## What changes were proposed in this pull request?
Fix fastSquaredDistance to calculate dense-dense situation calculation performance problem and meanwhile enhance the calculation accuracy.
## How was this patch tested?
From different point to test after add this patch, the dense-dense calculation situation performance is enhanced and will do influence other calculation situation like (sparse-sparse, sparse-dense)
**For calculation logic test**
There is my test for sparse-sparse, dense-dense, sparse-dense case
There is test result:
First we need define some branch path logic for sparse-sparse and sparse-dense case
if meet precisionBound1, we define it as LOGIC1
if not meet precisionBound1, and not meet precisionBound2, we define it as LOGIC2
if not meet precisionBound1, but meet precisionBound2, we define it as LOGIC3
(There is a trick, you can manually change the precision value to meet above situation)
sparse- sparse case time cost situation (milliseconds)
LOGIC1
Before add patch: 7786, 7970, 8086
After add patch: 7729, 7653, 7903
LOGIC2
Before add patch: 8412, 9029, 8606
After add patch: 8603, 8724, 9024
LOGIC3
Before add patch: 19365, 19146, 19351
After add patch: 18917, 19007, 19074
sparse-dense case time cost situation (milliseconds)
LOGIC1
Before add patch: 4195, 4014, 4409
After add patch: 4081,3971, 4151
LOGIC2
Before add patch: 4968, 5579, 5080
After add patch: 4980, 5472, 5148
LOGIC3
Before add patch: 11848, 12077, 12168
After add patch: 11718, 11874, 11743
And for dense-dense case like we already discussed in comment, only use sqdist to calculate distance
dense-dense case time cost situation (milliseconds)
Before add patch: 7340, 7816, 7672
After add patch: 5752, 5800, 5753
**For real world data test**
There is my test data situation
I use the data
http://archive.ics.uci.edu/ml/datasets/Condition+monitoring+of+hydraulic+systems
extract file (PS1, PS2, PS3, PS4, PS5, PS6) to form the test data
total instances are 13230
the attributes for line are 6000
Result for sparse-sparse situation time cost (milliseconds)
Before Enhance: 7670, 7704, 7652
After Enhance: 7634, 7729, 7645
Closes#22893 from KyleLi1985/updatekmeanpatch.
Authored-by: 李亮 <liang.li.work@outlook.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file!
This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context.
We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy.
Basically this PR proposes to break down `pyspark/sql/tests.py` into ...:
```bash
pyspark
...
├── sql
...
│ ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py'
│ │ │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can
│ │ │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ...
│ │ ├── __init__.py
│ │ ├── test_appsubmit.py
│ │ ├── test_arrow.py
│ │ ├── test_catalog.py
│ │ ├── test_column.py
│ │ ├── test_conf.py
│ │ ├── test_context.py
│ │ ├── test_dataframe.py
│ │ ├── test_datasources.py
│ │ ├── test_functions.py
│ │ ├── test_group.py
│ │ ├── test_pandas_udf.py
│ │ ├── test_pandas_udf_grouped_agg.py
│ │ ├── test_pandas_udf_grouped_map.py
│ │ ├── test_pandas_udf_scalar.py
│ │ ├── test_pandas_udf_window.py
│ │ ├── test_readwriter.py
│ │ ├── test_serde.py
│ │ ├── test_session.py
│ │ ├── test_streaming.py
│ │ ├── test_types.py
│ │ ├── test_udf.py
│ │ └── test_utils.py
...
├── testing # Includes testing utils that can be used in unittests.
│ ├── __init__.py
│ └── sqlutils.py
...
```
## How was this patch tested?
Existing tests should cover.
`cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran.
Each test (not officially) can be ran via:
```
SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar
```
Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`.
Closes#23021 from HyukjinKwon/SPARK-25344.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`Dataset.groupByKey` will bring in new attributes from serializer. If key type is the same as original Dataset's object type, they have same serializer output and so the attribute names will conflict.
