### What changes were proposed in this pull request?
Remove meaning less tooltip from All pages.
### Why are the changes needed?
If we can't come up with meaningful tooltips, then tooltips not require to add.
### Does this PR introduce any user-facing change?
Yes
tooltips like highlight in above image got removed
### How was this patch tested?
Manual test.
Closes#27043 from 07ARB/SPARK-30383.
Authored-by: 07ARB <ankitrajboudh@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/26780
In https://github.com/apache/spark/pull/26780, a new Avro data source option `actualSchema` is introduced for setting the original Avro schema in function `from_avro`, while the expected schema is supposed to be set in the parameter `jsonFormatSchema` of `from_avro`.
However, there is another Avro data source option `avroSchema`. It is used for setting the expected schema in readiong and writing.
This PR is to use the option `avroSchema` option for reading Avro data with an evolved schema and remove the new one `actualSchema`
### Why are the changes needed?
Unify and simplify the Avro data source options.
### Does this PR introduce any user-facing change?
Yes.
To deserialize Avro data with an evolved schema, before changes:
```
from_avro('col, expectedSchema, ("actualSchema" -> actualSchema))
```
After changes:
```
from_avro('col, actualSchema, ("avroSchema" -> expectedSchema))
```
The second parameter is always the actual Avro schema after changes.
### How was this patch tested?
Update the existing tests in https://github.com/apache/spark/pull/26780Closes#27045 from gengliangwang/renameAvroOption.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In `SqlBase.g4`, `database` is defined as
```
database : DATABASE | SCHEMA;
```
and it is being used as `(database | NAMESPACE)` in many places.
This PR proposes to define the following and use it as discussed in https://github.com/apache/spark/pull/26847/files#r359754778:
```
namespace : NAMESPACE | DATABASE | SCHEMA;
```
### Why are the changes needed?
To simplify the grammar.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
There is no change in the actual grammar, so the existing tests should be sufficient.
Closes#27027 from imback82/sqlbase_namespace.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch fixes the flaky test failure on MasterSuite, "SPARK-27510: Master should avoid dead loop while launching executor failed in Worker".
The culprit of test failure was ironically the test ran too fast; the interval of `eventually` is by default "15 ms", but it took only "8 ms" from submitting driver to removing app from master.
```
19/12/23 15:45:06.533 dispatcher-event-loop-6 INFO Master: Registering worker localhost:9999 with 10 cores, 3.6 GiB RAM
19/12/23 15:45:06.534 dispatcher-event-loop-6 INFO Master: Driver submitted org.apache.spark.FakeClass
19/12/23 15:45:06.535 dispatcher-event-loop-6 INFO Master: Launching driver driver-20191223154506-0000 on worker 10001
19/12/23 15:45:06.536 dispatcher-event-loop-9 INFO Master: Registering app name
19/12/23 15:45:06.537 dispatcher-event-loop-9 INFO Master: Registered app name with ID app-20191223154506-0000
19/12/23 15:45:06.537 dispatcher-event-loop-9 INFO Master: Launching executor app-20191223154506-0000/0 on worker 10001
19/12/23 15:45:06.537 dispatcher-event-loop-10 INFO Master: Removing executor app-20191223154506-0000/0 because it is FAILED
...
19/12/23 15:45:06.542 dispatcher-event-loop-19 ERROR Master: Application name with ID app-20191223154506-0000 failed 10 times; removing it
```
Given the interval is already tiny, instead of lowering interval, the patch considers above case as well when verifying the status.
### Why are the changes needed?
We observed intermittent test failure in Jenkins build which should be fixed.
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/115664/testReport/
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Modified UT.
Closes#27004 from HeartSaVioR/SPARK-30348.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Remove it from `CoarseGrainedSchedulerBackend` when `HeartbeatReceiver` recognizes a lost executor.
### Why are the changes needed?
Currently, an application may hang if we don't remove a lost executor from `CoarseGrainedSchedulerBackend` as it may happens due to:
1) In `expireDeadHosts()`, `HeartbeatReceiver` calls `scheduler.executorLost()`;
2) Before `HeartbeatReceiver` calls `sc.killAndReplaceExecutor()`(which would mark the lost executor as "pendingToRemove") in a separate thread, `CoarseGrainedSchedulerBackend` may begins to launch tasks on that executor without realizing it has been lost indeed.
3) If that lost executor doesn't shut down gracefully, `CoarseGrainedSchedulerBackend ` may never receive a disconnect event. As a result, tasks launched on that lost executor become orphans. While at the same time, driver just thinks that those tasks are still running and waits forever.
Removing the lost executor from `CoarseGrainedSchedulerBackend` would let `TaskSetManager` mark those tasks as failed which avoids app hang. Furthermore, it cleans up records in `executorDataMap`, which may never be removed in such case.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Updated existed tests.
