24996 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
HyukjinKwon
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d25cbd44ee |
[SPARK-28839][CORE] Avoids NPE in context cleaner when dynamic allocation and shuffle service are on
### What changes were proposed in this pull request? This PR proposes to avoid to thrown NPE at context cleaner when shuffle service is on - it is kind of a small followup of https://github.com/apache/spark/pull/24817 Seems like it sets `null` for `shuffleIds` to track when the service is on. Later, `removeShuffle` tries to remove an element at `shuffleIds` which leads to NPE. It fixes it by explicitly not sending the event (`ShuffleCleanedEvent`) in this case. See the code path below: |
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Xiao Li
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07c4b9bd1f |
Revert "[SPARK-25474][SQL] Support spark.sql.statistics.fallBackToHdfs in data source tables"
This reverts commit
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Gengliang Wang
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8258660f67 |
[SPARK-28741][SQL] Optional mode: throw exceptions when casting to integers causes overflow
## What changes were proposed in this pull request? To follow ANSI SQL, we should support a configurable mode that throws exceptions when casting to integers causes overflow. The behavior is similar to https://issues.apache.org/jira/browse/SPARK-26218, which throws exceptions on arithmetical operation overflow. To unify it, the configuration is renamed from "spark.sql.arithmeticOperations.failOnOverFlow" to "spark.sql.failOnIntegerOverFlow" ## How was this patch tested? Unit test Closes #25461 from gengliangwang/AnsiCastIntegral. Authored-by: Gengliang Wang <gengliang.wang@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Ali Afroozeh
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1472e664ba |
[SPARK-28716][SQL] Add id to Exchange and Subquery's stringArgs method for easier identifying their reuses in query plans
## What changes were proposed in this pull request? Add id to Exchange and Subquery's stringArgs method for easier identifying their reuses in query plans, for example: ``` ReusedExchange d_date_sk#827, BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint))) [id=#2710] ``` Where `2710` is the id of the reused exchange. ## How was this patch tested? Passes existing tests Closes #25434 from dbaliafroozeh/ImplementStringArgsExchangeSubqueryExec. Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com> Signed-off-by: herman <herman@databricks.com> |
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Ali Afroozeh
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aef7ca1f0b |
[SPARK-28836][SQL] Remove the canonicalize(attributes) method from PlanExpression
### What changes were proposed in this pull request? This PR removes the `canonicalize(attrs: AttributeSeq)` from `PlanExpression` and taking care of normalizing expressions in `QueryPlan`. ### Why are the changes needed? `Expression` has already a `canonicalized` method and having the `canonicalize` method in `PlanExpression` is confusing. ### Does this PR introduce any user-facing change? Removes the `canonicalize` plan from `PlanExpression`. Also renames the `normalizeExprId` to `normalizeExpressions` in query plan. ### How was this patch tested? This PR is a refactoring and passes the existing tests Closes #25534 from dbaliafroozeh/ImproveCanonicalizeAPI. Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com> Signed-off-by: herman <herman@databricks.com> |
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Dongjoon Hyun
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1fd7f290ab |
[SPARK-28857][INFRA] Clean up the comments of PR template during merging
### What changes were proposed in this pull request? This PR aims to clean up the commit logs by removing the comments of our PR template. ### Why are the changes needed? Apache Spark PR template has comments. Sometime we forget to clean up them because GitHub hides them nicely. It would be great if we clean up this. Otherwise, this makes the commit logs too verbose. (There are a few commits already.) ### Does this PR introduce any user-facing change? No. (only for committers) ### How was this patch tested? Manually with Python2/Python3. Closes #25564 from dongjoon-hyun/SPARK-28857. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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terryk
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98e1a4cea4 |
[SPARK-28319][SQL] Implement SHOW TABLES for Data Source V2 Tables
## What changes were proposed in this pull request? Implements the SHOW TABLES logical and physical plans for data source v2 tables. ## How was this patch tested? Added unit tests to `DataSourceV2SQLSuite`. Closes #25247 from imback82/dsv2_show_tables. Lead-authored-by: terryk <yuminkim@gmail.com> Co-authored-by: Terry Kim <yuminkim@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Ali Afroozeh
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9976b876f1 |
[SPARK-28835][SQL][TEST] Add TPCDSSchema trait
### What changes were proposed in this pull request? This PR extracts the schema information of TPCDS tables into a separate class called `TPCDSSchema` which can be reused for other testing purposes ### How was this patch tested? This PR is only a refactoring for tests and passes existing tests Closes #25535 from dbaliafroozeh/IntroduceTPCDSSchema. Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Jungtaek Lim (HeartSaVioR)
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406c5331ff
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[SPARK-28025][SS] Fix FileContextBasedCheckpointFileManager leaking crc files
### What changes were proposed in this pull request? This PR fixes the leak of crc files from CheckpointFileManager when FileContextBasedCheckpointFileManager is being used. Spark hits the Hadoop bug, [HADOOP-16255](https://issues.apache.org/jira/browse/HADOOP-16255) which seems to be a long-standing issue. This is there're two `renameInternal` methods: ``` public void renameInternal(Path src, Path dst) public void renameInternal(final Path src, final Path dst, boolean overwrite) ``` which should be overridden to handle all cases but ChecksumFs only overrides method with 2 params, so when latter is called FilterFs.renameInternal(...) is called instead, and it will do rename with RawLocalFs as underlying filesystem. The bug is related to FileContext, so FileSystemBasedCheckpointFileManager is not affected. [SPARK-17475](https://issues.apache.org/jira/browse/SPARK-17475) took a workaround for this bug, but [SPARK-23966](https://issues.apache.org/jira/browse/SPARK-23966) seemed to bring regression. This PR deletes crc file as "best-effort" when renaming, as failing to delete crc file is not that critical to fail the task. ### Why are the changes needed? This PR prevents crc files not being cleaned up even purging batches. Too many files in same directory often hurts performance, as well as each crc file occupies more space than its own size so possible to occupy nontrivial amount of space when batches go up to 100000+. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Some unit tests are modified to check leakage of crc files. Closes #25488 from HeartSaVioR/SPARK-28025. Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com> Signed-off-by: Shixiong Zhu <zsxwing@gmail.com> |
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Gengliang Wang
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895c90b582 |
[SPARK-28730][SQL] Configurable type coercion policy for table insertion
## What changes were proposed in this pull request? After all the discussions in the dev list: http://apache-spark-developers-list.1001551.n3.nabble.com/Discuss-Follow-ANSI-SQL-on-table-insertion-td27531.html#a27562. Here I propose that we can make the store assignment rules in the analyzer configurable, and the behavior of V1 and V2 should be consistent. When inserting a value into a column with a different data type, Spark will perform type coercion. After this PR, we support 2 policies for the type coercion rules: legacy and strict. 1. With legacy policy, Spark allows casting any value to any data type. The legacy policy is the only behavior in Spark 2.x and it is compatible with Hive. 2. With strict policy, Spark doesn't allow any possible precision loss or data truncation in type coercion, e.g. `int` and `long`, `float` -> `double` are not allowed. Eventually, the "legacy" mode will be removed, so it is disallowed in data source V2. To ensure backward compatibility with existing queries, the default store assignment policy for data source V1 is "legacy". ## How was this patch tested? Unit test Closes #25453 from gengliangwang/tableInsertRule. Authored-by: Gengliang Wang <gengliang.wang@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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shivusondur
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23bed0d3c0 |
[SPARK-28702][SQL] Display useful error message (instead of NPE) for invalid Dataset operations
### What changes were proposed in this pull request?
Added proper message instead of NPE for invalid Dataset operations (e.g. calling actions inside of transformations) similar to SPARK-5063 for RDD
### Why are the changes needed?
To report the user about the exact issue instead of NPE
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually tested
```scala
test code snap
"import spark.implicits._
val ds1 = spark.sparkContext.parallelize(1 to 100, 100).toDS()
val ds2 = spark.sparkContext.parallelize(1 to 100, 100).toDS()
ds1.map(x => {
// scalastyle:off
println(ds2.count + x)
x
}).collect()"
```
Closes #25503 from shivusondur/jira28702.
Authored-by: shivusondur <shivusondur@gmail.com>
Signed-off-by: Josh Rosen <rosenville@gmail.com>
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Kousuke Saruta
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33e45ec7b8 |
[SPARK-28769][CORE] Improve warning message of BarrierExecutionMode when required slots > maximum slots
### What changes were proposed in this pull request? Improved warning message in Barrier Execution Mode when required slots > maximum slots. The new message contains information about required slots, maximum slots and how many times retry failed. ### Why are the changes needed? Providing to users with the number of required slots, maximum slots and how many times retry failed might help users to decide what they should do. For example, continuing to wait for retry succeeded or killing jobs. ### Does this PR introduce any user-facing change? Yes. If `spark.scheduler.barrier.maxConcurrentTaskCheck.maxFailures=3`, we get following warning message. Before applying this change: ``` 19/08/18 15:18:09 WARN DAGScheduler: The job 2 requires to run a barrier stage that requires more slots than the total number of slots in the cluster currently. 19/08/18 15:18:24 WARN DAGScheduler: The job 2 requires to run a barrier stage that requires more slots than the total number of slots in the cluster currently. 19/08/18 15:18:39 WARN DAGScheduler: The job 2 requires to run a barrier stage that requires more slots than the total number of slots in the cluster currently. 19/08/18 15:18:54 WARN DAGScheduler: The job 2 requires to run a barrier stage that requires more slots than the total number of slots in the cluster currently. org.apache.spark.scheduler.BarrierJobSlotsNumberCheckFailed: [SPARK-24819]: Barrier execution mode does not allow run a barrier stage that requires more slots than the total number of slots in the cluster currently. Please init a new cluster with more CPU cores or repartition the input RDD(s) to reduce the number of slots required to run this barrier stage. at org.apache.spark.scheduler.DAGScheduler.checkBarrierStageWithNumSlots(DAGScheduler.scala:439) at org.apache.spark.scheduler.DAGScheduler.createResultStage(DAGScheduler.scala:453) at org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:983) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2140) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2132) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2121) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:749) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2080) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2120) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2145) at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:961) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:366) at org.apache.spark.rdd.RDD.collect(RDD.scala:960) ... 