Check for partition column null-ability while building the partition spec.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#10001 from dilipbiswal/spark-11997.
If `--private-ips` is required but not provided, spark_ec2.py may behave inappropriately, including attempting to ssh to localhost in attempts to verify ssh connectivity to the cluster.
This fixes that behavior by raising a `UsageError` exception if `get_dns_name` is unable to determine a hostname as a result.
Author: Jeremy Derr <jcderr@radius.com>
Closes#9975 from jcderr/SPARK-11991/ec_spark.py_hostname_check.
Fix regression test for SPARK-11778.
marmbrus
Could you please take a look?
Thank you very much!!
Author: Huaxin Gao <huaxing@oc0558782468.ibm.com>
Closes#9890 from huaxingao/spark-11778-regression-test.
Reference: https://jdbc.postgresql.org/documentation/head/query.html#query-with-cursor
In order for PostgreSQL to honor the fetchSize non-zero setting, its Connection.autoCommit needs to be set to false. Otherwise, it will just quietly ignore the fetchSize setting.
This adds a new side-effecting dialect specific beforeFetch method that will fire before a select query is ran.
Author: mariusvniekerk <marius.v.niekerk@gmail.com>
Closes#9861 from mariusvniekerk/SPARK-11881.
In the previous implementation, the driver needs to know the executor listening address to send the thread dump request. However, in Netty RPC, the executor doesn't listen to any port, so the executor thread dump feature is broken.
This patch makes the driver use the endpointRef stored in BlockManagerMasterEndpoint to send the thread dump request to fix it.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9976 from zsxwing/executor-thread-dump.
This is a followup for https://github.com/apache/spark/pull/9959.
I added more documentation and rewrote some monadic code into simpler ifs.
Author: Reynold Xin <rxin@databricks.com>
Closes#9995 from rxin/SPARK-11973.
If we need to download Hive/Hadoop artifacts, try to download a Hadoop that matches the Hadoop used by Spark. If the Hadoop artifact cannot be resolved (e.g. Hadoop version is a vendor specific version like 2.0.0-cdh4.1.1), we will use Hadoop 2.4.0 (we used to hard code this version as the hadoop that we will download from maven) and we will not share Hadoop classes.
I tested this match in my laptop with the following confs (these confs are used by our builds). All tests are good.
```
build/sbt -Phadoop-1 -Dhadoop.version=1.2.1 -Pkinesis-asl -Phive-thriftserver -Phive
build/sbt -Phadoop-1 -Dhadoop.version=2.0.0-mr1-cdh4.1.1 -Pkinesis-asl -Phive-thriftserver -Phive
build/sbt -Pyarn -Phadoop-2.2 -Pkinesis-asl -Phive-thriftserver -Phive
build/sbt -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -Pkinesis-asl -Phive-thriftserver -Phive
```
Author: Yin Huai <yhuai@databricks.com>
Closes#9979 from yhuai/versionsSuite.
this is based on https://github.com/apache/spark/pull/9844, with some bug fix and clean up.
The problems is that, normal operator should be resolved based on its child, but `Sort` operator can also be resolved based on its grandchild. So we have 3 rules that can resolve `Sort`: `ResolveReferences`, `ResolveSortReferences`(if grandchild is `Project`) and `ResolveAggregateFunctions`(if grandchild is `Aggregate`).
For example, `select c1 as a , c2 as b from tab group by c1, c2 order by a, c2`, we need to resolve `a` and `c2` for `Sort`. Firstly `a` will be resolved in `ResolveReferences` based on its child, and when we reach `ResolveAggregateFunctions`, we will try to resolve both `a` and `c2` based on its grandchild, but failed because `a` is not a legal aggregate expression.
whoever merge this PR, please give the credit to dilipbiswal
Author: Dilip Biswal <dbiswal@us.ibm.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9961 from cloud-fan/sort.
Currently, filter can't be pushed through aggregation with alias or literals, this patch fix that.
