## What changes were proposed in this pull request?
PySpark worker daemon reads from stdin the worker PIDs to kill. 1bb60ab839/python/pyspark/daemon.py (L127)
However, the worker process is a forked process from the worker daemon process and we didn't close stdin on the child after fork. This means the child and user program can read stdin as well, which blocks daemon from receiving the PID to kill. This can cause issues because the task reaper might detect the task was not terminated and eventually kill the JVM.
This PR fix this by redirecting the standard input of the forked child to devnull.
## How was this patch tested?
Manually test.
In `pyspark`, run:
```
import subprocess
def task(_):
subprocess.check_output(["cat"])
sc.parallelize(range(1), 1).mapPartitions(task).count()
```
Before:
The job will get stuck and press Ctrl+C to exit the job but the python worker process do not exit.
After:
The job finish correctly. The "cat" print nothing (because the dummay stdin is "/dev/null").
The python worker process exit normally.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25138 from WeichenXu123/SPARK-26175.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR makes the optimizer rule PullupCorrelatedPredicates idempotent.
## How was this patch tested?
A new test PullupCorrelatedPredicatesSuite
Closes#25268 from dilipbiswal/pr-25164.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
As part of the shuffle storage API proposed in SPARK-25299, this introduces an API for persisting shuffle data in arbitrary storage systems.
This patch introduces several concepts:
* `ShuffleDataIO`, which is the root of the entire plugin tree that will be proposed over the course of the shuffle API project.
* `ShuffleExecutorComponents` - the subset of plugins for managing shuffle-related components for each executor. This will in turn instantiate shuffle readers and writers.
* `ShuffleMapOutputWriter` interface - instantiated once per map task. This provides child `ShufflePartitionWriter` instances for persisting the bytes for each partition in the map task.
The default implementation of these plugins exactly mirror what was done by the existing shuffle writing code - namely, writing the data to local disk and writing an index file. We leverage the APIs in the `BypassMergeSortShuffleWriter` only. Follow-up PRs will use the APIs in `SortShuffleWriter` and `UnsafeShuffleWriter`, but are left as future work to minimize the review surface area.
## How was this patch tested?
New unit tests were added. Micro-benchmarks indicate there's no slowdown in the affected code paths.
Closes#25007 from mccheah/spark-shuffle-writer-refactor.
Lead-authored-by: mcheah <mcheah@palantir.com>
Co-authored-by: mccheah <mcheah@palantir.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
Launcher is implemented as a Java application and sometimes I'd like to apply Java options.
One situation I have met is the time I try to attach debugger to Launcher.
Launcher is launched from bin/spark-class but there is no room to apply Java options.
```
build_command() {
"$RUNNER" -Xmx128m -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$"
printf "%d\0" $?
}
```
Considering that it's not so many times to apply Java options to Launcher, one compromise would just modify spark-class by user like as follows.
```
build_command() {
"$RUNNER" -Xmx128m $SPARK_LAUNCHER_OPTS -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$"
printf "%d\0" $?
}
```
But it doesn't work when any text related to Java options is output to standard output because whole output is used as command-string for spark-shell and spark-submit in current implementation.
One example is jdwp. When apply agentlib option to use jdwp for debug, we will get output like as follows.
```
Listening for transport dt_socket at address: 9876
```
The output shown above is not a command-string so spark-submit and spark-shell will fail.
To enable Java options for Launcher, we need treat command-string and others.
I changed launcher/Main.java and bin/spark-class to print separator-character and treat it.
## How was this patch tested?
Tested manually using Spark Shell with / without LAUNCHER_JAVA_OPTIONS like as follows.
```
SPARK_LAUNCHER_OPTS="-agentlib:jdwp=transport=dt_socket,suspend=y,address=localhost:9876,server=y" bin/spark-shell
```
Closes#25265 from sarutak/add-spark-launcher-opts.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Implement `RobustScaler`
Since the transformation is quite similar to `StandardScaler`, I refactor the transform function so that it can be reused in both scalers.
## How was this patch tested?
existing and added tests
Closes#25160 from zhengruifeng/robust_scaler.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
… more efficiently
This PR improves the performance of scheduling speculative tasks to be O(1) instead of O(numSpeculativeTasks), using the same approach used for scheduling regular tasks. The performance of this method is particularly important because a lock is held on the TaskSchedulerImpl which is a bottleneck for all scheduling operations. We ran a Join query on a large dataset with speculation enabled and out of 100000 tasks for the ShuffleMapStage, the maximum number of speculatable tasks that was noted was close to 7900-8000 at a point. That is when we start seeing the bottleneck on the scheduler lock.
In particular, this works by storing a separate stack of tasks by executor, node, and rack locality preferences. Then when trying to schedule a speculative task, rather than scanning all speculative tasks to find ones which match the given executor (or node, or rack) preference, we can jump to a quick check of tasks matching the resource offer. This technique was already used for regular tasks -- this change refactors the code to allow sharing with regular and speculative task execution.
## What changes were proposed in this pull request?
Have split the main queue "speculatableTasks" into 5 separate queues based on locality preference similar to how normal tasks are enqueued. Thus, the "dequeueSpeculativeTask" method will avoid performing locality checks for each task at runtime and simply return the preferable task to be executed.
## How was this patch tested?
We ran a spark job that performed a join on a 10 TB dataset to test the code change.
Original Code:
<img width="1433" alt="screen shot 2019-01-28 at 5 07 22 pm" src="https://user-images.githubusercontent.com/22228190/51873321-572df280-2322-11e9-9149-0aae08d5edc6.png">
Optimized Code:
<img width="1435" alt="screen shot 2019-01-28 at 5 08 19 pm" src="https://user-images.githubusercontent.com/22228190/51873343-6745d200-2322-11e9-947b-2cfd0f06bcab.png">
As you can see, the run time of the ShuffleMapStage came down from 40 min to 6 min approximately, thus, reducing the overall running time of the spark job by a significant amount.
Another example for the same job:
Original Code:
<img width="1440" alt="screen shot 2019-01-28 at 5 11 30 pm" src="https://user-images.githubusercontent.com/22228190/51873355-70cf3a00-2322-11e9-9c3a-af035449a306.png">
Optimized Code:
<img width="1440" alt="screen shot 2019-01-28 at 5 12 16 pm" src="https://user-images.githubusercontent.com/22228190/51873367-7dec2900-2322-11e9-8d07-1b1b49285f71.png">
Closes#23677 from pgandhi999/SPARK-26755.
Lead-authored-by: pgandhi <pgandhi@verizonmedia.com>
Co-authored-by: pgandhi <pgandhi@oath.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Support multiple catalogs in the following InsertInto use cases:
- DataFrameWriter.insertInto("catalog.db.tbl")
Support matrix:
SaveMode|Partitioned Table|Partition Overwrite Mode|Action
--------|-----------------|------------------------|------
Append|*|*|AppendData
Overwrite|no|*|OverwriteByExpression(true)
Overwrite|yes|STATIC|OverwriteByExpression(true)
Overwrite|yes|DYNAMIC|OverwritePartitionsDynamic
## How was this patch tested?
New tests.
All existing catalyst and sql/core tests.
Closes#24980 from jzhuge/SPARK-28178-pr.
Authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Right now `Error` is not sent to `QueryExecutionListener.onFailure`. If there is any `Error` (such as `AssertionError`) when running a query, `QueryExecutionListener.onFailure` cannot be triggered.
This PR changes `onFailure` to accept a `Throwable` instead.
## How was this patch tested?
Jenkins
Closes#25292 from zsxwing/fix-QueryExecutionListener.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
New function `make_timestamp()` takes 6 columns `year`, `month`, `day`, `hour`, `min`, `sec` + optionally `timezone`, and makes new column of the `TIMESTAMP` type. If values in the input columns are `null` or out of valid ranges, the function returns `null`. Valid ranges are:
- `year` - `[1, 9999]`
- `month` - `[1, 12]`
- `day` - `[1, 31]`
- `hour` - `[0, 23]`
- `min` - `[0, 59]`
- `sec` - `[0, 60]`. If the `sec` argument equals to 60, the seconds field is set to 0 and 1 minute is added to the final timestamp.
- `timezone` - an identifier of timezone. Actual database of timezones can be found there: https://www.iana.org/time-zones.
Also constructed timestamp must be valid otherwise `make_timestamp` returns `null`.
The function is implemented similarly to `make_timestamp` in PostgreSQL: https://www.postgresql.org/docs/11/functions-datetime.html to maintain feature parity with it.
Here is an example:
```sql
select make_timestamp(2014, 12, 28, 6, 30, 45.887);
2014-12-28 06:30:45.887
select make_timestamp(2014, 12, 28, 6, 30, 45.887, 'CET');
2014-12-28 10:30:45.887
select make_timestamp(2019, 6, 30, 23, 59, 60)
2019-07-01 00:00:00
```
Returned value has Spark Catalyst type `TIMESTAMP` which is similar to Oracle's `TIMESTAMP WITH LOCAL TIME ZONE` (see https://docs.oracle.com/cd/B28359_01/server.111/b28298/ch4datetime.htm#i1006169) where data is stored in the session time zone, and the time zone offset is not stored as part of the column data. When users retrieve the data, Spark returns it in the session time zone specified by the SQL config `spark.sql.session.timeZone`.
## How was this patch tested?
Added new tests to `DateExpressionsSuite`, and uncommented a test for `make_timestamp` in `pgSQL/timestamp.sql`.
Closes#25220 from MaxGekk/make_timestamp.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Currently, if we run the Kubernetes integration tests with `SPARK_HOME` already set, it refers the `SPARK_HOME` even when `--spark-tgz` is specified.
This PR proposes to unset `SPARK_HOME` to let the docker-image-tool script detect `SPARK_HOME`. Otherwise, it cannot indicate the unpacked directory as its home.
## How was this patch tested?
```bash
export SPARK_HOME=`pwd`
dev/make-distribution.sh --pip --tgz -Phadoop-2.7 -Pkubernetes
resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh --deploy-mode docker-for-desktop --spark-tgz $PWD/spark-*.tgz
```
**Before:**
```
+ /.../spark/resource-managers/kubernetes/integration-tests/target/spark-dist-unpacked/bin/docker-image-tool.sh -r docker.io/kubespark -t 650B51C8-BBED-47C9-AEAB-E66FC9A0E64E -p /.../spark/resource-managers/kubernetes/integration-tests/target/spark-dist-unpacked/kubernetes/dockerfiles/spark/bindings/python/Dockerfile build
cp: resource-managers/kubernetes/docker/src/main/dockerfiles: No such file or directory
cp: assembly/target/scala-2.12/jars: No such file or directory
cp: resource-managers/kubernetes/integration-tests/tests: No such file or directory
cp: examples/target/scala-2.12/jars/*: No such file or directory
cp: resource-managers/kubernetes/docker/src/main/dockerfiles: No such file or directory
cp: resource-managers/kubernetes/docker/src/main/dockerfiles: No such file or directory
Cannot find docker image. This script must be run from a runnable distribution of Apache Spark.
...
[INFO] Spark Project Kubernetes Integration Tests ......... FAILURE [ 4.870 s]
[INFO] ------------------------------------------------------------------------
[INFO] BUILD FAILURE
```
**After:**
```
+ /.../spark/resource-managers/kubernetes/integration-tests/target/spark-dist-unpacked/bin/docker-image-tool.sh -r docker.io/kubespark -t 2BA5883A-A0AC-4D2B-8D00-702D31B59B23 -p /.../spark/resource-managers/kubernetes/integration-tests/target/spark-dist-unpacked/kubernetes/dockerfiles/spark/bindings/python/Dockerfile build
Sending build context to Docker daemon 250.2MB
Step 1/15 : FROM openjdk:8-alpine
---> a3562aa0b991
...
Successfully built 8614fb5ac279
Successfully tagged kubespark/spark:2BA5883A-A0AC-4D2B-8D00-702D31B59B23
```
Closes#25283 from HyukjinKwon/SPARK-28550.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This pr is used to support using hostpath/PV volume mounts as local storage. In KubernetesExecutorBuilder.scala, the LocalDrisFeatureStep is built before MountVolumesFeatureStep which means we cannot use any volumes mount later. This pr adjust the order of feature building steps which moves localDirsFeature at last so that we can check if directories in SPARK_LOCAL_DIRS are set to volumes mounted such as hostPath, PV, or others.
## How was this patch tested?
Unit tests
Closes#24879 from chenjunjiedada/SPARK-28042.
Lead-authored-by: Junjie Chen <jimmyjchen@tencent.com>
Co-authored-by: Junjie Chen <cjjnjust@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
These are what I found during working on #22282.
- Remove unused value: `UnsafeArraySuite#defaultTz`
- Remove redundant new modifier to the case class, `KafkaSourceRDDPartition`
- Remove unused variables from `RDD.scala`
- Remove trailing space from `structured-streaming-kafka-integration.md`
- Remove redundant parameter from `ArrowConvertersSuite`: `nullable` is `true` by default.
- Remove leading empty line: `UnsafeRow`
- Remove trailing empty line: `KafkaTestUtils`
- Remove unthrown exception type: `UnsafeMapData`
- Replace unused declarations: `expressions`
- Remove duplicated default parameter: `AnalysisErrorSuite`
- `ObjectExpressionsSuite`: remove duplicated parameters, conversions and unused variable
Closes#25251 from dongjinleekr/cleanup/201907.
Authored-by: Lee Dongjin <dongjin@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR aims to add a SQL function alias `random` to the existing `rand` function.
Please note that this adds the alias to SQL layer only because this is for PostgreSQL feature parity.
- [PostgreSQL Random function](https://www.postgresql.org/docs/11/functions-math.html)
- [SPARK-23160 Port window.sql](https://github.com/apache/spark/pull/24881/files#diff-14489bae6b27814d4cde0456a7ae75c8R702)
- [SPARK-28406 Port union.sql](https://github.com/apache/spark/pull/25163/files#diff-23a3430e0e1ff88830cbb43701da1f2cR402)
## How was this patch tested?
Manual.
```sql
spark-sql> DESCRIBE FUNCTION random;
Function: random
Class: org.apache.spark.sql.catalyst.expressions.Rand
Usage: random([seed]) - Returns a random value with independent and identically distributed (i.i.d.) uniformly distributed values in [0, 1).