This won't be a problem at most of cases, if we don't refer conflict attributes:
```scala
val ds: Dataset[(ClassData, Long)] = Seq(ClassData("one", 1), ClassData("two", 2)).toDS()
.map(c => ClassData(c.a, c.b + 1))
.groupByKey(p => p).count()
```
But if we use conflict attributes, `Analyzer` will complain about ambiguous references:
```scala
val ds = Seq(1, 2, 3).toDS()
val agg = ds.groupByKey(_ >= 2).agg(sum("value").as[Long], sum($"value" + 1).as[Long])
```
We have discussed two fixes https://github.com/apache/spark/pull/22944#discussion_r230977212:
1. Implicitly add alias to key attribute:
Works for primitive type. But for product type, we can't implicitly add aliases to key attributes because we might need to access key attributes by names in methods like `mapGroups`.
2. Detect conflict from key attributes and warn users to add alias manually
This might work, but needs to add some hacks to Analyzer or AttributeSeq.resolve.
This patch applies another simpler fix. We resolve aggregate expressions with `AppendColumns`'s children, instead of `AppendColumns`. `AppendColumns`'s output contains its children's output and serializer output, aggregate expressions shouldn't use serializer output.
## How was this patch tested?
Added test.
Closes#22944 from viirya/dataset_agg.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
An expressions with a subquery can be pushed down as a data source filter. Despite the filter is not actively used, this causes anyway a re-execution of the subquery becuase the `ReuseSubquery` optimization rule is ineffective in this case.
The PR avoids this problem by forbidding the push down of filters containing a subquery.
## How was this patch tested?
added UT
Closes#22518 from mgaido91/SPARK-25482.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Remove *.crc from .gitignore as there are actual .crc files in the test source dirs and IJ warns about it
## How was this patch tested?
N/A
Closes#23020 from srowen/gitignore.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
When sorting rows, ShuffleExchangeExec uses the entire row instead of just the columns references in SortOrder to create the RangePartitioner. This causes the RangePartitioner to sample entire rows to create rangeBounds and can cause OOM issues on the driver when rows contain large fields.
This change creates a projection and only use columns involved in the SortOrder for the RangePartitioner
## How was this patch tested?
Existing tests in spark-sql.
Plus
Started a local spark-shell with a small spark.driver.maxResultSize:
```
spark-shell --master 'local[16]' --conf spark.driver.maxResultSize=128M --driver-memory 4g
```
and ran the following script:
```
import com.google.common.io.Files
import org.apache.spark.SparkContext
import org.apache.spark.sql.SparkSession
import scala.util.Random
transient val sc = SparkContext.getOrCreate()
transient val spark = SparkSession.builder().getOrCreate()
import spark.implicits._
val path = Files.createTempDir().toString
// this creates a dataset with 1024 entries, each 1MB in size, across 16 partitions
sc.parallelize(0 until (1 << 10), sc.defaultParallelism).
map(_ => Array.fill(1 << 18)(Random.nextInt)).
toDS.
write.mode("overwrite").parquet(path)
spark.read.parquet(path).
orderBy('value (0)).
write.mode("overwrite").parquet(s"$path-sorted")
spark.read.parquet(s"$path-sorted").show
```
execution would fail when initializing RangePartitioner without this change.
execution succeeds and generates a correctly sorted dataset with this change.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22961 from mu5358271/sort-improvement.
Authored-by: mu5358271 <shuheng.dai@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose new method for debugging queries by dumping info about their execution to a file. It saves logical, optimized and physical plan similar to the `explain()` method + generated code. One of the advantages of the method over `explain` is it does not materializes full output as one string in memory which can cause OOMs.
## How was this patch tested?
Added a few tests to `QueryExecutionSuite` to check positive and negative scenarios.
Closes#23018 from MaxGekk/truncated-plan-to-file.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
Running a barrier job after a normal spark job causes the barrier job to run without a BarrierTaskContext. This is because while python worker reuse, BarrierTaskContext._getOrCreate() will still return a TaskContext after firstly submit a normal spark job, we'll get a `AttributeError: 'TaskContext' object has no attribute 'barrier'`. Fix this by adding check logic in BarrierTaskContext._getOrCreate() and make sure it will return BarrierTaskContext in this scenario.
## How was this patch tested?
Add new UT in pyspark-core.
Closes#22962 from xuanyuanking/SPARK-25921.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Since Spark 2.4.0 is already in maven repo, we can Bump previousSparkVersion in MimaBuild.scala to be 2.4.0.