47 elided ``` After applying this change: ``` 19/08/18 16:52:23 WARN DAGScheduler: The job 0 requires to run a barrier stage that requires 3 slots than the total number of slots(2) in the cluster currently. 19/08/18 16:52:38 WARN DAGScheduler: The job 0 requires to run a barrier stage that requires 3 slots than the total number of slots(2) in the cluster currently (Retry 1/3 failed). 19/08/18 16:52:53 WARN DAGScheduler: The job 0 requires to run a barrier stage that requires 3 slots than the total number of slots(2) in the cluster currently (Retry 2/3 failed). 19/08/18 16:53:08 WARN DAGScheduler: The job 0 requires to run a barrier stage that requires 3 slots than the total number of slots(2) in the cluster currently (Retry 3/3 failed). org.apache.spark.scheduler.BarrierJobSlotsNumberCheckFailed: [SPARK-24819]: Barrier execution mode does not allow run a barrier stage that requires more slots than the total number of slots in the cluster currently. Please init a new cluster with more CPU cores or repartition the input RDD(s) to reduce the number of slots required to run this barrier stage. at org.apache.spark.scheduler.DAGScheduler.checkBarrierStageWithNumSlots(DAGScheduler.scala:439) at org.apache.spark.scheduler.DAGScheduler.createResultStage(DAGScheduler.scala:453) at org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:983) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2140) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2132) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2121) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:749) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2080) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2120) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2145) at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:961) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:366) at org.apache.spark.rdd.RDD.collect(RDD.scala:960) ... 47 elided ``` ### How was this patch tested? I tested manually using Spark Shell with following configuration and script. And then, checked log message. ``` $ bin/spark-shell --master local[2] --conf spark.scheduler.barrier.maxConcurrentTasksCheck.maxFailures=3 scala> sc.parallelize(1 to 100, sc.defaultParallelism+1).barrier.mapPartitions(identity(_)).collect ``` Closes #25487 from sarutak/barrier-exec-mode-warning-message. Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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zhengruifeng
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defb65ed1a |
[SPARK-13677][ML] Implement Tree-Based Feature Transformation for ML
## What changes were proposed in this pull request? Tree-based feature transformation is a widely used feature and already implemented in many famous libraries, like sklearn/xgboost/lightgbm/catboost. But is still missing in ML. The previous discussions and design doc can be found in [SPARK-13677](https://issues.apache.org/jira/browse/SPARK-13677), which is the only left subtask in 'GBT improvement umbrella' [SPARK-14047](https://issues.apache.org/jira/browse/SPARK-14047). This pr is to add tree-based feature transformation. ## How was this patch tested? existing and added suites Closes #25383 from zhengruifeng/tree_path. Authored-by: zhengruifeng <ruifengz@foxmail.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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heleny
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fb1f868d4f |
[SPARK-28776][ML] SparkML Writer gets hadoop conf from session state
<!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. --> ### What changes were proposed in this pull request? SparkML writer gets hadoop conf from session state, instead of the spark context. <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> ### Why are the changes needed? Allow for multiple sessions in the same context that have different hadoop configurations. <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> ### Does this PR introduce any user-facing change? <!-- If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If no, write 'No'. --> No ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> Tested in pyspark.ml.tests.test_persistence.PersistenceTest test_default_read_write Closes #25505 from helenyugithub/SPARK-28776. Authored-by: heleny <heleny@palantir.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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Dongjoon Hyun
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36da2e3384 |
[SPARK-28847][TEST] Annotate HiveExternalCatalogVersionsSuite with ExtendedHiveTest
### What changes were proposed in this pull request? This PR aims to annotate `HiveExternalCatalogVersionsSuite` with `ExtendedHiveTest`. ### Why are the changes needed? `HiveExternalCatalogVersionsSuite` is an outstanding test in terms of testing time. This PR aims to allow skipping this test suite when we use `ExtendedHiveTest`.  ### Does this PR introduce any user-facing change? No ### How was this patch tested? Since Jenkins doesn't exclude `ExtendedHiveTest`, there is no difference in Jenkins testing. This PR should be tested by manually by the following. **BEFORE** ``` $ cd sql/hive $ mvn package -Dtest=none -DwildcardSuites=org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite -Dtest.exclude.tags=org.apache.spark.tags.ExtendedHiveTest ... Run starting. Expected test count is: 1 HiveExternalCatalogVersionsSuite: 22:32:16.218 WARN org.apache.hadoop.util.NativeCodeLoader: Unable to load ... ``` **AFTER** ``` $ cd sql/hive $ mvn package -Dtest=none -DwildcardSuites=org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite -Dtest.exclude.tags=org.apache.spark.tags.ExtendedHiveTest ... Run starting. Expected test count is: 0 HiveExternalCatalogVersionsSuite: Run completed in 772 milliseconds. Total number of tests run: 0 Suites: completed 2, aborted 0 Tests: succeeded 0, failed 0, canceled 0, ignored 0, pending 0 No tests were executed. ... ``` Closes #25550 from dongjoon-hyun/SPARK-28847. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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triplesheep
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48578a41b5 |
[SPARK-28844][SQL] Fix typo in SQLConf FILE_COMRESSION_FACTOR
### What changes were proposed in this pull request? Fix minor typo in SQLConf. `FILE_COMRESSION_FACTOR` -> `FILE_COMPRESSION_FACTOR` ### Why are the changes needed? Make conf more understandable. ### Does this PR introduce any user-facing change? No. (`spark.sql.sources.fileCompressionFactor` is unchanged.) ### How was this patch tested? Pass the Jenkins with the existing tests. Closes #25538 from triplesheep/TYPO-FIX. Authored-by: triplesheep <triplesheep0419@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Sean Owen
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9ea37b09cf |
[SPARK-17875][CORE][BUILD] Remove dependency on Netty 3
### What changes were proposed in this pull request? Spark uses Netty 4 directly, but also includes Netty 3 only because transitive dependencies do. The dependencies (Hadoop HDFS, Zookeeper, Avro) don't seem to need this dependency as used in Spark. I think we can forcibly remove it to slim down the dependencies. Previous attempts were blocked by its usage in Flume, but that dependency has gone away. https://github.com/apache/spark/pull/15436 ### Why are the changes needed? Mostly to reduce the transitive dependency size and complexity a little bit and avoid triggering spurious security alerts on Netty 3.x usage. ### Does this PR introduce any user-facing change? No ### How was this patch tested? Existing tests Closes #25544 from srowen/SPARK-17875. Authored-by: Sean Owen <sean.owen@databricks.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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maryannxue
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aefb2e70e7 |
[SPARK-28739][SQL] Add a simple cost check for Adaptive Query Execution
### What changes were proposed in this pull request? This PR adds a simple cost model and a mechanism to compare the costs of the before and after plans of each re-optimization in Adaptive Query Execution. Now the workflow of AQE re-optimization is changed to: If the cost of the plan after re-optimization is lower than or equal to that of the plan before re-optimization and the plan has been changed after re-optimization (if equal), the current physical plan will be updated to the plan after re-optimization, otherwise it will remain unchanged until the next re-optimization. ### Why are the changes needed? This new mechanism is to prevent regressions in Adaptive Query Execution caused by change of the plan introducing extra cost, in this PR specifically, change of SMJ to BHJ leading to extra `ShuffleExchangeExec`s. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Added UT. Closes #25456 from maryannxue/aqe-cost. Authored-by: maryannxue <maryannxue@apache.org> Signed-off-by: gatorsmile <gatorsmile@gmail.com> |
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Wenchen Fan
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ed3ea6734c |
[SPARK-28837][SQL] CTAS/RTAS should use nullable schema
<!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. --> ### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> When running CTAS/RTAS, use the nullable schema of the input query to create the table. ### Why are the changes needed? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> It's very likely to run CTAS/RTAS with non-nullable input query, e.g. `CREATE TABLE t AS SELECT 1`. However, it's surprising to users if they can't write null to this table later. Non-nullable is kind of a constraint of the column and should be specified by users explicitly. For reference, Postgres also use nullable schema for CTAS: ``` > create table t1(i int not null); > insert into t1 values (1); > create table t2 as select i from t1; > \d+ t1; Column | Type | Collation | Nullable | Default | Storage | Stats target | Description --------+---------+-----------+----------+---------+---------+--------------+------------- i | integer | | not null | | plain | | > \d+ t2; Column | Type | Collation | Nullable | Default | Storage | Stats target | Description --------+---------+-----------+----------+---------+---------+--------------+------------- i | integer | | | | plain | | ``` File source V1 has the same behavior. ### Does this PR introduce any user-facing change? <!-- If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If no, write 'No'. --> Yes, after this PR CTAS/RTAS creates tables with nullable schema, then users can insert null values later. ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> new test Closes #25536 from cloud-fan/ctas. Authored-by: Wenchen Fan <wenchen@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Wenchen Fan
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97b046f06f |
[SPARK-28635][SQL][FOLLOWUP] CatalogManager should reflect the changes of default catalog
<!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. --> ### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> The current namespace/catalog should be set to None at the beginning, so that we can read the new configs when reporting currennt namespace/catalog later. ### Why are the changes needed? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> Fix a bug in CatalogManager, to reflect the change of default catalog config when reporting current catalog. ### Does this PR introduce any user-facing change? <!-- If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If no, write 'No'. --> No. The current namespace/catalog stuff is still internal right now. ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> a new test suite Closes #25521 from cloud-fan/fix. Authored-by: Wenchen Fan <wenchen@databricks.com> Signed-off-by: Burak Yavuz <brkyvz@gmail.com> |
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Yuanjian Li
|
2d9cc42aa8 |
[SPARK-28699][SQL] Disable using radix sort for ShuffleExchangeExec in repartition case
