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## 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.
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## 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.
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## 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.
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## 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.
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## 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.
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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.
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## 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.
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dev/make-distribution.sh script.
## What changes were proposed in this pull request?
Fixed a typo in `dev/make-distribution.sh` script that sets the MAVEN_OPTS variable, introduced [here](https://github.com/apache/spark/commit/0e24054#diff-ba2c046d92a1d2b5b417788bfb5cb5f8R149).
## How was this patch tested?
Run `dev/make-distribution.sh` manually.
Author: Shuai Lin <linshuai2012@gmail.com>
Closes #16984 from lins05/fix-spark-make-distribution-after-removing-java7.
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## What changes were proposed in this pull request?
We recently add the spark.svmLinear API for SparkR. We need to add an example and update the vignettes.
## How was this patch tested?
Manually run example.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes #16969 from wangmiao1981/example.
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quickly
## What changes were proposed in this pull request?
The streaming thread in StreamExecution uses the following ways to check if it should exit:
- Catch an InterruptException.
- `StreamExecution.state` is TERMINATED.
When starting and stopping a query quickly, the above two checks may both fail:
- Hit [HADOOP-14084](https://issues.apache.org/jira/browse/HADOOP-14084) and swallow InterruptException
- StreamExecution.stop is called before `state` becomes `ACTIVE`. Then [runBatches](https://github.com/apache/spark/blob/dcc2d540a53f0bd04baead43fdee1c170ef2b9f3/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala#L252) changes the state from `TERMINATED` to `ACTIVE`.
If the above cases both happen, the query will hang forever.
This PR changes `state` to `AtomicReference` and uses`compareAndSet` to make sure we only change the state from `INITIALIZING` to `ACTIVE`. It also removes the `runUninterruptibly` hack from ``HDFSMetadata`, because HADOOP-14084 won't cause any problem after we fix the race condition.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #16947 from zsxwing/SPARK-19617.
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## What changes were proposed in this pull request?
stop session at end of example
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes #16973 from felixcheung/rexamples.
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## What changes were proposed in this pull request?
SparkR ```approxQuantile``` supports input multiple columns.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #16951 from yanboliang/spark-19619.
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## What changes were proposed in this pull request?
This patch fixes a bug in `KafkaSource` with the (de)serialization of the length of the JSON string that contains the initial partition offsets.
## How was this patch tested?
I ran the test suite for spark-sql-kafka-0-10.
Author: Roberto Agostino Vitillo <ra.vitillo@gmail.com>
Closes #16857 from vitillo/kafka_source_fix.
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spill before calling its iterator
## What changes were proposed in this pull request?
`ExternalAppendOnlyMap.forceSpill` now uses an assert to check if an iterator is not null in the map. However, the assertion is only true after the map is asked for iterator. Before it, if another memory consumer asks more memory than currently available, `ExternalAppendOnlyMap.forceSpill` is also be called too. In this case, we will see failure like this:
[info] java.lang.AssertionError: assertion failed
[info] at scala.Predef$.assert(Predef.scala:156)
[info] at org.apache.spark.util.collection.ExternalAppendOnlyMap.forceSpill(ExternalAppendOnlyMap.scala:196)
[info] at org.apache.spark.util.collection.Spillable.spill(Spillable.scala:111)
[info] at org.apache.spark.util.collection.ExternalAppendOnlyMapSuite$$anonfun$13.apply$mcV$sp(ExternalAppendOnlyMapSuite.scala:294)
This fixing is motivated by http://apache-spark-developers-list.1001551.n3.nabble.com/java-lang-AssertionError-assertion-failed-tc20277.html.
## 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 #16387 from viirya/fix-externalappendonlymap.
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## What changes were proposed in this pull request?
Radix sort require that half of array as free (as temporary space), so we use 0.5 as the scale factor to make sure that BytesToBytesMap will not have more items than 1/2 of capacity. Turned out this is not true, the current implementation of append() could leave 1 more item than the threshold (1/2 of capacity) in the array, which break the requirement of radix sort (fail the assert in 2.2, or fail to insert into InMemorySorter in 2.1).
This PR fix the off-by-one bug in BytesToBytesMap.
