## What changes were proposed in this pull request?
Adding `BinaryType` support for Arrow in pyspark, conditional on using pyarrow >= 0.10.0. Earlier versions will continue to raise a TypeError.
## How was this patch tested?
Additional unit tests in pyspark for code paths that use Arrow for createDataFrame, toPandas, and scalar pandas_udfs.
Closes#20725 from BryanCutler/arrow-binary-type-support-SPARK-23555.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Fixing typos is sometimes very hard. It's not so easy to visually review them. Recently, I discovered a very useful tool for it, [misspell](https://github.com/client9/misspell).
This pull request fixes minor typos detected by [misspell](https://github.com/client9/misspell) except for the false positives. If you would like me to work on other files as well, let me know.
## How was this patch tested?
### before
```
$ misspell . | grep -v '.js'
R/pkg/R/SQLContext.R:354:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:424:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:445:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:495:43: "definiton" is a misspelling of "definition"
NOTICE-binary:454:16: "containd" is a misspelling of "contained"
R/pkg/R/context.R:46:43: "definiton" is a misspelling of "definition"
R/pkg/R/context.R:74:43: "definiton" is a misspelling of "definition"
R/pkg/R/DataFrame.R:591:48: "persistance" is a misspelling of "persistence"
R/pkg/R/streaming.R:166:44: "occured" is a misspelling of "occurred"
R/pkg/inst/worker/worker.R:65:22: "ouput" is a misspelling of "output"
R/pkg/tests/fulltests/test_utils.R:106:25: "environemnt" is a misspelling of "environment"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/InMemoryStoreSuite.java:38:39: "existant" is a misspelling of "existent"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java:83:39: "existant" is a misspelling of "existent"
common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:243:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:234:19: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:238:63: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:244:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:276:39: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
common/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala:195:15: "orgin" is a misspelling of "origin"
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:621:39: "gauranteed" is a misspelling of "guaranteed"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/main/scala/org/apache/spark/storage/DiskStore.scala:282:18: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/util/ListenerBus.scala:64:17: "overriden" is a misspelling of "overridden"
core/src/test/scala/org/apache/spark/ShuffleSuite.scala:211:7: "substracted" is a misspelling of "subtracted"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:2468:84: "truely" is a misspelling of "truly"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:25:18: "persistance" is a misspelling of "persistence"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:26:69: "persistance" is a misspelling of "persistence"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
dev/run-pip-tests:55:28: "enviroments" is a misspelling of "environments"
dev/run-pip-tests:91:37: "virutal" is a misspelling of "virtual"
dev/merge_spark_pr.py:377:72: "accross" is a misspelling of "across"
dev/merge_spark_pr.py:378:66: "accross" is a misspelling of "across"
dev/run-pip-tests:126:25: "enviroments" is a misspelling of "environments"
docs/configuration.md:1830:82: "overriden" is a misspelling of "overridden"
docs/structured-streaming-programming-guide.md:525:45: "processs" is a misspelling of "processes"
docs/structured-streaming-programming-guide.md:1165:61: "BETWEN" is a misspelling of "BETWEEN"
docs/sql-programming-guide.md:1891:810: "behaivor" is a misspelling of "behavior"
examples/src/main/python/sql/arrow.py:98:8: "substract" is a misspelling of "subtract"
examples/src/main/python/sql/arrow.py:103:27: "substract" is a misspelling of "subtract"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala:230:24: "inital" is a misspelling of "initial"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala:237:26: "descripiton" is a misspelling of "descriptions"
python/pyspark/find_spark_home.py:30:13: "enviroment" is a misspelling of "environment"
python/pyspark/context.py:937:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:938:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:939:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:940:12: "supress" is a misspelling of "suppress"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:713:8: "probabilty" is a misspelling of "probability"
python/pyspark/ml/clustering.py:1038:8: "Currenlty" is a misspelling of "Currently"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/ml/regression.py:1378:20: "paramter" is a misspelling of "parameter"
python/pyspark/mllib/stat/_statistics.py:262:8: "probabilty" is a misspelling of "probability"
python/pyspark/rdd.py:1363:32: "paramter" is a misspelling of "parameter"
python/pyspark/streaming/tests.py:825:42: "retuns" is a misspelling of "returns"
python/pyspark/sql/tests.py:768:29: "initalization" is a misspelling of "initialization"
python/pyspark/sql/tests.py:3616:31: "initalize" is a misspelling of "initialize"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala:120:39: "arbitary" is a misspelling of "arbitrary"
resource-managers/mesos/src/test/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArgumentsSuite.scala:26:45: "sucessfully" is a misspelling of "successfully"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala:358:27: "constaints" is a misspelling of "constraints"
resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala:111:24: "senstive" is a misspelling of "sensitive"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1063:5: "overwirte" is a misspelling of "overwrite"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala:1348:17: "compatability" is a misspelling of "compatibility"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:77:36: "paramter" is a misspelling of "parameter"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:1374:22: "precendence" is a misspelling of "precedence"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:238:27: "unnecassary" is a misspelling of "unnecessary"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala:212:17: "whn" is a misspelling of "when"
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinHelper.scala:147:60: "timestmap" is a misspelling of "timestamp"
sql/core/src/test/scala/org/apache/spark/sql/TPCDSQuerySuite.scala:150:45: "precentage" is a misspelling of "percentage"
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala:135:29: "infered" is a misspelling of "inferred"
sql/hive/src/test/resources/golden/udf_instr-1-2e76f819563dbaba4beb51e3a130b922:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_instr-2-32da357fc754badd6e3898dcc8989182:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-1-6e41693c9c6dceea4d7fab4c02884e4e:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-2-d9b5934457931447874d6bb7c13de478:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:9:79: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:13:110: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/annotate_stats_join.q:46:105: "distint" is a misspelling of "distinct"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/auto_sortmerge_join_11.q:29:3: "Currenly" is a misspelling of "Currently"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/avro_partitioned.q:72:15: "existant" is a misspelling of "existent"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/decimal_udf.q:25:3: "substraction" is a misspelling of "subtraction"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby2_map_multi_distinct.q:16:51: "funtion" is a misspelling of "function"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby_sort_8.q:15:30: "issueing" is a misspelling of "issuing"
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala:669:52: "wiht" is a misspelling of "with"
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java:474:9: "Refering" is a misspelling of "Referring"
```
### after
```
$ misspell . | grep -v '.js'
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
```
Closes#22070 from seratch/fix-typo.
Authored-by: Kazuhiro Sera <seratch@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
The `dropFieldIfAllNull` parameter of the `json` method wasn't set as an option. This PR fixes that.
## How was this patch tested?
I added a test to `sql/test.py`
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#22002 from MaxGekk/drop-field-if-all-null.
## What changes were proposed in this pull request?
Fix a typo in pyspark sql tests
Author: William Sheu <william.sheu@databricks.com>
Closes#21833 from PenguinToast/fix-test-typo.
## What changes were proposed in this pull request?
The PR is a followup to move the test cases introduced by the original PR in their proper location.
## How was this patch tested?
moved UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21751 from mgaido91/SPARK-24208_followup.
## What changes were proposed in this pull request?
A self-join on a dataset which contains a `FlatMapGroupsInPandas` fails because of duplicate attributes. This happens because we are not dealing with this specific case in our `dedupAttr` rules.
The PR fix the issue by adding the management of the specific case
## How was this patch tested?
added UT + manual tests
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#21737 from mgaido91/SPARK-24208.
## What changes were proposed in this pull request?
Address comments in #21370 and add more test.
## How was this patch tested?
Enhance test in pyspark/sql/test.py and DataFrameSuite
Author: Yuanjian Li <xyliyuanjian@gmail.com>
Closes#21553 from xuanyuanking/SPARK-24215-follow.
## What changes were proposed in this pull request?
Currently, a `pandas_udf` of type `PandasUDFType.GROUPED_MAP` will assign the resulting columns based on index of the return pandas.DataFrame. If a new DataFrame is returned and constructed using a dict, then the order of the columns could be arbitrary and be different than the defined schema for the UDF. If the schema types still match, then no error will be raised and the user will see column names and column data mixed up.
This change will first try to assign columns using the return type field names. If a KeyError occurs, then the column index is checked if it is string based. If so, then the error is raised as it is most likely a naming mistake, else it will fallback to assign columns by position and raise a TypeError if the field types do not match.
## How was this patch tested?
Added a test that returns a new DataFrame with column order different than the schema.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#21427 from BryanCutler/arrow-grouped-map-mixesup-cols-SPARK-24324.
## What changes were proposed in this pull request?
Fix for SyntaxWarning on tests.py
## How was this patch tested?
./dev/run-tests
Author: Rekha Joshi <rekhajoshm@gmail.com>
Closes#21604 from rekhajoshm/SPARK-24614.
