Adds a test case that ensures that Executors gracefully handle a task that fails to deserialize, by sending back a reasonable failure message. This does not change any behavior (the prior behavior was already correct), it just adds a test case to prevent regression.
Author: Imran Rashid <irashid@cloudera.com>
Closes#16930 from squito/executor_task_deserialization.
This commit improves the tests that check the case when a
ShuffleMapTask completes successfully on an executor that has
failed. This commit improves the commenting around the existing
test for this, and adds some additional checks to make it more
clear what went wrong if the tests fail (the fact that these
tests are hard to understand came up in the context of markhamstra's
proposed fix for #16620).
This commit also removes a test that I realized tested exactly
the same functionality.
markhamstra, I verified that the new version of the test still fails (and
in a more helpful way) for your proposed change for #16620.
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#16892 from kayousterhout/SPARK-19560.
## What changes were proposed in this pull request?
Currently we can only check the estimated stats in logical plans by debugging. We need to provide an easier and more efficient way for developers/users.
In this pr, we add EXPLAIN COST command to show stats in the optimized logical plan.
E.g.
```
spark-sql> EXPLAIN COST select count(1) from store_returns;
...
== Optimized Logical Plan ==
Aggregate [count(1) AS count(1)#24L], Statistics(sizeInBytes=16.0 B, rowCount=1, isBroadcastable=false)
+- Project, Statistics(sizeInBytes=4.3 GB, rowCount=5.76E+8, isBroadcastable=false)
+- Relation[sr_returned_date_sk#3,sr_return_time_sk#4,sr_item_sk#5,sr_customer_sk#6,sr_cdemo_sk#7,sr_hdemo_sk#8,sr_addr_sk#9,sr_store_sk#10,sr_reason_sk#11,sr_ticket_number#12,sr_return_quantity#13,sr_return_amt#14,sr_return_tax#15,sr_return_amt_inc_tax#16,sr_fee#17,sr_return_ship_cost#18,sr_refunded_cash#19,sr_reversed_charge#20,sr_store_credit#21,sr_net_loss#22] parquet, Statistics(sizeInBytes=28.6 GB, rowCount=5.76E+8, isBroadcastable=false)
...
```
## How was this patch tested?
Add test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16594 from wzhfy/showStats.
## What changes were proposed in this pull request?
Fixed the line ending of `FilterEstimation.scala` (It's still using `\n\r`). Also improved the tests to cover the cases where the literals are on the left side of a binary operator.
## How was this patch tested?
Existing unit tests.
Author: Shuai Lin <linshuai2012@gmail.com>
Closes#17051 from lins05/fix-cbo-filter-file-encoding.
## What changes were proposed in this pull request?
This PR adds tests hive-hash by comparing the outputs generated against Hive 1.2.1. Following datatypes are covered by this PR:
- null
- boolean
- byte
- short
- int
- long
- float
- double
- string
- array
- map
- struct
Datatypes that I have _NOT_ covered but I will work on separately are:
- Decimal (handled separately in https://github.com/apache/spark/pull/17056)
- TimestampType
- DateType
- CalendarIntervalType
## How was this patch tested?
NA
Author: Tejas Patil <tejasp@fb.com>
Closes#17049 from tejasapatil/SPARK-17495_remaining_types.
## What changes were proposed in this pull request?
Because yarn#client will reset the `spark.yarn.keytab` configuration to point to the location in distributed file, so if user still uses the old `SparkConf` to create `SparkSession` with Hive enabled, it will read keytab from the path in distributed cached. This is OK for yarn cluster mode, but in yarn client mode where driver is running out of container, it will be failed to fetch the keytab.
So here we should avoid reseting this configuration in the `yarn#client` and only overwriting it for AM, so using `spark.yarn.keytab` could get correct keytab path no matter running in client (keytab in local fs) or cluster (keytab in distributed cache) mode.
## How was this patch tested?
Verified in security cluster.
Author: jerryshao <sshao@hortonworks.com>
Closes#16923 from jerryshao/SPARK-19038.