This PR also fix a bug that the array will never grow if it fail to grow once (stay as initial capacity), introduced by #15722 .
## How was this patch tested?
Added regression test.
Author: Davies Liu <davies@databricks.com>
Closes #16844 from davies/off_by_one.
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button to search.
## What changes were proposed in this pull request?
The search function of paged table is not available because of we don't skip the hash data of the reqeust path.
## How was this patch tested?
Tested manually with my browser.
Author: Stan Zhai <zhaishidan@haizhi.com>
Closes #16953 from stanzhai/fix-webui-paged-table.
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## What changes were proposed in this pull request?
Fix typo in docstring.
Author: Rolando Espinoza <rndmax84@gmail.com>
Closes #16967 from rolando/pyspark-doc-typo.
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## What changes were proposed in this pull request?
This PR proposes to close stale PRs.
What I mean by "stale" here includes that there are some review comments by reviewers but the author looks inactive without any answer to them more than a month.
I left some comments roughly a week ago to ping and the author looks still inactive in these PR below
These below includes some PR suggested to be closed and a PR against another branch which seems obviously inappropriate.
Given the comments in the last three PRs below, they are probably worth being taken over by anyone who is interested in it.
Closes #7963
Closes #8374
Closes #11192
Closes #11374
Closes #11692
Closes #12243
Closes #12583
Closes #12620
Closes #12675
Closes #12697
Closes #12800
Closes #13715
Closes #14266
Closes #15053
Closes #15159
Closes #15209
Closes #15264
Closes #15267
Closes #15871
Closes #15861
Closes #16319
Closes #16324
Closes #16890
Closes #12398
Closes #12933
Closes #14517
## How was this patch tested?
N/A
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #16937 from HyukjinKwon/stale-prs-close.
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for DataFrameWriter methods
## What changes were proposed in this pull request?
We only notify `QueryExecutionListener` for several `Dataset` operations, e.g. collect, take, etc. We should also do the notification for `DataFrameWriter` operations.
## How was this patch tested?
new regression test
close https://github.com/apache/spark/pull/16664
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16962 from cloud-fan/insert.
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## What changes were proposed in this pull request?
If a new option `wholeFile` is set to `true` the JSON reader will parse each file (instead of a single line) as a value. This is done with Jackson streaming and it should be capable of parsing very large documents, assuming the row will fit in memory.
Because the file is not buffered in memory the corrupt record handling is also slightly different when `wholeFile` is enabled: the corrupt column will contain the filename instead of the literal JSON if there is a parsing failure. It would be easy to extend this to add the parser location (line, column and byte offsets) to the output if desired.
These changes have allowed types other than `String` to be parsed. Support for `UTF8String` and `Text` have been added (alongside `String` and `InputFormat`) and no longer require a conversion to `String` just for parsing.
I've also included a few other changes that generate slightly better bytecode and (imo) make it more obvious when and where boxing is occurring in the parser. These are included as separate commits, let me know if they should be flattened into this PR or moved to a new one.
## How was this patch tested?
New and existing unit tests. No performance or load tests have been run.
Author: Nathan Howell <nhowell@godaddy.com>
Closes #16386 from NathanHowell/SPARK-18352.
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dev/mima
## What changes were proposed in this pull request?
Use JAVA_HOME/bin/java if JAVA_HOME is set in dev/mima script to run MiMa
This follows on https://github.com/apache/spark/pull/16871 -- it's a slightly separate issue, but, is currently causing a build failure.
## How was this patch tested?
Manually tested.
Author: Sean Owen <sowen@cloudera.com>
Closes #16957 from srowen/SPARK-19550.2.
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## What changes were proposed in this pull request?
1, check the behavior with illegal `quantiles` and `relativeError`
2, add tests for `relativeError` > 1
3, update tests for `null` data
4, update some docs for javadoc8
## How was this patch tested?
local test in spark-shell
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Closes #16776 from zhengruifeng/fix_approxQuantile.
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## What changes were proposed in this pull request?
These error below seems caused by unidoc that does not understand double commented block.
