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* [SPARK-17215][SQL] Method `SQLContext.parseDataType(dataTypeString: String)` ↵jiangxingbo2016-08-241-3/+3
| | | | | | | | | | | | | | | | | could be removed. ## What changes were proposed in this pull request? Method `SQLContext.parseDataType(dataTypeString: String)` could be removed, we should use `SparkSession.parseDataType(dataTypeString: String)` instead. This require updating PySpark. ## How was this patch tested? Existing test cases. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #14790 from jiangxb1987/parseDataType.
* [SPARK-16324][SQL] regexp_extract should doc that it returns empty string ↵Sean Owen2016-08-101-1/+5
| | | | | | | | | | | | | | | | when match fails ## What changes were proposed in this pull request? Doc that regexp_extract returns empty string when regex or group does not match ## How was this patch tested? Jenkins test, with a few new test cases Author: Sean Owen <sowen@cloudera.com> Closes #14525 from srowen/SPARK-16324.
* [SPARK-16409][SQL] regexp_extract with optional groups causes NPESean Owen2016-08-071-0/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? regexp_extract actually returns null when it shouldn't when a regex matches but the requested optional group did not. This makes it return an empty string, as apparently designed. ## How was this patch tested? Additional unit test Author: Sean Owen <sowen@cloudera.com> Closes #14504 from srowen/SPARK-16409.
* [SPARK-16772] Correct API doc references to PySpark classes + formatting fixesNicholas Chammas2016-07-281-8/+13
| | | | | | | | | | | | | | | | | | ## What's Been Changed The PR corrects several broken or missing class references in the Python API docs. It also correct formatting problems. For example, you can see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.registerFunction) how Sphinx is not picking up the reference to `DataType`. That's because the reference is relative to the current module, whereas `DataType` is in a different module. You can also see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.createDataFrame) how the formatting for byte, tinyint, and so on is italic instead of monospace. That's because in ReST single backticks just make things italic, unlike in Markdown. ## Testing I tested this PR by [building the Python docs](https://github.com/apache/spark/tree/master/docs#generating-the-documentation-html) and reviewing the results locally in my browser. I confirmed that the broken or missing class references were resolved, and that the formatting was corrected. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Closes #14393 from nchammas/python-docstring-fixes.
* [MINOR][PYSPARK][DOC] Fix wrongly formatted examples in PySpark documentationhyukjinkwon2016-07-061-4/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes wrongly formatted examples in PySpark documentation as below: - **`SparkSession`** - **Before** ![2016-07-06 11 34 41](https://cloud.githubusercontent.com/assets/6477701/16605847/ae939526-436d-11e6-8ab8-6ad578362425.png) - **After** ![2016-07-06 11 33 56](https://cloud.githubusercontent.com/assets/6477701/16605845/ace9ee78-436d-11e6-8923-b76d4fc3e7c3.png) - **`Builder`** - **Before** ![2016-07-06 11 34 44](https://cloud.githubusercontent.com/assets/6477701/16605844/aba60dbc-436d-11e6-990a-c87bc0281c6b.png) - **After** ![2016-07-06 1 26 37](https://cloud.githubusercontent.com/assets/6477701/16607562/586704c0-437d-11e6-9483-e0af93d8f74e.png) This PR also fixes several similar instances across the documentation in `sql` PySpark module. ## How was this patch tested? N/A Author: hyukjinkwon <gurwls223@gmail.com> Closes #14063 from HyukjinKwon/minor-pyspark-builder.
* [SPARK-16289][SQL] Implement posexplode table generating functionDongjoon Hyun2016-06-301-0/+21
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive. **Before** ```scala scala> sql("select posexplode(map('a', 1, 'b', 2))").show org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7 ``` **After** ```scala scala> sql("select posexplode(map('a', 1, 'b', 2))").show +---+---+-----+ |pos|key|value| +---+---+-----+ | 0| a| 1| | 1| b| 2| +---+---+-----+ ``` For `array` argument, `after` is the same with `before`. ``` scala> sql("select posexplode(array(1, 2, 3))").show +---+---+ |pos|col| +---+---+ | 0| 1| | 1| 2| | 2| 3| +---+---+ ``` ## How was this patch tested? Pass the Jenkins tests with newly added testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13971 from dongjoon-hyun/SPARK-16289.