## What changes were proposed in this pull request?
Currently, the micro-batches in the MicroBatchExecution is not exposed to the user through any public API. This was because we did not want to expose the micro-batches, so that all the APIs we expose, we can eventually support them in the Continuous engine. But now that we have better sense of buiding a ContinuousExecution, I am considering adding APIs which will run only the MicroBatchExecution. I have quite a few use cases where exposing the microbatch output as a dataframe is useful.
- Pass the output rows of each batch to a library that is designed only the batch jobs (example, uses many ML libraries need to collect() while learning).
- Reuse batch data sources for output whose streaming version does not exists (e.g. redshift data source).
- Writer the output rows to multiple places by writing twice for each batch. This is not the most elegant thing to do for multiple-output streaming queries but is likely to be better than running two streaming queries processing the same data twice.
The proposal is to add a method `foreachBatch(f: Dataset[T] => Unit)` to Scala/Java/Python `DataStreamWriter`.
## How was this patch tested?
New unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#21571 from tdas/foreachBatch.
## What changes were proposed in this pull request?
This PR adds `foreach` for streaming queries in Python. Users will be able to specify their processing logic in two different ways.
- As a function that takes a row as input.
- As an object that has methods `open`, `process`, and `close` methods.
See the python docs in this PR for more details.
## How was this patch tested?
Added java and python unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#21477 from tdas/SPARK-24396.
## What changes were proposed in this pull request?
This PR enables using a grouped aggregate pandas UDFs as window functions. The semantics is the same as using SQL aggregation function as window functions.
```
>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame(
... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
... ("id", "v"))
>>> pandas_udf("double", PandasUDFType.GROUPED_AGG)
... def mean_udf(v):
... return v.mean()
>>> w = Window.partitionBy('id')
>>> df.withColumn('mean_v', mean_udf(df['v']).over(w)).show()
+---+----+------+
| id| v|mean_v|
+---+----+------+
| 1| 1.0| 1.5|
| 1| 2.0| 1.5|
| 2| 3.0| 6.0|
| 2| 5.0| 6.0|
| 2|10.0| 6.0|
+---+----+------+
```
The scope of this PR is somewhat limited in terms of:
(1) Only supports unbounded window, which acts essentially as group by.
(2) Only supports aggregation functions, not "transform" like window functions (n -> n mapping)
Both of these are left as future work. Especially, (1) needs careful thinking w.r.t. how to pass rolling window data to python efficiently. (2) is a bit easier but does require more changes therefore I think it's better to leave it as a separate PR.
## How was this patch tested?
WindowPandasUDFTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#21082 from icexelloss/SPARK-22239-window-udf.
## What changes were proposed in this pull request?
SPARK-23754 was fixed in #21383 by changing the UDF code to wrap the user function, but this required a hack to save its argspec. This PR reverts this change and fixes the `StopIteration` bug in the worker
## How does this work?
The root of the problem is that when an user-supplied function raises a `StopIteration`, pyspark might stop processing data, if this function is used in a for-loop. The solution is to catch `StopIteration`s exceptions and re-raise them as `RuntimeError`s, so that the execution fails and the error is reported to the user. This is done using the `fail_on_stopiteration` wrapper, in different ways depending on where the function is used:
- In RDDs, the user function is wrapped in the driver, because this function is also called in the driver itself.
- In SQL UDFs, the function is wrapped in the worker, since all processing happens there. Moreover, the worker needs the signature of the user function, which is lost when wrapping it, but passing this signature to the worker requires a not so nice hack.
## How was this patch tested?
Same tests, plus tests for pandas UDFs
Author: edorigatti <emilio.dorigatti@gmail.com>
Closes#21467 from e-dorigatti/fix_udf_hack.
## What changes were proposed in this pull request?
Currently column names of headers in CSV files are not checked against provided schema of CSV data. It could cause errors like showed in the [SPARK-23786](https://issues.apache.org/jira/browse/SPARK-23786) and https://github.com/apache/spark/pull/20894#issuecomment-375957777. I introduced new CSV option - `enforceSchema`. If it is enabled (by default `true`), Spark forcibly applies provided or inferred schema to CSV files. In that case, CSV headers are ignored and not checked against the schema. If `enforceSchema` is set to `false`, additional checks can be performed. For example, if column in CSV header and in the schema have different ordering, the following exception is thrown:
```
java.lang.IllegalArgumentException: CSV file header does not contain the expected fields
Header: depth, temperature
Schema: temperature, depth
CSV file: marina.csv
```
## How was this patch tested?
The changes were tested by existing tests of CSVSuite and by 2 new tests.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#20894 from MaxGekk/check-column-names.