```
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:69: error: class, interface, or enum expected
[error] * MapGroupsWithStateFunction<String, Integer, Integer, String> mappingFunction =
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:69: error: class, interface, or enum expected
[error] * MapGroupsWithStateFunction<String, Integer, Integer, String> mappingFunction =
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:70: error: class, interface, or enum expected
[error] * new MapGroupsWithStateFunction<String, Integer, Integer, String>() {
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:70: error: class, interface, or enum expected
[error] * new MapGroupsWithStateFunction<String, Integer, Integer, String>() {
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:72: error: illegal character: '#'
[error] * @Override
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:72: error: class, interface, or enum expected
[error] * @Override
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:73: error: class, interface, or enum expected
[error] * public String call(String key, Iterator<Integer> value, KeyedState<Integer> state) {
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:73: error: class, interface, or enum expected
[error] * public String call(String key, Iterator<Integer> value, KeyedState<Integer> state) {
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:73: error: class, interface, or enum expected
[error] * public String call(String key, Iterator<Integer> value, KeyedState<Integer> state) {
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:73: error: class, interface, or enum expected
[error] * public String call(String key, Iterator<Integer> value, KeyedState<Integer> state) {
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:73: error: class, interface, or enum expected
[error] * public String call(String key, Iterator<Integer> value, KeyedState<Integer> state) {
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:76: error: class, interface, or enum expected
[error] * boolean shouldRemove = ...; // Decide whether to remove the state
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:77: error: class, interface, or enum expected
[error] * if (shouldRemove) {
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:79: error: class, interface, or enum expected
[error] * } else {
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:81: error: class, interface, or enum expected
[error] * state.update(newState); // Set the new state
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:82: error: class, interface, or enum expected
[error] * }
[error] ^
[error] .../forked/spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:85: error: class, interface, or enum expected
[error] * state.update(initialState);
[error] ^
[error] .../forked/spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:86: error: class, interface, or enum expected
[error] * }
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:90: error: class, interface, or enum expected
[error] * </code></pre>
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:92: error: class, interface, or enum expected
[error] * tparam S User-defined type of the state to be stored for each key. Must be encodable into
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:93: error: class, interface, or enum expected
[error] * Spark SQL types (see {link Encoder} for more details).
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:94: error: class, interface, or enum expected
[error] * since 2.1.1
[error] ^
```
And another link seems unrecognisable.
```
.../spark/sql/core/target/java/org/apache/spark/sql/KeyedState.java:16: error: reference not found
[error] * That is, in every batch of the {link streaming.StreamingQuery StreamingQuery},
[error]
```
Note that this PR does not fix the two breaks as below:
```
[error] .../spark/sql/core/target/java/org/apache/spark/sql/DataFrameStatFunctions.java:43: error: unexpected content
[error] * see {link DataFrameStatsFunctions.approxQuantile(col:Str* approxQuantile} for
[error] ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/DataFrameStatFunctions.java:52: error: bad use of '>'
[error] * param relativeError The relative target precision to achieve (>= 0).
[error] ^
[error]
```
because these seem probably fixed soon in https://github.com/apache/spark/pull/16776 and I intended to avoid potential conflicts.
## How was this patch tested?
Manually via `jekyll build`
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #16926 from HyukjinKwon/javadoc-break.
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- Move external/java8-tests tests into core, streaming, sql and remove
- Remove MaxPermGen and related options
- Fix some reflection / TODOs around Java 8+ methods
- Update doc references to 1.7/1.8 differences
- Remove Java 7/8 related build profiles
- Update some plugins for better Java 8 compatibility
- Fix a few Java-related warnings
For the future:
- Update Java 8 examples to fully use Java 8
- Update Java tests to use lambdas for simplicity
- Update Java internal implementations to use lambdas
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes #16871 from srowen/SPARK-19493.
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## What changes were proposed in this pull request?
This is 3ird batch of test case for IN/NOT IN subquery. In this PR, it has these test files:
`in-having.sql`
`in-joins.sql`
`in-multiple-columns.sql`
These are the queries and results from running on DB2.