* [MINOR] Fix Typos 'a -> an'Zheng RuiFeng2016-05-261-1/+1
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `a` -> `an` I use regex to generate potential error lines: `grep -in ' a [aeiou]' mllib/src/main/scala/org/apache/spark/ml/*/*scala` and review them line by line. ## How was this patch tested? local build `lint-java` checking Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13317 from zhengruifeng/a_an.
* [SPARK-15397][SQL] fix string udf locate as hiveDaoyuan Wang2016-05-231-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? in hive, `locate("aa", "aaa", 0)` would yield 0, `locate("aa", "aaa", 1)` would yield 1 and `locate("aa", "aaa", 2)` would yield 2, while in Spark, `locate("aa", "aaa", 0)` would yield 1, `locate("aa", "aaa", 1)` would yield 2 and `locate("aa", "aaa", 2)` would yield 0. This results from the different understanding of the third parameter in udf `locate`. It means the starting index and starts from 1, so when we use 0, the return would always be 0. ## How was this patch tested? tested with modified `StringExpressionsSuite` and `StringFunctionsSuite` Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #13186 from adrian-wang/locate.
* [SPARK-15464][ML][MLLIB][SQL][TESTS] Replace SQLContext and SparkContext ↵WeichenXu2016-05-231-75/+78
| | | | | | | | | | | | | | | | with SparkSession using builder pattern in python test code ## What changes were proposed in this pull request? Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code. ## How was this patch tested? Existing test. Author: WeichenXu <WeichenXu123@outlook.com> Closes #13242 from WeichenXu123/python_doctest_update_sparksession.
* [MINOR][SQL][DOCS] Add notes of the deterministic assumption on UDF functionsDongjoon Hyun2016-05-231-0/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Spark assumes that UDF functions are deterministic. This PR adds explicit notes about that. ## How was this patch tested? It's only about docs. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13087 from dongjoon-hyun/SPARK-15282.
* [SPARK-14639] [PYTHON] [R] Add `bround` function in Python/R.Dongjoon Hyun2016-04-191-3/+16
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This issue aims to expose Scala `bround` function in Python/R API. `bround` function is implemented in SPARK-14614 by extending current `round` function. We used the following semantics from Hive. ```java public static double bround(double input, int scale) { if (Double.isNaN(input) || Double.isInfinite(input)) { return input; } return BigDecimal.valueOf(input).setScale(scale, RoundingMode.HALF_EVEN).doubleValue(); } ``` After this PR, `pyspark` and `sparkR` also support `bround` function. **PySpark** ```python >>> from pyspark.sql.functions import bround >>> sqlContext.createDataFrame([(2.5,)], ['a']).select(bround('a', 0).alias('r')).collect() [Row(r=2.0)] ``` **SparkR** ```r > df = createDataFrame(sqlContext, data.frame(x = c(2.5, 3.5))) > head(collect(select(df, bround(df$x, 0)))) bround(x, 0) 1 2 2 4 ``` ## How was this patch tested? Pass the Jenkins tests (including new testcases). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12509 from dongjoon-hyun/SPARK-14639.
* [SPARK-14353] Dataset Time Window `window` API for Python, and SQLBurak Yavuz2016-04-051-0/+49
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The `window` function was added to Dataset with [this PR](https://github.com/apache/spark/pull/12008). This PR adds the Python, and SQL, API for this function. With this PR, SQL, Java, and Scala will share the same APIs as in users can use: - `window(timeColumn, windowDuration)` - `window(timeColumn, windowDuration, slideDuration)` - `window(timeColumn, windowDuration, slideDuration, startTime)` In Python, users can access all APIs above, but in addition they can do - In Python: `window(timeColumn, windowDuration, startTime=...)` that is, they can provide the startTime without providing the `slideDuration`. In this case, we will generate tumbling windows. ## How was this patch tested? Unit tests + manual tests Author: Burak Yavuz <brkyvz@gmail.com> Closes #12136 from brkyvz/python-windows.