## What changes were proposed in this pull request? Disable using radix sort in ShuffleExchangeExec when we do repartition. In #20393, we fixed the indeterministic result in the shuffle repartition case by performing a local sort before repartitioning. But for the newly added sort operation, we use radix sort which is wrong because binary data can't be compared by only the prefix. This makes the sort unstable and fails to solve the indeterminate shuffle output problem. ### Why are the changes needed? Fix the correctness bug caused by repartition after a shuffle. ### Does this PR introduce any user-facing change? Yes, user will get the right result in the case of repartition stage rerun. ## How was this patch tested? Test with `local-cluster[5, 2, 5120]`, use the integrated test below, it can return a right answer 100000000. ``` import scala.sys.process._ import org.apache.spark.TaskContext val res = spark.range(0, 10000 * 10000, 1).map{ x => (x % 1000, x)} // kill an executor in the stage that performs repartition(239) val df = res.repartition(113).map{ x => (x._1 + 1, x._2)}.repartition(239).map { x => if (TaskContext.get.attemptNumber == 0 && TaskContext.get.partitionId < 1 && TaskContext.get.stageAttemptNumber == 0) { throw new Exception("pkill -f -n java".!!) } x } val r2 = df.distinct.count() ``` Closes #25491 from xuanyuanking/SPARK-28699-fix. Authored-by: Yuanjian Li <xyliyuanjian@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Ali Afroozeh
|
4dc3093513 |
[SPARK-28715][SQL] Introduce collectInPlanAndSubqueries and subqueriesAll in QueryPlan
## What changes were proposed in this pull request? Introduces the collectInPlanAndSubqueries and subqueriesAll methods in QueryPlan that consider all the plans in the query plan, including the ones in nested subqueries. ## How was this patch tested? Unit test added Closes #25433 from dbaliafroozeh/IntroduceCollectInPlanAndSubqueries. Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com> Signed-off-by: herman <herman@databricks.com> |
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zhengruifeng
|
bdef7125b7 |
[SPARK-28540][WEBUI] Document Environment page
## What changes were proposed in this pull request? Document Environment page ## How was this patch tested? locally building  Closes #25430 from zhengruifeng/doc_ui_conf. Authored-by: zhengruifeng <ruifengz@foxmail.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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zhengruifeng
|
49ffbff2fc |
[SPARK-28780][ML] Delete the incorrect setWeightCol method in LinearSVCModel
### What changes were proposed in this pull request? Delete the incorrect method `def setWeightCol(value: Double): this.type = set(threshold, value)` in `LinearSVCModel` ### Why are the changes needed? `LinearSVCModel` should not provide this setter, moreover, this method is wrongly defined. ### Does this PR introduce any user-facing change? yes, a public method is removed ### How was this patch tested? existing suites Closes #25510 from zhengruifeng/linearsvc_model_set_weightcol. Authored-by: zhengruifeng <ruifengz@foxmail.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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WeichenXu
|
9779a82ea0 |
[SPARK-28483][CORE][FOLLOW-UP] Dealing with interrupted exception in BarrierTaskContext.barrier()
<!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. --> ### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> Dealing with interrupted exception in BarrierTaskContext.barrier() ### Why are the changes needed? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> Interrupted exception will happen in the case sparkContext local property "spark.job.interruptOnCancel" set true. ### Does this PR introduce any user-facing change? <!-- If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If no, write 'No'. --> No. ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> UT. Closes #25519 from WeichenXu123/barrier_fl. Authored-by: WeichenXu <weichen.xu@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Robert (Bobby) Evans
|
fac469e2e0 |
[SPARK-28774][SQL] Fix exchange reuse for columnar data
### What changes were proposed in this pull request? The rule ReuseExchange optimization rule will look for instances of Exchange that have the same plan and convert dedupe them to them to a ReuseExchangeExec instance. In the current Spark codebase all Exchange instances are row based, but if we use the spark.sql.extensions config to put in our own columnar based exchange implementation reuse will throw an exception saying that there was a columnar mismatch. ### Why are the changes needed? Without it Reused Columnar Exchanges throw an exception ### Does this PR introduce any user-facing change? No ### How was this patch tested? I tested this patch by running it against a query that was showing this exact issue and it fixed it. I also added a very simple unit test that shows the issue. Closes #25499 from revans2/reused-columnar-exchange. Authored-by: Robert (Bobby) Evans <bobby@apache.org> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Burak Yavuz
|
4855bfe16b |
[SPARK-28554][SQL] Adds a v1 fallback writer implementation for v2 data source codepaths
## What changes were proposed in this pull request? This PR adds a V1 fallback interface for writing to V2 Tables using V1 Writer interfaces. The only supported SaveMode that will be called on the target table will be an Append. The target table must use V2 interfaces such as `SupportsOverwrite` or `SupportsTruncate` to support Overwrite operations. It is up to the target DataSource implementation if this operation can be atomic or not. We do not support dynamicPartitionOverwrite, as we cannot call a `commit` method that actually cleans up the data in the partitions that were touched through this fallback. ## How was this patch tested? Will add tests and example implementation after comments + feedback. This is a proposal at this point. Closes #25348 from brkyvz/v1WriteFallback. Lead-authored-by: Burak Yavuz <brkyvz@gmail.com> Co-authored-by: Burak Yavuz <burak@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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zhengruifeng
|
c4257b18a1 |
[SPARK-28541][WEBUI] Document Storage page
## What changes were proposed in this pull request? add an example for storage tab ## How was this patch tested? locally building Closes #25445 from zhengruifeng/doc_ui_storage. Authored-by: zhengruifeng <ruifengz@foxmail.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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Marco Gaido
|
0bfcf9c210 |
[SPARK-28322][SQL] Add support to Decimal type for integral divide
## What changes were proposed in this pull request? The expression `IntegralDivide`, which corresponds to the `div` operator, support only integral type. Postgres, though, allows it to work also with decimals. The PR adds the support to decimal operands for this operation in order to have feature parity with postgres. ## How was this patch tested? added UTs Closes #25136 from mgaido91/SPARK-28322. Authored-by: Marco Gaido <marcogaido91@gmail.com> Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org> |
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Dhruve Ashar
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a50959a7f6 |
[SPARK-27937][CORE] Revert partial logic for auto namespace discovery
## What changes were proposed in this pull request? This change reverts the logic which was introduced as a part of SPARK-24149 and a subsequent followup PR. With existing logic: - Spark fails to launch with HDFS federation enabled while trying to get a path to a logical nameservice. - It gets tokens for unrelated namespaces if they are used in HDFS Federation - Automatic namespace discovery is supported only if these are on the same cluster. Rationale for change: - For accessing data from related namespaces, viewfs should handle getting tokens for spark - For accessing data from unrelated namespaces(user explicitly specifies them using existing configs) as these could be on the same or different cluster. (Please fill in changes proposed in this fix) Revert the changes. ## How was this patch tested? Ran few manual tests and unit test. Closes #24785 from dhruve/bug/SPARK-27937. Authored-by: Dhruve Ashar <dhruveashar@gmail.com> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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maryannxue
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39c11273e0 |
[SPARK-28753][SQL] Dynamically reuse subqueries in AQE
### What changes were proposed in this pull request? This PR changes subquery reuse in Adaptive Query Execution from compile-time static reuse to execution-time dynamic reuse. This PR adds a `ReuseAdaptiveSubquery` rule that applies to a query stage after it is created and before it is executed. The new dynamic reuse enables subqueries to be reused across all different subquery levels. ### Why are the changes needed? This is an improvement to the current subquery reuse in Adaptive Query Execution, which allows subquery reuse to happen in a lazy fashion as well as at different subquery levels. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Passed existing tests. Closes #25471 from maryannxue/aqe-dynamic-sub-reuse. Authored-by: maryannxue <maryannxue@apache.org> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Wenchen Fan
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d04522187a |
[SPARK-28635][SQL] create CatalogManager to track registered v2 catalogs
## What changes were proposed in this pull request? This is a pure refactor PR, which creates a new class `CatalogManager` to track the registered v2 catalogs, and provide the catalog up functionality. `CatalogManager` also tracks the current catalog/namespace. We will implement corresponding commands in other PRs, like `USE CATALOG my_catalog` ## How was this patch tested? existing tests Closes #25368 from cloud-fan/refactor. Authored-by: Wenchen Fan <wenchen@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Jungtaek Lim (HeartSaVioR)
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b37c8d5cea
|
[SPARK-28650][SS][DOC] Correct explanation of guarantee for ForeachWriter
# What changes were proposed in this pull request? This patch modifies the explanation of guarantee for ForeachWriter as it doesn't guarantee same output for `(partitionId, epochId)`. Refer the description of [SPARK-28650](https://issues.apache.org/jira/browse/SPARK-28650) for more details. Spark itself still guarantees same output for same epochId (batch) if the preconditions are met, 1) source is always providing the same input records for same offset request. 2) the query is idempotent in overall (indeterministic calculation like now(), random() can break this). Assuming breaking preconditions as an exceptional case (the preconditions are implicitly required even before), we still can describe the guarantee with `epochId`, though it will be harder to leverage the guarantee: 1) ForeachWriter should implement a feature to track whether all the partitions are written successfully for given `epochId` 2) There's pretty less chance to leverage the fact, as the chance for Spark to successfully write all partitions and fail to checkpoint the batch is small. Credit to zsxwing on discovering the broken guarantee. ## How was this patch tested? This is just a documentation change, both on javadoc and guide doc. Closes #25407 from HeartSaVioR/SPARK-28650. Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com> Signed-off-by: Shixiong Zhu <zsxwing@gmail.com> |
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lihao
|
79464bed2f |
[SPARK-28662][SQL] Create Hive Partitioned Table DDL should fail when partition column type missed
## What changes were proposed in this pull request? Create Hive Partitioned Table without specifying data type for partition column will success unexpectedly. ```HiveQL // create a hive table partition by b, but the data type of b isn't specified. CREATE TABLE tbl(a int) PARTITIONED BY (b) STORED AS parquet ``` In https://issues.apache.org/jira/browse/SPARK-26435 , PARTITIONED BY clause are extended to support Hive CTAS as following: ```ANTLR // Before (PARTITIONED BY '(' partitionColumns=colTypeList ')' // After (PARTITIONED BY '(' partitionColumns=colTypeList ')'| PARTITIONED BY partitionColumnNames=identifierList) | ``` Create Table Statement like above case will pass the syntax check, and recognized as (PARTITIONED BY partitionColumnNames=identifierList) 。 This PR will check this case in visitCreateHiveTable and throw a exception which contains explicit error message to user. ## How was this patch tested? Added tests. Closes #25390 from lidinghao/hive-ddl-fix. Authored-by: lihao <lihaowhu@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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WeichenXu
|