## What changes were proposed in this pull request?
Currently in Spark there're two issues when we add jars with invalid path:
* If the jar path is a empty string {--jar ",dummy.jar"}, then Spark will resolve it to the current directory path and add to classpath / file server, which is unwanted. This is happened in our programatic way to submit Spark application. From my understanding Spark should defensively filter out such empty path.
* If the jar path is a invalid path (file doesn't exist), `addJar` doesn't check it and will still add to file server, the exception will be delayed until job running. Actually this local path could be checked beforehand, no need to wait until task running. We have similar check in `addFile`, but lacks similar similar mechanism in `addJar`.
## How was this patch tested?
Add unit test and local manual verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#17038 from jerryshao/SPARK-19707.
## What changes were proposed in this pull request?
Replaces `UserDefinedFunction` object returned from `udf` with a function wrapper providing docstring and arguments information as proposed in [SPARK-19161](https://issues.apache.org/jira/browse/SPARK-19161).
### Backward incompatible changes:
- `pyspark.sql.functions.udf` will return a `function` instead of `UserDefinedFunction`. To ensure backward compatible public API we use function attributes to mimic `UserDefinedFunction` API (`func` and `returnType` attributes). This should have a minimal impact on the user code.
An alternative implementation could use dynamical sub-classing. This would ensure full backward compatibility but is more fragile in practice.
### Limitations:
Full functionality (retained docstring and argument list) is achieved only in the recent Python version. Legacy Python version will preserve only docstrings, but not argument list. This should be an acceptable trade-off between achieved improvements and overall complexity.
### Possible impact on other tickets:
This can affect [SPARK-18777](https://issues.apache.org/jira/browse/SPARK-18777).
## How was this patch tested?
Existing unit tests to ensure backward compatibility, additional tests targeting proposed changes.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16534 from zero323/SPARK-19161.
## What changes were proposed in this pull request?
In [SPARK-15959](https://issues.apache.org/jira/browse/SPARK-15959), we bring back the `hive.metastore.warehouse.dir` , while in the logic, when use the value of `spark.sql.warehouse.dir` to overwrite `hive.metastore.warehouse.dir` , it set it to `sparkContext.conf` which does not overwrite the value is hadoopConf, I think it should put in `sparkContext.hadoopConfiguration` and overwrite the original value of hadoopConf
https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/internal/SharedState.scala#L64
## How was this patch tested?
N/A
Author: windpiger <songjun@outlook.com>
Closes#16996 from windpiger/hivemetawarehouseConf.
## What changes were proposed in this pull request?
We traverse predicate and evaluate the logical expressions to compute the selectivity of a FILTER operator.
## How was this patch tested?
We add a new test suite to test various logical operators.
Author: Ron Hu <ron.hu@huawei.com>
Closes#16395 from ron8hu/filterSelectivity.
## What changes were proposed in this pull request?
Fixed the PySpark Params.copy method to behave like the Scala implementation. The main issue was that it did not account for the _defaultParamMap and merged it into the explicitly created param map.
## How was this patch tested?
Added new unit test to verify the copy method behaves correctly for copying uid, explicitly created params, and default params.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#16772 from BryanCutler/pyspark-ml-param_copy-Scala_sync-SPARK-14772.
## What changes were proposed in this pull request?
follow up pr of #16949.
## How was this patch tested?
jenkins
Author: uncleGen <hustyugm@gmail.com>
Closes#17033 from uncleGen/doc-restapi-environment.
## What changes were proposed in this pull request?
In SQLListener.getExecutionMetrics, driver accumulator updates don't belong to the execution should be ignored when merging all accumulator updates to prevent NoSuchElementException.
## How was this patch tested?
Updated unit test.
Author: Carson Wang <carson.wang@intel.com>
Closes#17009 from carsonwang/FixSQLMetrics.
This commit moves developer-specific information from the release-
specific documentation in this repo to the developer tools page on
the main Spark website. This commit relies on this PR on the
Spark website: https://github.com/apache/spark-website/pull/33.
srowen
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#17018 from kayousterhout/SPARK-19684.