Close#24350.
Closes#26980 from Ngone51/SPARK-27348.
Lead-authored-by: yi.wu <yi.wu@databricks.com>
Co-authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
using `MetadataUtils.getNumFeatures` to extract the numFeatures
### Why are the changes needed?
may avoid `first`/`head` job if metadata has attrGroup
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#27037 from zhengruifeng/unify_numFeatures.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
This reverts commit f926809a1f.
Closes#27032 from gatorsmile/revertSPARK-29390.
Authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Updated the document for LIST FILE/JAR command.
### Why are the changes needed?
LIST FILE/JAR can take multiple filenames as argument and it returns the files which were added as resources.
### Does this PR introduce any user-facing change?
Yes. Documentation updated for LIST FILE/JAR command
### How was this patch tested?
Manually
Closes#26996 from iRakson/SPARK-30342.
Authored-by: root1 <raksonrakesh@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
compute count and quantile on one pass
### Why are the changes needed?
to avoid extra pass
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#26990 from zhengruifeng/quantile_count_lsh.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
1, remove unused imports and variables
2, remove `countAccum: LongAccumulator`, since `costAccum: DoubleAccumulator` also records the count
3, mark `clusterCentersWithNorm` in KMeansModel trasient and lazy, since it is only used in transformation and can be directly generated from the centers.
### Why are the changes needed?
1,remove unused codes
2,avoid repeated computation
3,reduce broadcasted size
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#27014 from zhengruifeng/kmeans_clean_up.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
Currently SPARK history server display the classpath entries in the Environment tab with classpaths redacted, this is because EventLog file has the entry values redacted while writing. But when same is seen from a running application UI, its seen that it is not redacted. Classpath entries redact is not needed and can be avoided
### What changes were proposed in this pull request?
Event logs will not redect the classpath entries
### Why are the changes needed?
Redact of classpath entries is not needed
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
Tested manually to verify on UI
Closes#27016 from ajithme/redactui.
Authored-by: Ajith <ajith2489@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
precompute the `DecisionTreeMetadata` and reuse it for all trees
### Why are the changes needed?
In existing impl, each `DecisionTreeRegressor` needs a pass on the whole dataset to calculate the same `DecisionTreeMetadata` repeatedly.
In this PR, with default depth=5, it is about 8% faster then existing impl
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#27011 from zhengruifeng/gbt_reuse_instr_meta.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
Create temporary or permanent function it should throw AnalysisException if the resource is not found. Need to keep behavior consistent across permanent and temporary functions.
## How was this patch tested?
Added UT and also tested manually
**Before Fix**
If the UDF resource is not present then on creation of temporary function it throws AnalysisException where as for permanent function it does not throw. Permanent funtcion throws AnalysisException only after select operation is performed.
**After Fix**
For temporary and permanent function check for the resource, if the UDF resource is not found then throw AnalysisException
Closes#25399 from sandeep-katta/funcIssue.
Authored-by: sandeep katta <sandeep.katta2007@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This patch fixes the intermittent test failure on ThriftServerQueryTestSuite/ThriftServerWithSparkContextSuite, getting ConnectException when querying to thrift server.
(https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/115646/testReport/)
The relevant unit test log messages are following:
```
19/12/23 13:33:01.875 pool-1-thread-1 INFO AbstractService: Service:ThriftBinaryCLIService is started.
19/12/23 13:33:01.875 pool-1-thread-1 INFO AbstractService: Service:HiveServer2 is started.
...
19/12/23 13:33:01.888 pool-1-thread-1 INFO ThriftServerWithSparkContextSuite: HiveThriftServer2 started successfully
...
19/12/23 13:33:01.909 pool-1-thread-1-ScalaTest-running-ThriftServerWithSparkContextSuite INFO ThriftServerWithSparkContextSuite:
===== TEST OUTPUT FOR o.a.s.sql.hive.thriftserver.ThriftServerWithSparkContextSuite: 'SPARK-29911: Uncache cached tables when session closed' =====
...
19/12/23 13:33:02.017 pool-1-thread-1-ScalaTest-running-ThriftServerWithSparkContextSuite INFO Utils: Supplied authorities: localhost:15441
19/12/23 13:33:02.018 pool-1-thread-1-ScalaTest-running-ThriftServerWithSparkContextSuite INFO Utils: Resolved authority: localhost:15441
19/12/23 13:33:02.078 HiveServer2-Background-Pool: Thread-213 INFO BaseSessionStateBuilder$$anon$2: Optimization rule 'org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation' is excluded from the optimizer.
19/12/23 13:33:02.078 HiveServer2-Background-Pool: Thread-213 INFO BaseSessionStateBuilder$$anon$2: Optimization rule 'org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation' is excluded from the optimizer.