* [SPARK-14267] [SQL] [PYSPARK] execute multiple Python UDFs within single batchDavies Liu2016-03-311-2/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR support multiple Python UDFs within single batch, also improve the performance. ```python >>> from pyspark.sql.types import IntegerType >>> sqlContext.registerFunction("double", lambda x: x * 2, IntegerType()) >>> sqlContext.registerFunction("add", lambda x, y: x + y, IntegerType()) >>> sqlContext.sql("SELECT double(add(1, 2)), add(double(2), 1)").explain(True) == Parsed Logical Plan == 'Project [unresolvedalias('double('add(1, 2)), None),unresolvedalias('add('double(2), 1), None)] +- OneRowRelation$ == Analyzed Logical Plan == double(add(1, 2)): int, add(double(2), 1): int Project [double(add(1, 2))#14,add(double(2), 1)#15] +- Project [double(add(1, 2))#14,add(double(2), 1)#15] +- Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15] +- EvaluatePython [add(pythonUDF1#17, 1)], [pythonUDF0#18] +- EvaluatePython [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17] +- OneRowRelation$ == Optimized Logical Plan == Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15] +- EvaluatePython [add(pythonUDF1#17, 1)], [pythonUDF0#18] +- EvaluatePython [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17] +- OneRowRelation$ == Physical Plan == WholeStageCodegen : +- Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15] : +- INPUT +- !BatchPythonEvaluation [add(pythonUDF1#17, 1)], [pythonUDF0#16,pythonUDF1#17,pythonUDF0#18] +- !BatchPythonEvaluation [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17] +- Scan OneRowRelation[] ``` ## How was this patch tested? Added new tests. Using the following script to benchmark 1, 2 and 3 udfs, ``` df = sqlContext.range(1, 1 << 23, 1, 4) double = F.udf(lambda x: x * 2, LongType()) print df.select(double(df.id)).count() print df.select(double(df.id), double(df.id + 1)).count() print df.select(double(df.id), double(df.id + 1), double(df.id + 2)).count() ``` Here is the results: N | Before | After | speed up ---- |------------ | -------------|------ 1 | 22 s | 7 s | 3.1X 2 | 38 s | 13 s | 2.9X 3 | 58 s | 16 s | 3.6X This benchmark ran locally with 4 CPUs. For 3 UDFs, it launched 12 Python before before this patch, 4 process after this patch. After this patch, it will use less memory for multiple UDFs than before (less buffering). Author: Davies Liu <davies@databricks.com> Closes #12057 from davies/multi_udfs.
* [SPARK-14215] [SQL] [PYSPARK] Support chained Python UDFsDavies Liu2016-03-291-5/+11
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR brings the support for chained Python UDFs, for example ```sql select udf1(udf2(a)) select udf1(udf2(a) + 3) select udf1(udf2(a) + udf3(b)) ``` Also directly chained unary Python UDFs are put in single batch of Python UDFs, others may require multiple batches. For example, ```python >>> sqlContext.sql("select double(double(1))").explain() == Physical Plan == WholeStageCodegen : +- Project [pythonUDF#10 AS double(double(1))#9] : +- INPUT +- !BatchPythonEvaluation double(double(1)), [pythonUDF#10] +- Scan OneRowRelation[] >>> sqlContext.sql("select double(double(1) + double(2))").explain() == Physical Plan == WholeStageCodegen : +- Project [pythonUDF#19 AS double((double(1) + double(2)))#16] : +- INPUT +- !BatchPythonEvaluation double((pythonUDF#17 + pythonUDF#18)), [pythonUDF#17,pythonUDF#18,pythonUDF#19] +- !BatchPythonEvaluation double(2), [pythonUDF#17,pythonUDF#18] +- !BatchPythonEvaluation double(1), [pythonUDF#17] +- Scan OneRowRelation[] ``` TODO: will support multiple unrelated Python UDFs in one batch (another PR). ## How was this patch tested? Added new unit tests for chained UDFs. Author: Davies Liu <davies@databricks.com> Closes #12014 from davies/py_udfs.