[in-having DB2 version](https://github.com/apache/spark/files/772668/in-having.sql.db2.txt)
[output of in-having](https://github.com/apache/spark/files/772670/in-having.sql.db2.out.txt)
[in-joins DB2 version](https://github.com/apache/spark/files/772672/in-joins.sql.db2.txt)
[output of in-joins](https://github.com/apache/spark/files/772673/in-joins.sql.db2.out.txt)
[in-multiple-columns DB2 version](https://github.com/apache/spark/files/772678/in-multiple-columns.sql.db2.txt)
[output of in-multiple-columns](https://github.com/apache/spark/files/772680/in-multiple-columns.sql.db2.out.txt)
## How was this patch tested?
This pr is adding new test cases. We compare the result from spark with the result from another RDBMS(We used DB2 LUW). If the results are the same, we assume the result is correct.
Author: Kevin Yu <qyu@us.ibm.com>
Closes #16841 from kevinyu98/spark-18871-33.
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vs SQL
## What changes were proposed in this pull request?
Jira: https://issues.apache.org/jira/browse/SPARK-19618
Moved the check for validating number of buckets from `DataFrameWriter` to `BucketSpec` creation
## How was this patch tested?
- Added more unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes #16948 from tejasapatil/SPARK-19618_max_buckets.
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## What changes were proposed in this pull request?
This is 4th batch of test case for IN/NOT IN subquery. In this PR, it has these test files:
`in-set-operations.sql`
`in-with-cte.sql`
`not-in-joins.sql`
Here are the queries and results from running on DB2.
[in-set-operations DB2 version](https://github.com/apache/spark/files/772846/in-set-operations.sql.db2.txt)
[Output of in-set-operations](https://github.com/apache/spark/files/772848/in-set-operations.sql.db2.out.txt)
[in-with-cte DB2 version](https://github.com/apache/spark/files/772849/in-with-cte.sql.db2.txt)
[Output of in-with-cte](https://github.com/apache/spark/files/772856/in-with-cte.sql.db2.out.txt)
[not-in-joins DB2 version](https://github.com/apache/spark/files/772851/not-in-joins.sql.db2.txt)
[Output of not-in-joins](https://github.com/apache/spark/files/772852/not-in-joins.sql.db2.out.txt)
## How was this patch tested?
This pr is adding new test cases. We compare the result from spark with the result from another RDBMS(We used DB2 LUW). If the results are the same, we assume the result is correct.
Author: Kevin Yu <qyu@us.ibm.com>
Closes #16915 from kevinyu98/spark-18871-44.
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## What changes were proposed in this pull request?
`StreamingQuery.explain` doesn't show the correct streaming physical plan right now because `ExplainCommand` receives a runtime batch plan and its `logicalPlan.isStreaming` is always false.
This PR adds `streaming` parameter to `ExplainCommand` to allow `StreamExecution` to specify that it's a streaming plan.
Examples of the explain outputs:
- streaming DataFrame.explain()
```
== Physical Plan ==
*HashAggregate(keys=[value#518], functions=[count(1)])
+- StateStoreSave [value#518], OperatorStateId(<unknown>,0,0), Append, 0
+- *HashAggregate(keys=[value#518], functions=[merge_count(1)])
+- StateStoreRestore [value#518], OperatorStateId(<unknown>,0,0)
+- *HashAggregate(keys=[value#518], functions=[merge_count(1)])
+- Exchange hashpartitioning(value#518, 5)
+- *HashAggregate(keys=[value#518], functions=[partial_count(1)])
+- *SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#518]
+- *MapElements <function1>, obj#517: java.lang.String
+- *DeserializeToObject value#513.toString, obj#516: java.lang.String
+- StreamingRelation MemoryStream[value#513], [value#513]
```
- StreamingQuery.explain(extended = false)
```
== Physical Plan ==
*HashAggregate(keys=[value#518], functions=[count(1)])
+- StateStoreSave [value#518], OperatorStateId(...,0,0), Complete, 0
+- *HashAggregate(keys=[value#518], functions=[merge_count(1)])
+- StateStoreRestore [value#518], OperatorStateId(...,0,0)
+- *HashAggregate(keys=[value#518], functions=[merge_count(1)])
+- Exchange hashpartitioning(value#518, 5)
+- *HashAggregate(keys=[value#518], functions=[partial_count(1)])
+- *SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#518]
+- *MapElements <function1>, obj#517: java.lang.String
+- *DeserializeToObject value#543.toString, obj#516: java.lang.String
+- LocalTableScan [value#543]
```
- StreamingQuery.explain(extended = true)