19/12/23 13:33:02.121 pool-1-thread-1-ScalaTest-running-ThriftServerWithSparkContextSuite WARN HiveConnection: Failed to connect to localhost:15441
19/12/23 13:33:02.124 pool-1-thread-1-ScalaTest-running-ThriftServerWithSparkContextSuite INFO ThriftServerWithSparkContextSuite:
===== FINISHED o.a.s.sql.hive.thriftserver.ThriftServerWithSparkContextSuite: 'SPARK-29911: Uncache cached tables when session closed' =====
19/12/23 13:33:02.143 Thread-35 INFO ThriftCLIService: Starting ThriftBinaryCLIService on port 15441 with 5...500 worker threads
19/12/23 13:33:02.327 pool-1-thread-1 INFO HiveServer2: Shutting down HiveServer2
19/12/23 13:33:02.328 pool-1-thread-1 INFO ThriftCLIService: Thrift server has stopped
```
(Here the error is logged as `WARN HiveConnection: Failed to connect to localhost:15441` - the actual stack trace can be seen on Jenkins test summary.)
The reason of test failure: Thrift(Binary|Http)CLIService prepare and launch the service asynchronously (in new thread), which suites are not waiting for completion and just start running tests, ends up with race condition.
That can be easily reproduced, via adding artificial sleep in `ThriftBinaryCLIService.run()` here:
ba3f6330dd/sql/hive-thriftserver/v2.3/src/main/java/org/apache/hive/service/cli/thrift/ThriftBinaryCLIService.java (L49)
(Note that `sleep` should be added before initializing server socket. E.g. Line 57)
This patch changes the test initialization logic to try executing simple query to wait until the service is available. The patch also refactors the code to apply the change both ThriftServerQueryTestSuite and ThriftServerWithSparkContextSuite easily.
### Why are the changes needed?
This patch fixes the intermittent failure observed here:
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/115646/testReport/
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Artificially made the test fail consistently (by the approach described above), and confirmed the patch fixed the test.
Closes#27001 from HeartSaVioR/SPARK-30345.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Unify `DriverEndpoint. executorIsAlive()` and `CoarseGrainedSchedulerBackend .isExecutorActive()`.
### Why are the changes needed?
`DriverEndPoint` has method `executorIsAlive()` to check wether an executor is alive/active, while `CoarseGrainedSchedulerBackend` has method `isExecutorActive()` to do the same work. But, `isExecutorActive()` seems forget to consider `executorsPendingLossReason`. Unify these two methods makes behavior be consistent between `DriverEndPoint` and `CoarseGrainedSchedulerBackend` and make code more easier to maintain.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass Jenkins.
Closes#27012 from Ngone51/unify-is-executor-alive.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
supports instance weighting in GMM
### Why are the changes needed?
ML should support instance weighting
### Does this PR introduce any user-facing change?
yes, a new param `weightCol` is exposed
### How was this patch tested?
added testsuits
Closes#26735 from zhengruifeng/gmm_support_weight.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
add a corresponding class MultivariateGaussian containing a vector and a matrix on the py side, so gaussian can be used on the py side.
### Does this PR introduce any user-facing change?
add Python class ```MultivariateGaussian```
### How was this patch tested?
doctest
Closes#27020 from huaxingao/spark-30247.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Update the Spark SQL document menu and join strategy hints.
### Why are the changes needed?
- Several new changes in the Spark SQL document didn't change the menu-sql.yaml correspondingly.
- Update the demo code for join strategy hints.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Document change only.
Closes#26917 from xuanyuanking/SPARK-30278.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### Why are the changes needed?
`EnsureRequirements` adds `ShuffleExchangeExec` (RangePartitioning) after Sort if `RoundRobinPartitioning` behinds it. This will cause 2 shuffles, and the number of partitions in the final stage is not the number specified by `RoundRobinPartitioning.
**Example SQL**
```
SELECT /*+ REPARTITION(5) */ * FROM test ORDER BY a
```
**BEFORE**
```
== Physical Plan ==
*(1) Sort [a#0 ASC NULLS FIRST], true, 0
+- Exchange rangepartitioning(a#0 ASC NULLS FIRST, 200), true, [id=#11]
+- Exchange RoundRobinPartitioning(5), false, [id=#9]
+- Scan hive default.test [a#0, b#1], HiveTableRelation `default`.`test`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [a#0, b#1]
```
**AFTER**
```
== Physical Plan ==
*(1) Sort [a#0 ASC NULLS FIRST], true, 0
+- Exchange rangepartitioning(a#0 ASC NULLS FIRST, 5), true, [id=#11]
+- Scan hive default.test [a#0, b#1], HiveTableRelation `default`.`test`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [a#0, b#1]
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Run suite Tests and add new test for this.
Closes#26946 from stczwd/RoundRobinPartitioning.