Note that, seems we forgot to do it for branch 2.4, so this PR also updates MimaExcludes.scala
## How was this patch tested?
N/A
Closes#22977 from cloud-fan/mima.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In `SQLAppStatusListener.aggregateMetrics`, we use the `metricIds` only to filter the relevant metrics. And this is a Seq which is also sorted. When there are many metrics involved, this can be pretty inefficient. The PR proposes to use a Set for it.
## How was this patch tested?
NA
Closes#23002 from mgaido91/SPARK-26003.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Passing current value of SQL config `spark.sql.columnNameOfCorruptRecord` to `CSVOptions` inside of `DataFrameReader`.`csv()`.
## How was this patch tested?
Added a test where default value of `spark.sql.columnNameOfCorruptRecord` is changed.
Closes#23006 from MaxGekk/csv-corrupt-sql-config.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
changes in vignette only to disable eval
## How was this patch tested?
Jenkins
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#23007 from felixcheung/rjavavervig.
## What changes were proposed in this pull request?
R tools 3.5.1 is released few months ago. Spark currently uses 3.4.0. We should better upgrade in AppVeyor.
## How was this patch tested?
AppVeyor builds.
Closes#23011 from HyukjinKwon/SPARK-26013.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Based on the release description of ANTRL 4.7.1., https://github.com/antlr/antlr4/releases, let us upgrade our parser to 4.7.1.
## How was this patch tested?
N/A
Closes#23005 from gatorsmile/upgradeAntlr4.7.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
As comment in https://github.com/apache/spark/pull/22326#issuecomment-424923967, we test the new added optimizer rule by end-to-end test in python side, need to add suites under `org.apache.spark.sql.catalyst.optimizer` like other optimizer rules.
## How was this patch tested?
new added UT
Closes#22955 from xuanyuanking/SPARK-25949.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Clarify Bucketizer handleInvalid docs. Just a resubmit of https://github.com/apache/spark/pull/17169
## How was this patch tested?
N/A
Closes#23003 from srowen/SPARK-19714.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Added JSON options for `json()` in streaming.py that are presented in the similar method in readwriter.py. In particular, missed options are `dropFieldIfAllNull` and `encoding`.
Closes#22973 from MaxGekk/streaming-missed-options.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
* Implement (optional) use of KryoPool in KryoSerializer, an alternative to the existing implementation of caching a Kryo instance inside KryoSerializerInstance
* Add config key & documentation of spark.kryo.pool in order to turn this on
* Add benchmark KryoSerializerBenchmark to compare new and old implementation
* Add results of benchmark
## How was this patch tested?
Added new tests inside KryoSerializerSuite to test the pool implementation as well as added the pool option to the existing regression testing for SPARK-7766
This is my original work and I license the work to the project under the project’s open source license.
Closes#22855 from patrickbrownsync/kryo-pool.
Authored-by: Patrick Brown <patrick.brown@blyncsy.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Deprecated in Java 11, replace Class.newInstance with Class.getConstructor.getInstance, and primtive wrapper class constructors with valueOf or equivalent
## How was this patch tested?
Existing tests.
Closes#22988 from srowen/SPARK-25984.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently, Spark writes Spark version number into Hive Table properties with `spark.sql.create.version`.
```
parameters:{
spark.sql.sources.schema.part.0={
"type":"struct",
"fields":[{"name":"a","type":"integer","nullable":true,"metadata":{}}]
},
transient_lastDdlTime=1541142761,
spark.sql.sources.schema.numParts=1,
spark.sql.create.version=2.4.0
}
```
This PR aims to write Spark versions to ORC/Parquet file metadata with `org.apache.spark.sql.create.version` because we used `org.apache.` prefix in Parquet metadata already. It's different from Hive Table property key `spark.sql.create.version`, but it seems that we cannot change Hive Table property for backward compatibility.
After this PR, ORC and Parquet file generated by Spark will have the following metadata.
**ORC (`native` and `hive` implmentation)**
```
$ orc-tools meta /tmp/o
File Version: 0.12 with ...
...
User Metadata:
org.apache.spark.sql.create.version=3.0.0
```
**PARQUET**
```
$ parquet-tools meta /tmp/p
...
creator: parquet-mr version 1.10.0 (build 031a6654009e3b82020012a18434c582bd74c73a)
extra: org.apache.spark.sql.create.version = 3.0.0
extra: org.apache.spark.sql.parquet.row.metadata = {"type":"struct","fields":[{"name":"id","type":"long","nullable":false,"metadata":{}}]}
```
## How was this patch tested?
Pass the Jenkins with newly added test cases.