## What changes were proposed in this pull request?
Make sure that `StopIteration`s raised in users' code do not silently interrupt processing by spark, but are raised as exceptions to the users. The users' functions are wrapped in `safe_iter` (in `shuffle.py`), which re-raises `StopIteration`s as `RuntimeError`s
## How was this patch tested?
Unit tests, making sure that the exceptions are indeed raised. I am not sure how to check whether a `Py4JJavaError` contains my exception, so I simply looked for the exception message in the java exception's `toString`. Can you propose a better way?
## License
This is my original work, licensed in the same way as spark
Author: e-dorigatti <emilio.dorigatti@gmail.com>
Author: edorigatti <emilio.dorigatti@gmail.com>
Closes#21383 from e-dorigatti/fix_spark_23754.
## What changes were proposed in this pull request?
Logical `Range` node has been added with `outputOrdering` recently. It's used to eliminate redundant `Sort` during optimization. However, this `outputOrdering` doesn't not propagate to physical `RangeExec` node.
We also add correct `outputPartitioning` to `RangeExec` node.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21291 from viirya/SPARK-24242.
## What changes were proposed in this pull request?
Right now `ArrayWriter` used to output Arrow data for array type, doesn't do `clear` or `reset` after each batch. It produces wrong output.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21312 from viirya/SPARK-24259.
This avoids polluting and leaving garbage behind in /tmp, and allows the
usual build tools to clean up any leftover files.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#21198 from vanzin/SPARK-24126.
## What changes were proposed in this pull request?
When `PyArrow` or `Pandas` are not available, the corresponding PySpark tests are skipped automatically. Currently, PySpark tests fail when we are not using `-Phive`. This PR aims to skip Hive related PySpark tests when `-Phive` is not given.
**BEFORE**
```bash
$ build/mvn -DskipTests clean package
$ python/run-tests.py --python-executables python2.7 --modules pyspark-sql
File "/Users/dongjoon/spark/python/pyspark/sql/readwriter.py", line 295, in pyspark.sql.readwriter.DataFrameReader.table
...
IllegalArgumentException: u"Error while instantiating 'org.apache.spark.sql.hive.HiveExternalCatalog':"
**********************************************************************
1 of 3 in pyspark.sql.readwriter.DataFrameReader.table
***Test Failed*** 1 failures.
```
**AFTER**
```bash
$ build/mvn -DskipTests clean package
$ python/run-tests.py --python-executables python2.7 --modules pyspark-sql
...
Tests passed in 138 seconds
Skipped tests in pyspark.sql.tests with python2.7:
...
test_hivecontext (pyspark.sql.tests.HiveSparkSubmitTests) ... skipped 'Hive is not available.'
```
## How was this patch tested?
This is a test-only change. First, this should pass the Jenkins. Then, manually do the following.
```bash
build/mvn -DskipTests clean package
python/run-tests.py --python-executables python2.7 --modules pyspark-sql
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#21141 from dongjoon-hyun/SPARK-23853.
## What changes were proposed in this pull request?
I propose to support the `samplingRatio` option for schema inferring of CSV datasource similar to the same option of JSON datasource:
b14993e1fc/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala (L49-L50)
## How was this patch tested?
Added 2 tests for json and 2 tests for csv datasources. The tests checks that only subset of input dataset is used for schema inferring.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#20959 from MaxGekk/csv-sampling.
## What changes were proposed in this pull request?
I propose new option for JSON datasource which allows to specify encoding (charset) of input and output files. Here is an example of using of the option:
```
spark.read.schema(schema)
.option("multiline", "true")
.option("encoding", "UTF-16LE")
.json(fileName)
```
If the option is not specified, charset auto-detection mechanism is used by default.
The option can be used for saving datasets to jsons. Currently Spark is able to save datasets into json files in `UTF-8` charset only. The changes allow to save data in any supported charset. Here is the approximate list of supported charsets by Oracle Java SE: https://docs.oracle.com/javase/8/docs/technotes/guides/intl/encoding.doc.html . An user can specify the charset of output jsons via the charset option like `.option("charset", "UTF-16BE")`. By default the output charset is still `UTF-8` to keep backward compatibility.