```
== Parsed Logical Plan ==
Aggregate [value#518], [value#518, count(1) AS count(1)#524L]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#518]
+- MapElements <function1>, class java.lang.String, [StructField(value,StringType,true)], obj#517: java.lang.String
+- DeserializeToObject cast(value#543 as string).toString, obj#516: java.lang.String
+- LocalRelation [value#543]
== Analyzed Logical Plan ==
value: string, count(1): bigint
Aggregate [value#518], [value#518, count(1) AS count(1)#524L]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#518]
+- MapElements <function1>, class java.lang.String, [StructField(value,StringType,true)], obj#517: java.lang.String
+- DeserializeToObject cast(value#543 as string).toString, obj#516: java.lang.String
+- LocalRelation [value#543]
== Optimized Logical Plan ==
Aggregate [value#518], [value#518, count(1) AS count(1)#524L]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#518]
+- MapElements <function1>, class java.lang.String, [StructField(value,StringType,true)], obj#517: java.lang.String
+- DeserializeToObject value#543.toString, obj#516: java.lang.String
+- LocalRelation [value#543]
== Physical Plan ==
*HashAggregate(keys=[value#518], functions=[count(1)], output=[value#518, count(1)#524L])
+- StateStoreSave [value#518], OperatorStateId(...,0,0), Complete, 0
+- *HashAggregate(keys=[value#518], functions=[merge_count(1)], output=[value#518, count#530L])
+- StateStoreRestore [value#518], OperatorStateId(...,0,0)
+- *HashAggregate(keys=[value#518], functions=[merge_count(1)], output=[value#518, count#530L])
+- Exchange hashpartitioning(value#518, 5)
+- *HashAggregate(keys=[value#518], functions=[partial_count(1)], output=[value#518, count#530L])
+- *SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#518]
+- *MapElements <function1>, obj#517: java.lang.String
+- *DeserializeToObject value#543.toString, obj#516: java.lang.String
+- LocalTableScan [value#543]
```
## How was this patch tested?
The updated unit test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #16934 from zsxwing/SPARK-19603.
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## What changes were proposed in this pull request?
This pull request includes python API and examples for LSH. The API changes was based on yanboliang 's PR #15768 and resolved conflicts and API changes on the Scala API. The examples are consistent with Scala examples of MinHashLSH and BucketedRandomProjectionLSH.
## How was this patch tested?
API and examples are tested using spark-submit:
`bin/spark-submit examples/src/main/python/ml/min_hash_lsh.py`
`bin/spark-submit examples/src/main/python/ml/bucketed_random_projection_lsh.py`
User guide changes are generated and manually inspected:
`SKIP_API=1 jekyll build`
Author: Yun Ni <yunn@uber.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Author: Yunni <Euler57721@gmail.com>
Closes #16715 from Yunni/spark-18080.
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## What changes were proposed in this pull request?
SPARK-19464 removed support for Hadoop 2.5 and earlier, so we can do some cleanup for HDFSMetadataLog.
This PR includes the following changes:
- ~~Remove the workaround codes for HADOOP-10622.~~ Unfortunately, there is another issue [HADOOP-14084](https://issues.apache.org/jira/browse/HADOOP-14084) that prevents us from removing the workaround codes.
- Remove unnecessary `writer: (T, OutputStream) => Unit` and just call `serialize` directly.
- Remove catching FileNotFoundException.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #16932 from zsxwing/metadata-cleanup.
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## What changes were proposed in this pull request?
Right now, we only have info level log after we finish the tests of a Python test file. We should also log the start of a test. So, if a test is hanging, we can tell which test file is running.
## How was this patch tested?
This is a change for python tests.
Author: Yin Huai <yhuai@databricks.com>
Closes #16935 from yhuai/SPARK-19604.
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## What changes were proposed in this pull request?
This is a follow-up pr of #16308.
This pr enables timezone support in CSV/JSON parsing.
We should introduce `timeZone` option for CSV/JSON datasources (the default value of the option is session local timezone).
The datasources should use the `timeZone` option to format/parse to write/read timestamp values.