```
Closes#25282 from dongjoon-hyun/SPARK-28086.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR aims to recover our K8s integration test suite by extending node affinity in order to pass `PVTestsSuite` in `DockerForDesktop` environment, too. Previously, `PVTestsSuite` fails at `--deploy-mode docker-for-desktop` option because the node affinity requires `minibase` node.
For `Docker Desktop`, there are two node names like the following. Note that Spark testing needs K8s v1.13 and above. So, this PR should be verified with `Docker Desktop (Edge)` version. This PR adds both because next stable `Docker Desktop` will have K8s v1.14.3.
**Docker Desktop (Stable, K8s v1.10.11)**
```
$ kubectl get node
NAME STATUS ROLES AGE VERSION
docker-for-desktop Ready master 52s v1.10.11
```
**Docker Desktop 2.1.0.0 (Edge, K8s v1.14.3, Released 2019-07-26)**
```
$ kubectl get node
NAME STATUS ROLES AGE VERSION
docker-desktop Ready master 16h v1.14.3
```
## How was this patch tested?
Pass the Jenkins K8s integration test (`minibase`) and install `Docker Desktop 2.1.0.0 (Edge)` and run the integration test in `DockerForDesktop`. Note that this fixes only `PVTestsSuite`.
```
$ dev/make-distribution.sh --pip --tgz -Phadoop-2.7 -Pkubernetes
$ resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh --deploy-mode docker-for-desktop --spark-tgz $PWD/spark-*.tgz
...
KubernetesSuite:
...
- PVs with local storage
...
```
Closes#25269 from dongjoon-hyun/SPARK-28534.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to use `uuuu` for years instead of `yyyy` in date/timestamp patterns without the era pattern `G` (https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html). **Parsing/formatting of positive years (current era) will be the same.** The difference is in formatting negative years belong to previous era - BC (Before Christ).
I replaced the `yyyy` pattern by `uuuu` everywhere except:
1. Test, Suite & Benchmark. Existing tests must work as is.
2. `SimpleDateFormat` because it doesn't support the `uuuu` pattern.
3. Comments and examples (except comments related to already replaced patterns).
Before the changes, the year of common era `100` and the year of BC era `-99`, showed similarly as `100`. After the changes negative years will be formatted with the `-` sign.
Before:
```Scala
scala> Seq(java.time.LocalDate.of(-99, 1, 1)).toDF().show
+----------+
| value|
+----------+
|0100-01-01|
+----------+
```
After:
```Scala
scala> Seq(java.time.LocalDate.of(-99, 1, 1)).toDF().show
+-----------+
| value|
+-----------+
|-0099-01-01|
+-----------+
```
## How was this patch tested?
By existing test suites, and added tests for negative years to `DateFormatterSuite` and `TimestampFormatterSuite`.
Closes#25230 from MaxGekk/year-pattern-uuuu.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Code is not generated for LocalTableScanExec although proper situations.
If a LocalTableScanExec plan has the direct parent plan which supports WholeStageCodegen,
the LocalTableScanExec plan also should be within a WholeStageCodegen domain.
But code is not generated for LocalTableScanExec and InputAdapter is inserted for now.
```
val df1 = spark.createDataset(1 to 10).toDF
val df2 = spark.createDataset(1 to 10).toDF
val df3 = df1.join(df2, df1("value") === df2("value"))
df3.explain(true)
...
== Physical Plan ==
*(1) BroadcastHashJoin [value#1], [value#6], Inner, BuildRight
:- LocalTableScan [value#1] // LocalTableScanExec is not within a WholeStageCodegen domain
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)))
+- LocalTableScan [value#6]
```
```
scala> df3.queryExecution.executedPlan.children.head.children.head.getClass
res4: Class[_ <: org.apache.spark.sql.execution.SparkPlan] = class org.apache.spark.sql.execution.InputAdapter
```
For the current implementation of LocalTableScanExec, codegen is enabled in case `parent` is not null
but `parent` is set in `consume`, which is called after `insertInputAdapter` so it doesn't work as intended.
After applying this cnahge, we can get following plan, which means LocalTableScanExec is within a WholeStageCodegen domain.
```
== Physical Plan ==
*(1) BroadcastHashJoin [value#63], [value#68], Inner, BuildRight
:- *(1) LocalTableScan [value#63]
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)))
+- LocalTableScan [value#68]
## How was this patch tested?
New test cases are added into WholeStageCodegenSuite.
Closes#25260 from sarutak/localtablescan-improvement.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
Fix for ```SPARK-28441 (PythonUDF used in correlated scalar subquery causes UnsupportedOperationException)``` is in. Re-enable the commented out test for ```udf(max(udf(column))) ```
## How was this patch tested?
use existing test ```udf-except.sql```
Closes#25278 from huaxingao/spark-28277n.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
In case of CatalogFileIndex datasource table, sizeInBytes is always coming as default size in bytes, which is 8.0EB (Even when the user give fallBackToHdfsForStatsEnabled=true) . So, the datasource table which has CatalogFileIndex, always prefer SortMergeJoin, instead of BroadcastJoin, even though the size is below broadcast join threshold.
In this PR, In case of CatalogFileIndex table, if we enable "fallBackToHdfsForStatsEnabled=true", then the computeStatistics get the sizeInBytes from the hdfs and we get the actual size of the table. Hence, during join operation, when the table size is below broadcast threshold, it will prefer broadCastHashJoin instead of SortMergeJoin.
Added UT
Closes#22502 from shahidki31/SPARK-25474.
Authored-by: shahid <shahidki31@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Add indexOf method for ml.feature.HashingTF.
## How was this patch tested?
Add Unit test.
Closes#25250 from huaxingao/spark-21481.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
`ObjectAggregationIterator` shows a directional info message to increase `spark.sql.objectHashAggregate.sortBased.fallbackThreshold` when the size of the in-memory hash map grows too large and it falls back to sort-based aggregation.
However, we don't know how much we need to increase. This PR adds the size of the current in-memory hash map size to the log message.
**BEFORE**
```
15:21:41.669 Executor task launch worker for task 0 INFO
ObjectAggregationIterator: Aggregation hash map reaches threshold capacity (2 entries), ...
```
**AFTER**
```
15:20:05.742 Executor task launch worker for task 0 INFO
ObjectAggregationIterator: Aggregation hash map size 2 reaches threshold capacity (2 entries), ...
```
## How was this patch tested?
Manual. For example, run `ObjectHashAggregateSuite.scala`'s `typed_count fallback to sort-based aggregation` and search the above message in `target/unit-tests.log`.
Closes#25276 from dongjoon-hyun/SPARK-28545.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR aims to update `zstd-jni` library to bring the latest improvement and bug fixes in `1.4.1` and `1.4.2`.
- https://github.com/facebook/zstd/releases/tag/v1.4.1 (4.5 ~ 11.8% performance improvement from v1.4.0 and bug fixes)
- https://github.com/facebook/zstd/releases/tag/v1.4.2 (bug fixes)
## How was this patch tested?
Pass the Jenkins.
Closes#25275 from dongjoon-hyun/SPARK-28544.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR add support typed `interval` expression:
```sql
spark-sql> select interval 'interval 3 year 1 hour';
interval 3 years 1 hours
spark-sql>
```
Please note that this pr did not add a cast alias for `interval` type like [other types](2d74f14d74/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala (L529-L541)) because neither PostgreSQL nor Hive supports this syntax.