The solution has the following restrictions for per-line mode (`multiline = false`):
- If charset is different from UTF-8, the lineSep option must be specified. The option required because Hadoop LineReader cannot detect the line separator correctly. Here is the ticket for solving the issue: https://issues.apache.org/jira/browse/SPARK-23725
- Encoding with [BOM](https://en.wikipedia.org/wiki/Byte_order_mark) are not supported. For example, the `UTF-16` and `UTF-32` encodings are blacklisted. The problem can be solved by https://github.com/MaxGekk/spark-1/pull/2
## How was this patch tested?
I added the following tests:
- reads an json file in `UTF-16LE` encoding with BOM in `multiline` mode
- read json file by using charset auto detection (`UTF-32BE` with BOM)
- read json file using of user's charset (`UTF-16LE`)
- saving in `UTF-32BE` and read the result by standard library (not by Spark)
- checking that default charset is `UTF-8`
- handling wrong (unsupported) charset
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#20937 from MaxGekk/json-encoding-line-sep.
## What changes were proposed in this pull request?
This PR proposes to remove duplicated dependency checking logics and also print out skipped tests from unittests.
For example, as below:
```
Skipped tests in pyspark.sql.tests with pypy:
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
...
Skipped tests in pyspark.sql.tests with python3:
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
...
```
Currently, it's not printed out in the console. I think we should better print out skipped tests in the console.
## How was this patch tested?
Manually tested. Also, fortunately, Jenkins has good environment to test the skipped output.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21107 from HyukjinKwon/skipped-tests-print.
## What changes were proposed in this pull request?
Added the `samplingRatio` option to the `json()` method of PySpark DataFrame Reader. Improving existing tests for Scala API according to review of the PR: https://github.com/apache/spark/pull/20959
## How was this patch tested?
Added new test for PySpark, updated 2 existing tests according to reviews of https://github.com/apache/spark/pull/20959 and added new negative test
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21056 from MaxGekk/json-sampling.
## What changes were proposed in this pull request?
This PR proposes to add `collect` to a query executor as an action.
Seems `collect` / `collect` with Arrow are not recognised via `QueryExecutionListener` as an action. For example, if we have a custom listener as below:
```scala
package org.apache.spark.sql
import org.apache.spark.internal.Logging
import org.apache.spark.sql.execution.QueryExecution
import org.apache.spark.sql.util.QueryExecutionListener
class TestQueryExecutionListener extends QueryExecutionListener with Logging {
override def onSuccess(funcName: String, qe: QueryExecution, durationNs: Long): Unit = {
logError("Look at me! I'm 'onSuccess'")
}
override def onFailure(funcName: String, qe: QueryExecution, exception: Exception): Unit = { }
}
```
and set `spark.sql.queryExecutionListeners` to `org.apache.spark.sql.TestQueryExecutionListener`
Other operations in PySpark or Scala side seems fine:
```python
>>> sql("SELECT * FROM range(1)").show()
```
```
18/04/09 17:02:04 ERROR TestQueryExecutionListener: Look at me! I'm 'onSuccess'
+---+
| id|
+---+
| 0|
+---+
```
```scala
scala> sql("SELECT * FROM range(1)").collect()
```
```
18/04/09 16:58:41 ERROR TestQueryExecutionListener: Look at me! I'm 'onSuccess'
res1: Array[org.apache.spark.sql.Row] = Array([0])
```
but ..
**Before**
```python
>>> sql("SELECT * FROM range(1)").collect()
```
```
[Row(id=0)]
```
```python
>>> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
>>> sql("SELECT * FROM range(1)").toPandas()
```
```
id
0 0
```
**After**
```python
>>> sql("SELECT * FROM range(1)").collect()
```
```
18/04/09 16:57:58 ERROR TestQueryExecutionListener: Look at me! I'm 'onSuccess'
[Row(id=0)]
```
```python
>>> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
>>> sql("SELECT * FROM range(1)").toPandas()
```
```
18/04/09 17:53:26 ERROR TestQueryExecutionListener: Look at me! I'm 'onSuccess'
id
0 0
```
## How was this patch tested?
I have manually tested as described above and unit test was added.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21007 from HyukjinKwon/SPARK-23942.
## What changes were proposed in this pull request?
There was a mistake in `tests.py` missing `assertEquals`.
## How was this patch tested?
Fixed tests.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21035 from HyukjinKwon/SPARK-23847.
## What changes were proposed in this pull request?
Column.scala and Functions.scala have asc_nulls_first, asc_nulls_last, desc_nulls_first and desc_nulls_last. Add the corresponding python APIs in column.py and functions.py
## How was this patch tested?
Add doctest
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20962 from huaxingao/spark-23847.