Notice that while reading, if the timestampFormat has the timezone info, the timezone will not be used because we should respect the timezone in the values.
For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT`, the values written with the default timezone option, which is `"GMT"` because session local timezone is `"GMT"` here, are:
```scala
scala> spark.conf.set("spark.sql.session.timeZone", "GMT")
scala> val df = Seq(new java.sql.Timestamp(1451606400000L)).toDF("ts")
df: org.apache.spark.sql.DataFrame = [ts: timestamp]
scala> df.show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
scala> df.write.json("/path/to/gmtjson")
```
```sh
$ cat /path/to/gmtjson/part-*
{"ts":"2016-01-01T00:00:00.000Z"}
```
whereas setting the option to `"PST"`, they are:
```scala
scala> df.write.option("timeZone", "PST").json("/path/to/pstjson")
```
```sh
$ cat /path/to/pstjson/part-*
{"ts":"2015-12-31T16:00:00.000-08:00"}
```
We can properly read these files even if the timezone option is wrong because the timestamp values have timezone info:
```scala
scala> val schema = new StructType().add("ts", TimestampType)
schema: org.apache.spark.sql.types.StructType = StructType(StructField(ts,TimestampType,true))
scala> spark.read.schema(schema).json("/path/to/gmtjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
scala> spark.read.schema(schema).option("timeZone", "PST").json("/path/to/gmtjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
```
And even if `timezoneFormat` doesn't contain timezone info, we can properly read the values with setting correct timezone option:
```scala
scala> df.write.option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").option("timeZone", "JST").json("/path/to/jstjson")
```
```sh
$ cat /path/to/jstjson/part-*
{"ts":"2016-01-01T09:00:00"}
```
```scala
// wrong result
scala> spark.read.schema(schema).option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").json("/path/to/jstjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 09:00:00|
+-------------------+
// correct result
scala> spark.read.schema(schema).option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").option("timeZone", "JST").json("/path/to/jstjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
```
This pr also makes `JsonToStruct` and `StructToJson` `TimeZoneAwareExpression` to be able to evaluate values with timezone option.
## How was this patch tested?
Existing tests and added some tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes #16750 from ueshin/issues/SPARK-18937.
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pre-existing location should succeed
## What changes were proposed in this pull request?
when we insert data into a datasource table use `sqlText`, and the table has an not exists location,
this will throw an Exception.
example:
```
spark.sql("create table t(a string, b int) using parquet")
spark.sql("alter table t set location '/xx'")
spark.sql("insert into table t select 'c', 1")
```
Exception:
```
com.google.common.util.concurrent.UncheckedExecutionException: org.apache.spark.sql.AnalysisException: Path does not exist: /xx;
at com.google.common.cache.LocalCache$LocalLoadingCache.getUnchecked(LocalCache.java:4814)
at com.google.common.cache.LocalCache$LocalLoadingCache.apply(LocalCache.java:4830)
at org.apache.spark.sql.hive.HiveMetastoreCatalog.lookupRelation(HiveMetastoreCatalog.scala:122)
at org.apache.spark.sql.hive.HiveSessionCatalog.lookupRelation(HiveSessionCatalog.scala:69)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$lookupTableFromCatalog(Analyzer.scala:456)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$8.applyOrElse(Analyzer.scala:465)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$8.applyOrElse(Analyzer.scala:463)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:60)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:463)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:453)
```
As discussed following comments, we should unify the action when we reading from or writing to a datasource table with a non pre-existing locaiton:
1. reading from a datasource table: return 0 rows
2. writing to a datasource table: write data successfully
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes #16672 from windpiger/insertNotExistLocation.
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## What changes were proposed in this pull request?
#16940 adds a test case which does not stop the spark job. It causes many failures of other test cases.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.7/2403/consoleFull
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7/2600/consoleFull
```
[info] org.apache.spark.SparkException: Only one SparkContext may be running in this JVM (see SPARK-2243). To ignore this error, set spark.driver.allowMultipleContexts = true. The currently running SparkContext was created at:
```
## How was this patch tested?
Pass the Jenkins test.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #16943 from dongjoon-hyun/SPARK-19607-2.