## What changes were proposed in this pull request?
to be consistent with the scala API, we should also add `contains` to `Column` in pyspark.
## How was this patch tested?
updated unit test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17036 from cloud-fan/pyspark.
## What changes were proposed in this pull request?
This pr added a logic to put malformed tokens into a new field when parsing CSV data in case of permissive modes. In the current master, if the CSV parser hits these malformed ones, it throws an exception below (and then a job fails);
```
Caused by: java.lang.IllegalArgumentException
at java.sql.Date.valueOf(Date.java:143)
at org.apache.spark.sql.catalyst.util.DateTimeUtils$.stringToTime(DateTimeUtils.scala:137)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$$anonfun$castTo$6.apply$mcJ$sp(CSVInferSchema.scala:272)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$$anonfun$castTo$6.apply(CSVInferSchema.scala:272)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$$anonfun$castTo$6.apply(CSVInferSchema.scala:272)
at scala.util.Try.getOrElse(Try.scala:79)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$.castTo(CSVInferSchema.scala:269)
at
```
In case that users load large CSV-formatted data, the job failure makes users get some confused. So, this fix set NULL for original columns and put malformed tokens in a new field.
## How was this patch tested?
Added tests in `CSVSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#16928 from maropu/SPARK-18699-2.
## What changes were proposed in this pull request?
This PR adds a special streaming deduplication operator to support `dropDuplicates` with `aggregation` and watermark. It reuses the `dropDuplicates` API but creates new logical plan `Deduplication` and new physical plan `DeduplicationExec`.
The following cases are supported:
- one or multiple `dropDuplicates()` without aggregation (with or without watermark)
- `dropDuplicates` before aggregation
Not supported cases:
- `dropDuplicates` after aggregation
Breaking changes:
- `dropDuplicates` without aggregation doesn't work with `complete` or `update` mode.
## How was this patch tested?
The new unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16970 from zsxwing/dedup.
## What changes were proposed in this pull request?
The `[[` method is supposed to take a single index and return a column. This is different from base R which takes a vector index. We should check for this and issue warning or error when vector index is supplied (which is very likely given the behavior in base R).
Currently I'm issuing a warning message and just take the first element of the vector index. We could change this to an error it that's better.
## How was this patch tested?
new tests
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#17017 from actuaryzhang/sparkRSubsetter.
## What changes were proposed in this pull request?
This PR is a small follow-up on https://github.com/apache/spark/pull/16804. This PR also adds support for nested char/varchar fields in orc.
## How was this patch tested?
I have added a regression test to the OrcSourceSuite.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17030 from hvanhovell/SPARK-19459-follow-up.
## What changes were proposed in this pull request?
This pr fixed a class-cast exception below;
```
scala> spark.range(10).selectExpr("cast (id as decimal) as x").selectExpr("percentile(x, 0.5)").collect()
java.lang.ClassCastException: org.apache.spark.sql.types.Decimal cannot be cast to java.lang.Number
at org.apache.spark.sql.catalyst.expressions.aggregate.Percentile.update(Percentile.scala:141)
at org.apache.spark.sql.catalyst.expressions.aggregate.Percentile.update(Percentile.scala:58)
at org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate.update(interfaces.scala:514)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$1$$anonfun$applyOrElse$1.apply(AggregationIterator.scala:171)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$1$$anonfun$applyOrElse$1.apply(AggregationIterator.scala:171)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$generateProcessRow$1.apply(AggregationIterator.scala:187)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$generateProcessRow$1.apply(AggregationIterator.scala:181)
at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.processInputs(ObjectAggregationIterator.scala:151)
at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.<init>(ObjectAggregationIterator.scala:78)
at org.apache.spark.sql.execution.aggregate.ObjectHashAggregateExec$$anonfun$doExecute$1$$anonfun$2.apply(ObjectHashAggregateExec.scala:109)
at
```
This fix simply converts catalyst values (i.e., `Decimal`) into scala ones by using `CatalystTypeConverters`.