This closes#22255.
Closes#22932 from dongjoon-hyun/SPARK-25102.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The evaluators BinaryClassificationEvaluator, RegressionEvaluator, and MulticlassClassificationEvaluator and the corresponding metrics classes BinaryClassificationMetrics, RegressionMetrics and MulticlassMetrics should use sample weight data.
I've closed the PR: https://github.com/apache/spark/pull/16557
as recommended in favor of creating three pull requests, one for each of the evaluators (binary/regression/multiclass) to make it easier to review/update.
Note: I've updated the JIRA to:
https://issues.apache.org/jira/browse/SPARK-24101
Which is a child of JIRA:
https://issues.apache.org/jira/browse/SPARK-18693
## How was this patch tested?
I added tests to the metrics class.
Closes#17086 from imatiach-msft/ilmat/multiclass-evaluate.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Very minor parser bug, but possibly problematic for code-generated queries:
Consider the following two queries:
```
SELECT avg(k) OVER (w) FROM kv WINDOW w AS (PARTITION BY v ORDER BY w) ORDER BY 1
```
and
```
SELECT avg(k) OVER w FROM kv WINDOW w AS (PARTITION BY v ORDER BY w) ORDER BY 1
```
The former, with parens around the OVER condition, fails to parse while the latter, without parens, succeeds:
```
Error in SQL statement: ParseException:
mismatched input '(' expecting {<EOF>, ',', 'FROM', 'WHERE', 'GROUP', 'ORDER', 'HAVING', 'LIMIT', 'LATERAL', 'WINDOW', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', 'SORT', 'CLUSTER', 'DISTRIBUTE'}(line 1, pos 19)
== SQL ==
SELECT avg(k) OVER (w) FROM kv WINDOW w AS (PARTITION BY v ORDER BY w) ORDER BY 1
-------------------^^^
```
This was found when running the cockroach DB tests.
I tried PostgreSQL, The SQL with parentheses is also workable.
## How was this patch tested?
Unit test
Closes#22987 from gengliangwang/windowParentheses.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
When the queries do not use the column names with the same case, users might hit various errors. Below is a typical test failure they can hit.
```
Expected only partition pruning predicates: ArrayBuffer(isnotnull(tdate#237), (cast(tdate#237 as string) >= 2017-08-15));
org.apache.spark.sql.AnalysisException: Expected only partition pruning predicates: ArrayBuffer(isnotnull(tdate#237), (cast(tdate#237 as string) >= 2017-08-15));
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogUtils$.prunePartitionsByFilter(ExternalCatalogUtils.scala:146)
at org.apache.spark.sql.catalyst.catalog.InMemoryCatalog.listPartitionsByFilter(InMemoryCatalog.scala:560)
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.listPartitionsByFilter(SessionCatalog.scala:925)
```
## How was this patch tested?
Added two test cases.
Closes#22990 from gatorsmile/fix1283.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
HistoryPage.scala counts applications (with a predicate depending on if it is displaying incomplete or complete applications) to check if it must display the dataTable.
Since it only checks if allAppsSize > 0, we could use exists method on the iterator. This way we stop iterating at the first occurence found.
Such a change has been relevant (roughly 12s improvement on page loading) on our cluster that runs tens of thousands of jobs per day.
Closes#22982 from Willymontaz/SPARK-25973.
Authored-by: William Montaz <w.montaz@criteo.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add `Locale.ROOT` to all internal calls to String `toLowerCase`, `toUpperCase`
## How was this patch tested?
existing tests
Closes#22975 from zhengruifeng/Tokenizer_Locale.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove `BenchmarkWithCodegen` as we don't use it anymore.
More details: https://github.com/apache/spark/pull/22484#discussion_r221397904
## How was this patch tested?
N/A
Closes#22985 from wangyum/SPARK-25510.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to add new option `locale` into CSVOptions/JSONOptions to make parsing date/timestamps in local languages possible. Currently the locale is hard coded to `Locale.US`.