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Move `SQLViewSuite` from `sql/hive` to `sql/core`, so we can test the view supports without hive metastore. Also moved the test cases that specified to hive to `HiveSQLViewSuite`.
Improve the test coverage of SQLViewSuite, cover the following cases:
1. view resolution(possibly a referenced table/view have changed after the view creation);
2. handle a view with user specified column names;
3. improve the test cases for a nested view.
Also added a test case for cyclic view reference, which is a known issue that is not fixed yet.
N/A
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes #16674 from jiangxb1987/view-test.
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## What changes were proposed in this pull request?
Add coalesce on DataFrame for down partitioning without shuffle and coalesce on Column
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes #16739 from felixcheung/rcoalesce.
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## What changes were proposed in this pull request?
This PR adds `udf` decorator syntax as proposed in [SPARK-19160](https://issues.apache.org/jira/browse/SPARK-19160).
This allows users to define UDF using simplified syntax:
```python
from pyspark.sql.decorators import udf
udf(IntegerType())
def add_one(x):
"""Adds one"""
if x is not None:
return x + 1
```
without need to define a separate function and udf.
## How was this patch tested?
Existing unit tests to ensure backward compatibility and additional unit tests covering new functionality.
Author: zero323 <zero323@users.noreply.github.com>
Closes #16533 from zero323/SPARK-19160.
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pyspark
## What changes were proposed in this pull request?
This PR is to document the changes on QuantileDiscretizer in pyspark for PR:
https://github.com/apache/spark/pull/15428
## How was this patch tested?
No test needed
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Closes #16922 from VinceShieh/spark-19590.
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delimiter
## What changes were proposed in this pull request?
A follow-up to disallow space as the delimiter in broadcast hint.
## How was this patch tested?
Jenkins test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes #16941 from viirya/disallow-space-delimiter.
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## What changes were proposed in this pull request?
This PR adds the third and final set of tests for EXISTS subquery.
File name | Brief description
------------------------| -----------------
exists-cte.sql |Tests Exist subqueries referencing CTE
exists-joins-and-set-ops.sql|Tests Exists subquery used in Joins (Both when joins occurs in outer and suquery blocks)
DB2 results are attached here as reference :
[exists-cte-db2.txt](https://github.com/apache/spark/files/752091/exists-cte-db2.txt)
[exists-joins-and-set-ops-db2.txt](https://github.com/apache/spark/files/753283/exists-joins-and-set-ops-db2.txt) (updated)
## How was this patch tested?
The test result is compared with the result run from another SQL engine (in this case is IBM DB2). If the result are equivalent, we assume the result is correct.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes #16802 from dilipbiswal/exists-pr3.
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scalar subquery in predicate context
## What changes were proposed in this pull request?
This PR adds new test cases for scalar subquery in predicate context
## How was this patch tested?
The test result is compared with the result run from another SQL engine (in this case is IBM DB2). If the result are equivalent, we assume the result is correct.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes #16798 from nsyca/18873-2.
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## What changes were proposed in this pull request?
This is 2nd batch of test case for IN/NOT IN subquery. In this PR, it has these test cases:
`in-limit.sql`
`in-order-by.sql`
`not-in-group-by.sql`
These are the queries and results from running on DB2.
[in-limit DB2 version](https://github.com/apache/spark/files/743267/in-limit.sql.db2.out.txt)
[in-order-by DB2 version](https://github.com/apache/spark/files/743269/in-order-by.sql.db2.txt)
[not-in-group-by DB2 version](https://github.com/apache/spark/files/743271/not-in-group-by.sql.db2.txt)
[output of in-limit.sql DB2](https://github.com/apache/spark/files/743276/in-limit.sql.db2.out.txt)
[output of in-order-by.sql DB2](https://github.com/apache/spark/files/743278/in-order-by.sql.db2.out.txt)
[output of not-in-group-by.sql DB2](https://github.com/apache/spark/files/743279/not-in-group-by.sql.db2.out.txt)
## How was this patch tested?
This pr is adding new test cases.
Author: Kevin Yu <qyu@us.ibm.com>
Closes #16759 from kevinyu98/spark-18871-2.
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statistics
## What changes were proposed in this pull request?
Support cardinality estimation and stats propagation for all join types.