## How was this patch tested?
Added a test in `DataFrameSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17028 from maropu/SPARK-19691.
## What changes were proposed in this pull request?
This pr comes from #16928 and fixed a json behaviour along with the CSV one.
## How was this patch tested?
Added tests in `JsonSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17023 from maropu/SPARK-19695.
## What changes were proposed in this pull request?
add rest api for job environment.
## How was this patch tested?
existing ut.
Author: uncleGen <hustyugm@gmail.com>
Closes#16949 from uncleGen/SPARK-16122.
## What changes were proposed in this pull request?
SPARK-15615 proposes replacing the sqlContext.read.json(rdd) with a dataset equivalent.
SPARK-15463 adds a CSV API for reading from Dataset[String] so this keeps the API consistent.
I am deprecating the existing RDD based APIs.
## How was this patch tested?
There are existing tests. I left most tests to use the existing APIs as they delegate to the new json API.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: pj.fanning <pj.fanning@workday.com>
Author: PJ Fanning <pjfanning@users.noreply.github.com>
Closes#16895 from pjfanning/SPARK-15615.
### What changes were proposed in this pull request?
Currently, if `NumPartitions` is not set in RepartitionByExpression, we will set it using `spark.sql.shuffle.partitions` during Planner. However, this is not following the general resolution process. This PR is to set it in `Parser` and then `Optimizer` can use the value for plan optimization.
### How was this patch tested?
Added a test case.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#16988 from gatorsmile/resolveRepartition.
Allow an application to use the History Server URL as the tracking
URL in the YARN RM, so there's still a link to the web UI somewhere
in YARN even if the driver's UI is disabled. This is useful, for
example, if an admin wants to disable the driver UI by default for
applications, since it's harder to secure it (since it involves non
trivial ssl certificate and auth management that admins may not want
to expose to user apps).
This needs to be opt-in, because of the way the YARN proxy works, so
a new configuration was added to enable the option.
The YARN RM will proxy requests to live AMs instead of redirecting
the client, so pages in the SHS UI will not render correctly since
they'll reference invalid paths in the RM UI. The proxy base support
in the SHS cannot be used since that would prevent direct access to
the SHS.
So, to solve this problem, for the feature to work end-to-end, a new
YARN-specific filter was added that detects whether the requests come
from the proxy and redirects the client appropriatly. The SHS admin has
to add this filter manually if they want the feature to work.
Tested with new unit test, and by running with the documented configuration
set in a test cluster. Also verified the driver UI is used when it's
enabled.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16946 from vanzin/SPARK-19554.
## What changes were proposed in this pull request?
This PR proposes to fix two.
**Skip a property without a getter in beans**
Currently, if we use a JavaBean without the getter as below:
```java
public static class BeanWithoutGetter implements Serializable {
private String a;
public void setA(String a) {
this.a = a;
}
}
BeanWithoutGetter bean = new BeanWithoutGetter();
List<BeanWithoutGetter> data = Arrays.asList(bean);
spark.createDataFrame(data, BeanWithoutGetter.class).show();
```
- Before
It throws an exception as below:
```
java.lang.NullPointerException
at org.spark_project.guava.reflect.TypeToken.method(TypeToken.java:465)
at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$2.apply(JavaTypeInference.scala:126)
at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$2.apply(JavaTypeInference.scala:125)
```
- After
```
++
||
++
||
++
```
**Supports empty bean in encoder creation**
```java
public static class EmptyBean implements Serializable {}
EmptyBean bean = new EmptyBean();
List<EmptyBean> data = Arrays.asList(bean);
spark.createDataset(data, Encoders.bean(EmptyBean.class)).show();
```
- Before
throws an exception as below:
```
java.lang.UnsupportedOperationException: Cannot infer type for class EmptyBean because it is not bean-compliant
at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$serializerFor(JavaTypeInference.scala:436)
at org.apache.spark.sql.catalyst.JavaTypeInference$.serializerFor(JavaTypeInference.scala:341)
```
- After
```
++
||
++
||
++
```
## How was this patch tested?
Unit test in `JavaDataFrameSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17013 from HyukjinKwon/SPARK-19666.