bc75ed675b |
[SPARK-28483][CORE] Fix canceling a spark job using barrier mode but barrier tasks blocking on BarrierTaskContext.barrier()
## What changes were proposed in this pull request?
Fix canceling a spark job using barrier mode but barrier tasks do not exit.
Currently, when spark tasks are killed, `BarrierTaskContext.barrier()` cannot be killed (it will blocking on RPC request), cause the task blocking and cannot exit.
In my PR I implement an interface for RPC which support `abort` in class `RpcEndpointRef`
```
def askAbortable[T: ClassTag](
message: Any,
timeout: RpcTimeout): AbortableRpcFuture[T]
```
The returned `AbortableRpcFuture` instance include an `abort` method so that we can abort the RPC before it timeout.
## How was this patch tested?
Unit test added.
Manually test:
### Test code
launch spark-shell via `spark-shell --master local[4]`
and run following code:
```
sc.setLogLevel("INFO")
import org.apache.spark.BarrierTaskContext
val n = 4
def taskf(iter: Iterator[Int]): Iterator[Int] = {
val context = BarrierTaskContext.get()
val x = iter.next()
if (x % 2 == 0) {
// sleep 6000000 seconds with task killed checking
for (i <- 0 until 6000000) {
Thread.sleep(1000)
if (context.isInterrupted()) {
throw new org.apache.spark.TaskKilledException()
}
}
}
context.barrier()
return Iterator.empty
}
// launch spark job, including 4 tasks, tasks 1/3 will enter `barrier()`, and tasks 0/2 will enter `sleep`
sc.parallelize((0 to n), n).barrier().mapPartitions(taskf).collect()
```
And then press Ctrl+C to exit the running job.
### Before
press Ctrl+C to exit the running job, then open spark UI we can see 2 tasks (task 1/3) are not killed. They are blocking.
### After
press Ctrl+C to exit the running job, we can see in spark UI all tasks killed successfully.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes #25235 from WeichenXu123/sc_14848.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
|
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Yuanjian Li
|
0d3a783cc5 |
[SPARK-28699][CORE] Fix a corner case for aborting indeterminate stage
### What changes were proposed in this pull request?
Change the logic of collecting the indeterminate stage, we should look at stages from mapStage, not failedStage during handle FetchFailed.
### Why are the changes needed?
In the fetch failed error handle logic, the original logic of collecting indeterminate stage from the fetch failed stage. And in the scenario of the fetch failed happened in the first task of this stage, this logic will cause the indeterminate stage to resubmit partially. Eventually, we are capable of getting correctness bug.
### Does this PR introduce any user-facing change?
It makes the corner case of indeterminate stage abort as expected.
### How was this patch tested?
New UT in DAGSchedulerSuite.
Run below integrated test with `local-cluster[5, 2, 5120]`, and set `spark.sql.execution.sortBeforeRepartition`=false, it will abort the indeterminate stage as expected:
```
import scala.sys.process._
import org.apache.spark.TaskContext
val res = spark.range(0, 10000 * 10000, 1).map{ x => (x % 1000, x)}
// kill an executor in the stage that performs repartition(239)
val df = res.repartition(113).map{ x => (x._1 + 1, x._2)}.repartition(239).map { x =>
if (TaskContext.get.attemptNumber == 0 && TaskContext.get.partitionId < 1 && TaskContext.get.stageAttemptNumber == 0) {
throw new Exception("pkill -f -n java".!!)
}
x
}
val r2 = df.distinct.count()
```
Closes #25498 from xuanyuanking/SPARK-28699-followup.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
|
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darrentirto
|
a787bc2884 |
[SPARK-28777][PYTHON][DOCS] Fix format_string doc string with the correct parameters
### What changes were proposed in this pull request? The parameters doc string of the function format_string was changed from _col_, _d_ to _format_, _cols_ which is what the actual function declaration states ### Why are the changes needed? The parameters stated by the documentation was inaccurate ### Does this PR introduce any user-facing change? Yes. **BEFORE**  **AFTER**  ### How was this patch tested? N/A: documentation only <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> Closes #25506 from darrentirto/SPARK-28777. Authored-by: darrentirto <darrentirto@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Sean Owen
|
3b4e345fa1 |
[SPARK-28775][CORE][TESTS] Skip date 8633 in Kwajalein due to changes in tzdata2018i that only some JDK 8s use
### What changes were proposed in this pull request? Some newer JDKs use the tzdata2018i database, which changes how certain (obscure) historical dates and timezones are handled. As previously, we can pretty much safely ignore these in tests, as the value may vary by JDK. ### Why are the changes needed? Test otherwise fails using, for example, JDK 1.8.0_222. https://bugs.openjdk.java.net/browse/JDK-8215982 has a full list of JDKs which has this. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Existing tests Closes #25504 from srowen/SPARK-28775. Authored-by: Sean Owen <sean.owen@databricks.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Mick Jermsurawong
|
b79cf0d143 |
[SPARK-28224][SQL] Check overflow in decimal Sum aggregate
## What changes were proposed in this pull request? - Currently `sum` in aggregates for decimal type can overflow and return null. - `Sum` expression codegens arithmetic on `sql.Decimal` and the output which preserves scale and precision goes into `UnsafeRowWriter`. Here overflowing will be converted to null when writing out. - It also does not go through this branch in `DecimalAggregates` because it's expecting precision of the sum (not the elements to be summed) to be less than 5. |
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Takuya UESHIN
|
26f344354b |
[SPARK-27905][SQL][FOLLOW-UP] Add prettyNames
### What changes were proposed in this pull request? This is a follow-up of #24761 which added a higher-order function `ArrayForAll`. The PR mistakenly removed the `prettyName` from `ArrayExists` and forgot to add it to `ArrayForAll`. ### Why are the changes needed? This reverts the `prettyName` back to `ArrayExists` not to affect explained plans, and adds it to `ArrayForAll` to clarify the `prettyName` as the same as the expressions around. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Existing tests. Closes #25501 from ueshin/issues/SPARK-27905/pretty_names. Authored-by: Takuya UESHIN <ueshin@databricks.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Sean Owen
|
fa7fd8f2a4 |
[SPARK-28434][TESTS][ML] Fix values in dummy tree in DecisionTreeSuite
### What changes were proposed in this pull request? Fix dummy tree created in decision tree tests to have actually consistent stats, so that it can be compared in tests more completely. The current one has values for, say, impurity that don't even match internally. With this, the tests can assert more about stats staying correct after load. ### Why are the changes needed? Fixes a TODO and improves the test slightly. ### Does this PR introduce any user-facing change? None ### How was this patch tested? Existing tests. Closes #25485 from srowen/SPARK-28434. Authored-by: Sean Owen <sean.owen@databricks.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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Marcelo Vanzin
|
5f6eb5d20d |
[SPARK-28634][YARN] Ignore kerberos login config in client mode AM
This change makes the client mode AM ignore any login configuration, which is now always handled by the driver. The previous code tried to achieve that by modifying the configuration visible to the AM, but that missed the case where old configuration names were being used. Tested in real cluster with reproduction provided in the bug. Closes #25467 from vanzin/SPARK-28634. Authored-by: Marcelo Vanzin <vanzin@cloudera.com> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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HyukjinKwon
|
1de4a22c52 |
Revert "[SPARK-28759][BUILD] Upgrade scala-maven-plugin to 4.1.1"
This reverts commit
|
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|
HyukjinKwon
|
2fd83c2820 |
[SPARK-28756][R][FOLLOW-UP] Specify minimum and maximum Java versions
<!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. --> ### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> This PR proposes to set minimum and maximum Java version specification. (see https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Writing-portable-packages). Seems there is not the standard way to specify both given the documentation and other packages (see https://gist.github.com/glin/bd36cf1eb0c7f8b1f511e70e2fb20f8d). I found two ways from existing packages on CRAN. ``` Package (<= 1 & > 2) Package (<= 1, > 2) ``` The latter seems closer to other standard notations such as `R (>= 2.14.0), R (>= r56550)`. So I have chosen the latter way. ### Why are the changes needed? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> Seems the package might be rejected by CRAN. See https://github.com/apache/spark/pull/25472#issuecomment-522405742 ### Does this PR introduce any user-facing change? <!-- If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If no, write 'No'. --> No. ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> JDK 8 ```bash ./build/mvn -DskipTests -Psparkr clean package ./R/run-tests.sh ... basic tests for CRAN: ............. ... ``` JDK 11 ```bash ./build/mvn -DskipTests -Psparkr -Phadoop-3.2 clean package ./R/run-tests.sh ... basic tests for CRAN: ............. ... ``` Closes #25490 from HyukjinKwon/SPARK-28756. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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Huaxin Gao
|
ec14b6eb65 |
[SPARK-28393][SQL][PYTHON][TESTS] Convert and port 'pgSQL/join.sql' into UDF test base
## What changes were proposed in this pull request? This PR adds some tests converted from ```pgSQL/join.sql``` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921). <details><summary>Diff comparing to 'join.sql'</summary> <p> ```diff diff --git a/sql/core/src/test/resources/sql-tests/results/pgSQL/join.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-join.sql.out index f75fe05196..ad2b5dd0db 100644 --- a/sql/core/src/test/resources/sql-tests/results/pgSQL/join.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-join.sql.out -240,10 +240,10 struct<> -- !query 27 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(i), udf(j), udf(t) FROM J1_TBL AS tx -- !query 27 schema -struct<xxx:string,i:int,j:int,t:string> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string> -- !query 27 output 0 NULL zero 1 4 one -259,10 +259,10 struct<xxx:string,i:int,j:int,t:string> -- !query 28 -SELECT '' AS `xxx`, * +SELECT udf(udf('')) AS `xxx`, udf(udf(i)), udf(j), udf(t) FROM J1_TBL tx -- !query 28 schema -struct<xxx:string,i:int,j:int,t:string> +struct<xxx:string,CAST(udf(cast(cast(udf(cast(i as string)) as int) as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string> -- !query 28 output 0 NULL zero 1 4 one -278,10 +278,10 struct<xxx:string,i:int,j:int,t:string> -- !query 29 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, a, udf(udf(b)), c FROM J1_TBL AS t1 (a, b, c) -- !query 29 schema -struct<xxx:string,a:int,b:int,c:string> +struct<xxx:string,a:int,CAST(udf(cast(cast(udf(cast(b as string)) as int) as string)) AS INT):int,c:string> -- !query 29 output 0 NULL zero 1 4 one -297,10 +297,10 struct<xxx:string,a:int,b:int,c:string> -- !query 30 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(a), udf(b), udf(udf(c)) FROM J1_TBL t1 (a, b, c) -- !query 30 schema -struct<xxx:string,a:int,b:int,c:string> +struct<xxx:string,CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(c as string)) as string) as string)) AS STRING):string> -- !query 30 output 0 NULL zero 1 4 one -316,10 +316,10 struct<xxx:string,a:int,b:int,c:string> -- !query 31 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(a), b, udf(c), udf(d), e FROM J1_TBL t1 (a, b, c), J2_TBL t2 (d, e) -- !query 31 schema -struct<xxx:string,a:int,b:int,c:string,d:int,e:int> +struct<xxx:string,CAST(udf(cast(a as string)) AS INT):int,b:int,CAST(udf(cast(c as string)) AS STRING):string,CAST(udf(cast(d as string)) AS INT):int,e:int> -- !query 31 output 0 NULL zero 0 NULL 0 NULL zero 1 -1 -423,7 +423,7 struct<xxx:string,a:int,b:int,c:string,d:int,e:int> -- !query 32 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, * FROM