## How was this patch tested?
Added two tests for parsing a date from CSV/JSON - `ноя 2018`.
Closes#22951 from MaxGekk/locale.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Since Spark 2.4.0 is released, we should test it in HiveExternalCatalogVersionsSuite
## How was this patch tested?
N/A
Closes#22984 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
1. OrcReadBenchmark is under hive module, so the way to run it should be
```
build/sbt "hive/test:runMain <this class>"
```
2. The benchmark "String with Nulls Scan" should be with case "String with Nulls Scan(5%/50%/95%)", not "(0.05%/0.5%/0.95%)"
3. Add the null value percentages in the test case names of DataSourceReadBenchmark, for the benchmark "String with Nulls Scan" .
## How was this patch tested?
Re-run benchmarks
Closes#22965 from gengliangwang/fixHiveOrcReadBenchmark.
Lead-authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Co-authored-by: Gengliang Wang <ltnwgl@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
#22970 fixed Scala 2.12 build error, and this PR updates the function according to the review comments.
## How was this patch tested?
This is also manually tested with Scala 2.12 build.
Closes#22978 from dongjoon-hyun/SPARK-25676-3.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #20014 which introduced `SparkOutOfMemoryError` to avoid killing the entire executor when an `OutOfMemoryError` is thrown.
We should throw `SparkOutOfMemoryError` in `HashAggregateExec`, too.
## How was this patch tested?
Existing tests.
Closes#22969 from ueshin/issues/SPARK-22827/oome.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently, `SQLQueryTestSuite` is sensitive in terms of the bytes of parquet files in table partitions. If we change the default file format (from Parquet to ORC) or update the metadata of them, the test case should be changed accordingly. This PR aims to make `SQLQueryTestSuite` more robust by ignoring the partition byte statistics.
```
-Partition Statistics 1144 bytes, 2 rows
+Partition Statistics [not included in comparison] bytes, 2 rows
```
## How was this patch tested?
Pass the Jenkins with the newly updated test cases.
Closes#22972 from dongjoon-hyun/SPARK-25971.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to port existing JSON tests from `JsonFunctionsSuite` that are applicable for CSV, and put them to `CsvFunctionsSuite`. In particular:
- roundtrip `from_csv` to `to_csv`, and `to_csv` to `from_csv`
- using `schema_of_csv` in `from_csv`
- Java API `from_csv`
- using `from_csv` and `to_csv` in exprs.
Closes#22960 from MaxGekk/csv-additional-tests.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR fixes an issue when the corrupt record column specified via `spark.sql.columnNameOfCorruptRecord` or JSON options `columnNameOfCorruptRecord` is propagated to JacksonParser, and returned row breaks an assumption in `FailureSafeParser` that the row must contain only actual data. The issue is fixed by passing actual schema without the corrupt record field into `JacksonParser`.
## How was this patch tested?
Added a test with the corrupt record column in the middle of user's schema.
Closes#22958 from MaxGekk/from_json-corrupt-record-schema.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR fixes the Scala-2.12 build.
## How was this patch tested?
Manual build with Scala-2.12 profile.
Closes#22970 from dongjoon-hyun/SPARK-25676-2.12.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
- Remove some AccumulableInfo .apply() methods
- Remove non-label-specific multiclass precision/recall/fScore in favor of accuracy
- Remove toDegrees/toRadians in favor of degrees/radians (SparkR: only deprecated)
- Remove approxCountDistinct in favor of approx_count_distinct (SparkR: only deprecated)
- Remove unused Python StorageLevel constants
- Remove Dataset unionAll in favor of union
- Remove unused multiclass option in libsvm parsing
- Remove references to deprecated spark configs like spark.yarn.am.port
- Remove TaskContext.isRunningLocally
- Remove ShuffleMetrics.shuffle* methods
- Remove BaseReadWrite.context in favor of session
- Remove Column.!== in favor of =!=
- Remove Dataset.explode
- Remove Dataset.registerTempTable
- Remove SQLContext.getOrCreate, setActive, clearActive, constructors
Not touched yet
- everything else in MLLib
- HiveContext
- Anything deprecated more recently than 2.0.0, generally
## How was this patch tested?
Existing tests
Closes#22921 from srowen/SPARK-25908.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR explicitly specifies `flake8` and `pydocstyle` versions.