After this patch, the time of TPC-DS query 4 go down to 13 seconds from 141 seconds (10x improvements).
cc nongli yhuai
Author: Davies Liu <davies@databricks.com>
Closes#9959 from davies/push_filter2.
In the previous codes, `newDaemonCachedThreadPool` uses `SynchronousQueue`, which is wrong. `SynchronousQueue` is an empty queue that cannot cache any task. This patch uses `LinkedBlockingQueue` to fix it along with other fixes to make sure `newDaemonCachedThreadPool` can use at most `maxThreadNumber` threads, and after that, cache tasks to `LinkedBlockingQueue`.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9978 from zsxwing/cached-threadpool.
Added Python test cases for the function `isnan`, `isnull`, `nanvl` and `json_tuple`.
Fixed a bug in the function `json_tuple`
rxin , could you help me review my changes? Please let me know anything is missing.
Thank you! Have a good Thanksgiving day!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9977 from gatorsmile/json_tuple.
Right now, the expended start will include the name of expression as prefix for column, that's not better than without expending, we should not have the prefix.
Author: Davies Liu <davies@databricks.com>
Closes#9984 from davies/expand_star.
On the live web UI, there is a SQL tab which provides valuable information for the SQL query. But once the workload is finished, we won't see the SQL tab on the history server. It will be helpful if we support SQL UI on the history server so we can analyze it even after its execution.
To support SQL UI on the history server:
1. I added an `onOtherEvent` method to the `SparkListener` trait and post all SQL related events to the same event bus.
2. Two SQL events `SparkListenerSQLExecutionStart` and `SparkListenerSQLExecutionEnd` are defined in the sql module.
3. The new SQL events are written to event log using Jackson.
4. A new trait `SparkHistoryListenerFactory` is added to allow the history server to feed events to the SQL history listener. The SQL implementation is loaded at runtime using `java.util.ServiceLoader`.
Author: Carson Wang <carson.wang@intel.com>
Closes#9297 from carsonwang/SqlHistoryUI.
This change does a couple of different things to make sure that the RpcEnv-level
code and the network library agree about the status of outstanding RPCs.
For RPCs that do not expect a reply ("RpcEnv.send"), support for one way
messages (hello CORBA!) was added to the network layer. This is a
"fire and forget" message that does not require any state to be kept
by the TransportClient; as a result, the RpcEnv 'Ack' message is not needed
anymore.
For RPCs that do expect a reply ("RpcEnv.ask"), the network library now
returns the internal RPC id; if the RpcEnv layer decides to time out the
RPC before the network layer does, it now asks the TransportClient to
forget about the RPC, so that if the network-level timeout occurs, the
client is not killed.
As part of implementing the above, I cleaned up some of the code in the
netty rpc backend, removing types that were not necessary and factoring
out some common code. Of interest is a slight change in the exceptions
when posting messages to a stopped RpcEnv; that's mostly to avoid nasty
error messages from the local-cluster backend when shutting down, which
pollutes the terminal output.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#9917 from vanzin/SPARK-11866.
The Python exception track in TransformFunction and TransformFunctionSerializer is not sent back to Java. Py4j just throws a very general exception, which is hard to debug.
This PRs adds `getFailure` method to get the failure message in Java side.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9922 from zsxwing/SPARK-11935.
`ExecutorAdded` can only be sent to `AppClient` when worker report back the executor state as `LOADING`, otherwise because of concurrency issue, `AppClient` will possibly receive `ExectuorAdded` at first, then `ExecutorStateUpdated` with `LOADING` state.
Also Master will change the executor state from `LAUNCHING` to `RUNNING` (`AppClient` report back the state as `RUNNING`), then to `LOADING` (worker report back to state as `LOADING`), it should be `LAUNCHING` -> `LOADING` -> `RUNNING`.
Also it is wrongly shown in master UI, the state of executor should be `RUNNING` rather than `LOADING`:
Author: jerryshao <sshao@hortonworks.com>
Closes#8714 from jerryshao/SPARK-10558.