## What changes were proposed in this pull request?
This is a follow-up PR of #16800
When doing SPARK-19456, we found that "" should be consider a NULL column name and should not be set. aggregationDepth should be exposed as an expert parameter.
## How was this patch tested?
Existing tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#16945 from wangmiao1981/svc.
- Add dependency on aws-java-sdk-sts
- Replace SerializableAWSCredentials with new SerializableCredentialsProvider interface
- Make KinesisReceiver take SerializableCredentialsProvider as argument and
pass credential provider to KCL
- Add new implementations of KinesisUtils.createStream() that take STS
arguments
- Make JavaKinesisStreamSuite test the entire KinesisUtils Java API
- Update KCL/AWS SDK dependencies to 1.7.x/1.11.x
## What changes were proposed in this pull request?
[JIRA link with detailed description.](https://issues.apache.org/jira/browse/SPARK-19405)
* Replace SerializableAWSCredentials with new SerializableKCLAuthProvider class that takes 5 optional config params for configuring AWS auth and returns the appropriate credential provider object
* Add new public createStream() APIs for specifying these parameters in KinesisUtils
## How was this patch tested?
* Manually tested using explicit keypair and instance profile to read data from Kinesis stream in separate account (difficult to write a test orchestrating creation and assumption of IAM roles across separate accounts)
* Expanded JavaKinesisStreamSuite to test the entire Java API in KinesisUtils
## License acknowledgement
This contribution is my original work and that I license the work to the project under the project’s open source license.
Author: Budde <budde@amazon.com>
Closes#16744 from budde/master.
## What changes were proposed in this pull request?
This is a small change to make GeneratorOuter always unresolved. It is mostly no-op change but makes it more clear since GeneratorOuter shouldn't survive analysis phase.
This requires also handling in ResolveAliases rule.
## How was this patch tested?
Existing generator tests.
Author: Bogdan Raducanu <bogdan@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#17026 from bogdanrdc/PR16958.
## What changes were proposed in this pull request?
Destroy broadcasted object without blocking
use `find mllib -name '*.scala' | xargs -i bash -c 'egrep "destroy" -n {} && echo {}'`
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#17016 from zhengruifeng/destroy_without_block.
## What changes were proposed in this pull request?
Add missing 'setTopicDistributionCol' for LDAModel
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#17021 from zhengruifeng/lda_outputCol.
### What changes were proposed in this pull request?
Bucketed table reading and writing does not need Hive support. We can move the test cases from `sql/hive` to `sql/core`. After this PR, we can improve the test case coverage. Bucket table reading and writing can be tested with and without Hive support.
### How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17004 from gatorsmile/mvTestCaseForBuckets.
The REST API has a security filter that performs auth checks
based on the UI root's security manager. That works fine when
the UI root is the app's UI, but not when it's the history server.
In the SHS case, all users would be allowed to see all applications
through the REST API, even if the UI itself wouldn't be available
to them.
This change adds auth checks for each app access through the API
too, so that only authorized users can see the app's data.
The change also modifies the existing security filter to use
`HttpServletRequest.getRemoteUser()`, which is used in other
places. That is not necessarily the same as the principal's
name; for example, when using Hadoop's SPNEGO auth filter,
the remote user strips the realm information, which then matches
the user name registered as the owner of the application.
I also renamed the UIRootFromServletContext trait to a more generic
name since I'm using it to store more context information now.
Tested manually with an authentication filter enabled.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16978 from vanzin/SPARK-19652.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/14065, we introduced a configurable credential manager for Spark running on YARN. Also two configs `spark.yarn.credentials.renewalTime` and `spark.yarn.credentials.updateTime` were added, one is for the credential renewer and the other updater. But now we just query `spark.yarn.credentials.renewalTime` by mistake during CREDENTIALS UPDATING, where should be actually `spark.yarn.credentials.updateTime` .
This PR fixes this mistake.