J1_TBL CROSS JOIN J2_TBL -- !query 32 schema struct<xxx:string,i:int,j:int,t:string,i:int,k:int> -530,20 +530,20 struct<xxx:string,i:int,j:int,t:string,i:int,k:int> -- !query 33 -SELECT '' AS `xxx`, i, k, t +SELECT udf('') AS `xxx`, udf(i) AS i, udf(k), udf(t) AS t FROM J1_TBL CROSS JOIN J2_TBL -- !query 33 schema struct<> -- !query 33 output org.apache.spark.sql.AnalysisException -Reference 'i' is ambiguous, could be: default.j1_tbl.i, default.j2_tbl.i.; line 1 pos 20 +Reference 'i' is ambiguous, could be: default.j1_tbl.i, default.j2_tbl.i.; line 1 pos 29 -- !query 34 -SELECT '' AS `xxx`, t1.i, k, t +SELECT udf('') AS `xxx`, udf(t1.i) AS i, udf(k), udf(t) FROM J1_TBL t1 CROSS JOIN J2_TBL t2 -- !query 34 schema -struct<xxx:string,i:int,k:int,t:string> +struct<xxx:string,i:int,CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string> -- !query 34 output 0 -1 zero 0 -3 zero -647,11 +647,11 struct<xxx:string,i:int,k:int,t:string> -- !query 35 -SELECT '' AS `xxx`, ii, tt, kk +SELECT udf(udf('')) AS `xxx`, udf(udf(ii)) AS ii, udf(udf(tt)) AS tt, udf(udf(kk)) FROM (J1_TBL CROSS JOIN J2_TBL) AS tx (ii, jj, tt, ii2, kk) -- !query 35 schema -struct<xxx:string,ii:int,tt:string,kk:int> +struct<xxx:string,ii:int,tt:string,CAST(udf(cast(cast(udf(cast(kk as string)) as int) as string)) AS INT):int> -- !query 35 output 0 zero -1 0 zero -3 -755,10 +755,10 struct<xxx:string,ii:int,tt:string,kk:int> -- !query 36 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(udf(j1_tbl.i)), udf(j), udf(t), udf(a.i), udf(a.k), udf(b.i), udf(b.k) FROM J1_TBL CROSS JOIN J2_TBL a CROSS JOIN J2_TBL b -- !query 36 schema -struct<xxx:string,i:int,j:int,t:string,i:int,k:int,i:int,k:int> +struct<xxx:string,CAST(udf(cast(cast(udf(cast(i as string)) as int) as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(k as string)) AS INT):int> -- !query 36 output 0 NULL zero 0 NULL 0 NULL 0 NULL zero 0 NULL 1 -1 -1654,10 +1654,10 struct<xxx:string,i:int,j:int,t:string,i:int,k:int,i:int,k:int> -- !query 37 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(i) AS i, udf(j), udf(t) AS t, udf(k) FROM J1_TBL INNER JOIN J2_TBL USING (i) -- !query 37 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,i:int,CAST(udf(cast(j as string)) AS INT):int,t:string,CAST(udf(cast(k as string)) AS INT):int> -- !query 37 output 0 NULL zero NULL 1 4 one -1 -1669,10 +1669,10 struct<xxx:string,i:int,j:int,t:string,k:int> -- !query 38 -SELECT '' AS `xxx`, * +SELECT udf(udf('')) AS `xxx`, udf(i), udf(j) AS j, udf(t), udf(k) AS k FROM J1_TBL JOIN J2_TBL USING (i) -- !query 38 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,j:int,CAST(udf(cast(t as string)) AS STRING):string,k:int> -- !query 38 output 0 NULL zero NULL 1 4 one -1 -1684,9 +1684,9 struct<xxx:string,i:int,j:int,t:string,k:int> -- !query 39 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, * FROM J1_TBL t1 (a, b, c) JOIN J2_TBL t2 (a, d) USING (a) - ORDER BY a, d + ORDER BY udf(udf(a)), udf(d) -- !query 39 schema struct<xxx:string,a:int,b:int,c:string,d:int> -- !query 39 output -1700,10 +1700,10 struct<xxx:string,a:int,b:int,c:string,d:int> -- !query 40 -SELECT '' AS `xxx`, * +SELECT udf(udf('')) AS `xxx`, udf(i), udf(j), udf(t), udf(k) FROM J1_TBL NATURAL JOIN J2_TBL -- !query 40 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int> -- !query 40 output 0 NULL zero NULL 1 4 one -1 -1715,10 +1715,10 struct<xxx:string,i:int,j:int,t:string,k:int> -- !query 41 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(udf(udf(a))) AS a, udf(b), udf(c), udf(d) FROM J1_TBL t1 (a, b, c) NATURAL JOIN J2_TBL t2 (a, d) -- !query 41 schema -struct<xxx:string,a:int,b:int,c:string,d:int> +struct<xxx:string,a:int,CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(c as string)) AS STRING):string,CAST(udf(cast(d as string)) AS INT):int> -- !query 41 output 0 NULL zero NULL 1 4 one -1 -1730,10 +1730,10 struct<xxx:string,a:int,b:int,c:string,d:int> -- !query 42 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(udf(a)), udf(udf(b)), udf(udf(c)) AS c, udf(udf(udf(d))) AS d FROM J1_TBL t1 (a, b, c) NATURAL JOIN J2_TBL t2 (d, a) -- !query 42 schema -struct<xxx:string,a:int,b:int,c:string,d:int> +struct<xxx:string,CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(b as string)) as int) as string)) AS INT):int,c:string,d:int> -- !query 42 output 0 NULL zero NULL 2 3 two 2 -1741,10 +1741,10 struct<xxx:string,a:int,b:int,c:string,d:int> -- !query 43 -SELECT '' AS `xxx`, * - FROM J1_TBL JOIN J2_TBL ON (J1_TBL.i = J2_TBL.i) +SELECT udf('') AS `xxx`, udf(J1_TBL.i), udf(udf(J1_TBL.j)), udf(J1_TBL.t), udf(J2_TBL.i), udf(J2_TBL.k) + FROM J1_TBL JOIN J2_TBL ON (udf(J1_TBL.i) = J2_TBL.i) -- !query 43 schema -struct<xxx:string,i:int,j:int,t:string,i:int,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(j as string)) as int) as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(k as string)) AS INT):int> -- !query 43 output 0 NULL zero 0 NULL 1 4 one 1 -1 -1756,10 +1756,10 struct<xxx:string,i:int,j:int,t:string,i:int,k:int> -- !query 44 -SELECT '' AS `xxx`, * - FROM J1_TBL JOIN J2_TBL ON (J1_TBL.i = J2_TBL.k) +SELECT udf('') AS `xxx`, udf(udf(J1_TBL.i)), udf(udf(J1_TBL.j)), udf(udf(J1_TBL.t)), J2_TBL.i, J2_TBL.k + FROM J1_TBL JOIN J2_TBL ON (J1_TBL.i = udf(J2_TBL.k)) -- !query 44 schema -struct<xxx:string,i:int,j:int,t:string,i:int,k:int> +struct<xxx:string,CAST(udf(cast(cast(udf(cast(i as string)) as int) as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(j as string)) as int) as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(t as string)) as string) as string)) AS STRING):string,i:int,k:int> -- !query 44 output 0 NULL zero NULL 0 2 3 two 2 2 -1767,10 +1767,10 struct<xxx:string,i:int,j:int,t:string,i:int,k:int> -- !query 45 -SELECT '' AS `xxx`, * - FROM J1_TBL JOIN J2_TBL ON (J1_TBL.i <= J2_TBL.k) +SELECT udf('') AS `xxx`, udf(J1_TBL.i), udf(J1_TBL.j), udf(J1_TBL.t), udf(J2_TBL.i), udf(J2_TBL.k) + FROM J1_TBL JOIN J2_TBL ON (udf(J1_TBL.i) <= udf(udf(J2_TBL.k))) -- !query 45 schema -struct<xxx:string,i:int,j:int,t:string,i:int,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(k as string)) AS INT):int> -- !query 45 output 0 NULL zero 2 2 0 NULL zero 2 4 -1784,11 +1784,11 struct<xxx:string,i:int,j:int,t:string,i:int,k:int> -- !query 46 -SELECT '' AS `xxx`, * +SELECT udf(udf('')) AS `xxx`, udf(i), udf(j), udf(t), udf(k) FROM J1_TBL LEFT OUTER JOIN J2_TBL USING (i) - ORDER BY i, k, t + ORDER BY udf(udf(i)), udf(k), udf(t) -- !query 46 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int> -- !query 46 output NULL NULL null NULL NULL 0 zero NULL -1806,11 +1806,11 struct<xxx:string,i:int,j:int,t:string,k:int> -- !query 47 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(i), udf(j), udf(t), udf(k) FROM J1_TBL LEFT JOIN J2_TBL USING (i) - ORDER BY i, k, t + ORDER BY udf(i), udf(udf(k)), udf(t) -- !query 47 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int> -- !query 47 output NULL NULL null NULL NULL 0 zero NULL -1828,10 +1828,10 struct<xxx:string,i:int,j:int,t:string,k:int> -- !query 48 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(udf(i)), udf(j), udf(t), udf(k) FROM J1_TBL RIGHT OUTER JOIN J2_TBL USING (i) -- !query 48 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,CAST(udf(cast(cast(udf(cast(i as string)) as int) as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int> -- !query 48 output 0 NULL zero NULL 1 4 one -1 -1845,10 +1845,10 struct<xxx:string,i:int,j:int,t:string,k:int> -- !query 49 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(i), udf(udf(j)), udf(t), udf(k) FROM J1_TBL RIGHT JOIN J2_TBL USING (i) -- !query 49 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(j as string)) as int) as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int> -- !query 49 output 0 NULL zero NULL 1 4 one -1 -1862,11 +1862,11 struct<xxx:string,i:int,j:int,t:string,k:int> -- !query 50 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(i), udf(j), udf(udf(t)), udf(k) FROM J1_TBL FULL OUTER JOIN J2_TBL USING (i) - ORDER BY i, k, t + ORDER BY udf(udf(i)), udf(k), udf(t) -- !query 50 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(t as string)) as string) as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int> -- !query 50 output NULL NULL NULL NULL NULL NULL null NULL -1886,11 +1886,11 struct<xxx:string,i:int,j:int,t:string,k:int> -- !query 51 -SELECT '' AS `xxx`, * +SELECT udf('') AS `xxx`, udf(i), udf(j), t, udf(udf(k)) FROM J1_TBL FULL JOIN J2_TBL USING (i) - ORDER BY i, k, t + ORDER BY udf(udf(i)), udf(k), udf(udf(t)) -- !query 51 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,t:string,CAST(udf(cast(cast(udf(cast(k as string)) as int) as string)) AS INT):int> -- !query 51 output NULL NULL NULL NULL NULL NULL null NULL -1910,19 +1910,19 struct<xxx:string,i:int,j:int,t:string,k:int> -- !query 52 -SELECT '' AS `xxx`, * - FROM J1_TBL LEFT JOIN J2_TBL USING (i) WHERE (k = 1) +SELECT udf('') AS `xxx`, udf(i), udf(j), udf(t), udf(udf(k)) + FROM J1_TBL LEFT JOIN J2_TBL USING (i) WHERE (udf(k) = 1) -- !query 52 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(cast(udf(cast(k as string)) as int) as string)) AS INT):int> -- !query 52 output -- !query 53 -SELECT '' AS `xxx`, * - FROM J1_TBL LEFT JOIN J2_TBL USING (i) WHERE (i = 1) +SELECT udf('') AS `xxx`, udf(i), udf(j), udf(t), udf(k) + FROM J1_TBL LEFT JOIN J2_TBL USING (i) WHERE (udf(udf(i)) = udf(1)) -- !query 53 schema -struct<xxx:string,i:int,j:int,t:string,k:int> +struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int> -- !query 53 output 1 4 one -1 -2020,9 +2020,9 ee NULL 42 NULL -- !query 65 SELECT * FROM -(SELECT * FROM t2) as s2 +(SELECT udf(name) as name, t2.n FROM t2) as s2 INNER JOIN -(SELECT * FROM t3) s3 +(SELECT udf(udf(name)) as name, t3.n FROM t3) s3 USING (name) -- !query 65 schema struct<name:string,n:int,n:int> -2033,9 +2033,9 cc 22 23 -- !query 66 SELECT * FROM -(SELECT * FROM t2) as s2 +(SELECT udf(udf(name)) as name, t2.n FROM t2) as s2 LEFT JOIN -(SELECT * FROM t3) s3 +(SELECT udf(name) as name, t3.n FROM t3) s3 USING (name) -- !query 66 schema struct<name:string,n:int,n:int> -2046,13 +2046,13 ee 42 NULL -- !query 67 -SELECT * FROM +SELECT udf(name), udf(udf(s2.n)), udf(s3.n) FROM (SELECT * FROM t2) as s2 FULL JOIN (SELECT * FROM t3) s3 USING (name) -- !query 67 schema -struct<name:string,n:int,n:int> +struct<CAST(udf(cast(name as string)) AS STRING):string,CAST(udf(cast(cast(udf(cast(n as string)) as int) as string)) AS INT):int,CAST(udf(cast(n as string)) AS INT):int> -- !query 67 output bb 12 13 cc 22 23 -2062,9 +2062,9 ee 42 NULL -- !query 68 SELECT * FROM -(SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2 +(SELECT udf(udf(name)) as name, udf(n) as s2_n, udf(2) as s2_2 FROM t2) as s2 NATURAL INNER JOIN -(SELECT name, n as s3_n, 3 as s3_2 FROM t3) s3 +(SELECT udf(name) as name, udf(udf(n)) as s3_n, udf(3) as s3_2 FROM t3) s3 -- !query 68 schema struct<name:string,s2_n:int,s2_2:int,s3_n:int,s3_2:int> -- !query 68 output -2074,9 +2074,9 cc 22 2 23 3 -- !query 69 SELECT * FROM -(SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2 +(SELECT udf(name) as name, udf(udf(n)) as s2_n, 2 as s2_2 FROM t2) as s2 NATURAL LEFT JOIN -(SELECT name, n as s3_n, 3 as s3_2 FROM t3) s3 +(SELECT udf(udf(name)) as name, udf(n) as s3_n, 3 as s3_2 FROM t3) s3 -- !query 69 schema struct<name:string,s2_n:int,s2_2:int,s3_n:int,s3_2:int> -- !query 69 output -2087,9 +2087,9 ee 42 2 NULL NULL -- !query 70 SELECT * FROM -(SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2 +(SELECT udf(name) as name, udf(n) as s2_n, 2 as s2_2 FROM t2) as s2 NATURAL FULL JOIN -(SELECT name, n as s3_n, 3 as s3_2 FROM t3) s3 +(SELECT udf(udf(name)) as name, udf(udf(n)) as s3_n, 3 as s3_2 FROM t3) s3 -- !query 70 schema struct<name:string,s2_n:int,s2_2:int,s3_n:int,s3_2:int> -- !query 70 output -2101,11 +2101,11 ee 42 2 NULL NULL -- !query 71 SELECT * FROM -(SELECT name, n as s1_n, 1 as s1_1 FROM t1) as s1 +(SELECT udf(udf(name)) as name, udf(n) as s1_n, 1 as s1_1 FROM t1) as s1 NATURAL INNER JOIN -(SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2 +(SELECT udf(name) as name, udf(n) as s2_n, 2 as s2_2 FROM t2) as s2 NATURAL INNER JOIN -(SELECT name, n as s3_n, 3 as s3_2 FROM t3) s3 +(SELECT udf(udf(udf(name))) as name, udf(n) as s3_n, 3 as s3_2 FROM t3) s3 -- !query 71 schema struct<name:string,s1_n:int,s1_1:int,s2_n:int,s2_2:int,s3_n:int,s3_2:int> -- !query 71 output -2114,11 +2114,11 bb 11 1 12 2 13 3 -- !query 72 SELECT * FROM -(SELECT name, n