* On windows the `bin/load-spark-env.cmd` tries to load `spark-env.cmd` from `%~dp0..\..\conf`, where `~dp0` points to `bin` and `conf` is only one level up.
* Updated `bin/load-spark-env.cmd` to load `spark-env.cmd` from `%~dp0..\conf`, instead of `%~dp0..\..\conf`
Author: wangt <wangtao.upc@gmail.com>
Closes#9863 from toddwan/master.
Currently the Web UI navbar has a minimum width of 1200px; so if a window is resized smaller than that the app name goes off screen. The 1200px width seems to have been chosen since it fits the longest example app name without wrapping.
To work with smaller window widths I made the tabs wrap since it looked better than wrapping the app name. This is a distinct change in how the navbar looks and I'm not sure if it's what we actually want to do.
Other notes:
- min-width set to 600px to keep the tabs from wrapping individually (will need to be adjusted if tabs are added)
- app name will also wrap (making three levels) if a really really long app name is used
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#9874 from ajbozarth/spark10864.
Currently, we does not have visualization for SQL query from Python, this PR fix that.
cc zsxwing
Author: Davies Liu <davies@databricks.com>
Closes#9949 from davies/pyspark_sql_ui.
deleting the temp dir like that
```
scala> import scala.collection.mutable
import scala.collection.mutable
scala> val a = mutable.Set(1,2,3,4,7,0,8,98,9)
a: scala.collection.mutable.Set[Int] = Set(0, 9, 1, 2, 3, 7, 4, 8, 98)
scala> a.foreach(x => {a.remove(x) })
scala> a.foreach(println(_))
98
```
You may not modify a collection while traversing or iterating over it.This can not delete all element of the collection
Author: Zhongshuai Pei <peizhongshuai@huawei.com>
Closes#9951 from DoingDone9/Bug_RemainDir.
- NettyRpcEnv::openStream() now correctly propagates errors to
the read side of the pipe.
- NettyStreamManager now throws if the file being transferred does
not exist.
- The network library now correctly handles zero-sized streams.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#9941 from vanzin/SPARK-11956.
This issue was addressed in https://github.com/apache/spark/pull/5494, but the fix in that PR, while safe in the sense that it will prevent the SparkContext from shutting down, misses the actual bug. The intent of `submitMissingTasks` should be understood as "submit the Tasks that are missing for the Stage, and run them as part of the ActiveJob identified by jobId". Because of a long-standing bug, the `jobId` parameter was never being used. Instead, we were trying to use the jobId with which the Stage was created -- which may no longer exist as an ActiveJob, hence the crash reported in SPARK-6880.
The correct fix is to use the ActiveJob specified by the supplied jobId parameter, which is guaranteed to exist at the call sites of submitMissingTasks.
This fix should be applied to all maintenance branches, since it has existed since 1.0.
kayousterhout pankajarora12
Author: Mark Hamstra <markhamstra@gmail.com>
Author: Imran Rashid <irashid@cloudera.com>
Closes#6291 from markhamstra/SPARK-6880.
Except inner join, maybe the other join types are also useful when users are using the joinWith function. Thus, added the joinType into the existing joinWith call in Dataset APIs.
Also providing another joinWith interface for the cartesian-join-like functionality.
Please provide your opinions. marmbrus rxin cloud-fan Thank you!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9921 from gatorsmile/joinWith.
This solves the following exception caused when empty state RDD is checkpointed and recovered. The root cause is that an empty OpenHashMapBasedStateMap cannot be deserialized as the initialCapacity is set to zero.