Limitations:
- For inner/outer joins without any equal condition, we estimate it like cartesian product.
- For left semi/anti joins, since we can't apply the heuristics for inner join to it, for now we just propagate the statistics from left side. We should support them when other advanced stats (e.g. histograms) are available in spark.
## How was this patch tested?
Add a new test suite.
Author: Zhenhua Wang <wzh_zju@163.com>
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes #16228 from wzhfy/joinEstimate.
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partition columns
## What changes were proposed in this pull request?
We will throw an exception if bucket columns are part of partition columns, this should also apply to sort columns.
This PR also move the checking logic from `DataFrameWriter` to `PreprocessTableCreation`, which is the central place for checking and normailization.
## How was this patch tested?
updated test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16931 from cloud-fan/bucket.
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## What changes were proposed in this pull request?
A small update to https://github.com/apache/spark/pull/16925
1. Rename SubstituteHints -> ResolveHints to be more consistent with rest of the rules.
2. Added more documentation in the rule and be more defensive / future proof to skip views as well as CTEs.
## How was this patch tested?
This pull request contains no real logic change and all behavior should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes #16939 from rxin/SPARK-16475.
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## What changes were proposed in this pull request?
Implementing a mapping between executionId and corresponding QueryExecution in SQLExecution.
## How was this patch tested?
Adds a unit test.
Author: Ala Luszczak <ala@databricks.com>
Closes #16940 from ala/execution-id.
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## What changes were proposed in this pull request?
Linear SVM classifier is newly added into ML and python API has been added. This JIRA is to add R side API.
Marked as WIP, as I am designing unit tests.
## How was this patch tested?
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes #16800 from wangmiao1981/svc.
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batch mode
## What changes were proposed in this pull request?
Revision to structured-streaming-kafka-integration.md to reflect new Batch query specification and options.
zsxwing tdas
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Tyson Condie <tcondie@gmail.com>
Closes #16918 from tcondie/kafka-docs.
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user in case-sensitive manner.
## What changes were proposed in this pull request?
The reason for test failure is that the property “oracle.jdbc.mapDateToTimestamp” set by the test was getting converted into all lower case. Oracle database expects this property in case-sensitive manner.
This test was passing in previous releases because connection properties were sent as user specified for the test case scenario. Fixes to handle all option uniformly in case-insensitive manner, converted the JDBC connection properties also to lower case.
This PR enhances CaseInsensitiveMap to keep track of input case-sensitive keys , and uses those when creating connection properties that are passed to the JDBC connection.
Alternative approach PR https://github.com/apache/spark/pull/16847 is to pass original input keys to JDBC data source by adding check in the Data source class and handle case-insensitivity in the JDBC source code.
## How was this patch tested?
Added new test cases to JdbcSuite , and OracleIntegrationSuite. Ran docker integration tests passed on my laptop, all tests passed successfully.
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes #16891 from sureshthalamati/jdbc_case_senstivity_props_fix-SPARK-19318.
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## What changes were proposed in this pull request?
This pull request introduces a simple hint infrastructure to SQL and implements broadcast join hint using the infrastructure.
The hint syntax looks like the following:
```
SELECT /*+ BROADCAST(t) */ * FROM t
```
For broadcast hint, we accept "BROADCAST", "BROADCASTJOIN", and "MAPJOIN", and a sequence of relation aliases can be specified in the hint. A broadcast hint plan node will be inserted on top of any relation (that is not aliased differently), subquery, or common table expression that match the specified name.
The hint resolution works by recursively traversing down the query plan to find a relation or subquery that matches one of the specified broadcast aliases. The traversal does not go past beyond any existing broadcast hints, subquery aliases. This rule happens before common table expressions.
Note that there was an earlier patch in https://github.com/apache/spark/pull/14426. This is a rewrite of that patch, with different semantics and simpler test cases.
## How was this patch tested?
Added a new unit test suite for the broadcast hint rule (SubstituteHintsSuite) and new test cases for parser change (in PlanParserSuite). Also added end-to-end test case in BroadcastSuite.
Author: Reynold Xin <rxin@databricks.com>
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #16925 from rxin/SPARK-16475-broadcast-hint.
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