as s1_n, 1 as s1_1 FROM t1) as s1 +(SELECT udf(name) as name, udf(n) as s1_n, udf(udf(1)) as s1_1 FROM t1) as s1 NATURAL FULL JOIN -(SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2 +(SELECT udf(name) as name, udf(udf(n)) as s2_n, udf(2) as s2_2 FROM t2) as s2 NATURAL FULL JOIN -(SELECT name, n as s3_n, 3 as s3_2 FROM t3) s3 +(SELECT udf(udf(name)) as name, udf(n) as s3_n, udf(3) as s3_2 FROM t3) s3 -- !query 72 schema struct<name:string,s1_n:int,s1_1:int,s2_n:int,s2_2:int,s3_n:int,s3_2:int> -- !query 72 output -2129,16 +2129,16 ee NULL NULL 42 2 NULL NULL -- !query 73 -SELECT * FROM -(SELECT name, n as s1_n FROM t1) as s1 +SELECT name, udf(udf(s1_n)), udf(s2_n), udf(s3_n) FROM +(SELECT name, udf(udf(n)) as s1_n FROM t1) as s1 NATURAL FULL JOIN (SELECT * FROM - (SELECT name, n as s2_n FROM t2) as s2 + (SELECT name, udf(n) as s2_n FROM t2) as s2 NATURAL FULL JOIN - (SELECT name, n as s3_n FROM t3) as s3 + (SELECT name, udf(udf(n)) as s3_n FROM t3) as s3 ) ss2 -- !query 73 schema -struct<name:string,s1_n:int,s2_n:int,s3_n:int> +struct<name:string,CAST(udf(cast(cast(udf(cast(s1_n as string)) as int) as string)) AS INT):int,CAST(udf(cast(s2_n as string)) AS INT):int,CAST(udf(cast(s3_n as string)) AS INT):int> -- !query 73 output bb 11 12 13 cc NULL 22 23 -2151,9 +2151,9 SELECT * FROM (SELECT name, n as s1_n FROM t1) as s1 NATURAL FULL JOIN (SELECT * FROM - (SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2 + (SELECT name, udf(udf(n)) as s2_n, 2 as s2_2 FROM t2) as s2 NATURAL FULL JOIN - (SELECT name, n as s3_n FROM t3) as s3 + (SELECT name, udf(n) as s3_n FROM t3) as s3 ) ss2 -- !query 74 schema struct<name:string,s1_n:int,s2_n:int,s2_2:int,s3_n:int> -2165,13 +2165,13 ee NULL 42 2 NULL -- !query 75 -SELECT * FROM - (SELECT name, n as s1_n FROM t1) as s1 +SELECT s1.name, udf(s1_n), s2.name, udf(udf(s2_n)) FROM + (SELECT name, udf(n) as s1_n FROM t1) as s1 FULL JOIN (SELECT name, 2 as s2_n FROM t2) as s2 -ON (s1_n = s2_n) +ON (udf(udf(s1_n)) = udf(s2_n)) -- !query 75 schema -struct<name:string,s1_n:int,name:string,s2_n:int> +struct<name:string,CAST(udf(cast(s1_n as string)) AS INT):int,name:string,CAST(udf(cast(cast(udf(cast(s2_n as string)) as int) as string)) AS INT):int> -- !query 75 output NULL NULL bb 2 NULL NULL cc 2 -2200,9 +2200,9 struct<> -- !query 78 -select * from x +select udf(udf(x1)), udf(x2) from x -- !query 78 schema -struct<x1:int,x2:int> +struct<CAST(udf(cast(cast(udf(cast(x1 as string)) as int) as string)) AS INT):int,CAST(udf(cast(x2 as string)) AS INT):int> -- !query 78 output 1 11 2 22 -2212,9 +2212,9 struct<x1:int,x2:int> -- !query 79 -select * from y +select udf(y1), udf(udf(y2)) from y -- !query 79 schema -struct<y1:int,y2:int> +struct<CAST(udf(cast(y1 as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(y2 as string)) as int) as string)) AS INT):int> -- !query 79 output 1 111 2 222 -2223,7 +2223,7 struct<y1:int,y2:int> -- !query 80 -select * from x left join y on (x1 = y1 and x2 is not null) +select * from x left join y on (udf(x1) = udf(udf(y1)) and udf(x2) is not null) -- !query 80 schema struct<x1:int,x2:int,y1:int,y2:int> -- !query 80 output -2235,7 +2235,7 struct<x1:int,x2:int,y1:int,y2:int> -- !query 81 -select * from x left join y on (x1 = y1 and y2 is not null) +select * from x left join y on (udf(udf(x1)) = udf(y1) and udf(y2) is not null) -- !query 81 schema struct<x1:int,x2:int,y1:int,y2:int> -- !query 81 output -2247,8 +2247,8 struct<x1:int,x2:int,y1:int,y2:int> -- !query 82 -select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2) -on (x1 = xx1) +select * from (x left join y on (udf(x1) = udf(udf(y1)))) left join x xx(xx1,xx2) +on (udf(udf(x1)) = udf(xx1)) -- !query 82 schema struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 82 output -2260,8 +2260,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 83 -select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2) -on (x1 = xx1 and x2 is not null) +select * from (x left join y on (udf(x1) = udf(y1))) left join x xx(xx1,xx2) +on (udf(x1) = xx1 and udf(x2) is not null) -- !query 83 schema struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 83 output -2273,8 +2273,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 84 -select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2) -on (x1 = xx1 and y2 is not null) +select * from (x left join y on (x1 = udf(y1))) left join x xx(xx1,xx2) +on (udf(x1) = udf(udf(xx1)) and udf(y2) is not null) -- !query 84 schema struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 84 output -2286,8 +2286,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 85 -select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2) -on (x1 = xx1 and xx2 is not null) +select * from (x left join y on (udf(x1) = y1)) left join x xx(xx1,xx2) +on (udf(udf(x1)) = udf(xx1) and udf(udf(xx2)) is not null) -- !query 85 schema struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 85 output -2299,8 +2299,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 86 -select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2) -on (x1 = xx1) where (x2 is not null) +select * from (x left join y on (udf(udf(x1)) = udf(udf(y1)))) left join x xx(xx1,xx2) +on (udf(x1) = udf(xx1)) where (udf(x2) is not null) -- !query 86 schema struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 86 output -2310,8 +2310,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 87 -select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2) -on (x1 = xx1) where (y2 is not null) +select * from (x left join y on (udf(x1) = udf(y1))) left join x xx(xx1,xx2) +on (udf(x1) = xx1) where (udf(y2) is not null) -- !query 87 schema struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 87 output -2321,8 +2321,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 88 -select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2) -on (x1 = xx1) where (xx2 is not null) +select * from (x left join y on (udf(x1) = udf(y1))) left join x xx(xx1,xx2) +on (x1 = udf(xx1)) where (xx2 is not null) -- !query 88 schema struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 88 output -2332,75 +2332,75 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int> -- !query 89 -select count(*) from tenk1 a where unique1 in - (select unique1 from tenk1 b join tenk1 c using (unique1) - where b.unique2 = 42) +select udf(udf(count(*))) from tenk1 a where udf(udf(unique1)) in + (select udf(unique1) from tenk1 b join tenk1 c using (unique1) + where udf(udf(b.unique2)) = udf(42)) -- !query 89 schema -struct<count(1):bigint> +struct<CAST(udf(cast(cast(udf(cast(count(1) as string)) as bigint) as string)) AS BIGINT):bigint> -- !query 89 output 1 -- !query 90 -select count(*) from tenk1 x where - x.unique1 in (select a.f1 from int4_tbl a,float8_tbl b where a.f1=b.f1) and - x.unique1 = 0 and - x.unique1 in (select aa.f1 from int4_tbl aa,float8_tbl bb where aa.f1=bb.f1) +select udf(count(*)) from tenk1 x where + udf(x.unique1) in (select udf(a.f1) from int4_tbl a,float8_tbl b where udf(udf(a.f1))=b.f1) and + udf(x.unique1) = 0 and + udf(x.unique1) in (select aa.f1 from int4_tbl aa,float8_tbl bb where aa.f1=udf(udf(bb.f1))) -- !query 90 schema -struct<count(1):bigint> +struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint> -- !query 90 output 1 -- !query 91 -select count(*) from tenk1 x where - x.unique1 in (select a.f1 from int4_tbl a,float8_tbl b where a.f1=b.f1) and - x.unique1 = 0 and - x.unique1 in (select aa.f1 from int4_tbl aa,float8_tbl bb where aa.f1=bb.f1) +select udf(udf(count(*))) from tenk1 x where + udf(x.unique1) in (select udf(a.f1) from int4_tbl a,float8_tbl b where udf(udf(a.f1))=b.f1) and + udf(x.unique1) = 0 and + udf(udf(x.unique1)) in (select udf(aa.f1) from int4_tbl aa,float8_tbl bb where udf(aa.f1)=udf(udf(bb.f1))) -- !query 91 schema -struct<count(1):bigint> +struct<CAST(udf(cast(cast(udf(cast(count(1) as string)) as bigint) as string)) AS BIGINT):bigint> -- !query 91 output 1 -- !query 92 select * from int8_tbl i1 left join (int8_tbl i2 join - (select 123 as x) ss on i2.q1 = x) on i1.q2 = i2.q2 -order by 1, 2 + (select udf(123) as x) ss on udf(udf(i2.q1)) = udf(x)) on udf(udf(i1.q2)) = udf(udf(i2.q2)) +order by udf(udf(1)), 2 -- !query 92 schema struct<q1:bigint,q2:bigint,q1:bigint,q2:bigint,x:int> -- !query 92 output -123 456 123 456 123 -123 4567890123456789 123 4567890123456789 123 4567890123456789 -4567890123456789 NULL NULL NULL 4567890123456789 123 NULL NULL NULL +123 456 123 456 123 +123 4567890123456789 123 4567890123456789 123 4567890123456789 4567890123456789 123 4567890123456789 123 -- !query 93 -select count(*) +select udf(count(*)) from - (select t3.tenthous as x1, coalesce(t1.stringu1, t2.stringu1) as x2 + (select udf(t3.tenthous) as x1, udf(coalesce(udf(t1.stringu1), udf(t2.stringu1))) as x2 from tenk1 t1 - left join tenk1 t2 on t1.unique1 = t2.unique1 - join tenk1 t3 on t1.unique2 = t3.unique2) ss, + left join tenk1 t2 on udf(t1.unique1) = udf(t2.unique1) + join tenk1 t3 on t1.unique2 = udf(t3.unique2)) ss, tenk1 t4, tenk1 t5 -where t4.thousand = t5.unique1 and ss.x1 = t4.tenthous and ss.x2 = t5.stringu1 +where udf(t4.thousand) = udf(t5.unique1) and udf(udf(ss.x1)) = t4.tenthous and udf(ss.x2) = udf(udf(t5.stringu1)) -- !query 93 schema -struct<count(1):bigint> +struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint> -- !query 93 output 1000 -- !query 94 -select a.f1, b.f1, t.thousand, t.tenthous from +select udf(a.f1), udf(b.f1), udf(t.thousand), udf(t.tenthous) from tenk1 t, - (select sum(f1)+1 as f1 from int4_tbl i4a) a, - (select sum(f1) as f1 from int4_tbl i4b) b -where b.f1 = t.thousand and a.f1 = b.f1 and (a.f1+b.f1+999) = t.tenthous + (select udf(udf(sum(udf(f1))+1)) as f1 from int4_tbl i4a) a, + (select udf(sum(udf(f1))) as f1 from int4_tbl i4b) b +where b.f1 = udf(t.thousand) and udf(a.f1) = udf(b.f1) and udf((udf(a.f1)+udf(b.f1)+999)) = udf(udf(t.tenthous)) -- !query 94 schema -struct<f1:bigint,f1:bigint,thousand:int,tenthous:int> +struct<CAST(udf(cast(f1 as string)) AS BIGINT):bigint,CAST(udf(cast(f1 as string)) AS BIGINT):bigint,CAST(udf(cast(thousand as string)) AS INT):int,CAST(udf(cast(tenthous as string)) AS INT):int> -- !query 94 output -2408,8 +2408,8 struct<f1:bigint,f1:bigint,thousand:int,tenthous:int> -- !query 95 select * from j1_tbl full join - (select * from j2_tbl order by j2_tbl.i desc, j2_tbl.k asc) j2_tbl - on j1_tbl.i = j2_tbl.i and j1_tbl.i = j2_tbl.k + (select * from j2_tbl order by udf(udf(j2_tbl.i)) desc, udf(j2_tbl.k) asc) j2_tbl + on udf(j1_tbl.i) = udf(j2_tbl.i) and udf(j1_tbl.i) = udf(j2_tbl.k) -- !query 95 schema struct<i:int,j:int,t:string,i:int,k:int> -- !query 95 output -2435,13 +2435,13 NULL NULL null NULL NULL -- !query 96 -select count(*) from - (select * from tenk1 x order by x.thousand, x.twothousand, x.fivethous) x +select udf(count(*)) from + (select * from tenk1 x order by udf(x.thousand), udf(udf(x.twothousand)), x.fivethous) x left join - (select * from tenk1 y order by y.unique2) y - on x.thousand = y.unique2 and x.twothousand = y.hundred and x.fivethous = y.unique2 + (select * from tenk1 y order by udf(y.unique2)) y + on udf(x.thousand) = y.unique2 and x.twothousand = udf(y.hundred) and x.fivethous = y.unique2 -- !query 96 schema -struct<count(1):bigint> +struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint> -- !query 96 output 10000 -2507,7 +2507,7 struct<> -- !query 104 -select tt1.*, tt2.* from tt1 left join tt2 on tt1.joincol = tt2.joincol +select tt1.*, tt2.* from tt1 left join tt2 on udf(udf(tt1.joincol)) = udf(tt2.joincol) -- !query 104 schema struct<tt1_id:int,joincol:int,tt2_id:int,joincol:int> -- !query 104 output -2517,7 +2517,7 struct<tt1_id:int,joincol:int,tt2_id:int,joincol:int> -- !query 105 -select tt1.*, tt2.* from tt2 right join tt1 on tt1.joincol = tt2.joincol +select tt1.*, tt2.