```
Job aborted due to stage failure: Task 0 in stage 6.0 failed 1 times, most recent failure: Lost task 0.0 in stage 6.0 (TID 20, localhost): java.lang.IllegalArgumentException: requirement failed: Invalid initial capacity
at scala.Predef$.require(Predef.scala:233)
at org.apache.spark.streaming.util.OpenHashMapBasedStateMap.<init>(StateMap.scala:96)
at org.apache.spark.streaming.util.OpenHashMapBasedStateMap.<init>(StateMap.scala:86)
at org.apache.spark.streaming.util.OpenHashMapBasedStateMap.readObject(StateMap.scala:291)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:76)
at org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:181)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:921)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:921)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:88)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:744)
```
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#9958 from tdas/SPARK-11979.
This patch makes it consistent to use varargs in all DataFrameReader methods, including Parquet, JSON, text, and the generic load function.
Also added a few more API tests for the Java API.
Author: Reynold Xin <rxin@databricks.com>
Closes#9945 from rxin/SPARK-11967.
This PR is to provide two common `coalesce` and `repartition` in Dataset APIs.
After reading the comments of SPARK-9999, I am unclear about the plan for supporting re-partitioning in Dataset APIs. Currently, both RDD APIs and Dataframe APIs provide users such a flexibility to control the number of partitions.
In most traditional RDBMS, they expose the number of partitions, the partitioning columns, the table partitioning methods to DBAs for performance tuning and storage planning. Normally, these parameters could largely affect the query performance. Since the actual performance depends on the workload types, I think it is almost impossible to automate the discovery of the best partitioning strategy for all the scenarios.
I am wondering if Dataset APIs are planning to hide these APIs from users? Feel free to reject my PR if it does not match the plan.
Thank you for your answers. marmbrus rxin cloud-fan
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9899 from gatorsmile/coalesce.
When using remote Hive metastore, `hive.metastore.uris` is set to the metastore URI. However, it overrides `javax.jdo.option.ConnectionURL` unexpectedly, thus the execution Hive client connects to the actual remote Hive metastore instead of the Derby metastore created in the temporary directory. Cleaning this configuration for the execution Hive client fixes this issue.
Author: Cheng Lian <lian@databricks.com>
Closes#9895 from liancheng/spark-11783.clean-remote-metastore-config.
After calling spill() on SortedIterator, the array inside InMemorySorter is not needed, it should be freed during spilling, this could help to join multiple tables with limited memory.
Author: Davies Liu <davies@databricks.com>
Closes#9793 from davies/free_array.
In the default Spark distribution, there are currently two separate
log4j config files, with different default values for the root logger,
so that when running the shell you have a different default log level.
This makes the shell more usable, since the logs don't overwhelm the
output.
But if you install a custom log4j.properties, you lose that, because
then it's going to be used no matter whether you're running a regular
app or the shell.
With this change, the overriding of the log level is done differently;
the log level repl's main class (org.apache.spark.repl.Main) is used
to define the root logger's level when running the shell, defaulting
to WARN if it's not set explicitly.
On a somewhat related change, the shell output about the "sc" variable
was changed a bit to contain a little more useful information about
the application, since when the root logger's log level is WARN, that
information is never shown to the user.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#9816 from vanzin/shell-logging.
Currently pivot's signature looks like
```scala
scala.annotation.varargs
def pivot(pivotColumn: Column, values: Column*): GroupedData
scala.annotation.varargs
def pivot(pivotColumn: String, values: Any*): GroupedData
```
I think we can remove the one that takes "Column" types, since callers should always be passing in literals. It'd also be more clear if the values are not varargs, but rather Seq or java.util.List.
I also made similar changes for Python.
Author: Reynold Xin <rxin@databricks.com>
Closes#9929 from rxin/SPARK-11946.
This is continuation of SPARK-11761
Andrew suggested adding this protection. See tail of https://github.com/apache/spark/pull/9741
Author: tedyu <yuzhihong@gmail.com>
Closes#9852 from tedyu/master.
Add read/write support to LDA, similar to ALS.
save/load for ml.LocalLDAModel is done.
For DistributedLDAModel, I'm not sure if we can invoke save on the mllib.DistributedLDAModel directly. I'll send update after some test.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#9894 from hhbyyh/ldaMLsave.
