674 from maropu/AddGraphLoaderSuite.
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Takeshi YAMAMURO authored
[SPARK-12392][CORE] Optimize a location order of broadcast blocks by considering preferred local hosts When multiple workers exist in a host, we can bypass unnecessary remote access for broadcasts; block managers fetch broadcast blocks from the same host instead of remote hosts. Author: Takeshi YAMAMURO <linguin.m.s@gmail.com> Closes #10346 from maropu/OptimizeBlockLocationOrder.
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gatorsmile authored
Based on the suggestions from marmbrus , added logical/physical operators for Range for improving the performance. Also added another API for resolving the JIRA Spark-12150. Could you take a look at my implementation, marmbrus ? If not good, I can rework it. : ) Thank you very much! Author: gatorsmile <gatorsmile@gmail.com> Closes #10335 from gatorsmile/rangeOperators.
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Wenchen Fan authored
An alternative solution for https://github.com/apache/spark/pull/10295 , instead of implementing json format for all logical/physical plans and expressions, use reflection to implement it in `TreeNode`. Here I use pre-order traversal to flattern a plan tree to a plan list, and add an extra field `num-children` to each plan node, so that we can reconstruct the tree from the list. example json: logical plan tree: ``` [ { "class" : "org.apache.spark.sql.catalyst.plans.logical.Sort", "num-children" : 1, "order" : [ [ { "class" : "org.apache.spark.sql.catalyst.expressions.SortOrder", "num-children" : 1, "child" : 0, "direction" : "Ascending" }, { "class" : "org.apache.spark.sql.catalyst.expressions.AttributeReference", "num-children" : 0, "name" : "i", "dataType" : "integer", "nullable" : true, "metadata" : { }, "exprId" : { "id" : 10, "jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6" }, "qualifiers" : [ ] } ] ], "global" : false, "child" : 0 }, { "class" : "org.apache.spark.sql.catalyst.plans.logical.Project", "num-children" : 1, "projectList" : [ [ { "class" : "org.apache.spark.sql.catalyst.expressions.Alias", "num-children" : 1, "child" : 0, "name" : "i", "exprId" : { "id" : 10, "jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6" }, "qualifiers" : [ ] }, { "class" : "org.apache.spark.sql.catalyst.expressions.Add", "num-children" : 2, "left" : 0, "right" : 1 }, { "class" : "org.apache.spark.sql.catalyst.expressions.AttributeReference", "num-children" : 0, "name" : "a", "dataType" : "integer", "nullable" : true, "metadata" : { }, "exprId" : { "id" : 0, "jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6" }, "qualifiers" : [ ] }, { "class" : "org.apache.spark.sql.catalyst.expressions.Literal", "num-children" : 0, "value" : "1", "dataType" : "integer" } ], [ { "class" : "org.apache.spark.sql.catalyst.expressions.Alias", "num-children" : 1, "child" : 0, "name" : "j", "exprId" : { "id" : 11, "jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6" }, "qualifiers" : [ ] }, { "class" : "org.apache.spark.sql.catalyst.expressions.Multiply", "num-children" : 2, "left" : 0, "right" : 1 }, { "class" : "org.apache.spark.sql.catalyst.expressions.AttributeReference", "num-children" : 0, "name" : "a", "dataType" : "integer", "nullable" : true, "metadata" : { }, "exprId" : { "id" : 0, "jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6" }, "qualifiers" : [ ] }, { "class" : "org.apache.spark.sql.catalyst.expressions.Literal", "num-children" : 0, "value" : "2", "dataType" : "integer" } ] ], "child" : 0 }, { "class" : "org.apache.spark.sql.catalyst.plans.logical.LocalRelation", "num-children" : 0, "output" : [ [ { "class" : "org.apache.spark.sql.catalyst.expressions.AttributeReference", "num-children" : 0, "name" : "a", "dataType" : "integer", "nullable" : true, "metadata" : { }, "exprId" : { "id" : 0, "jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6" }, "qualifiers" : [ ] } ] ], "data" : [ ] } ] ``` Author: Wenchen Fan <wenchen@databricks.com> Closes #10311 from cloud-fan/toJson-reflection.
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Dilip Biswal authored
When a DataFrame or Dataset has a long schema, we should intelligently truncate to avoid flooding the screen with unreadable information. // Standard output [a: int, b: int] // Truncate many top level fields [a: int, b, string ... 10 more fields] // Truncate long inner structs [a: struct<a: Int ... 10 more fields>] Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #10373 from dilipbiswal/spark-12398.
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Jeff Zhang authored
No jira is created since this is a trivial change. davies Please help review it Author: Jeff Zhang <zjffdu@apache.org> Closes #10143 from zjffdu/pyspark_typo.
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Sean Owen authored
Only load explainedVariance in PCAModel if it was written with Spark > 1.6.x jkbradley is this kind of what you had in mind? Author: Sean Owen <sowen@cloudera.com> Closes #10327 from srowen/SPARK-12349.
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- Dec 20, 2015
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Bryan Cutler authored
Added catch for casting Long to Int exception when PySpark ALS Ratings are serialized. It is easy to accidentally use Long IDs for user/product and before, it would fail with a somewhat cryptic "ClassCastException: java.lang.Long cannot be cast to java.lang.Integer." Now if this is done, a more descriptive error is shown, e.g. "PickleException: Ratings id 1205640308657491975 exceeds max integer value of 2147483647." Author: Bryan Cutler <bjcutler@us.ibm.com> Closes #9361 from BryanCutler/als-pyspark-long-id-error-SPARK-10158.