## How was this patch tested?
existing test
cc jerryshao vanzin
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#16955 from yaooqinn/cred_update.
## What changes were proposed in this pull request?
Documentation and examples (Java, scala, python, R) for LinearSVC
## How was this patch tested?
local doc generation
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#16968 from hhbyyh/mlsvmdoc.
## What changes were proposed in this pull request?
This PR proposes to fix new test failures on WIndows as below:
**Before**
```
KafkaRelationSuite:
- test late binding start offsets *** FAILED *** (7 seconds, 679 milliseconds)
Cause: java.nio.file.FileSystemException: C:\projects\spark\target\tmp\spark-4c4b0cd1-4cb7-4908-949d-1b0cc8addb50\topic-4-0\00000000000000000000.log -> C:\projects\spark\target\tmp\spark-4c4b0cd1-4cb7-4908-949d-1b0cc8addb50\topic-4-0\00000000000000000000.log.deleted: The process cannot access the file because it is being used by another process.
KafkaSourceSuite:
- deserialization of initial offset with Spark 2.1.0 *** FAILED *** (3 seconds, 542 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-97ef64fc-ae61-4ce3-ac59-287fd38bd824
- deserialization of initial offset written by Spark 2.1.0 *** FAILED *** (60 milliseconds)
java.nio.file.InvalidPathException: Illegal char <:> at index 2: /C:/projects/spark/external/kafka-0-10-sql/target/scala-2.11/test-classes/kafka-source-initial-offset-version-2.1.0.b
HiveDDLSuite:
- partitioned table should always put partition columns at the end of table schema *** FAILED *** (657 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-f1b83d09-850a-4bba-8e43-a2a28dfaa757;
DDLSuite:
- create a data source table without schema *** FAILED *** (94 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-a3f3c161-afae-4d6f-9182-e8642f77062b;
- SET LOCATION for managed table *** FAILED *** (219 milliseconds)
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree:
Exchange SinglePartit
+- *HashAggregate(keys=[], functions=[partial_count(1)], output=[count#99367L])
+- *FileScan parquet default.tbl[] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/C:projectsspark arget mpspark-15be2f2f-4ea9-4c47-bfee-1b7b49363033], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<>
- insert data to a data source table which has a not existed location should succeed *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-34987671-e8d1-4624-ba5b-db1012e1246b;
- insert into a data source table with no existed partition location should succeed *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-4c6ccfbf-4091-4032-9fbc-3d40c58267d5;
- read data from a data source table which has a not existed location should succeed *** FAILED *** (0 milliseconds)
- read data from a data source table with no existed partition location should succeed *** FAILED *** (0 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-6af39e37-abd1-44e8-ac68-e2dfcf67a2f3;
InputOutputMetricsSuite:
- output metrics on records written *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-cd69ee77-88f2-4202-bed6-19c0ee05ef55\InputOutputMetricsSuite, expected: file:///
- output metrics on records written - new Hadoop API *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-b69e8fcb-047b-4de8-9cdf-5f026efb6762\InputOutputMetricsSuite, expected: file:///
```
**After**
```
KafkaRelationSuite:
- test late binding start offsets !!! CANCELED !!! (62 milliseconds)
KafkaSourceSuite:
- deserialization of initial offset with Spark 2.1.0 (5 seconds, 341 milliseconds)
- deserialization of initial offset written by Spark 2.1.0 (910 milliseconds)
HiveDDLSuite:
- partitioned table should always put partition columns at the end of table schema (2 seconds)
DDLSuite:
- create a data source table without schema (828 milliseconds)
- SET LOCATION for managed table (406 milliseconds)
- insert data to a data source table which has a not existed location should succeed (406 milliseconds)
- insert into a data source table with no existed partition location should succeed (453 milliseconds)
- read data from a data source table which has a not existed location should succeed (94 milliseconds)
- read data from a data source table with no existed partition location should succeed (265 milliseconds)
InputOutputMetricsSuite:
- output metrics on records written (172 milliseconds)
- output metrics on records written - new Hadoop API (297 milliseconds)
```
## How was this patch tested?
Fixed tests in `InputOutputMetricsSuite`, `KafkaRelationSuite`, `KafkaSourceSuite`, `DDLSuite.scala` and `HiveDDLSuite`.