## How was this patch tested?
unit tests
Closes#25241 from wangyum/SPARK-28424.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Replace duplicate code by function `_memory_limit`
## How was this patch tested?
Existing UTs
Closes#25273 from WangGuangxin/python_memory_limit.
Authored-by: wangguangxin.cn <wangguangxin.cn@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
This test tries to detect correct behavior in racy code, where the event
thread is racing with the executor thread that's trying to kill the running
task.
If the event that signals the stage end arrives first, any delay in the
delivery of the message to kill the task causes the code to rapidly process
elements, and may cause the test to assert. Adding a 10ms delay in
LocalSchedulerBackend before the task kill makes the test run through
~1000 elements. A longer delay can easily cause the 10000 elements to
be processed.
Instead, by adding a small delay (10ms) in the test code that processes
elements, there's a much lower probability that the kill event will not
arrive before the end; that leaves a window of 100s for the event
to be delivered to the executor. And because each element only sleeps for
10ms, the test is not really slowed down at all.
Closes#25270 from vanzin/SPARK-28535.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
We should add `assume(shouldTestPythonUDFs)`. Maybe it's not a biggie in general but it can matter in other venders' testing base. For instance, if somebody launches a test in a minimal docker image, it might make the tests failed suddenly.
This skipping stuff isn't completely new in our test base. See `TestUtils.testCommandAvailable` for instance.
## How was this patch tested?
Manually tested.
Closes#25272 from HyukjinKwon/SPARK-28441.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Since for AQP the cost for joins can change between multiple runs, there is no reason that we have an idempotence enforcement on this optimizer batch. We thus make it `FixedPoint(1)` instead of `Once`.
## How was this patch tested?
Existing UTs.
Closes#25266 from yeshengm/SPARK-28530.
Lead-authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the Catalyst optimizer, the batch subquery actually calls the optimizer recursively. Therefore it makes no sense to enforce idempotence on it and we change this batch to `FixedPoint(1)`.
## How was this patch tested?
Existing UTs.
Closes#25267 from yeshengm/SPARK-28532.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In SPARK-15370, We checked the expression at the root of the correlated subquery, in order to fix count bug. If a `PythonUDF` in in the checking path, evaluating it causes the failure as we can't statically evaluate `PythonUDF`. The Python UDF test added at SPARK-28277 shows this issue.
If we can statically evaluate the expression, we intercept NULL values coming from the outer join and replace them with the value that the subquery's expression like before, if it is not, we replace them with the `PythonUDF` expression, with statically evaluated parameters.
After this, the last query in `udf-except.sql` which throws `java.lang.UnsupportedOperationException` can be run:
```
SELECT t1.k
FROM t1
WHERE t1.v <= (SELECT udf(max(udf(t2.v)))
FROM t2
WHERE udf(t2.k) = udf(t1.k))
MINUS
SELECT t1.k
FROM t1
WHERE udf(t1.v) >= (SELECT min(udf(t2.v))
FROM t2
WHERE t2.k = t1.k)
-- !query 2 schema
struct<k:string>
-- !query 2 output
two
```
Note that this issue is also for other non-foldable expressions, like rand. As like PythonUDF, we can't call `eval` on this kind of expressions in optimization. The evaluation needs to defer to query runtime.
## How was this patch tested?
Added tests.
Closes#25204 from viirya/SPARK-28441.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The motivation for these additional metrics is to help in troubleshooting and monitoring task execution workload when running on a cluster. Currently available metrics include executor threadpool metrics for task completed and for active tasks. The addition of threadpool taskStarted metric will allow for example to collect info on the (approximate) number of failed tasks by computing the difference thread started – (active threads + completed tasks and/or successfully finished tasks).
The proposed metric finishedTasks is also intended for this type of troubleshooting. The difference between finshedTasks and threadpool.completeTasks, is that the latter is a (dropwizard library) gauge taken from the threadpool, while the former is a (dropwizard) counter computed in the [[Executor]] class, when a task successfully finishes, together with several other task metrics counters.
Note, there are similarities with some of the metrics introduced in SPARK-24398, however there are key differences, coming from the fact that this PR concerns the executor source, therefore providing metric values per executor + metric values do not require to pass through the listerner bus in this case.
## How was this patch tested?
Manually tested on a YARN cluster
Closes#22290 from LucaCanali/AddMetricExecutorStartedTasks.
Lead-authored-by: Luca Canali <luca.canali@cern.ch>
Co-authored-by: LucaCanali <luca.canali@cern.ch>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
How to reproduce this issue:
```shell
build/sbt clean package -Phive -Phive-thriftserver -Phadoop-3.2
export SPARK_PREPEND_CLASSES=true
sbin/start-thriftserver.sh
[rootspark-3267648 spark]# bin/beeline -u jdbc:hive2://localhost:10000/default -e "select cast(1 as decimal(38, 18));"
Connecting to jdbc:hive2://localhost:10000/default
Connected to: Spark SQL (version 3.0.0-SNAPSHOT)
Driver: Hive JDBC (version 2.3.5)
Transaction isolation: TRANSACTION_REPEATABLE_READ
Error: java.lang.ClassCastException: java.math.BigDecimal incompatible with org.apache.hadoop.hive.common.type.HiveDecimal (state=,code=0)
Closing: 0: jdbc:hive2://localhost:10000/default
```
This pr fix this issue.
## How was this patch tested?
unit tests
Closes#25217 from wangyum/SPARK-28463.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
remove deprecated ``` def context(self, sqlContext)``` from ```pyspark/ml/util.py```
## How was this patch tested?
test with existing ml PySpark test suites
Closes#25246 from huaxingao/spark-28507.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
1, avoid calling param getter in udf;
2, for constant dims, precompute the transformed result;
3, for usual dims, precompute `scale / originalRange(i)` to skip a division;
## How was this patch tested?
existing suites
Closes#25244 from zhengruifeng/minmax_opt.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
`KafkaOffsetRangeCalculator.getRanges` may drop offsets due to round off errors. The test added in this PR is one example.
This PR rewrites the logic in `KafkaOffsetRangeCalculator.getRanges` to ensure it never drops offsets.
## How was this patch tested?
The regression test.
Closes#25237 from zsxwing/fix-range.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In adaptive query processing (AQE), query plans are optimized on the fly during execution. However, a few `Once` rules can be problematic for such optimization since they can either generate wrong plan/unnecessary intermediate plan nodes.
This PR enforces idempotence for "Once" batches that are supposed to run once. This is a key enabler for AQE re-optimization and can improve robustness for existing optimizer rules.
Once batches that are currently not idempotent are marked in a blacklist. We will submit followup PRs to fix idempotence of these rules.
## How was this patch tested?
Existing UTs. Failing Once rules are temporarily blacklisted.