Lead-authored-by: lijunqing <lijunqing@baidu.com>
Co-authored-by: stczwd <qcsd2011@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Implement Factorization Machines as a ml-pipeline component
1. loss function supports: logloss, mse
2. optimizer: GD, adamW
### Why are the changes needed?
Factorization Machines is widely used in advertising and recommendation system to estimate CTR(click-through rate).
Advertising and recommendation system usually has a lot of data, so we need Spark to estimate the CTR, and Factorization Machines are common ml model to estimate CTR.
References:
1. S. Rendle, “Factorization machines,” in Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 995–1000, 2010.
https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
run unit tests
Closes#27000 from mob-ai/ml/fm.
Authored-by: zhanjf <zhanjf@mob.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Settings to quiet for Class `ExecutorAllocationManager` that request message too verbose. otherwise, this class generates too many messages like
`INFO spark.ExecutorAllocationManager: Request to remove executorIds: 890`
when we enabled DRA.
### Why are the changes needed?
Log level improvement.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Closes#26925 from cfmcgrady/quiet-request-executor-remove-message.
Authored-by: Fu Chen <cfmcgrady@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR fixes `ScalaReflection.arrayClassFor()` to use an empty array instead of a one-element array for getting its class object by reflection.
### Why are the changes needed?
Because it may reduce unnecessary memory allocation.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Ran the existing unit tests for sql/catalyst and confirmed that all of them succeeded.
Closes#27005 from sekikn/SPARK-30350.
Authored-by: Kengo Seki <sekikn@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
The filter predicate for aggregate expression is an `ANSI SQL`.
```
<aggregate function> ::=
COUNT <left paren> <asterisk> <right paren> [ <filter clause> ]
| <general set function> [ <filter clause> ]
| <binary set function> [ <filter clause> ]
| <ordered set function> [ <filter clause> ]
| <array aggregate function> [ <filter clause> ]
| <row pattern count function> [ <filter clause> ]
```
There are some mainstream database support this syntax.
**PostgreSQL:**
https://www.postgresql.org/docs/current/sql-expressions.html#SYNTAX-AGGREGATES
For example:
```
SELECT
year,
count(*) FILTER (WHERE gdp_per_capita >= 40000)
FROM
countries
GROUP BY
year
```
```
SELECT
year,
code,
gdp_per_capita,
count(*)
FILTER (WHERE gdp_per_capita >= 40000)
OVER (PARTITION BY year)
FROM
countries
```
**jOOQ:**
https://blog.jooq.org/2014/12/30/the-awesome-postgresql-9-4-sql2003-filter-clause-for-aggregate-functions/
**Notice:**
1.This PR only supports FILTER predicate without codegen. maropu will create another PR is related to SPARK-30027 to support codegen.
2.This PR only supports FILTER predicate without DISTINCT. I will create another PR is related to SPARK-30276 to support this.
3.This PR only supports FILTER predicate that can't reference the outer query. I created ticket SPARK-30219 to support it.
4.This PR only supports FILTER predicate that can't use IN/EXISTS predicate sub-queries. I created ticket SPARK-30220 to support it.
5.Spark SQL cannot supports a SQL with nested aggregate. I created ticket SPARK-30182 to support it.
There are some show of the PR on my production environment.
```
spark-sql> desc gja_test_partition;
key string NULL
value string NULL
other string NULL
col2 int NULL
# Partition Information
# col_name data_type comment
col2 int NULL
Time taken: 0.79 s
```
```
spark-sql> select * from gja_test_partition;
a A ao 1
b B bo 1
c C co 1
d D do 1
e E eo 2
g G go 2
h H ho 2
j J jo 2
f F fo 3
k K ko 3
l L lo 4
i I io 4
Time taken: 1.75 s
```
```
spark-sql> select count(key), sum(col2) from gja_test_partition;
12 26
Time taken: 1.848 s
```
```
spark-sql> select count(key) filter (where col2 > 1) from gja_test_partition;
8
Time taken: 2.926 s
```
```
spark-sql> select sum(col2) filter (where col2 > 2) from gja_test_partition;
14
Time taken: 2.087 s
```
```
spark-sql> select count(key) filter (where col2 > 1), sum(col2) filter (where col2 > 2) from gja_test_partition;
8 14
Time taken: 2.847 s
```
```
spark-sql> select count(key), count(key) filter (where col2 > 1), sum(col2), sum(col2) filter (where col2 > 2) from gja_test_partition;
12 8 26 14
Time taken: 1.787 s
```
```
spark-sql> desc student;
id int NULL
name string NULL
sex string NULL
class_id int NULL
Time taken: 0.206 s
```
```
spark-sql> select * from student;
1 张三 man 1
2 李四 man 1
3 王五 man 2
4 赵六 man 2
5 钱小花 woman 1
6 赵九红 woman 2
7 郭丽丽 woman 2
Time taken: 0.786 s
```
```
spark-sql> select class_id, count(id), sum(id) from student group by class_id;
1 3 8
2 4 20
Time taken: 18.783 s
```
```
spark-sql> select class_id, count(id) filter (where sex = 'man'), sum(id) filter (where sex = 'woman') from student group by class_id;
1 2 5
2 2 13
Time taken: 3.887 s
```
### Why are the changes needed?
Add new SQL feature.