* [SPARK-14061][SQL] implement CreateMapWenchen Fan2016-03-251-0/+20
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? As we have `CreateArray` and `CreateStruct`, we should also have `CreateMap`. This PR adds the `CreateMap` expression, and the DataFrame API, and python API. ## How was this patch tested? various new tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #11879 from cloud-fan/create_map.
* [MINOR] Fix typo in 'hypot' docstringTristan Reid2016-03-091-1/+1
| | | | | | | | | | Minor typo: docstring for pyspark.sql.functions: hypot has extra characters N/A Author: Tristan Reid <treid@netflix.com> Closes #11616 from tristanreid/master.
* [SPARK-12720][SQL] SQL Generation Support for Cube, Rollup, and Grouping Setsgatorsmile2016-03-051-7/+7
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? This PR is for supporting SQL generation for cube, rollup and grouping sets. For example, a query using rollup: ```SQL SELECT count(*) as cnt, key % 5, grouping_id() FROM t1 GROUP BY key % 5 WITH ROLLUP ``` Original logical plan: ``` Aggregate [(key#17L % cast(5 as bigint))#47L,grouping__id#46], [(count(1),mode=Complete,isDistinct=false) AS cnt#43L, (key#17L % cast(5 as bigint))#47L AS _c1#45L, grouping__id#46 AS _c2#44] +- Expand [List(key#17L, value#18, (key#17L % cast(5 as bigint))#47L, 0), List(key#17L, value#18, null, 1)], [key#17L,value#18,(key#17L % cast(5 as bigint))#47L,grouping__id#46] +- Project [key#17L, value#18, (key#17L % cast(5 as bigint)) AS (key#17L % cast(5 as bigint))#47L] +- Subquery t1 +- Relation[key#17L,value#18] ParquetRelation ``` Converted SQL: ```SQL SELECT count( 1) AS `cnt`, (`t1`.`key` % CAST(5 AS BIGINT)), grouping_id() AS `_c2` FROM `default`.`t1` GROUP BY (`t1`.`key` % CAST(5 AS BIGINT)) GROUPING SETS (((`t1`.`key` % CAST(5 AS BIGINT))), ()) ``` #### How was the this patch tested? Added eight test cases in `LogicalPlanToSQLSuite`. Author: gatorsmile <gatorsmile@gmail.com> Author: xiaoli <lixiao1983@gmail.com> Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local> Closes #11283 from gatorsmile/groupingSetsToSQL.
* [SPARK-13594][SQL] remove typed operations(e.g. map, flatMap) from python ↵Wenchen Fan2016-03-021-2/+2
| | | | | | | | | | | | | | | | DataFrame ## What changes were proposed in this pull request? Remove `map`, `flatMap`, `mapPartitions` from python DataFrame, to prepare for Dataset API in the future. ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #11445 from cloud-fan/python-clean.
* [SPARK-13467] [PYSPARK] abstract python function to simplify pyspark codeWenchen Fan2016-02-241-5/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? When we pass a Python function to JVM side, we also need to send its context, e.g. `envVars`, `pythonIncludes`, `pythonExec`, etc. However, it's annoying to pass around so many parameters at many places. This PR abstract python function along with its context, to simplify some pyspark code and make the logic more clear. ## How was the this patch tested? by existing unit tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #11342 from cloud-fan/python-clean.
* [MINOR][DOCS] Fix all typos in markdown files of `doc` and similar patterns ↵Dongjoon Hyun2016-02-221-3/+3
| | | | | | | | | | | | | | | | | in other comments ## What changes were proposed in this pull request? This PR tries to fix all typos in all markdown files under `docs` module, and fixes similar typos in other comments, too. ## How was the this patch tested? manual tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11300 from dongjoon-hyun/minor_fix_typos.