## What changes were proposed in this pull request?
This PR proposes to add lineSep option for a configurable line separator in text datasource.
It supports this option by using `LineRecordReader`'s functionality with passing it to the constructor.
The approach is similar with https://github.com/apache/spark/pull/20727; however, one main difference is, it uses text datasource's `lineSep` option to parse line by line in JSON's schema inference.
## How was this patch tested?
Manually tested and unit tests were added.
Author: hyukjinkwon <gurwls223@apache.org>
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20877 from HyukjinKwon/linesep-json.
## What changes were proposed in this pull request?
When using Arrow for createDataFrame or toPandas and an error is encountered with fallback disabled, this will raise the same type of error instead of a RuntimeError. This change also allows for the traceback of the error to be retained and prevents the accidental chaining of exceptions with Python 3.
## How was this patch tested?
Updated existing tests to verify error type.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20839 from BryanCutler/arrow-raise-same-error-SPARK-23699.
## What changes were proposed in this pull request?
This PR proposes to add `lineSep` option for a configurable line separator in text datasource.
It supports this option by using `LineRecordReader`'s functionality with passing it to the constructor.
## How was this patch tested?
Manual tests and unit tests were added.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20727 from HyukjinKwon/linesep-text.
## What changes were proposed in this pull request?
d6632d185e added an useful util
```python
contextmanager
def sql_conf(self, pairs):
...
```
to allow configuration set/unset within a block:
```python
with self.sql_conf({"spark.blah.blah.blah", "blah"})
# test codes
```
This PR proposes to use this util where possible in PySpark tests.
Note that there look already few places affecting tests without restoring the original value back in unittest classes.
## How was this patch tested?
Manually tested via:
```
./run-tests --modules=pyspark-sql --python-executables=python2
./run-tests --modules=pyspark-sql --python-executables=python3
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20830 from HyukjinKwon/cleanup-sql-conf.
## What changes were proposed in this pull request?
This PR proposes to support an alternative function from with group aggregate pandas UDF.
The current form:
```
def foo(pdf):
return ...
```
Takes a single arg that is a pandas DataFrame.
With this PR, an alternative form is supported:
```
def foo(key, pdf):
return ...
```
The alternative form takes two argument - a tuple that presents the grouping key, and a pandas DataFrame represents the data.
## How was this patch tested?
GroupbyApplyTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20295 from icexelloss/SPARK-23011-groupby-apply-key.
## What changes were proposed in this pull request?
This PR adds a configuration to control the fallback of Arrow optimization for `toPandas` and `createDataFrame` with Pandas DataFrame.
## How was this patch tested?
Manually tested and unit tests added.
You can test this by:
**`createDataFrame`**
```python
spark.conf.set("spark.sql.execution.arrow.enabled", False)
pdf = spark.createDataFrame([[{'a': 1}]]).toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", True)
spark.createDataFrame(pdf, "a: map<string, int>")
```
```python
spark.conf.set("spark.sql.execution.arrow.enabled", False)
pdf = spark.createDataFrame([[{'a': 1}]]).toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", False)
spark.createDataFrame(pdf, "a: map<string, int>")
```
**`toPandas`**
```python
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", True)
spark.createDataFrame([[{'a': 1}]]).toPandas()
```
```python
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", False)
spark.createDataFrame([[{'a': 1}]]).toPandas()
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20678 from HyukjinKwon/SPARK-23380-conf.
## What changes were proposed in this pull request?
Check python version to determine whether to use `inspect.getargspec` or `inspect.getfullargspec` before applying `pandas_udf` core logic to a function. The former is python2.7 (deprecated in python3) and the latter is python3.x. The latter correctly accounts for type annotations, which are syntax errors in python2.x.
## How was this patch tested?
Locally, on python 2.7 and 3.6.
Author: Michael (Stu) Stewart <mstewart141@gmail.com>
Closes#20728 from mstewart141/pandas_udf_fix.
## What changes were proposed in this pull request?
This PR explicitly specifies and checks the types we supported in `toPandas`. This was a hole. For example, we haven't finished the binary type support in Python side yet but now it allows as below:
```python
spark.conf.set("spark.sql.execution.arrow.enabled", "false")
df = spark.createDataFrame([[bytearray("a")]])
df.toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
df.toPandas()
```
```
_1
0 [97]
_1
0 a
```
This should be disallowed. I think the same things also apply to nested timestamps too.