### Does this PR introduce any user-facing change?
'No'.
### How was this patch tested?
Exists UT and new UT.
Closes#26656 from beliefer/support-aggregate-clause.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch removes TODO comments which are left to address changing case classes having vars to normal classes in spark-sql-kafka module - the pattern is actually discouraged, but still worth to break it, as we already use automatic toString implementation and we may be using more.
### Why are the changes needed?
Described above.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UTs.
Closes#26992 from HeartSaVioR/SPARK-30337.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
I execute some query as` select get_json_object(ytag, '$.y1') AS y1 from t4`; SparkSQL return null but Hive return correct results.
In my production environment, ytag is a json wrapped by single quotes,as follows
```
{'y1': 'shuma', 'y2': 'shuma:shouji'}
{'y1': 'jiaoyu', 'y2': 'jiaoyu:gaokao'}
{'y1': 'yule', 'y2': 'yule:mingxing'}
```
Then l realized some functions including get_json_object and json_tuple does not support single quotes json parsing.
So l provide this PR to resolve the question.
### Why are the changes needed?
Enabled for Hive compatibility
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
NEW TESTS
Closes#26965 from wenfang6/enableSingleQuotesJsonForSparkSQL.
Authored-by: wenfang <wenfang@360.cn>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
LibSVMDataSource attach AttributeGroup
### Why are the changes needed?
LibSVMDataSource will attach a special metadata to indicate numFeatures:
```scala
scala> val data = spark.read.format("libsvm").load("/data0/Dev/Opensource/spark/data/mllib/sample_multiclass_classification_data.txt")
scala> data.schema("features").metadata
res0: org.apache.spark.sql.types.Metadata = {"numFeatures":4}
```
However, all ML impls will try to obtain vector size via AttributeGroup, which can not use this metadata:
```scala
scala> import org.apache.spark.ml.attribute._
import org.apache.spark.ml.attribute._
scala> AttributeGroup.fromStructField(data.schema("features")).size
res1: Int = -1
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
added tests
Closes#27003 from zhengruifeng/libsvm_attr_group.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Rename `spark.sql.streaming.forceDeleteTempCheckpointLocation` to `spark.sql.streaming.forceDeleteTempCheckpointLocation.enabled`.
### Why are the changes needed?
To follow the other boolean conf naming convention.
### Does this PR introduce any user-facing change?
No, as this config is newly added in 3.0.
### How was this patch tested?
Pass Jenkins.
Closes#26981 from Ngone51/SPARK-26389-FOLLOWUP.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
accuracyExpression can accept Long which may cause overflow error.
accuracyExpression can accept fractions which are implicitly floored.
accuracyExpression can accept null which is implicitly changed to 0.
percentageExpression can accept null but cause MatchError.
percentageExpression can accept ArrayType(_, nullable=true) in which the nulls are implicitly changed to zeros.
##### cases
```sql
select percentile_approx(10.0, 0.5, 2147483648); -- overflow and fail
select percentile_approx(10.0, 0.5, 4294967297); -- overflow but success
select percentile_approx(10.0, 0.5, null); -- null cast to 0
select percentile_approx(10.0, 0.5, 1.2); -- 1.2 cast to 1
select percentile_approx(10.0, null, 1); -- scala.MatchError
select percentile_approx(10.0, array(0.2, 0.4, null), 1); -- null cast to zero.