Closes#25249 from yeshengm/idempotence-checker.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Because the local default locale isn't in available locales at `Locale`, when I did some tests locally with python code, `StopWordsRemover` related python test hits some errors, like:
```
Traceback (most recent call last):
File "/spark-1/python/pyspark/ml/tests/test_feature.py", line 87, in test_stopwordsremover
stopWordRemover = StopWordsRemover(inputCol="input", outputCol="output")
File "/spark-1/python/pyspark/__init__.py", line 111, in wrapper
return func(self, **kwargs)
File "/spark-1/python/pyspark/ml/feature.py", line 2646, in __init__
self.uid)
File "/spark-1/python/pyspark/ml/wrapper.py", line 67, in _new_java_obj
return java_obj(*java_args)
File /spark-1/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1554, in __call__
answer, self._gateway_client, None, self._fqn)
File "/spark-1/python/pyspark/sql/utils.py", line 93, in deco
raise converted
pyspark.sql.utils.IllegalArgumentException: 'StopWordsRemover_4598673ee802 parameter locale given invalid value en_TW.'
```
As per HyukjinKwon's advice, instead of setting up locale to pass test, it is better to have a workable locale if system default locale can't be found in available locales in JVM. Otherwise, users have to manually change system locale or accessing a private property _jvm in PySpark.
## How was this patch tested?
Added test and manual test.
```
scala> val remover = new StopWordsRemover().setInputCol("raw").setOutputCol("filtered")
19/07/14 19:20:03 WARN StopWordsRemover: Default locale set was [en_TW]; however, it was not found in available locales in JVM, falling back to en_US locale. Set param `locale` in order to respect another locale.
```
Closes#25133 from viirya/pytest-default-locale.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds some tests converted from 'union.sql' to test UDFs
<details><summary>Diff comparing to 'union.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/union.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-union.sql.out
index b023df825d..84b5e10dbe 100644
--- a/sql/core/src/test/resources/sql-tests/results/union.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-union.sql.out
-19,10 +19,10 struct<>
-- !query 2
-SELECT *
-FROM (SELECT * FROM t1
+SELECT udf(c1) as c1, udf(c2) as c2
+FROM (SELECT udf(c1) as c1, udf(c2) as c2 FROM t1
UNION ALL
- SELECT * FROM t1)
+ SELECT udf(c1) as c1, udf(c2) as c2 FROM t1)
-- !query 2 schema
struct<c1:int,c2:string>
-- !query 2 output
-33,12 +33,12 struct<c1:int,c2:string>
-- !query 3
-SELECT *
-FROM (SELECT * FROM t1
+SELECT udf(c1) as c1, udf(c2) as c2
+FROM (SELECT udf(c1) as c1, udf(c2) as c2 FROM t1
UNION ALL
- SELECT * FROM t2
+ SELECT udf(c1) as c1, udf(c2) as c2 FROM t2
UNION ALL
- SELECT * FROM t2)
+ SELECT udf(c1) as c1, udf(c2) as c2 FROM t2)
-- !query 3 schema
struct<c1:decimal(11,1),c2:string>
-- !query 3 output
-51,11 +51,11 struct<c1:decimal(11,1),c2:string>
-- !query 4
-SELECT a
-FROM (SELECT 0 a, 0 b
+SELECT udf(udf(a)) as a
+FROM (SELECT udf(0) a, udf(0) b
UNION ALL
- SELECT SUM(1) a, CAST(0 AS BIGINT) b
- UNION ALL SELECT 0 a, 0 b) T
+ SELECT udf(SUM(1)) a, udf(CAST(0 AS BIGINT)) b
+ UNION ALL SELECT udf(0) a, udf(0) b) T
-- !query 4 schema
struct<a:bigint>
-- !query 4 output
-89,13 +89,13 struct<>
-- !query 8
-SELECT 1 AS x,
- col
-FROM (SELECT col AS col
- FROM (SELECT p1.col AS col
+SELECT udf(1) AS x,
+ udf(col) as col
+FROM (SELECT udf(col) AS col
+ FROM (SELECT udf(p1.col) AS col
FROM p1 CROSS JOIN p2
UNION ALL
- SELECT col
+ SELECT udf(col)
FROM p3) T1) T2
-- !query 8 schema
struct<x:int,col:int>
-105,9 +105,9 struct<x:int,col:int>
-- !query 9
-SELECT map(1, 2), 'str'
+SELECT map(1, 2), udf('str') as str
UNION ALL
-SELECT map(1, 2, 3, NULL), 1
+SELECT map(1, 2, 3, NULL), udf(1)
-- !query 9 schema
struct<map(1, 2):map<int,int>,str:string>
-- !query 9 output
-116,9 +116,9 struct<map(1, 2):map<int,int>,str:string>
-- !query 10
-SELECT array(1, 2), 'str'
+SELECT array(1, 2), udf('str') as str
UNION ALL
-SELECT array(1, 2, 3, NULL), 1
+SELECT array(1, 2, 3, NULL), udf(1)
-- !query 10 schema
struct<array(1, 2):array<int>,str:string>
-- !query 10 output
```
</p>
</details>
## How was this patch tested?
Tested as guided in SPARK-27921.
Closes#25202 from yiheng/fix_28289.
Authored-by: Yiheng Wang <yihengw@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR fixes the log messages like `attempt 0stage 9.0` by adding a comma followed by space. These are all instances in `DataWritingSparkTask` which was introduced at 6d16b9885d. This should be fixed in `branch-2.4`, too.
```
19/07/25 18:35:01 INFO DataWritingSparkTask: Commit authorized for partition 65 (task 153, attempt 0stage 9.0)
19/07/25 18:35:01 INFO DataWritingSparkTask: Committed partition 65 (task 153, attempt 0stage 9.0)
```
## How was this patch tested?
This only changes log messages. Pass the Jenkins with the existing tests.
Closes#25257 from dongjoon-hyun/DataWritingSparkTask.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Support multiple catalogs in the following InsertTable use cases:
- INSERT INTO [TABLE] catalog.db.tbl
- INSERT OVERWRITE TABLE catalog.db.tbl
Support matrix:
Overwrite|Partitioned Table|Partition Clause |Partition Overwrite Mode|Action
---------|-----------------|-----------------|------------------------|-----
false|*|*|*|AppendData
true|no|(empty)|*|OverwriteByExpression(true)
true|yes|p1,p2 or p1 or p2 or (empty)|STATIC|OverwriteByExpression(true)
true|yes|p2,p2 or p1 or p2 or (empty)|DYNAMIC|OverwritePartitionsDynamic
true|yes|p1=23,p2=3|*|OverwriteByExpression(p1=23 and p2=3)
true|yes|p1=23,p2 or p1=23|STATIC|OverwriteByExpression(p1=23)
true|yes|p1=23,p2 or p1=23|DYNAMIC|OverwritePartitionsDynamic
Notes:
- Assume the partitioned table has 2 partitions: p1 and p2.
- `STATIC` is the default Partition Overwrite Mode for data source tables.
- DSv2 tables currently do not support `IfPartitionNotExists`.
## How was this patch tested?
New tests.
All existing catalyst and sql/core tests.
Closes#24832 from jzhuge/SPARK-27845-pr.
Lead-authored-by: Ryan Blue <blue@apache.org>
Co-authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
## What changes were proposed in this pull request?
Unlike the other versions, `x.x.0-SNAPSHOT` causes `x.x.-1`. Although this will not happen in the tags (there is no `SNAPSHOT` postfix), we had better fix this.
```
$ dev/create-release/do-release-docker.sh -d /tmp/spark-3.0.0 -n
Output directory already exists. Overwrite and continue? [y/n] y
Branch [branch-2.4]: master
Current branch version is 3.0.0-SNAPSHOT.