* from tt2 right join tt1 on udf(udf(tt1.joincol)) = udf(udf(tt2.joincol)) -- !query 105 schema struct<tt1_id:int,joincol:int,tt2_id:int,joincol:int> -- !query 105 output -2527,10 +2527,10 struct<tt1_id:int,joincol:int,tt2_id:int,joincol:int> -- !query 106 -select count(*) from tenk1 a, tenk1 b - where a.hundred = b.thousand and (b.fivethous % 10) < 10 +select udf(count(*)) from tenk1 a, tenk1 b + where udf(a.hundred) = b.thousand and udf(udf((b.fivethous % 10)) < 10) -- !query 106 schema -struct<count(1):bigint> +struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint> -- !query 106 output 100000 -2584,14 +2584,14 struct<> -- !query 113 -SELECT a.f1 +SELECT udf(udf(a.f1)) as f1 FROM tt4 a LEFT JOIN ( SELECT b.f1 - FROM tt3 b LEFT JOIN tt3 c ON (b.f1 = c.f1) - WHERE c.f1 IS NULL -) AS d ON (a.f1 = d.f1) -WHERE d.f1 IS NULL + FROM tt3 b LEFT JOIN tt3 c ON udf(b.f1) = udf(c.f1) + WHERE udf(c.f1) IS NULL +) AS d ON udf(a.f1) = d.f1 +WHERE udf(udf(d.f1)) IS NULL -- !query 113 schema struct<f1:int> -- !query 113 output -2621,7 +2621,7 struct<> -- !query 116 -select * from tt5,tt6 where tt5.f1 = tt6.f1 and tt5.f1 = tt5.f2 - tt6.f2 +select * from tt5,tt6 where udf(tt5.f1) = udf(tt6.f1) and udf(tt5.f1) = udf(udf(tt5.f2) - udf(tt6.f2)) -- !query 116 schema struct<f1:int,f2:int,f1:int,f2:int> -- !query 116 output -2649,12 +2649,12 struct<> -- !query 119 -select yy.pkyy as yy_pkyy, yy.pkxx as yy_pkxx, yya.pkyy as yya_pkyy, - xxa.pkxx as xxa_pkxx, xxb.pkxx as xxb_pkxx +select udf(udf(yy.pkyy)) as yy_pkyy, udf(yy.pkxx) as yy_pkxx, udf(yya.pkyy) as yya_pkyy, + udf(xxa.pkxx) as xxa_pkxx, udf(xxb.pkxx) as xxb_pkxx from yy - left join (SELECT * FROM yy where pkyy = 101) as yya ON yy.pkyy = yya.pkyy - left join xx xxa on yya.pkxx = xxa.pkxx - left join xx xxb on coalesce (xxa.pkxx, 1) = xxb.pkxx + left join (SELECT * FROM yy where pkyy = 101) as yya ON udf(yy.pkyy) = udf(yya.pkyy) + left join xx xxa on udf(yya.pkxx) = udf(udf(xxa.pkxx)) + left join xx xxb on udf(udf(coalesce (xxa.pkxx, 1))) = udf(xxb.pkxx) -- !query 119 schema struct<yy_pkyy:int,yy_pkxx:int,yya_pkyy:int,xxa_pkxx:int,xxb_pkxx:int> -- !query 119 output -2693,9 +2693,9 struct<> -- !query 123 select * from - zt2 left join zt3 on (f2 = f3) - left join zt1 on (f3 = f1) -where f2 = 53 + zt2 left join zt3 on (udf(f2) = udf(udf(f3))) + left join zt1 on (udf(udf(f3)) = udf(f1)) +where udf(f2) = 53 -- !query 123 schema struct<f2:int,f3:int,f1:int> -- !query 123 output -2712,9 +2712,9 struct<> -- !query 125 select * from - zt2 left join zt3 on (f2 = f3) - left join zv1 on (f3 = f1) -where f2 = 53 + zt2 left join zt3 on (f2 = udf(f3)) + left join zv1 on (udf(f3) = f1) +where udf(udf(f2)) = 53 -- !query 125 schema struct<f2:int,f3:int,f1:int,junk:string> -- !query 125 output -2722,12 +2722,12 struct<f2:int,f3:int,f1:int,junk:string> -- !query 126 -select a.unique2, a.ten, b.tenthous, b.unique2, b.hundred -from tenk1 a left join tenk1 b on a.unique2 = b.tenthous -where a.unique1 = 42 and - ((b.unique2 is null and a.ten = 2) or b.hundred = 3) +select udf(a.unique2), udf(a.ten), udf(b.tenthous), udf(b.unique2), udf(b.hundred) +from tenk1 a left join tenk1 b on a.unique2 = udf(b.tenthous) +where udf(a.unique1) = 42 and + ((udf(b.unique2) is null and udf(a.ten) = 2) or udf(udf(b.hundred)) = udf(udf(3))) -- !query 126 schema -struct<unique2:int,ten:int,tenthous:int,unique2:int,hundred:int> +struct<CAST(udf(cast(unique2 as string)) AS INT):int,CAST(udf(cast(ten as string)) AS INT):int,CAST(udf(cast(tenthous as string)) AS INT):int,CAST(udf(cast(unique2 as string)) AS INT):int,CAST(udf(cast(hundred as string)) AS INT):int> -- !query 126 output -2749,7 +2749,7 struct<> -- !query 129 -select * from a left join b on i = x and i = y and x = i +select * from a left join b on udf(i) = x and i = udf(y) and udf(x) = udf(i) -- !query 129 schema struct<i:int,x:int,y:int> -- !query 129 output -2757,11 +2757,11 struct<i:int,x:int,y:int> -- !query 130 -select t1.q2, count(t2.*) -from int8_tbl t1 left join int8_tbl t2 on (t1.q2 = t2.q1) -group by t1.q2 order by 1 +select udf(t1.q2), udf(count(t2.*)) +from int8_tbl t1 left join int8_tbl t2 on (udf(udf(t1.q2)) = t2.q1) +group by udf(t1.q2) order by 1 -- !query 130 schema -struct<q2:bigint,count(q1, q2):bigint> +struct<CAST(udf(cast(q2 as string)) AS BIGINT):bigint,CAST(udf(cast(count(q1, q2) as string)) AS BIGINT):bigint> -- !query 130 output -4567890123456789 0 123 2 -2770,11 +2770,11 struct<q2:bigint,count(q1, q2):bigint> -- !query 131 -select t1.q2, count(t2.*) -from int8_tbl t1 left join (select * from int8_tbl) t2 on (t1.q2 = t2.q1) -group by t1.q2 order by 1 +select udf(udf(t1.q2)), udf(count(t2.*)) +from int8_tbl t1 left join (select * from int8_tbl) t2 on (udf(udf(t1.q2)) = udf(t2.q1)) +group by udf(udf(t1.q2)) order by 1 -- !query 131 schema -struct<q2:bigint,count(q1, q2):bigint> +struct<CAST(udf(cast(cast(udf(cast(q2 as string)) as bigint) as string)) AS BIGINT):bigint,CAST(udf(cast(count(q1, q2) as string)) AS BIGINT):bigint> -- !query 131 output -4567890123456789 0 123 2 -2783,13 +2783,13 struct<q2:bigint,count(q1, q2):bigint> -- !query 132 -select t1.q2, count(t2.*) +select udf(t1.q2) as q2, udf(udf(count(t2.*))) from int8_tbl t1 left join - (select q1, case when q2=1 then 1 else q2 end as q2 from int8_tbl) t2 - on (t1.q2 = t2.q1) + (select udf(q1) as q1, case when q2=1 then 1 else q2 end as q2 from int8_tbl) t2 + on (udf(t1.q2) = udf(t2.q1)) group by t1.q2 order by 1 -- !query 132 schema -struct<q2:bigint,count(q1, q2):bigint> +struct<q2:bigint,CAST(udf(cast(cast(udf(cast(count(q1, q2) as string)) as bigint) as string)) AS BIGINT):bigint> -- !query 132 output -4567890123456789 0 123 2 -2828,17 +2828,17 struct<> -- !query 136 -select c.name, ss.code, ss.b_cnt, ss.const +select udf(c.name), udf(ss.code), udf(ss.b_cnt), udf(ss.const) from c left join (select a.code, coalesce(b_grp.cnt, 0) as b_cnt, -1 as const from a left join - (select count(1) as cnt, b.a from b group by b.a) as b_grp - on a.code = b_grp.a + (select udf(count(1)) as cnt, b.a as a from b group by b.a) as b_grp + on udf(a.code) = udf(udf(b_grp.a)) ) as ss - on (c.a = ss.code) + on (udf(udf(c.a)) = udf(ss.code)) order by c.name -- !query 136 schema -struct<name:string,code:string,b_cnt:bigint,const:int> +struct<CAST(udf(cast(name as string)) AS STRING):string,CAST(udf(cast(code as string)) AS STRING):string,CAST(udf(cast(b_cnt as string)) AS BIGINT):bigint,CAST(udf(cast(const as string)) AS INT):int> -- !query 136 output A p 2 -1 B q 0 -1 -2852,15 +2852,15 LEFT JOIN ( SELECT sub3.key3, sub4.value2, COALESCE(sub4.value2, 66) as value3 FROM ( SELECT 1 as key3 ) sub3 LEFT JOIN - ( SELECT sub5.key5, COALESCE(sub6.value1, 1) as value2 FROM + ( SELECT udf(sub5.key5) as key5, udf(udf(COALESCE(sub6.value1, 1))) as value2 FROM ( SELECT 1 as key5 ) sub5 LEFT JOIN ( SELECT 2 as key6, 42 as value1 ) sub6 - ON sub5.key5 = sub6.key6 + ON sub5.key5 = udf(sub6.key6) ) sub4 - ON sub4.key5 = sub3.key3 + ON udf(sub4.key5) = sub3.key3 ) sub2 -ON sub1.key1 = sub2.key3 +ON udf(udf(sub1.key1)) = udf(udf(sub2.key3)) -- !query 137 schema struct<key1:int,key3:int,value2:int,value3:int> -- !query 137 output -2871,34 +2871,34 struct<key1:int,key3:int,value2:int,value3:int> SELECT * FROM ( SELECT 1 as key1 ) sub1 LEFT JOIN -( SELECT sub3.key3, value2, COALESCE(value2, 66) as value3 FROM +( SELECT udf(sub3.key3) as key3, udf(value2), udf(COALESCE(value2, 66)) as value3 FROM ( SELECT 1 as key3 ) sub3 LEFT JOIN ( SELECT sub5.key5, COALESCE(sub6.value1, 1) as value2 FROM ( SELECT 1 as key5 ) sub5 LEFT JOIN ( SELECT 2 as key6, 42 as value1 ) sub6 - ON sub5.key5 = sub6.key6 + ON udf(udf(sub5.key5)) = sub6.key6 ) sub4 ON sub4.key5 = sub3.key3 ) sub2 -ON sub1.key1 = sub2.key3 +ON sub1.key1 = udf(udf(sub2.key3)) -- !query 138 schema -struct<key1:int,key3:int,value2:int,value3:int> +struct<key1:int,key3:int,CAST(udf(cast(value2 as string)) AS INT):int,value3:int> -- !query 138 output 1 1 1 1 -- !query 139 -SELECT qq, unique1 +SELECT udf(qq), udf(udf(unique1)) FROM - ( SELECT COALESCE(q1, 0) AS qq FROM int8_tbl a ) AS ss1 + ( SELECT udf(COALESCE(q1, 0)) AS qq FROM int8_tbl a ) AS ss1 FULL OUTER JOIN - ( SELECT COALESCE(q2, -1) AS qq FROM int8_tbl b ) AS ss2 + ( SELECT udf(udf(COALESCE(q2, -1))) AS qq FROM int8_tbl b ) AS ss2 USING (qq) - INNER JOIN tenk1 c ON qq = unique2 + INNER JOIN tenk1 c ON udf(qq) = udf(unique2) -- !query 139 schema -struct<qq:bigint,unique1:int> +struct<CAST(udf(cast(qq as string)) AS BIGINT):bigint,CAST(udf(cast(cast(udf(cast(unique1 as string)) as int) as string)) AS INT):int> -- !query 139 output 123 4596 123 4596 -2936,19 +2936,19 struct<> -- !query 143 -select nt3.id +select udf(nt3.id) from nt3 as nt3 left join - (select nt2.*, (nt2.b1 and ss1.a3) AS b3 + (select nt2.*, (udf(nt2.b1) and udf(ss1.a3)) AS b3 from nt2 as nt2 left join - (select nt1.*, (nt1.id is not null) as a3 from nt1) as ss1 - on ss1.id = nt2.nt1_id + (select nt1.*, (udf(nt1.id) is not null) as a3 from nt1) as ss1 + on ss1.id = udf(udf(nt2.nt1_id)) ) as ss2 - on ss2.id = nt3.nt2_id -where nt3.id = 1 and ss2.b3 + on udf(ss2.id) = nt3.nt2_id +where udf(nt3.id) = 1 and udf(ss2.b3) -- !query 143 schema -struct<id:int> +struct<CAST(udf(cast(id as string)) AS INT):int> -- !query 143 output 1 -3003,73 +3003,73 NULL 2147483647 -- !query 146 -select count(*) from - tenk1 a join tenk1 b on a.unique1 = b.unique2 - left join tenk1 c on a.unique2 = b.unique1 and c.thousand = a.thousand - join int4_tbl on b.thousand = f1 +select udf(count(*)) from + tenk1 a join tenk1 b on udf(a.unique1) = udf(b.unique2) + left join tenk1 c on udf(a.unique2) = udf(b.unique1) and udf(c.thousand) = udf(udf(a.thousand)) + join int4_tbl on udf(b.thousand) = f1 -- !query 146 schema -struct<count(1):bigint> +struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint> -- !query 146 output 10 -- !query 147 -select b.unique1 from - tenk1 a join tenk1 b on a.unique1 = b.unique2 - left join tenk1 c on b.unique1 = 42 and c.thousand = a.thousand - join int4_tbl i1 on b.thousand = f1 - right join int4_tbl i2 on i2.f1 = b.tenthous - order by 1 +select udf(b.unique1) from + tenk1 a join tenk1 b on udf(a.unique1) = udf(b.unique2) + left join tenk1 c on udf(b.unique1) = 42 and c.thousand = udf(a.thousand) + join int4_tbl i1 on udf(b.thousand) = udf(udf(f1)) + right join int4_tbl i2 on udf(udf(i2.f1)) = udf(b.tenthous) + order by udf(1) -- !query 147 schema -struct<unique1:int> +struct<CAST(udf(cast(unique1 as string)) AS INT):int> -- !query 147 output NULL NULL +0 NULL NULL -0 -- !query 148 select * from ( - select unique1, q1, coalesce(unique1, -1) + q1 as fault - from int8_tbl left join tenk1 on (q2 = unique2) + select udf(unique1), udf(q1), udf(udf(coalesce(unique1, -1)) + udf(q1)) as fault + from int8_tbl left join tenk1 on (udf(q2) = udf(unique2)) ) ss -where fault = 122 -order by fault +where udf(fault) = udf(122) +order by udf(fault) -- !query 148 schema -struct<unique1:int,q1:bigint,fault:bigint> +struct<CAST(udf(cast(unique1 as string)) AS INT):int,CAST(udf(cast(q1 as string)) AS BIGINT):bigint,fault:bigint> -- !query 148 output NULL 123 122 -- !query 149 -select q1, unique2, thousand, hundred - from int8_tbl a left join tenk1 b on q1 = unique2 - where coalesce(thousand,123) = q1 and q1 = coalesce(hundred,123) +select udf(q1), udf(unique2), udf(thousand), udf(hundred) + from int8_tbl a left join tenk1 b on udf(q1) = udf(unique2) + where udf(coalesce(thousand,123)) = udf(q1) and udf(q1) = udf(udf(coalesce(hundred,123))) -- !query 149 schema -struct<q1:bigint,unique2:int,thousand:int,hundred:int> +struct<CAST(udf(cast(q1 as string)) AS BIGINT):bigint,CAST(udf(cast(unique2 as string)) AS INT):int,CAST(udf(cast(thousand as string)) AS INT):int,CAST(udf(cast(hundred as string)) AS INT):int> -- !query 149 output -- !query 150 -select f1, unique2, case when unique2 is null then f1 else 0 end - from int4_tbl a left join tenk1 b on f1 = unique2 - where (case when unique2 is null then f1 else 0 end) = 0 +select udf(f1), udf(unique2), case when udf(udf(unique2)) is null then udf(f1) else 0 end + from int4_tbl a left join tenk1 b on udf(f1) = udf(udf(unique2)) + where (case when udf(unique2) is null then udf(f1) else 0 end) = 0 -- !query 150 schema -struct<f1:int,unique2:int,CASE WHEN (unique2 IS NULL) THEN f1 ELSE 0 END:int> +struct<CAST(udf(cast(f1 as string)) AS INT):int,CAST(udf(cast(unique2 as string)) AS INT):int,CASE WHEN (CAST(udf(cast(cast(udf(cast(unique2 as string)) as int) as string)) AS INT) IS NULL) THEN CAST(udf(cast(f1 as string)) AS INT) ELSE 0 END:int> -- !query 150 output 0 0 0 -- !query 151 -select a.unique1, b.unique1, c.unique1, coalesce(b.twothousand, a.twothousand) - from tenk1 a left join tenk1 b on b.thousand = a.unique1 left join tenk1 c on c.unique2 = coalesce(b.twothousand, a.twothousand) - where a.unique2 < 10 and coalesce(b.twothousand, a.twothousand) = 44 +select udf(a.unique1), udf(b.unique1), udf(c.unique1), udf(coalesce(b.twothousand, a.twothousand)) + from tenk1 a left join tenk1 b on udf(b.thousand) = a.unique1 left join tenk1 c on udf(c.unique2) = udf(coalesce(b.twothousand, a.twothousand)) + where a.unique2 < udf(10) and udf(udf(coalesce(b.twothousand, a.twothousand))) = udf(44) -- !query 