Manually tested via AppVeyor as below:
`InputOutputMetricsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/ex8nvwa6tsh7rmto
`KafkaRelationSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/h8dlcowew52y8ncw
`KafkaSourceSuite`: https://ci.appveyor.com/project/spark-test/spark/build/634-20170219-windows-test/job/9ybgjl7yeubxcre4
`DDLSuite`: https://ci.appveyor.com/project/spark-test/spark/build/635-20170219-windows-test
`HiveDDLSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/up6o9n47er087ltb
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16999 from HyukjinKwon/windows-fix.
## What changes were proposed in this pull request?
Utils provides a helper function to bind service on port. This function can bind the service to a random free port. However, if the binding fails on a random free port, the retrying and final exception messages look confusing.
17/02/06 16:25:43 WARN Utils: Service 'sparkDriver' could not bind on port 0. Attempting port 1.
17/02/06 16:25:43 WARN Utils: Service 'sparkDriver' could not bind on port 0. Attempting port 1.
17/02/06 16:25:43 WARN Utils: Service 'sparkDriver' could not bind on port 0. Attempting port 1.
17/02/06 16:25:43 WARN Utils: Service 'sparkDriver' could not bind on port 0. Attempting port 1.
17/02/06 16:25:43 WARN Utils: Service 'sparkDriver' could not bind on port 0. Attempting port 1.
...
17/02/06 16:25:43 ERROR SparkContext: Error initializing SparkContext.
java.net.BindException: Can't assign requested address: Service 'sparkDriver' failed after 16 retries (starting from 0)! Consider explicitly setting the appropriate port for the service 'sparkDriver' (for example spark.ui.port for SparkUI) to an available port or increasing spark.port.maxRetries.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16851 from viirya/better-log-message.
## What changes were proposed in this pull request?
In [SPARK-19669](0733a54a45) change the sessionState access privileges from private to public, this lead to the compile failed in TestSQLContext
this pr is a hotfix for this.
## How was this patch tested?
N/A
Author: windpiger <songjun@outlook.com>
Closes#17008 from windpiger/hotfixcompile.
## What changes were proposed in this pull request?
To ease debugging, most of Spark SQL internals have public level visibility. Two of the most important internal states, sharedState and sessionState, however, are package private. It would make more sense to open these up as well with clear documentation that they are internal.
In addition, users currently have way to set active/default SparkSession, but no way to actually get them back. We should open those up as well.
## How was this patch tested?
N/A - only visibility change.
Author: Reynold Xin <rxin@databricks.com>
Closes#17002 from rxin/SPARK-19669.
## What changes were proposed in this pull request?
Use `BytesWritable.copyBytes`, not `getBytes`, because `getBytes` returns the underlying array, which may be reused when repeated reads don't need a different size, as is the case with binaryRecords APIs
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16974 from srowen/SPARK-19646.
## What changes were proposed in this pull request?
In `FileFormatWriter`, we will sort the input rows by partition columns and bucket id and sort columns, if we want to write data out partitioned or bucketed.
However, if the data is already sorted, we will sort it again, which is unnecssary.
This PR removes the sorting logic in `FileFormatWriter` and use `SortExec` instead. We will not add `SortExec` if the data is already sorted.
## How was this patch tested?
I did a micro benchmark manually
```
val df = spark.range(10000000).select($"id", $"id" % 10 as "part").sort("part")
spark.time(df.write.partitionBy("part").parquet("/tmp/test"))
```
The result was about 6.4 seconds before this PR, and is 5.7 seconds afterwards.
close https://github.com/apache/spark/pull/16724
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16898 from cloud-fan/writer.
## What changes were proposed in this pull request?
Convert tests to use Java 8 lambdas, and modest related fixes to surrounding code.
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16964 from srowen/SPARK-19534.