Release [3.0.-1]:
```
Since we already have `RELEASE_VERSION` by removing `SNAPSHOT`. This PR uses `RELEASE_VERSION` instead of `VERSION`.
```
$ dev/create-release/do-release-docker.sh -d /tmp/spark-3.0.0 -n
Branch [branch-2.4]: master
Current branch version is 3.0.0-SNAPSHOT.
Release [3.0.0]:
```
## How was this patch tested?
Manually do `dev/create-release/do-release-docker.sh -d /tmp/spark-3.0.0 -n` and see the default value of `Release`.
Closes#25254 from dongjoon-hyun/SPARK-28511.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR adds some tests converted from window.sql to test UDFs. Please see the contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'xxx.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/window.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-window.sql.out
index 367dc4f513..9354d5e311 100644
--- a/sql/core/src/test/resources/sql-tests/results/window.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-window.sql.out
-21,10 +21,10 struct<>
-- !query 1
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val ROWS CURRENT ROW) FROM testData
-ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY cate ORDER BY udf(val) ROWS CURRENT ROW) FROM testData
+ORDER BY cate, udf(val)
-- !query 1 schema
-struct<val:int,cate:string,count(val) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST ROWS BETWEEN CURRENT ROW AND CURRENT ROW):bigint>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,count(val) OVER (PARTITION BY cate ORDER BY CAST(udf(cast(val as string)) AS INT) ASC NULLS FIRST ROWS BETWEEN CURRENT ROW AND CURRENT ROW):bigint>
-- !query 1 output
NULL NULL 0
3 NULL 1
-38,10 +38,10 NULL a 0
-- !query 2
-SELECT val, cate, sum(val) OVER(PARTITION BY cate ORDER BY val
-ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, sum(val) OVER(PARTITION BY cate ORDER BY udf(val)
+ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING) FROM testData ORDER BY cate, udf(val)
-- !query 2 schema
-struct<val:int,cate:string,sum(val) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING):bigint>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,sum(val) OVER (PARTITION BY cate ORDER BY CAST(udf(cast(val as string)) AS INT) ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING):bigint>
-- !query 2 output
NULL NULL 3
3 NULL 3
-55,20 +55,20 NULL a 1
-- !query 3
-SELECT val_long, cate, sum(val_long) OVER(PARTITION BY cate ORDER BY val_long
-ROWS BETWEEN CURRENT ROW AND 2147483648 FOLLOWING) FROM testData ORDER BY cate, val_long
+SELECT val_long, udf(cate), sum(val_long) OVER(PARTITION BY cate ORDER BY udf(val_long)
+ROWS BETWEEN CURRENT ROW AND 2147483648 FOLLOWING) FROM testData ORDER BY udf(cate), val_long
-- !query 3 schema
struct<>
-- !query 3 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'ROWS BETWEEN CURRENT ROW AND 2147483648L FOLLOWING' due to data type mismatch: The data type of the upper bound 'bigint' does not match the expected data type 'int'.; line 1 pos 41
+cannot resolve 'ROWS BETWEEN CURRENT ROW AND 2147483648L FOLLOWING' due to data type mismatch: The data type of the upper bound 'bigint' does not match the expected data type 'int'.; line 1 pos 46
-- !query 4
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val RANGE 1 PRECEDING) FROM testData
-ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY val RANGE 1 PRECEDING) FROM testData
+ORDER BY cate, udf(val)
-- !query 4 schema
-struct<val:int,cate:string,count(val) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN 1 PRECEDING AND CURRENT ROW):bigint>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,count(val) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val ASC NULLS FIRST RANGE BETWEEN 1 PRECEDING AND CURRENT ROW):bigint>
-- !query 4 output
NULL NULL 0
3 NULL 1
-82,10 +82,10 NULL a 0
-- !query 5
-SELECT val, cate, sum(val) OVER(PARTITION BY cate ORDER BY val
-RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT val, udf(cate), sum(val) OVER(PARTITION BY udf(cate) ORDER BY val
+RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY udf(cate), val
-- !query 5 schema
-struct<val:int,cate:string,sum(val) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING):bigint>
+struct<val:int,CAST(udf(cast(cate as string)) AS STRING):string,sum(val) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING):bigint>
-- !query 5 output
NULL NULL NULL
3 NULL 3
-99,10 +99,10 NULL a NULL
-- !query 6
-SELECT val_long, cate, sum(val_long) OVER(PARTITION BY cate ORDER BY val_long
-RANGE BETWEEN CURRENT ROW AND 2147483648 FOLLOWING) FROM testData ORDER BY cate, val_long
+SELECT val_long, udf(cate), sum(val_long) OVER(PARTITION BY udf(cate) ORDER BY val_long
+RANGE BETWEEN CURRENT ROW AND 2147483648 FOLLOWING) FROM testData ORDER BY udf(cate), val_long
-- !query 6 schema
-struct<val_long:bigint,cate:string,sum(val_long) OVER (PARTITION BY cate ORDER BY val_long ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 2147483648 FOLLOWING):bigint>
+struct<val_long:bigint,CAST(udf(cast(cate as string)) AS STRING):string,sum(val_long) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val_long ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 2147483648 FOLLOWING):bigint>
-- !query 6 output
NULL NULL NULL
1 NULL 1
-116,10 +116,10 NULL b NULL
-- !query 7
-SELECT val_double, cate, sum(val_double) OVER(PARTITION BY cate ORDER BY val_double
-RANGE BETWEEN CURRENT ROW AND 2.5 FOLLOWING) FROM testData ORDER BY cate, val_double
+SELECT val_double, udf(cate), sum(val_double) OVER(PARTITION BY udf(cate) ORDER BY val_double
+RANGE BETWEEN CURRENT ROW AND 2.5 FOLLOWING) FROM testData ORDER BY udf(cate), val_double
-- !query 7 schema
-struct<val_double:double,cate:string,sum(val_double) OVER (PARTITION BY cate ORDER BY val_double ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND CAST(2.5 AS DOUBLE) FOLLOWING):double>
+struct<val_double:double,CAST(udf(cast(cate as string)) AS STRING):string,sum(val_double) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val_double ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND CAST(2.5 AS DOUBLE) FOLLOWING):double>
-- !query 7 output
NULL NULL NULL
1.0 NULL 1.0
-133,10 +133,10 NULL NULL NULL
-- !query 8
-SELECT val_date, cate, max(val_date) OVER(PARTITION BY cate ORDER BY val_date
-RANGE BETWEEN CURRENT ROW AND 2 FOLLOWING) FROM testData ORDER BY cate, val_date
+SELECT val_date, udf(cate), max(val_date) OVER(PARTITION BY udf(cate) ORDER BY val_date
+RANGE BETWEEN CURRENT ROW AND 2 FOLLOWING) FROM testData ORDER BY udf(cate), val_date
-- !query 8 schema
-struct<val_date:date,cate:string,max(val_date) OVER (PARTITION BY cate ORDER BY val_date ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 2 FOLLOWING):date>
+struct<val_date:date,CAST(udf(cast(cate as string)) AS STRING):string,max(val_date) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val_date ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 2 FOLLOWING):date>
-- !query 8 output
NULL NULL NULL
2017-08-01 NULL 2017-08-01