```
##### behavior before
```sql
+select percentile_approx(10.0, 0.5, 2147483648)
+org.apache.spark.sql.AnalysisException
+cannot resolve 'percentile_approx(10.0BD, CAST(0.5BD AS DOUBLE), CAST(2147483648L AS INT))' due to data type mismatch: The accuracy provided must be a positive integer literal (current value = -2147483648); line 1 pos 7
+
+select percentile_approx(10.0, 0.5, 4294967297)
+10.0
+
+select percentile_approx(10.0, 0.5, null)
+org.apache.spark.sql.AnalysisException
+cannot resolve 'percentile_approx(10.0BD, CAST(0.5BD AS DOUBLE), CAST(NULL AS INT))' due to data type mismatch: The accuracy provided must be a positive integer literal (current value = 0); line 1 pos 7
+
+select percentile_approx(10.0, 0.5, 1.2)
+10.0
+
+select percentile_approx(10.0, null, 1)
+scala.MatchError
+null
+
+
+select percentile_approx(10.0, array(0.2, 0.4, null), 1)
+[10.0,10.0,10.0]
```
##### behavior after
```sql
+select percentile_approx(10.0, 0.5, 2147483648)
+10.0
+
+select percentile_approx(10.0, 0.5, 4294967297)
+10.0
+
+select percentile_approx(10.0, 0.5, null)
+org.apache.spark.sql.AnalysisException
+cannot resolve 'percentile_approx(10.0BD, 0.5BD, NULL)' due to data type mismatch: argument 3 requires integral type, however, 'NULL' is of null type.; line 1 pos 7
+
+select percentile_approx(10.0, 0.5, 1.2)
+org.apache.spark.sql.AnalysisException
+cannot resolve 'percentile_approx(10.0BD, 0.5BD, 1.2BD)' due to data type mismatch: argument 3 requires integral type, however, '1.2BD' is of decimal(2,1) type.; line 1 pos 7
+
+select percentile_approx(10.0, null, 1)
+java.lang.IllegalArgumentException
+The value of percentage must be be between 0.0 and 1.0, but got null
+
+select percentile_approx(10.0, array(0.2, 0.4, null), 1)
+java.lang.IllegalArgumentException
+Each value of the percentage array must be be between 0.0 and 1.0, but got [0.2,0.4,null]
```
### Why are the changes needed?
bug fix
### Does this PR introduce any user-facing change?
yes, fix some improper usages of percentile_approx as cases list above
### How was this patch tested?
add ut
Closes#26905 from yaooqinn/SPARK-30266.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change config name from `spark.eventLog.allowErasureCoding` to `spark.eventLog.allowErasureCoding.enabled`.
### Why are the changes needed?
To follow the other boolean conf naming convention.
### Does this PR introduce any user-facing change?
No, it's newly added in Spark 3.0.
### How was this patch tested?
Tested manually and pass Jenkins.
Closes#26998 from Ngone51/SPARK-25855-FOLLOWUP.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Set `isFinalPlan=true` before posting the final AdaptiveSparkPlan event (`SparkListenerSQLAdaptiveExecutionUpdate`)
### Why are the changes needed?
Otherwise, any attempt to listen on the final event by pattern matching `isFinalPlan=true` would fail
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
All tests in `AdaptiveQueryExecSuite` are exteneded with a verification that a `SparkListenerSQLAdaptiveExecutionUpdate` event with `isFinalPlan=True` exists
Closes#26983 from manuzhang/spark-30331.
Authored-by: manuzhang <owenzhang1990@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We added shuffle block fetch optimization in SPARK-9853. In ShuffleBlockFetcherIterator, we merge single blocks into batch blocks. During merging, we should count merged blocks for `maxBlocksInFlightPerAddress`, not original single blocks.
### Why are the changes needed?
If `maxBlocksInFlightPerAddress` is specified, like set it to 1, it should mean one batch block, not one original single block. Otherwise, it will conflict with batch shuffle fetch.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit test.
Closes#26930 from viirya/respect-max-blocks-inflight.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
compute the medians/ranges more distributedly
### Why are the changes needed?
It is a bottleneck to collect the whole Array[QuantileSummaries] from executors,
since a QuantileSummaries is a large object, which maintains arrays of large sizes 10k(`defaultCompressThreshold`)/50k(`defaultHeadSize`).
In Spark-Shell with default params, I processed a dataset with numFeatures=69,200, and existing impl fail due to OOM.
After this PR, it will sucessfuly fit the model.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#26803 from zhengruifeng/robust_high_dim.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
1,modify the toString in SQLTransformer & VectorSizeHint
2,add toString in RegexTokenizer
### Why are the changes needed?
in SQLTransformer & VectorSizeHint , `toString` methods directly call getter of param without default values.
This will cause `java.util.NoSuchElementException` in REPL:
```scala
scala> val vs = new VectorSizeHint()
java.util.NoSuchElementException: Failed to find a default value for size
at org.apache.spark.ml.param.Params.$anonfun$getOrDefault$2(params.scala:780)
at scala.Option.getOrElse(Option.scala:189)
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#26999 from zhengruifeng/fix_toString.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
While querying using **desc** , if column name is not entered exactly as per the column name given during the table creation, the colstats are wrong. fetching of col stats has been made case insensitive.
### Why are the changes needed?
functions like **analyze**, etc support case insensitive retrieval of column data.
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
<!--
Unit test has been rewritten and tested.
Closes#26927 from PavithraRamachandran/desc_caseinsensitive.
Authored-by: Pavithra Ramachandran <pavi.rams@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to update the SQL migration guide and clarify semantic of string conversion to typed `TIMESTAMP` and `DATE` literals.