* [SPARK-12799] Simplify various string output for expressionsCheng Lian2016-02-211-15/+15
| | | | | | | | | | | | | | | | | | | | | | | | | This PR introduces several major changes: 1. Replacing `Expression.prettyString` with `Expression.sql` The `prettyString` method is mostly an internal, developer faced facility for debugging purposes, and shouldn't be exposed to users. 1. Using SQL-like representation as column names for selected fields that are not named expression (back-ticks and double quotes should be removed) Before, we were using `prettyString` as column names when possible, and sometimes the result column names can be weird. Here are several examples: Expression | `prettyString` | `sql` | Note ------------------ | -------------- | ---------- | --------------- `a && b` | `a && b` | `a AND b` | `a.getField("f")` | `a[f]` | `a.f` | `a` is a struct 1. Adding trait `NonSQLExpression` extending from `Expression` for expressions that don't have a SQL representation (e.g. Scala UDF/UDAF and Java/Scala object expressions used for encoders) `NonSQLExpression.sql` may return an arbitrary user facing string representation of the expression. Author: Cheng Lian <lian@databricks.com> Closes #10757 from liancheng/spark-12799.simplify-expression-string-methods.
* Revert "[SPARK-12567] [SQL] Add aes_{encrypt,decrypt} UDFs"Reynold Xin2016-02-191-37/+0
| | | | This reverts commit 4f9a66481849dc867cf6592d53e0e9782361d20a.
* [SPARK-12567] [SQL] Add aes_{encrypt,decrypt} UDFsKai Jiang2016-02-191-0/+37
| | | | | | Author: Kai Jiang <jiangkai@gmail.com> Closes #10527 from vectorijk/spark-12567.
* [SPARK-13296][SQL] Move UserDefinedFunction into sql.expressions.Reynold Xin2016-02-131-3/+3
| | | | | | | | | | | | | | | | This pull request has the following changes: 1. Moved UserDefinedFunction into expressions package. This is more consistent with how we structure the packages for window functions and UDAFs. 2. Moved UserDefinedPythonFunction into execution.python package, so we don't have a random private class in the top level sql package. 3. Move everything in execution/python.scala into the newly created execution.python package. Most of the diffs are just straight copy-paste. Author: Reynold Xin <rxin@databricks.com> Closes #11181 from rxin/SPARK-13296.
* [SPARK-12962] [SQL] [PySpark] PySpark support covar_samp and covar_popYanbo Liang2016-02-121-6/+35
| | | | | | | | | | PySpark support ```covar_samp``` and ```covar_pop```. cc rxin davies marmbrus Author: Yanbo Liang <ybliang8@gmail.com> Closes #10876 from yanboliang/spark-12962.
* [SPARK-12706] [SQL] grouping() and grouping_id()Davies Liu2016-02-101-0/+44
| | | | | | | | | | | | Grouping() returns a column is aggregated or not, grouping_id() returns the aggregation levels. grouping()/grouping_id() could be used with window function, but does not work in having/sort clause, will be fixed by another PR. The GROUPING__ID/grouping_id() in Hive is wrong (according to docs), we also did it wrongly, this PR change that to match the behavior in most databases (also the docs of Hive). Author: Davies Liu <davies@databricks.com> Closes #10677 from davies/grouping.
* [SPARK-13049] Add First/last with ignore nulls to functions.scalaHerman van Hovell2016-01-311-2/+24
| | | | | | | | | | | | | This PR adds the ability to specify the ```ignoreNulls``` option to the functions dsl, e.g: ```df.select($"id", last($"value", ignoreNulls = true).over(Window.partitionBy($"id").orderBy($"other"))``` This PR is some where between a bug fix (see the JIRA) and a new feature. I am not sure if we should backport to 1.6. cc yhuai Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #10957 from hvanhovell/SPARK-13049.
* [SPARK-12642][SQL] improve the hash expression to be decoupled from unsafe rowWenchen Fan2016-01-131-1/+1
| | | | | | | | https://issues.apache.org/jira/browse/SPARK-12642 Author: Wenchen Fan <wenchen@databricks.com> Closes #10694 from cloud-fan/hash-expr.
* [SPARK-12480][FOLLOW-UP] use a single column vararg for hashWenchen Fan2016-01-051-0/+12
| | | | | | | | | | address comments in #10435 This makes the API easier to use if user programmatically generate the call to hash, and they will get analysis exception if the arguments of hash is empty. Author: Wenchen Fan <wenchen@databricks.com> Closes #10588 from cloud-fan/hash.