-150,11 +150,11 NULL NULL NULL
-- !query 9
-SELECT val_timestamp, cate, avg(val_timestamp) OVER(PARTITION BY cate ORDER BY val_timestamp
+SELECT val_timestamp, udf(cate), avg(val_timestamp) OVER(PARTITION BY udf(cate) ORDER BY val_timestamp
RANGE BETWEEN CURRENT ROW AND interval 23 days 4 hours FOLLOWING) FROM testData
-ORDER BY cate, val_timestamp
+ORDER BY udf(cate), val_timestamp
-- !query 9 schema
-struct<val_timestamp:timestamp,cate:string,avg(CAST(val_timestamp AS DOUBLE)) OVER (PARTITION BY cate ORDER BY val_timestamp ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND interval 3 weeks 2 days 4 hours FOLLOWING):double>
+struct<val_timestamp:timestamp,CAST(udf(cast(cate as string)) AS STRING):string,avg(CAST(val_timestamp AS DOUBLE)) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val_timestamp ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND interval 3 weeks 2 days 4 hours FOLLOWING):double>
-- !query 9 output
NULL NULL NULL
2017-07-31 17:00:00 NULL 1.5015456E9
-168,10 +168,10 NULL NULL NULL
-- !query 10
-SELECT val, cate, sum(val) OVER(PARTITION BY cate ORDER BY val DESC
+SELECT val, udf(cate), sum(val) OVER(PARTITION BY cate ORDER BY val DESC
RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, val
-- !query 10 schema
-struct<val:int,cate:string,sum(val) OVER (PARTITION BY cate ORDER BY val DESC NULLS LAST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING):bigint>
+struct<val:int,CAST(udf(cast(cate as string)) AS STRING):string,sum(val) OVER (PARTITION BY cate ORDER BY val DESC NULLS LAST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING):bigint>
-- !query 10 output
NULL NULL NULL
3 NULL 3
-185,58 +185,58 NULL a NULL
-- !query 11
-SELECT val, cate, count(val) OVER(PARTITION BY cate
-ROWS BETWEEN UNBOUNDED FOLLOWING AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate)
+ROWS BETWEEN UNBOUNDED FOLLOWING AND 1 FOLLOWING) FROM testData ORDER BY cate, udf(val)
-- !query 11 schema
struct<>
-- !query 11 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'ROWS BETWEEN UNBOUNDED FOLLOWING AND 1 FOLLOWING' due to data type mismatch: Window frame upper bound '1' does not follow the lower bound 'unboundedfollowing$()'.; line 1 pos 33
+cannot resolve 'ROWS BETWEEN UNBOUNDED FOLLOWING AND 1 FOLLOWING' due to data type mismatch: Window frame upper bound '1' does not follow the lower bound 'unboundedfollowing$()'.; line 1 pos 38
-- !query 12
-SELECT val, cate, count(val) OVER(PARTITION BY cate
-RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate)
+RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, udf(val)
-- !query 12 schema
struct<>
-- !query 12 output
org.apache.spark.sql.AnalysisException
-cannot resolve '(PARTITION BY testdata.`cate` RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: A range window frame cannot be used in an unordered window specification.; line 1 pos 33
+cannot resolve '(PARTITION BY CAST(udf(cast(cate as string)) AS STRING) RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: A range window frame cannot be used in an unordered window specification.; line 1 pos 38
-- !query 13
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val, cate
-RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY udf(val), cate
+RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, udf(val)
-- !query 13 schema
struct<>
-- !query 13 output
org.apache.spark.sql.AnalysisException
-cannot resolve '(PARTITION BY testdata.`cate` ORDER BY testdata.`val` ASC NULLS FIRST, testdata.`cate` ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: A range window frame with value boundaries cannot be used in a window specification with multiple order by expressions: val#x ASC NULLS FIRST,cate#x ASC NULLS FIRST; line 1 pos 33
+cannot resolve '(PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY CAST(udf(cast(val as string)) AS INT) ASC NULLS FIRST, testdata.`cate` ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: A range window frame with value boundaries cannot be used in a window specification with multiple order by expressions: cast(udf(cast(val#x as string)) as int) ASC NULLS FIRST,cate#x ASC NULLS FIRST; line 1 pos 38
-- !query 14
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY current_timestamp
-RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY current_timestamp
+RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, udf(val)
-- !query 14 schema
struct<>
-- !query 14 output
org.apache.spark.sql.AnalysisException
-cannot resolve '(PARTITION BY testdata.`cate` ORDER BY current_timestamp() ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: The data type 'timestamp' used in the order specification does not match the data type 'int' which is used in the range frame.; line 1 pos 33
+cannot resolve '(PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY current_timestamp() ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: The data type 'timestamp' used in the order specification does not match the data type 'int' which is used in the range frame.; line 1 pos 38
-- !query 15
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val
-RANGE BETWEEN 1 FOLLOWING AND 1 PRECEDING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY val
+RANGE BETWEEN 1 FOLLOWING AND 1 PRECEDING) FROM testData ORDER BY udf(cate), val
-- !query 15 schema
struct<>
-- !query 15 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'RANGE BETWEEN 1 FOLLOWING AND 1 PRECEDING' due to data type mismatch: The lower bound of a window frame must be less than or equal to the upper bound; line 1 pos 33
+cannot resolve 'RANGE BETWEEN 1 FOLLOWING AND 1 PRECEDING' due to data type mismatch: The lower bound of a window frame must be less than or equal to the upper bound; line 1 pos 38
-- !query 16
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val
-RANGE BETWEEN CURRENT ROW AND current_date PRECEDING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY udf(val)
+RANGE BETWEEN CURRENT ROW AND current_date PRECEDING) FROM testData ORDER BY cate, val(val)
-- !query 16 schema
struct<>
-- !query 16 output
-245,48 +245,48 org.apache.spark.sql.catalyst.parser.ParseException
Frame bound value must be a literal.(line 2, pos 30)
== SQL ==
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val
-RANGE BETWEEN CURRENT ROW AND current_date PRECEDING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY udf(val)
+RANGE BETWEEN CURRENT ROW AND current_date PRECEDING) FROM testData ORDER BY cate, val(val)
------------------------------^^^
-- !query 17
-SELECT val, cate,
-max(val) OVER w AS max,
-min(val) OVER w AS min,
-min(val) OVER w AS min,
-count(val) OVER w AS count,
-sum(val) OVER w AS sum,
-avg(val) OVER w AS avg,
-stddev(val) OVER w AS stddev,
-first_value(val) OVER w AS first_value,
-first_value(val, true) OVER w AS first_value_ignore_null,
-first_value(val, false) OVER w AS first_value_contain_null,
-last_value(val) OVER w AS last_value,
-last_value(val, true) OVER w AS last_value_ignore_null,