### Why are the changes needed?
This is a follow-up of the PR https://github.com/apache/spark/pull/23541 which changed the behavior of `TIMESTAMP`/`DATE` literals, and can impact on results of user's queries.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
It should be checked by jenkins build.
Closes#26985 from MaxGekk/timestamp-date-constructors-followup.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch addresses review comments in #26960 (https://github.com/apache/spark/pull/26960#discussion_r360661930 / https://github.com/apache/spark/pull/26960#discussion_r360661947) which were not addressed in the patch. Addressing these review comments will let the code less dependent on actual implementation as it only relies on `Time` interface in Kafka.
### Why are the changes needed?
These were review comments in previous PR and they bring actual benefit though they're minors.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#26979 from HeartSaVioR/SPARK-29294-follow-up.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
This patch revises Kafka producer pool (cache) to implement 'expire' correctly.
Current implementation of Kafka producer cache leverages Guava cache, which decides cached producer instance to be expired if the instance is not "accessed" from cache. The behavior defines expiration time as "last accessed time + timeout", which is incorrect because some task may use the instance longer than timeout. There's no concept of "returning" in Guava cache as well, so it cannot be fixed with Guava cache.
This patch introduces a new pool implementation which tracks "reference count" of cached instance, and defines expiration time for the instance as "last returned time + timeout" if the reference count goes 0, otherwise Long.MaxValue (effectively no expire). Expiring instances will be done with evict thread explicitly instead of evicting in part of handling acquire. (It might bring more overhead, but it ensures clearing expired instances even the pool is idle.)
This patch also creates a new package `producer` under `kafka010`, to hide the details from `kafka010` package. In point of `kafka010` package's view, only acquire()/release()/reset() are available in pool, and even for CachedKafkaProducer the package cannot close the producer directly.
Explained above.
Yes, but only for the way of expiring cached instances. (The difference is described above.) Each executor leveraging spark-sql-kafka would have one eviction thread.
New and existing UTs.
Closes#26845 from HeartSaVioR/SPARK-21869-revised.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
Implement Factorization Machines as a ml-pipeline component
1. loss function supports: logloss, mse
2. optimizer: GD, adamW
### Why are the changes needed?
Factorization Machines is widely used in advertising and recommendation system to estimate CTR(click-through rate).
Advertising and recommendation system usually has a lot of data, so we need Spark to estimate the CTR, and Factorization Machines are common ml model to estimate CTR.
References:
1. S. Rendle, “Factorization machines,” in Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 995–1000, 2010.
https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
run unit tests
Closes#26124 from mob-ai/ml/fm.
Authored-by: zhanjf <zhanjf@mob.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This is an alternative solution to https://github.com/apache/spark/pull/25095.
SQLMetrics use -1 as init value as a work around for [SPARK-11013](https://issues.apache.org/jira/browse/SPARK-11013.) However, it may bring out some badcases as https://github.com/apache/spark/pull/26726 reporting. In fact, we only need to reserve -1 when doing min max statistics in `SQLMetrics.stringValue` so that we can filter out those not initialized accumulators.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UTs
Closes#26899 from WangGuangxin/sqlmetrics.
Authored-by: wangguangxin.cn <wangguangxin.cn@bytedance.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds and exposes the options, 'recursiveFileLookup' and 'pathGlobFilter' in file sources 'mergeSchema' in ORC, into documentation.
- `recursiveFileLookup` at file sources: https://github.com/apache/spark/pull/24830 ([SPARK-27627](https://issues.apache.org/jira/browse/SPARK-27627))
- `pathGlobFilter` at file sources: https://github.com/apache/spark/pull/24518 ([SPARK-27990](https://issues.apache.org/jira/browse/SPARK-27990))
- `mergeSchema` at ORC: https://github.com/apache/spark/pull/24043 ([SPARK-11412](https://issues.apache.org/jira/browse/SPARK-11412))
**Note that** `timeZone` option was not moved from `DataFrameReader.options` as I assume it will likely affect other datasources as well once DSv2 is complete.
### Why are the changes needed?
To document available options in sources properly.
### Does this PR introduce any user-facing change?
In PySpark, `pathGlobFilter` can be set via `DataFrameReader.(text|orc|parquet|json|csv)` and `DataStreamReader.(text|orc|parquet|json|csv)`.
### How was this patch tested?
Manually built the doc and checked the output. Option setting in PySpark is rather a logical change. I manually tested one only:
```bash
$ ls -al tmp
...
-rw-r--r-- 1 hyukjin.kwon staff 3 Dec 20 12:19 aa
-rw-r--r-- 1 hyukjin.kwon staff 3 Dec 20 12:19 ab
-rw-r--r-- 1 hyukjin.kwon staff 3 Dec 20 12:19 ac
-rw-r--r-- 1 hyukjin.kwon staff 3 Dec 20 12:19 cc
```
```python
>>> spark.read.text("tmp", pathGlobFilter="*c").show()
```
```
+-----+
|value|
+-----+
| ac|
| cc|
+-----+
```
Closes#26958 from HyukjinKwon/doc-followup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is a follow-up to https://github.com/apache/spark/pull/25001
### Why are the changes needed?
No
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Pass the Jenkins with the newly update test files.