## What changes were proposed in this pull request?
Convert Java tests to use lambdas, Java 8 features.
## How was this patch tested?
Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#16961 from srowen/SPARK-19533.
## What changes were proposed in this pull request?
`askSync` is already added in `RpcEndpointRef` (see SPARK-19347 and https://github.com/apache/spark/pull/16690#issuecomment-276850068) and `askWithRetry` is marked as deprecated.
As mentioned SPARK-18113(https://github.com/apache/spark/pull/16503#event-927953218):
>askWithRetry is basically an unneeded API, and a leftover from the akka days that doesn't make sense anymore. It's prone to cause deadlocks (exactly because it's blocking), it imposes restrictions on the caller (e.g. idempotency) and other things that people generally don't pay that much attention to when using it.
Since `askWithRetry` is just used inside spark and not in user logic. It might make sense to replace all of them with `askSync`.
## How was this patch tested?
This PR doesn't change code logic, existing unit test can cover.
Author: jinxing <jinxing@meituan.com>
Closes#16790 from jinxing64/SPARK-19450.
## What changes were proposed in this pull request?
Go back to selecting source/target 1.7 for Scala 2.10 builds, because the SBT-based build for 2.10 won't work otherwise.
## How was this patch tested?
Existing tests, but, we need to verify this vs what the SBT build would exactly run on Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes#16983 from srowen/SPARK-19550.3.
## What changes were proposed in this pull request?
The Range was modified to produce "recordsRead" metric instead of "generated rows". The tests were updated and partially moved to SQLMetricsSuite.
## How was this patch tested?
Unit tests.
Author: Ala Luszczak <ala@databricks.com>
Closes#16960 from ala/range-records-read.
In current `DAGScheduler handleTaskCompletion` code, when event.reason is `Success`, it will first do `stage.pendingPartitions -= task.partitionId`, which maybe a bug when `FetchFailed` happens.
**Think about below**
1. Stage 0 runs and generates shuffle output data.
2. Stage 1 reads the output from stage 0 and generates more shuffle data. It has two tasks: ShuffleMapTask1 and ShuffleMapTask2, and these tasks are launched on executorA.
3. ShuffleMapTask1 fails to fetch blocks locally and sends a FetchFailed to the driver. The driver marks executorA as lost and updates failedEpoch;
4. The driver resubmits stage 0 so the missing output can be re-generated, and then once it completes, resubmits stage 1 with ShuffleMapTask1x and ShuffleMapTask2x.
5. ShuffleMapTask2 (from the original attempt of stage 1) successfully finishes on executorA and sends Success back to driver. This causes DAGScheduler::handleTaskCompletion to remove partition 2 from stage.pendingPartitions (line 1149), but it does not add the partition to the set of output locations (line 1192), because the task’s epoch is less than the failure epoch for the executor (because of the earlier failure on executor A)
6. ShuffleMapTask1x successfully finishes on executorB, causing the driver to remove partition 1 from stage.pendingPartitions. Combined with the previous step, this means that there are no more pending partitions for the stage, so the DAGScheduler marks the stage as finished (line 1196). However, the shuffle stage is not available (line 1215) because the completion for ShuffleMapTask2 was ignored because of its epoch, so the DAGScheduler resubmits the stage.
7. ShuffleMapTask2x is still running, so when TaskSchedulerImpl::submitTasks is called for the re-submitted stage, it throws an error, because there’s an existing active task set
**In this fix**
If a task completion is from a previous stage attempt and the epoch is too low
(i.e., it was from a failed executor), don't remove the corresponding partition
from pendingPartitions.
Author: jinxing <jinxing@meituan.com>
Author: jinxing <jinxing6042@126.com>
Closes#16620 from jinxing64/SPARK-19263.
## What changes were proposed in this pull request?
Replace LeastSquaresAggregator with LogisticAggregator in the require statement of the merge op.
## How was this patch tested?
Simple message fix.
Author: Moussa Taifi <moutai10@gmail.com>
Closes#16903 from moutai/master.