* [SPARK-12600][SQL] Remove deprecated methods in Spark SQLReynold Xin2016-01-041-24/+0
| | | | | | Author: Reynold Xin <rxin@databricks.com> Closes #10559 from rxin/remove-deprecated-sql.
* Doc typo: ltrim = trim from left end, not rightpshearer2015-12-211-1/+1
| | | | | | Author: pshearer <pshearer@massmutual.com> Closes #10414 from pshearer/patch-1.
* [SPARK-11980][SPARK-10621][SQL] Fix json_tuple and add test cases forgatorsmile2015-11-251-10/+34
| | | | | | | | | | | | | | Added Python test cases for the function `isnan`, `isnull`, `nanvl` and `json_tuple`. Fixed a bug in the function `json_tuple` rxin , could you help me review my changes? Please let me know anything is missing. Thank you! Have a good Thanksgiving day! Author: gatorsmile <gatorsmile@gmail.com> Closes #9977 from gatorsmile/json_tuple.
* [SPARK-10621][SQL] Consistent naming for functions in SQL, Python, ScalaReynold Xin2015-11-241-17/+94
| | | | | | Author: Reynold Xin <rxin@databricks.com> Closes #9948 from rxin/SPARK-10621.
* [SPARK-11836][SQL] udf/cast should not create new SQLContextDavies Liu2015-11-231-3/+4
| | | | | | | | They should use the existing SQLContext. Author: Davies Liu <davies@databricks.com> Closes #9914 from davies/create_udf.
* [SPARK-11567] [PYTHON] Add Python API for corr Aggregate functionfelixcheung2015-11-101-0/+16
| | | | | | | | | | like `df.agg(corr("col1", "col2")` davies Author: felixcheung <felixcheung_m@hotmail.com> Closes #9536 from felixcheung/pyfunc.
* [SPARK-9830][SQL] Remove AggregateExpression1 and Aggregate Operator used to ↵Yin Huai2015-11-101-1/+1
| | | | | | | | | | | | | | | | | | | evaluate AggregateExpression1s https://issues.apache.org/jira/browse/SPARK-9830 This PR contains the following main changes. * Removing `AggregateExpression1`. * Removing `Aggregate` operator, which is used to evaluate `AggregateExpression1`. * Removing planner rule used to plan `Aggregate`. * Linking `MultipleDistinctRewriter` to analyzer. * Renaming `AggregateExpression2` to `AggregateExpression` and `AggregateFunction2` to `AggregateFunction`. * Updating places where we create aggregate expression. The way to create aggregate expressions is `AggregateExpression(aggregateFunction, mode, isDistinct)`. * Changing `val`s in `DeclarativeAggregate`s that touch children of this function to `lazy val`s (when we create aggregate expression in DataFrame API, children of an aggregate function can be unresolved). Author: Yin Huai <yhuai@databricks.com> Closes #9556 from yhuai/removeAgg1.
* [SPARK-9301][SQL] Add collect_set and collect_list aggregate functionsNick Buroojy2015-11-091-11/+14
| | | | | | | | | | | | | | | | | | | | | For now they are thin wrappers around the corresponding Hive UDAFs. One limitation with these in Hive 0.13.0 is they only support aggregating primitive types. I chose snake_case here instead of camelCase because it seems to be used in the majority of the multi-word fns. Do we also want to add these to `functions.py`? This approach was recommended here: https://github.com/apache/spark/pull/8592#issuecomment-154247089 marmbrus rxin Author: Nick Buroojy <nick.buroojy@civitaslearning.com> Closes #9526 from nburoojy/nick/udaf-alias. (cherry picked from commit a6ee4f989d020420dd08b97abb24802200ff23b2) Signed-off-by: Michael Armbrust <michael@databricks.com>
* [SPARK-11467][SQL] add Python API for stddev/varianceDavies Liu2015-11-031-0/+17
| | | | | | | | Add Python API for stddev/stddev_pop/stddev_samp/variance/var_pop/var_samp/skewness/kurtosis Author: Davies Liu <davies@databricks.com> Closes #9424 from davies/py_var.