Closes#26949 from beliefer/uncomment-like-escape-tests.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Fixed typo in `docs` directory and in other directories
1. Find typo in `docs` and apply fixes to files in all directories
2. Fix `the the` -> `the`
### Why are the changes needed?
Better readability of documents
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
No test needed
Closes#26976 from kiszk/typo_20191221.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch upgrades the version of Kafka to 2.4, which supports Scala 2.13.
There're some incompatible changes in Kafka 2.4 which the patch addresses as well:
* `ZkUtils` is removed -> Replaced with `KafkaZkClient`
* Majority of methods are removed in `AdminUtils` -> Replaced with `AdminZkClient`
* Method signature of `Scheduler.schedule` is changed (return type) -> leverage `DeterministicScheduler` to avoid implementing `ScheduledFuture`
### Why are the changes needed?
* Kafka 2.4 supports Scala 2.13
### Does this PR introduce any user-facing change?
No, as Kafka API is known to be compatible across versions.
### How was this patch tested?
Existing UTs
Closes#26960 from HeartSaVioR/SPARK-29294.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR update document for make Hive 2.3 dependency by default.
### Why are the changes needed?
The documentation is incorrect.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#26919 from wangyum/SPARK-30280.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
a followup of https://github.com/apache/spark/pull/26576, which mistakenly removes the UI update of the final plan.
### Why are the changes needed?
fix mistake.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26968 from cloud-fan/fix.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
When querying a partitioned table with format `org.apache.hive.hcatalog.data.JsonSerDe` and more than one task runs in each executor concurrently, the following exception is encountered:
`java.lang.ClassCastException: java.util.ArrayList cannot be cast to org.apache.hive.hcatalog.data.HCatRecord`
The exception occurs in `HadoopTableReader.fillObject`.
`org.apache.hive.hcatalog.data.JsonSerDe#initialize` populates a `cachedObjectInspector` field by calling `HCatRecordObjectInspectorFactory.getHCatRecordObjectInspector`, which is not thread-safe; this `cachedObjectInspector` is returned by `JsonSerDe#getObjectInspector`.
We protect against this Hive bug by synchronizing on an object when we need to call `initialize` on `org.apache.hadoop.hive.serde2.Deserializer` instances (which may be `JsonSerDe` instances). By doing so, the `ObjectInspector` for the `Deserializer` of the partitions of the JSON table and that of the table `SerDe` are the same cached `ObjectInspector` and `HadoopTableReader.fillObject` then works correctly. (If the `ObjectInspector`s are different, then a bug in `HCatRecordObjectInspector` causes an `ArrayList` to be created instead of an `HCatRecord`, resulting in the `ClassCastException` that is seen.)
### Why are the changes needed?
To avoid HIVE-15773 / HIVE-21752.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Tested manually on a cluster with a partitioned JSON table and running a query using more than one core per executor. Before this change, the ClassCastException happens consistently. With this change it does not happen.
Closes#26895 from wypoon/SPARK-17398.
Authored-by: Wing Yew Poon <wypoon@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
Remove usages of Guava that no longer work in Guava 27, and replace with workalikes. I'll comment on key types of changes below.
### Why are the changes needed?
Hadoop 3.2.1 uses Guava 27, so this helps us avoid problems running on Hadoop 3.2.1+ and generally lowers our exposure to Guava.
### Does this PR introduce any user-facing change?
Should not be, but see notes below on hash codes and toString.
### How was this patch tested?
Existing tests will verify whether these changes break anything for Guava 14.
I manually built with an updated version and it compiles with Guava 27; tests running manually locally now.
Closes#26911 from srowen/SPARK-30272.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Add batching support in Alter table add partition flow. Also calculate new partition sizes faster by doing listing in parallel.
### Why are the changes needed?
This PR split the the single createPartitions() call AlterTableAddPartition flow into smaller batches, which could prevent
- SocketTimeoutException: Adding thousand of partitions in Hive metastore itself takes lot of time. Because of this hive client fails with SocketTimeoutException.
- Hive metastore from OOM (caused by millions of partitions).
It will also try to gather stats (total size of all files in all new partitions) faster by parallely listing the new partition paths.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT.
Also tested on a cluster in HDI with 15000 partitions with remote metastore server. Without batching - operation fails with SocketTimeoutException, With batching it finishes in 25 mins.
Closes#26569 from prakharjain09/add_partition_batching_r1.
Authored-by: Prakhar Jain <prakharjain09@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