I also added some nicer message about `spark.sql.execution.arrow.enabled` in the error message.
## How was this patch tested?
Manually tested and tests added in `python/pyspark/sql/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20625 from HyukjinKwon/pandas_convertion_supported_type.
## What changes were proposed in this pull request?
This PR targets to explicitly specify supported types in Pandas UDFs.
The main change here is to add a deduplicated and explicit type checking in `returnType` ahead with documenting this; however, it happened to fix multiple things.
1. Currently, we don't support `BinaryType` in Pandas UDFs, for example, see:
```python
from pyspark.sql.functions import pandas_udf
pudf = pandas_udf(lambda x: x, "binary")
df = spark.createDataFrame([[bytearray(1)]])
df.select(pudf("_1")).show()
```
```
...
TypeError: Unsupported type in conversion to Arrow: BinaryType
```
We can document this behaviour for its guide.
2. Also, the grouped aggregate Pandas UDF fails fast on `ArrayType` but seems we can support this case.
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
foo = pandas_udf(lambda v: v.mean(), 'array<double>', PandasUDFType.GROUPED_AGG)
df = spark.range(100).selectExpr("id", "array(id) as value")
df.groupBy("id").agg(foo("value")).show()
```
```
...
NotImplementedError: ArrayType, StructType and MapType are not supported with PandasUDFType.GROUPED_AGG
```
3. Since we can check the return type ahead, we can fail fast before actual execution.
```python
# we can fail fast at this stage because we know the schema ahead
pandas_udf(lambda x: x, BinaryType())
```
## How was this patch tested?
Manually tested and unit tests for `BinaryType` and `ArrayType(...)` were added.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20531 from HyukjinKwon/pudf-cleanup.
## What changes were proposed in this pull request?
Expose range partitioning shuffle introduced by spark-22614
## How was this patch tested?
Unit test in dataframe.py
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: xubo245 <601450868@qq.com>
Closes#20456 from xubo245/SPARK22624_PysparkRangePartition.
## What changes were proposed in this pull request?
When tz_localize a tz-naive timetamp, pandas will throw exception if the timestamp is during daylight saving time period, e.g., `2015-11-01 01:30:00`. This PR fixes this issue by setting `ambiguous=False` when calling tz_localize, which is the same default behavior of pytz.
## How was this patch tested?
Add `test_timestamp_dst`
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20537 from icexelloss/SPARK-23314.
## What changes were proposed in this pull request?
Currently we use `tzlocal()` to get Python local timezone, but it sometimes causes unexpected behavior.
I changed the way to get Python local timezone to use pytz if the timezone is specified in environment variable, or timezone file via dateutil .
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20559 from ueshin/issues/SPARK-23360/master.
## What changes were proposed in this pull request?
This PR proposes to disallow default value None when 'to_replace' is not a dictionary.
It seems weird we set the default value of `value` to `None` and we ended up allowing the case as below:
```python
>>> df.show()
```
```
+----+------+-----+
| age|height| name|
+----+------+-----+
| 10| 80|Alice|
...
```
```python
>>> df.na.replace('Alice').show()
```
```
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
...
```
**After**
This PR targets to disallow the case above:
```python
>>> df.na.replace('Alice').show()
```
```
...
TypeError: value is required when to_replace is not a dictionary.
```
while we still allow when `to_replace` is a dictionary:
```python
>>> df.na.replace({'Alice': None}).show()
```
```
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
...
```
## How was this patch tested?
Manually tested, tests were added in `python/pyspark/sql/tests.py` and doctests were fixed.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20499 from HyukjinKwon/SPARK-19454-followup.
## What changes were proposed in this pull request?
This is a followup pr of #20487.
When importing module but it doesn't exists, the error message is slightly different between Python 2 and 3.
E.g., in Python 2:
```
No module named pandas
```
in Python 3:
```
No module named 'pandas'
```
So, one test to check an import error fails in Python 3 without pandas.
This pr fixes it.
## How was this patch tested?
Tested manually in my local environment.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20538 from ueshin/issues/SPARK-23319/fup1.
## What changes were proposed in this pull request?
This PR proposes to explicitly specify Pandas and PyArrow versions in PySpark tests to skip or test.