151 schema -struct<unique1:int,unique1:int,unique1:int,coalesce(twothousand, twothousand):int> +struct<CAST(udf(cast(unique1 as string)) AS INT):int,CAST(udf(cast(unique1 as string)) AS INT):int,CAST(udf(cast(unique1 as string)) AS INT):int,CAST(udf(cast(coalesce(twothousand, twothousand) as string)) AS INT):int> -- !query 151 output -3078,11 +3078,11 struct<unique1:int,unique1:int,unique1:int,coalesce(twothousand, twothousand):in select * from text_tbl t1 inner join int8_tbl i8 - on i8.q2 = 456 + on udf(i8.q2) = udf(udf(456)) right join text_tbl t2 - on t1.f1 = 'doh!' + on udf(t1.f1) = udf(udf('doh!')) left join int4_tbl i4 - on i8.q1 = i4.f1 + on udf(udf(i8.q1)) = i4.f1 -- !query 152 schema struct<f1:string,q1:bigint,q2:bigint,f1:string,f1:int> -- !query 152 output -3092,10 +3092,10 doh! 123 456 hi de ho neighbor NULL -- !query 153 select * from - (select 1 as id) as xx + (select udf(udf(1)) as id) as xx left join - (tenk1 as a1 full join (select 1 as id) as yy on (a1.unique1 = yy.id)) - on (xx.id = coalesce(yy.id)) + (tenk1 as a1 full join (select udf(1) as id) as yy on (udf(a1.unique1) = udf(yy.id))) + on (xx.id = udf(udf(coalesce(yy.id)))) -- !query 153 schema struct<id:int,unique1:int,unique2:int,two:int,four:int,ten:int,twenty:int,hundred:int,thousand:int,twothousand:int,fivethous:int,tenthous:int,odd:int,even:int,stringu1:string,stringu2:string,string4:string,id:int> -- !query 153 output -3103,11 +3103,11 struct<id:int,unique1:int,unique2:int,two:int,four:int,ten:int,twenty:int,hundre -- !query 154 -select a.q2, b.q1 - from int8_tbl a left join int8_tbl b on a.q2 = coalesce(b.q1, 1) - where coalesce(b.q1, 1) > 0 +select udf(a.q2), udf(b.q1) + from int8_tbl a left join int8_tbl b on udf(a.q2) = coalesce(b.q1, 1) + where udf(udf(coalesce(b.q1, 1)) > 0) -- !query 154 schema -struct<q2:bigint,q1:bigint> +struct<CAST(udf(cast(q2 as string)) AS BIGINT):bigint,CAST(udf(cast(q1 as string)) AS BIGINT):bigint> -- !query 154 output -4567890123456789 NULL 123 123 -3142,7 +3142,7 struct<> -- !query 157 -select p.* from parent p left join child c on (p.k = c.k) +select p.* from parent p left join child c on (udf(p.k) = udf(c.k)) -- !query 157 schema struct<k:int,pd:int> -- !query 157 output -3153,8 +3153,8 struct<k:int,pd:int> -- !query 158 select p.*, linked from parent p - left join (select c.*, true as linked from child c) as ss - on (p.k = ss.k) + left join (select c.*, udf(udf(true)) as linked from child c) as ss + on (udf(p.k) = udf(udf(ss.k))) -- !query 158 schema struct<k:int,pd:int,linked:boolean> -- !query 158 output -3165,8 +3165,8 struct<k:int,pd:int,linked:boolean> -- !query 159 select p.* from - parent p left join child c on (p.k = c.k) - where p.k = 1 and p.k = 2 + parent p left join child c on (udf(p.k) = c.k) + where p.k = udf(1) and udf(udf(p.k)) = udf(udf(2)) -- !query 159 schema struct<k:int,pd:int> -- !query 159 output -3175,8 +3175,8 struct<k:int,pd:int> -- !query 160 select p.* from - (parent p left join child c on (p.k = c.k)) join parent x on p.k = x.k - where p.k = 1 and p.k = 2 + (parent p left join child c on (udf(p.k) = c.k)) join parent x on p.k = udf(x.k) + where udf(p.k) = udf(1) and udf(udf(p.k)) = udf(udf(2)) -- !query 160 schema struct<k:int,pd:int> -- !query 160 output -3204,7 +3204,7 struct<> -- !query 163 -SELECT * FROM b LEFT JOIN a ON (b.a_id = a.id) WHERE (a.id IS NULL OR a.id > 0) +SELECT * FROM b LEFT JOIN a ON (udf(b.a_id) = udf(a.id)) WHERE (udf(udf(a.id)) IS NULL OR udf(a.id) > 0) -- !query 163 schema struct<id:int,a_id:int,id:int> -- !query 163 output -3212,7 +3212,7 struct<id:int,a_id:int,id:int> -- !query 164 -SELECT b.* FROM b LEFT JOIN a ON (b.a_id = a.id) WHERE (a.id IS NULL OR a.id > 0) +SELECT b.* FROM b LEFT JOIN a ON (udf(b.a_id) = udf(a.id)) WHERE (udf(a.id) IS NULL OR udf(udf(a.id)) > 0) -- !query 164 schema struct<id:int,a_id:int> -- !query 164 output -3231,13 +3231,13 struct<> -- !query 166 SELECT * FROM - (SELECT 1 AS x) ss1 + (SELECT udf(1) AS x) ss1 LEFT JOIN - (SELECT q1, q2, COALESCE(dat1, q1) AS y - FROM int8_tbl LEFT JOIN innertab ON q2 = id) ss2 + (SELECT udf(q1), udf(q2), udf(COALESCE(dat1, q1)) AS y + FROM int8_tbl LEFT JOIN innertab ON udf(udf(q2)) = id) ss2 ON true -- !query 166 schema -struct<x:int,q1:bigint,q2:bigint,y:bigint> +struct<x:int,CAST(udf(cast(q1 as string)) AS BIGINT):bigint,CAST(udf(cast(q2 as string)) AS BIGINT):bigint,y:bigint> -- !query 166 output 1 123 456 123 1 123 4567890123456789 123 -3248,27 +3248,27 struct<x:int,q1:bigint,q2:bigint,y:bigint> -- !query 167 select * from - int8_tbl x join (int4_tbl x cross join int4_tbl y) j on q1 = f1 + int8_tbl x join (int4_tbl x cross join int4_tbl y) j on udf(q1) = udf(f1) -- !query 167 schema struct<> -- !query 167 output org.apache.spark.sql.AnalysisException -Reference 'f1' is ambiguous, could be: j.f1, j.f1.; line 2 pos 63 +Reference 'f1' is ambiguous, could be: j.f1, j.f1.; line 2 pos 72 -- !query 168 select * from - int8_tbl x join (int4_tbl x cross join int4_tbl y) j on q1 = y.f1 + int8_tbl x join (int4_tbl x cross join int4_tbl y) j on udf(q1) = udf(y.f1) -- !query 168 schema struct<> -- !query 168 output org.apache.spark.sql.AnalysisException -cannot resolve '`y.f1`' given input columns: [j.f1, j.f1, x.q1, x.q2]; line 2 pos 63 +cannot resolve '`y.f1`' given input columns: [j.f1, j.f1, x.q1, x.q2]; line 2 pos 72 -- !query 169 select * from - int8_tbl x join (int4_tbl x cross join int4_tbl y(ff)) j on q1 = f1 + int8_tbl x join (int4_tbl x cross join int4_tbl y(ff)) j on udf(q1) = udf(udf(f1)) -- !query 169 schema struct<q1:bigint,q2:bigint,f1:int,ff:int> -- !query 169 output -3276,69 +3276,69 struct<q1:bigint,q2:bigint,f1:int,ff:int> -- !query 170 -select t1.uunique1 from - tenk1 t1 join tenk2 t2 on t1.two = t2.two +select udf(t1.uunique1) from + tenk1 t1 join tenk2 t2 on t1.two = udf(t2.two) -- !query 170 schema struct<> -- !query 170 output org.apache.spark.sql.AnalysisException -cannot resolve '`t1.uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 7 +cannot resolve '`t1.uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 11 -- !query 171 -select t2.uunique1 from - tenk1 t1 join tenk2 t2 on t1.two = t2.two +select udf(udf(t2.uunique1)) from + tenk1 t1 join tenk2 t2 on udf(t1.two) = t2.two -- !query 171 schema struct<> -- !query 171 output org.apache.spark.sql.AnalysisException -cannot resolve '`t2.uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 7 +cannot resolve '`t2.uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 15 -- !query 172 -select uunique1 from - tenk1 t1 join tenk2 t2 on t1.two = t2.two +select udf(uunique1) from + tenk1 t1 join tenk2 t2 on udf(t1.two) = udf(t2.two) -- !query 172 schema struct<> -- !query 172 output org.apache.spark.sql.AnalysisException -cannot resolve '`uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 7 +cannot resolve '`uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 11 -- !query 173 -select f1,g from int4_tbl a, (select f1 as g) ss +select udf(udf(f1,g)) from int4_tbl a, (select udf(udf(f1)) as g) ss -- !query 173 schema struct<> -- !query 173 output org.apache.spark.sql.AnalysisException -cannot resolve '`f1`' given input columns: []; line 1 pos 37 +cannot resolve '`f1`' given input columns: []; line 1 pos 55 -- !query 174 -select f1,g from int4_tbl a, (select a.f1 as g) ss +select udf(f1,g) from int4_tbl a, (select a.f1 as g) ss -- !query 174 schema struct<> -- !query 174 output org.apache.spark.sql.AnalysisException -cannot resolve '`a.f1`' given input columns: []; line 1 pos 37 +cannot resolve '`a.f1`' given input columns: []; line 1 pos 42 -- !query 175 -select f1,g from int4_tbl a cross join (select f1 as g) ss +select udf(udf(f1,g)) from int4_tbl a cross join (select udf(f1) as g) ss -- !query 175 schema struct<> -- !query 175 output org.apache.spark.sql.AnalysisException -cannot resolve '`f1`' given input columns: []; line 1 pos 47 +cannot resolve '`f1`' given input columns: []; line 1 pos 61 -- !query 176 -select f1,g from int4_tbl a cross join (select a.f1 as g) ss +select udf(f1,g) from int4_tbl a cross join (select udf(udf(a.f1)) as g) ss -- !query 176 schema struct<> -- !query 176 output org.apache.spark.sql.AnalysisException -cannot resolve '`a.f1`' given input columns: []; line 1 pos 47 +cannot resolve '`a.f1`' given input columns: []; line 1 pos 60 -- !query 177 -3383,8 +3383,8 struct<> -- !query 182 select * from j1 -inner join j2 on j1.id1 = j2.id1 and j1.id2 = j2.id2 -where j1.id1 % 1000 = 1 and j2.id1 % 1000 = 1 +inner join j2 on udf(j1.id1) = udf(j2.id1) and udf(udf(j1.id2)) = udf(j2.id2) +where udf(j1.id1) % 1000 = 1 and udf(udf(j2.id1) % 1000) = 1 -- !query 182 schema struct<id1:int,id2:int,id1:int,id2:int> -- !query 182 output ``` </p> </details> ## How was this patch tested? Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921). Closes #25371 from huaxingao/spark-28393. Authored-by: Huaxin Gao <huaxing@us.ibm.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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Wenchen Fan
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97dc4c0bfc |
[SPARK-28744][SQL][TEST] rename SharedSQLContext to SharedSparkSession
## What changes were proposed in this pull request?
The Spark SQL test framework needs to support 2 kinds of tests:
1. tests inside Spark to test Spark itself (extends `SparkFunSuite`)
2. test outside of Spark to test Spark applications (introduced at
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Peter Toth
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f999e00e9f |
[SPARK-28356][SHUFFLE][FOLLOWUP] Fix case with different pre-shuffle partition numbers
### What changes were proposed in this pull request? This PR reverts some of the latest changes in `ReduceNumShufflePartitions` to fix the case when there are different pre-shuffle partition numbers in the plan. Please see the new UT for an example. ### Why are the changes needed? Eliminate a bug. ### Does this PR introduce any user-facing change? Yes, some queries that failed will succeed now. ### How was this patch tested? Added new UT. Closes #25479 from peter-toth/SPARK-28356-followup. Authored-by: Peter Toth <peter.toth@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Dilip Biswal
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a5df5ff0fd |
[SPARK-28734][DOC] Initial table of content in the left hand side bar for SQL doc
## What changes were proposed in this pull request? This is a initial PR that creates the table of content for SQL reference guide. The left side bar will displays additional menu items corresponding to supported SQL constructs. One this PR is merged, we will fill in the content incrementally. Additionally this PR contains a minor change to make the left sidebar scrollable. Currently it is not possible to scroll in the left hand side window. ## How was this patch tested? Used jekyll build and serve to verify. Closes #25459 from dilipbiswal/ref-doc. Authored-by: Dilip Biswal <dbiswal@us.ibm.com> Signed-off-by: gatorsmile <gatorsmile@gmail.com> |
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Eyal Zituny
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d75a11d059 |
[SPARK-27330][SS] support task abort in foreach writer
## What changes were proposed in this pull request? in order to address cases where foreach writer task is failing without calling the close() method, (for example when a task is interrupted) added the option to implement an abort() method that will be called when the task is aborted. users should handle resource cleanup (such as connections) in the abort() method ## How was this patch tested? update existing unit tests. Closes #24382 from eyalzit/SPARK-27330-foreach-writer-abort. Lead-authored-by: Eyal Zituny <eyal.zituny@equalum.io> Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com> Co-authored-by: eyalzit <eyal.zituny@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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shivusondur
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c96b6154b7 |
[SPARK-28390][SQL][PYTHON][TESTS][FOLLOW-UP] Update the TODO with actual blocking JIRA IDs
## What changes were proposed in this pull request? only todo message updated. Need to add udf() for GroupBy Tests, after resolving following jira [SPARK-28386] and [SPARK-26741] ## How was this patch tested? NA, only TODO message updated. Closes #25415 from shivusondur/jiraFollowup. Authored-by: shivusondur <shivusondur@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