* [SPARK-10577] [PYSPARK] DataFrame hint for broadcast joinJian Feng2015-09-211-0/+9
| | | | | | | | https://issues.apache.org/jira/browse/SPARK-10577 Author: Jian Feng <jzhang.chs@gmail.com> Closes #8801 from Jianfeng-chs/master.
* [SPARK-10373] [PYSPARK] move @since into pyspark from sqlDavies Liu2015-09-081-2/+1
| | | | | | | | cc mengxr Author: Davies Liu <davies@databricks.com> Closes #8657 from davies/move_since.
* [DOCS] [SQL] [PYSPARK] Fix typo in ntile functionMoussa Taifi2015-08-191-1/+1
| | | | | | | | Fix typo in ntile function. Author: Moussa Taifi <moutai10@gmail.com> Closes #8261 from moutai/patch-2.
* [SPARK-9978] [PYSPARK] [SQL] fix Window.orderBy and doc of ntile()Davies Liu2015-08-141-3/+4
| | | | | | Author: Davies Liu <davies@databricks.com> Closes #8213 from davies/fix_window.
* [SPARK-9907] [SQL] Python crc32 is mistakenly calling md5Reynold Xin2015-08-121-2/+2
| | | | | | Author: Reynold Xin <rxin@databricks.com> Closes #8138 from rxin/SPARK-9907.
* [SPARK-9691] [SQL] PySpark SQL rand function treats seed 0 as no seedYin Huai2015-08-061-2/+2
| | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-9691 jkbradley rxin Author: Yin Huai <yhuai@databricks.com> Closes #7999 from yhuai/pythonRand and squashes the following commits: 4187e0c [Yin Huai] Regression test. a985ef9 [Yin Huai] Use "if seed is not None" instead "if seed" because "if seed" returns false when seed is 0.
* [SPARK-8266] [SQL] add function translatezhichao.li2015-08-061-0/+16
| | | | | | | | | | | | | | | | | ![translate](http://www.w3resource.com/PostgreSQL/postgresql-translate-function.png) Author: zhichao.li <zhichao.li@intel.com> Closes #7709 from zhichao-li/translate and squashes the following commits: 9418088 [zhichao.li] refine checking condition f2ab77a [zhichao.li] clone string 9d88f2d [zhichao.li] fix indent 6aa2962 [zhichao.li] style e575ead [zhichao.li] add python api 9d4bab0 [zhichao.li] add special case for fodable and refactor unittest eda7ad6 [zhichao.li] update to use TernaryExpression cdfd4be [zhichao.li] add function translate
* [SPARK-8231] [SQL] Add array_containsPedro Rodriguez2015-08-041-0/+17
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR is based on #7580 , thanks to EntilZha PR for work on https://issues.apache.org/jira/browse/SPARK-8231 Currently, I have an initial implementation for contains. Based on discussion on JIRA, it should behave same as Hive: https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFArrayContains.java#L102-L128 Main points are: 1. If the array is empty, null, or the value is null, return false 2. If there is a type mismatch, throw error 3. If comparison is not supported, throw error Closes #7580 Author: Pedro Rodriguez <prodriguez@trulia.com> Author: Pedro Rodriguez <ski.rodriguez@gmail.com> Author: Davies Liu <davies@databricks.com> Closes #7949 from davies/array_contains and squashes the following commits: d3c08bc [Davies Liu] use foreach() to avoid copy bc3d1fe [Davies Liu] fix array_contains 719e37d [Davies Liu] Merge branch 'master' of github.com:apache/spark into array_contains e352cf9 [Pedro Rodriguez] fixed diff from master 4d5b0ff [Pedro Rodriguez] added docs and another type check ffc0591 [Pedro Rodriguez] fixed unit test 7a22deb [Pedro Rodriguez] Changed test to use strings instead of long/ints which are different between python 2 an 3 b5ffae8 [Pedro Rodriguez] fixed pyspark test 4e7dce3 [Pedro Rodriguez] added more docs 3082399 [Pedro Rodriguez] fixed unit test 46f9789 [Pedro Rodriguez] reverted change d3ca013 [Pedro Rodriguez] Fixed type checking to match hive behavior, then added tests to insure this 