We declared the extra dependencies:
b8bfce51ab/python/setup.py (L204)
In case of PyArrow:
Currently we only check if pyarrow is installed or not without checking the version. It already fails to run tests. For example, if PyArrow 0.7.0 is installed:
```
======================================================================
ERROR: test_vectorized_udf_wrong_return_type (pyspark.sql.tests.ScalarPandasUDF)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/tests.py", line 4019, in test_vectorized_udf_wrong_return_type
f = pandas_udf(lambda x: x * 1.0, MapType(LongType(), LongType()))
File "/.../spark/python/pyspark/sql/functions.py", line 2309, in pandas_udf
return _create_udf(f=f, returnType=return_type, evalType=eval_type)
File "/.../spark/python/pyspark/sql/udf.py", line 47, in _create_udf
require_minimum_pyarrow_version()
File "/.../spark/python/pyspark/sql/utils.py", line 132, in require_minimum_pyarrow_version
"however, your version was %s." % pyarrow.__version__)
ImportError: pyarrow >= 0.8.0 must be installed on calling Python process; however, your version was 0.7.0.
----------------------------------------------------------------------
Ran 33 tests in 8.098s
FAILED (errors=33)
```
In case of Pandas:
There are few tests for old Pandas which were tested only when Pandas version was lower, and I rewrote them to be tested when both Pandas version is lower and missing.
## How was this patch tested?
Manually tested by modifying the condition:
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20487 from HyukjinKwon/pyarrow-pandas-skip.
## What changes were proposed in this pull request?
In b2ce17b4c9, I mistakenly renamed `VectorizedUDFTests` to `ScalarPandasUDF`. This PR fixes the mistake.
## How was this patch tested?
Existing tests.
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20489 from icexelloss/fix-scalar-udf-tests.
## What changes were proposed in this pull request?
In Python 2, when `pandas_udf` tries to return string type value created in the udf with `".."`, the execution fails. E.g.,
```python
from pyspark.sql.functions import pandas_udf, col
import pandas as pd
df = spark.range(10)
str_f = pandas_udf(lambda x: pd.Series(["%s" % i for i in x]), "string")
df.select(str_f(col('id'))).show()
```
raises the following exception:
```
...
java.lang.AssertionError: assertion failed: Invalid schema from pandas_udf: expected StringType, got BinaryType
at scala.Predef$.assert(Predef.scala:170)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:93)
...
```
Seems like pyarrow ignores `type` parameter for `pa.Array.from_pandas()` and consider it as binary type when the type is string type and the string values are `str` instead of `unicode` in Python 2.
This pr adds a workaround for the case.
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20507 from ueshin/issues/SPARK-23334.
## What changes were proposed in this pull request?
This is a follow-up pr of #19872 which uses `assertRaisesRegex` but it doesn't exist in Python 2, so some tests fail when running tests in Python 2 environment.
Unfortunately, we missed it because currently Python 2 environment of the pr builder doesn't have proper versions of pandas or pyarrow, so the tests were skipped.
This pr modifies to use `assertRaisesRegexp` instead of `assertRaisesRegex`.
## How was this patch tested?
Tested manually in my local environment.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20467 from ueshin/issues/SPARK-22274/fup1.
## What changes were proposed in this pull request?
In the current PySpark code, Python created `jsparkSession` doesn't add to JVM's defaultSession, this `SparkSession` object cannot be fetched from Java side, so the below scala code will be failed when loaded in PySpark application.
```scala
class TestSparkSession extends SparkListener with Logging {
override def onOtherEvent(event: SparkListenerEvent): Unit = {
event match {
case CreateTableEvent(db, table) =>
val session = SparkSession.getActiveSession.orElse(SparkSession.getDefaultSession)
assert(session.isDefined)
val tableInfo = session.get.sharedState.externalCatalog.getTable(db, table)
logInfo(s"Table info ${tableInfo}")
case e =>
logInfo(s"event $e")
}
}
}
```
So here propose to add fresh create `jsparkSession` to `defaultSession`.
## How was this patch tested?
Manual verification.
Author: jerryshao <sshao@hortonworks.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Saisai Shao <sai.sai.shao@gmail.com>
Closes#20404 from jerryshao/SPARK-23228.
## What changes were proposed in this pull request?
Rename the public APIs and names of pandas udfs.
- `PANDAS SCALAR UDF` -> `SCALAR PANDAS UDF`
- `PANDAS GROUP MAP UDF` -> `GROUPED MAP PANDAS UDF`
- `PANDAS GROUP AGG UDF` -> `GROUPED AGG PANDAS UDF`
## How was this patch tested?
The existing tests
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20428 from gatorsmile/renamePandasUDFs.