- It checks flake8 binary executable
- flake8 version check >= 3.5.0
- pydocstyle >= 3.0.0 (previously it was == 3.0.0)
## How was this patch tested?
Manually tested.
Closes#22963 from HyukjinKwon/SPARK-25962.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
The integration tests can now be run in sbt if the right profile
is enabled, using the "test" task under the respective project.
This avoids having to fall back to maven to run the tests, which
invalidates all your compiled stuff when you go back to sbt, making
development way slower than it should.
There's also a task to run the tests directly without refreshing
the docker images, which is helpful if you just made a change to
the submission code which should not affect the code in the images.
The sbt tasks currently are not very customizable; there's some
very minor things you can set in the sbt shell itself, but otherwise
it's hardcoded to run on minikube.
I also had to make some slight adjustments to the IT code itself,
mostly to remove assumptions about the existing harness.
Tested on sbt and maven.
Closes#22909 from vanzin/SPARK-25897.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Removal of intermediate structures in HighlyCompressedMapStatus will speed up its creation and deserialization time.
https://issues.apache.org/jira/browse/SPARK-25885
## How was this patch tested?
Additional tests are not necessary for the patch.
Closes#22894 from Koraseg/mapStatusesOptimization.
Authored-by: koraseg <artem.kupchinsky@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
JVMs can't allocate arrays of length exactly Int.MaxValue, so ensure we never try to allocate an array that big. This commit changes some defaults & configs to gracefully fallover to something that doesn't require one large array in some cases; in other cases it simply improves an error message for cases which will still fail.
Closes#22818 from squito/SPARK-25827.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Fix for `CsvToStructs` to take into account SQL config `spark.sql.columnNameOfCorruptRecord` similar to `from_json`.
## How was this patch tested?
Added new test where `spark.sql.columnNameOfCorruptRecord` is set to corrupt column name different from default.
Closes#22956 from MaxGekk/csv-tests.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Refactor BenchmarkWideTable to use main method.
Generate benchmark result:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.WideTableBenchmark"
```
## How was this patch tested?
manual tests
Closes#22823 from yucai/BenchmarkWideTable.
Lead-authored-by: yucai <yyu1@ebay.com>
Co-authored-by: Yucai Yu <yucai.yu@foxmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
update known_translations after running `translate-contributors.py` during 2.4.0 release
## How was this patch tested?
N/A
Closes#22949 from cloud-fan/contributors.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
The write ahead log expects to be able to call hflush, but that is a no-op when writing to a file with hdfs erasure coding. So ensure that file is always written with replication instead, regardless of filesystem defaults.
None yet. I'm posting this mostly to make it visible.
Closes#22882 from squito/SPARK-25871.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
When we added the `distanceMeasure`, we didn't update the `formatVersion` for `KMeans`. Despite this is not a big issue, as that information is used nowhere, we are returning a wrong information.
## How was this patch tested?
NA
Closes#22873 from mgaido91/SPARK-25866.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Every time a task is unschedulable because of the condition where no. of task failures < no. of executors available, we currently abort the taskSet - failing the job. This change tries to acquire new executors so that we can complete the job successfully. We try to acquire a new executor only when we can kill an existing idle executor. We fallback to the older implementation where we abort the job if we cannot find an idle executor.
## How was this patch tested?
I performed some manual tests to check and validate the behavior.
```scala
val rdd = sc.parallelize(Seq(1 to 10), 3)
import org.apache.spark.TaskContext
val mapped = rdd.mapPartitionsWithIndex ( (index, iterator) => { if (index == 2) { Thread.sleep(30 * 1000); val attemptNum = TaskContext.get.attemptNumber; if (attemptNum < 3) throw new Exception("Fail for blacklisting")}; iterator.toList.map (x => x + " -> " + index).iterator } )
mapped.collect
```
Closes#22288 from dhruve/bug/SPARK-22148.
Lead-authored-by: Dhruve Ashar <dhruveashar@gmail.com>
Co-authored-by: Dhruve Ashar <dhruve@users.noreply.github.com>
Co-authored-by: Tom Graves <tgraves@apache.org>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
Upgrade ASM to 7.x to support JDK11
## How was this patch tested?
Existing tests.
Closes#22953 from dbtsai/asm7.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