-last_value(val, false) OVER w AS last_value_contain_null,
+SELECT udf(val), cate,
+max(udf(val)) OVER w AS max,
+min(udf(val)) OVER w AS min,
+min(udf(val)) OVER w AS min,
+count(udf(val)) OVER w AS count,
+sum(udf(val)) OVER w AS sum,
+avg(udf(val)) OVER w AS avg,
+stddev(udf(val)) OVER w AS stddev,
+first_value(udf(val)) OVER w AS first_value,
+first_value(udf(val), true) OVER w AS first_value_ignore_null,
+first_value(udf(val), false) OVER w AS first_value_contain_null,
+last_value(udf(val)) OVER w AS last_value,
+last_value(udf(val), true) OVER w AS last_value_ignore_null,
+last_value(udf(val), false) OVER w AS last_value_contain_null,
rank() OVER w AS rank,
dense_rank() OVER w AS dense_rank,
cume_dist() OVER w AS cume_dist,
percent_rank() OVER w AS percent_rank,
ntile(2) OVER w AS ntile,
row_number() OVER w AS row_number,
-var_pop(val) OVER w AS var_pop,
-var_samp(val) OVER w AS var_samp,
-approx_count_distinct(val) OVER w AS approx_count_distinct,
-covar_pop(val, val_long) OVER w AS covar_pop,
-corr(val, val_long) OVER w AS corr,
-stddev_samp(val) OVER w AS stddev_samp,
-stddev_pop(val) OVER w AS stddev_pop,
-collect_list(val) OVER w AS collect_list,
-collect_set(val) OVER w AS collect_set,
-skewness(val_double) OVER w AS skewness,
-kurtosis(val_double) OVER w AS kurtosis
+var_pop(udf(val)) OVER w AS var_pop,
+var_samp(udf(val)) OVER w AS var_samp,
+approx_count_distinct(udf(val)) OVER w AS approx_count_distinct,
+covar_pop(udf(val), udf(val_long)) OVER w AS covar_pop,
+corr(udf(val), udf(val_long)) OVER w AS corr,
+stddev_samp(udf(val)) OVER w AS stddev_samp,
+stddev_pop(udf(val)) OVER w AS stddev_pop,
+collect_list(udf(val)) OVER w AS collect_list,
+collect_set(udf(val)) OVER w AS collect_set,
+skewness(udf(val_double)) OVER w AS skewness,
+kurtosis(udf(val_double)) OVER w AS kurtosis
FROM testData
-WINDOW w AS (PARTITION BY cate ORDER BY val)
-ORDER BY cate, val
+WINDOW w AS (PARTITION BY udf(cate) ORDER BY udf(val))
+ORDER BY cate, udf(val)
-- !query 17 schema
-struct<val:int,cate:string,max:int,min:int,min:int,count:bigint,sum:bigint,avg:double,stddev:double,first_value:int,first_value_ignore_null:int,first_value_contain_null:int,last_value:int,last_value_ignore_null:int,last_value_contain_null:int,rank:int,dense_rank:int,cume_dist:double,percent_rank:double,ntile:int,row_number:int,var_pop:double,var_samp:double,approx_count_distinct:bigint,covar_pop:double,corr:double,stddev_samp:double,stddev_pop:double,collect_list:array<int>,collect_set:array<int>,skewness:double,kurtosis:double>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,max:int,min:int,min:int,count:bigint,sum:bigint,avg:double,stddev:double,first_value:int,first_value_ignore_null:int,first_value_contain_null:int,last_value:int,last_value_ignore_null:int,last_value_contain_null:int,rank:int,dense_rank:int,cume_dist:double,percent_rank:double,ntile:int,row_number:int,var_pop:double,var_samp:double,approx_count_distinct:bigint,covar_pop:double,corr:double,stddev_samp:double,stddev_pop:double,collect_list:array<int>,collect_set:array<int>,skewness:double,kurtosis:double>
-- !query 17 output
NULL NULL NULL NULL NULL 0 NULL NULL NULL NULL NULL NULL NULL NULL NULL 1 1 0.5 0.0 1 1 NULL NULL 0 NULL NULL
NULL NULL [] [] NULL NULL
3 NULL 3 3 3 1 3 3.0 NaN NULL 3 NULL 3 3 3 2 2 1.0 1.0 2 2 0.0 NaN 1 0.0 NaN
NaN 0.0 [3] [3] NaN NaN
-300,9 +300,9 NULL a NULL NULL NULL 0 NULL NULL NULL NULL NULL NULL NULL NULL NULL 1 1 0.25 0.
-- !query 18
-SELECT val, cate, avg(null) OVER(PARTITION BY cate ORDER BY val) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, avg(null) OVER(PARTITION BY cate ORDER BY val) FROM testData ORDER BY cate, val
-- !query 18 schema
-struct<val:int,cate:string,avg(CAST(NULL AS DOUBLE)) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):double>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,avg(CAST(NULL AS DOUBLE)) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):double>
-- !query 18 output
NULL NULL NULL
3 NULL NULL
-316,7 +316,7 NULL a NULL
-- !query 19
-SELECT val, cate, row_number() OVER(PARTITION BY cate) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, row_number() OVER(PARTITION BY cate) FROM testData ORDER BY cate, udf(val)
-- !query 19 schema
struct<>
-- !query 19 output
-325,9 +325,9 Window function row_number() requires window to be ordered, please add ORDER BY
-- !query 20
-SELECT val, cate, sum(val) OVER(), avg(val) OVER() FROM testData ORDER BY cate, val
+SELECT udf(val), cate, sum(val) OVER(), avg(val) OVER() FROM testData ORDER BY cate, val
-- !query 20 schema
-struct<val:int,cate:string,sum(CAST(val AS BIGINT)) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING):bigint,avg(CAST(val AS BIGINT)) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING):double>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,sum(CAST(val AS BIGINT)) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING):bigint,avg(CAST(val AS BIGINT)) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING):double>
-- !query 20 output
NULL NULL 13 1.8571428571428572
3 NULL 13 1.8571428571428572
-341,7 +341,7 NULL a 13 1.8571428571428572
-- !query 21
-SELECT val, cate,
+SELECT udf(val), cate,
first_value(false) OVER w AS first_value,
first_value(true, true) OVER w AS first_value_ignore_null,
first_value(false, false) OVER w AS first_value_contain_null,
-352,7 +352,7 FROM testData
WINDOW w AS ()
ORDER BY cate, val
-- !query 21 schema
-struct<val:int,cate:string,first_value:boolean,first_value_ignore_null:boolean,first_value_contain_null:boolean,last_value:boolean,last_value_ignore_null:boolean,last_value_contain_null:boolean>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,first_value:boolean,first_value_ignore_null:boolean,first_value_contain_null:boolean,last_value:boolean,last_value_ignore_null:boolean,last_value_contain_null:boolean>
-- !query 21 output
NULL NULL false true false false true false
3 NULL false true false false true false
-366,12 +366,12 NULL a false true false false true false
-- !query 22
-SELECT cate, sum(val) OVER (w)
+SELECT udf(cate), sum(val) OVER (w)
FROM testData
WHERE val is not null
WINDOW w AS (PARTITION BY cate ORDER BY val)
-- !query 22 schema
-struct<cate:string,sum(CAST(val AS BIGINT)) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):bigint>
+struct<CAST(udf(cast(cate as string)) AS STRING):string,sum(CAST(val AS BIGINT)) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):bigint>
-- !query 22 output
NULL 3
a 2
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25195 from younggyuchun/master.
Authored-by: younggyu chun <younggyuchun@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the current implementation. complex types like Array/Map/StructType are allowed to upcast as StringType.
This is not safe casting. We should disallow it.
## How was this patch tested?
Update the existing test case
Closes#25242 from gengliangwang/fixUpCastStringType.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