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Reynold Xin authored
Author: Reynold Xin <rxin@databricks.com> Closes #10395 from rxin/SPARK-11808.
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- Dec 19, 2015
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Reynold Xin authored
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Reynold Xin authored
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Reynold Xin authored
Author: Reynold Xin <rxin@databricks.com> Closes #10387 from rxin/version-bump.
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Yanbo Liang authored
Fix mistake doc of join type for ```dataframe.join```. Author: Yanbo Liang <ybliang8@gmail.com> Closes #10378 from yanboliang/leftsemi.
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- Dec 18, 2015
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gatorsmile authored
The current default storage level of Python persist API is MEMORY_ONLY_SER. This is different from the default level MEMORY_ONLY in the official document and RDD APIs. davies Is this inconsistency intentional? Thanks! Updates: Since the data is always serialized on the Python side, the storage levels of JAVA-specific deserialization are not removed, such as MEMORY_ONLY. Updates: Based on the reviewers' feedback. In Python, stored objects will always be serialized with the [Pickle](https://docs.python.org/2/library/pickle.html) library, so it does not matter whether you choose a serialized level. The available storage levels in Python include `MEMORY_ONLY`, `MEMORY_ONLY_2`, `MEMORY_AND_DISK`, `MEMORY_AND_DISK_2`, `DISK_ONLY`, `DISK_ONLY_2` and `OFF_HEAP`. Author: gatorsmile <gatorsmile@gmail.com> Closes #10092 from gatorsmile/persistStorageLevel.
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Luc Bourlier authored
It is usually an invalid location on the remote machine executing the job. It is picked up by the Mesos support in cluster mode, and most of the time causes the job to fail. Fixes SPARK-12345 Author: Luc Bourlier <luc.bourlier@typesafe.com> Closes #10329 from skyluc/issue/SPARK_HOME.
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Shixiong Zhu authored
Added `channelActive` to `RpcHandler` so that `NettyRpcHandler` doesn't need `clients` any more. Author: Shixiong Zhu <shixiong@databricks.com> Closes #10301 from zsxwing/network-events.
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Nong Li authored
Previously, the rpc timeout was the default network timeout, which is the same value the driver uses to determine dead executors. This means if there is a network issue, the executor is determined dead after one heartbeat attempt. There is a separate config for the heartbeat interval which is a better value to use for the heartbeat RPC. With this change, the executor will make multiple heartbeat attempts even with RPC issues. Author: Nong Li <nong@databricks.com> Closes #10365 from nongli/spark-12411.
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Grace authored
In discussion (SPARK-9552), we proposed a force kill in `killExecutors`. But if there is nothing to kill, it will return back with true (acknowledgement). And then, it causes the certain executor(s) (which is not eligible to kill) adding to pendingToRemove list for further actions. In this patch, we'd like to change the return semantics. If there is nothing to kill, we will return "false". and therefore all those non-eligible executors won't be added to the pendingToRemove list. vanzin andrewor14 As the follow up of PR#7888, please let me know your comments. Author: Grace <jie.huang@intel.com> Author: Jie Huang <hjie@fosun.com> Author: Andrew Or <andrew@databricks.com> Closes #9796 from GraceH/emptyPendingToRemove.
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Burak Yavuz authored
- Provide example on `message handler` - Provide bit on KPL record de-aggregation - Fix typos Author: Burak Yavuz <brkyvz@gmail.com> Closes #9970 from brkyvz/kinesis-docs.
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Kousuke Saruta authored
Now `StaticInvoke` receives `Any` as a object and `StaticInvoke` can be serialized but sometimes the object passed is not serializable. For example, following code raises Exception because `RowEncoder#extractorsFor` invoked indirectly makes `StaticInvoke`. ``` case class TimestampContainer(timestamp: java.sql.Timestamp) val rdd = sc.parallelize(1 to 2).map(_ => TimestampContainer(System.currentTimeMillis)) val df = rdd.toDF val ds = df.as[TimestampContainer] val rdd2 = ds.rdd <----------------- invokes extractorsFor indirectory ``` I'll add test cases. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Author: Michael Armbrust <michael@databricks.com> Closes #10357 from sarutak/SPARK-12404.
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Yin Huai authored
JIRA: https://issues.apache.org/jira/browse/SPARK-12218 When creating filters for Parquet/ORC, we should not push nested AND expressions partially. Author: Yin Huai <yhuai@databricks.com> Closes #10362 from yhuai/SPARK-12218.
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Davies Liu authored
This could simplify the generated code for expressions that is not nullable. This PR fix lots of bugs about nullability. Author: Davies Liu <davies@databricks.com> Closes #10333 from davies/skip_nullable.
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Dilip Biswal authored
Description of the problem from cloud-fan Actually this line: https://github.com/apache/spark/blob/branch-1.5/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala#L689 When we use `selectExpr`, we pass in `UnresolvedFunction` to `DataFrame.select` and fall in the last case. A workaround is to do special handling for UDTF like we did for `explode`(and `json_tuple` in 1.6), wrap it with `MultiAlias`. Another workaround is using `expr`, for example, `df.select(expr("explode(a)").as(Nil))`, I think `selectExpr` is no longer needed after we have the `expr` function.... Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #9981 from dilipbiswal/spark-11619.
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Marcelo Vanzin authored
If a client requests a non-existent stream, just send a failure message back, without logging any error on the server side (since it's not a server error). On the executor side, avoid error logs by translating any errors during transfer to a `ClassNotFoundException`, so that loading the class is retried on a the parent class loader. This can mask IO errors during transmission, but the most common cause is that the class is not served by the remote end. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #10337 from vanzin/SPARK-12350.
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