8528027 [Pedro Rodriguez] added more tests 686e029 [Pedro Rodriguez] fix scala style d262e9d [Pedro Rodriguez] reworked type checking code and added more tests 2517a58 [Pedro Rodriguez] removed unused import 28b4f71 [Pedro Rodriguez] fixed bug with type conversions and re-added tests 12f8795 [Pedro Rodriguez] fix scala style checks e8a20a9 [Pedro Rodriguez] added python df (broken atm) 65b562c [Pedro Rodriguez] made array_contains nullable false 33b45aa [Pedro Rodriguez] reordered test 9623c64 [Pedro Rodriguez] fixed test 4b4425b [Pedro Rodriguez] changed Arrays in tests to Seqs 72cb4b1 [Pedro Rodriguez] added checkInputTypes and docs 69c46fb [Pedro Rodriguez] added tests and codegen 9e0bfc4 [Pedro Rodriguez] initial attempt at implementation
* [SPARK-9513] [SQL] [PySpark] Add python API for DataFrame functionsDavies Liu2015-08-041-247/+602
| | | | | | | | | | | | | | | | | This adds Python API for those DataFrame functions that is introduced in 1.5. There is issue with serialize byte_array in Python 3, so some of functions (for BinaryType) does not have tests. cc rxin Author: Davies Liu <davies@databricks.com> Closes #7922 from davies/python_functions and squashes the following commits: 8ad942f [Davies Liu] fix test 5fb6ec3 [Davies Liu] fix bugs 3495ed3 [Davies Liu] fix issues ea5f7bb [Davies Liu] Add python API for DataFrame functions
* [SPARK-8269] [SQL] string function: initcapHuJiayin2015-08-011-0/+12
| | | | | | | | | | | | | | | | | | | | | | | | This PR is based on #7208 , thanks to HuJiayin Closes #7208 Author: HuJiayin <jiayin.hu@intel.com> Author: Davies Liu <davies@databricks.com> Closes #7850 from davies/initcap and squashes the following commits: 54472e9 [Davies Liu] fix python test 17ffe51 [Davies Liu] Merge branch 'master' of github.com:apache/spark into initcap ca46390 [Davies Liu] Merge branch 'master' of github.com:apache/spark into initcap 3a906e4 [Davies Liu] implement title case in UTF8String 8b2506a [HuJiayin] Update functions.py 2cd43e5 [HuJiayin] fix python style check b616c0e [HuJiayin] add python api 1f5a0ef [HuJiayin] add codegen 7e0c604 [HuJiayin] Merge branch 'master' of https://github.com/apache/spark into initcap 6a0b958 [HuJiayin] add column c79482d [HuJiayin] support soundex 7ce416b [HuJiayin] support initcap rebase code
* [SPARK-8263] [SQL] substr/substring should also support binary typezhichao.li2015-08-011-1/+17
| | | | | | | | | | | | | | | | | This is based on #7641, thanks to zhichao-li Closes #7641 Author: zhichao.li <zhichao.li@intel.com> Author: Davies Liu <davies@databricks.com> Closes #7848 from davies/substr and squashes the following commits: 461b709 [Davies Liu] remove bytearry from tests b45377a [Davies Liu] Merge branch 'master' of github.com:apache/spark into substr 01d795e [zhichao.li] scala style 99aa130 [zhichao.li] add substring to dataframe 4f68bfe [zhichao.li] add binary type support for substring
* [SPARK-8232] [SQL] Add sort_array supportCheng Hao2015-08-011-0/+20
| | | | | | | | | | | | | | | | | This PR is based on #7581 , just fix the conflict. Author: Cheng Hao <hao.cheng@intel.com> Author: Davies Liu <davies@databricks.com> Closes #7851 from davies/sort_array and squashes the following commits: a80ef66 [Davies Liu] fix conflict 7cfda65 [Davies Liu] Merge branch 'master' of github.com:apache/spark into sort_array 664c960 [Cheng Hao] update the sort_array by using the ArrayData 276d2d5 [Cheng Hao] add empty line 0edab9c [Cheng Hao] Add asending/descending support for sort_array 80fc0f8 [Cheng Hao] Add type checking a42b678 [Cheng Hao] Add sort_array support
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