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* [SPARK-17007][SQL] Move test data files into a test-data folderpetermaxlee2016-08-101-6/+6
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch moves all the test data files in sql/core/src/test/resources to sql/core/src/test/resources/test-data, so we don't clutter the top level sql/core/src/test/resources. Also deleted sql/core/src/test/resources/old-repeated.parquet since it is no longer used. The change will make it easier to spot sql-tests directory. ## How was this patch tested? This is a test-only change. Author: petermaxlee <petermaxlee@gmail.com> Closes #14589 from petermaxlee/SPARK-17007.
* [SPARK-16706][SQL] support java map in encoderWenchen Fan2016-07-261-4/+54
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? finish the TODO, create a new expression `ExternalMapToCatalyst` to iterate the map directly. ## How was this patch tested? new test in `JavaDatasetSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #14344 from cloud-fan/java-map.
* [SPARK-15982][SPARK-16009][SPARK-16007][SQL] Harmonize the behavior of ↵Tathagata Das2016-06-201-0/+158
| | | | | | | | | | | | | | | | | | | | | | | | | DataFrameReader.text/csv/json/parquet/orc ## What changes were proposed in this pull request? Issues with current reader behavior. - `text()` without args returns an empty DF with no columns -> inconsistent, its expected that text will always return a DF with `value` string field, - `textFile()` without args fails with exception because of the above reason, it expected the DF returned by `text()` to have a `value` field. - `orc()` does not have var args, inconsistent with others - `json(single-arg)` was removed, but that caused source compatibility issues - [SPARK-16009](https://issues.apache.org/jira/browse/SPARK-16009) - user specified schema was not respected when `text/csv/...` were used with no args - [SPARK-16007](https://issues.apache.org/jira/browse/SPARK-16007) The solution I am implementing is to do the following. - For each format, there will be a single argument method, and a vararg method. For json, parquet, csv, text, this means adding json(string), etc.. For orc, this means adding orc(varargs). - Remove the special handling of text(), csv(), etc. that returns empty dataframe with no fields. Rather pass on the empty sequence of paths to the datasource, and let each datasource handle it right. For e.g, text data source, should return empty DF with schema (value: string) - Deduped docs and fixed their formatting. ## How was this patch tested? Added new unit tests for Scala and Java tests Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13727 from tdas/SPARK-15982.
* [SPARK-15898][SQL] DataFrameReader.text should return DataFrameWenchen Fan2016-06-121-2/+2
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? We want to maintain API compatibility for DataFrameReader.text, and will introduce a new API called DataFrameReader.textFile which returns Dataset[String]. affected PRs: https://github.com/apache/spark/pull/11731 https://github.com/apache/spark/pull/13104 https://github.com/apache/spark/pull/13184 ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #13604 from cloud-fan/revert.
* [SPARK-15086][CORE][STREAMING] Deprecate old Java accumulator APISean Owen2016-06-121-2/+2
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Deprecate old Java accumulator API; should use Scala now - Update Java tests and examples - Don't bother testing old accumulator API in Java 8 (too) - (fix a misspelling too) ## How was this patch tested? Jenkins tests Author: Sean Owen <sowen@cloudera.com> Closes #13606 from srowen/SPARK-15086.
* [SPARK-15696][SQL] Improve `crosstab` to have a consistent column orderDongjoon Hyun2016-06-091-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, `crosstab` returns a Dataframe having **random-order** columns obtained by just `distinct`. Also, the documentation of `crosstab` shows the result in a sorted order which is different from the current implementation. This PR explicitly constructs the columns in a sorted order in order to improve user experience. Also, this implementation gives the same result with the documentation. **Before** ```scala scala> spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3))).toDF("key", "value").stat.crosstab("key", "value").show() +---------+---+---+---+ |key_value| 3| 2| 1| +---------+---+---+---+ | 2| 1| 0| 2| | 1| 0| 1| 1| | 3| 1| 1| 0| +---------+---+---+---+ scala> spark.createDataFrame(Seq((1, "a"), (1, "b"), (2, "a"), (2, "a"), (2, "c"), (3, "b"), (3, "c"))).toDF("key", "value").stat.crosstab("key", "value").show() +---------+---+---+---+ |key_value| c| a| b| +---------+---+---+---+ | 2| 1| 2| 0| | 1| 0| 1| 1| | 3| 1| 0| 1| +---------+---+---+---+ ``` **After** ```scala scala> spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3))).toDF("key", "value").stat.crosstab("key", "value").show() +---------+---+---+---+ |key_value| 1| 2| 3| +---------+---+---+---+ | 2| 2| 0| 1| | 1| 1| 1| 0| | 3| 0| 1| 1| +---------+---+---+---+ scala> spark.createDataFrame(Seq((1, "a"), (1, "b"), (2, "a"), (2, "a"), (2, "c"), (3, "b"), (3, "c"))).toDF("key", "value").stat.crosstab("key", "value").show() +---------+---+---+---+ |key_value| a| b| c| +---------+---+---+---+ | 2| 2| 0| 1| | 1| 1| 1| 0| | 3| 0| 1| 1| +---------+---+---+---+ ``` ## How was this patch tested? Pass the Jenkins tests with updated testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13436 from dongjoon-hyun/SPARK-15696.
* [SPARK-15632][SQL] Typed Filter should NOT change the Dataset schemaSean Zhong2016-06-061-0/+13
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR makes sure the typed Filter doesn't change the Dataset schema. **Before the change:** ``` scala> val df = spark.range(0,9) scala> df.schema res12: org.apache.spark.sql.types.StructType = StructType(StructField(id,LongType,false)) scala> val afterFilter = df.filter(_=>true) scala> afterFilter.schema // !!! schema is CHANGED!!! Column name is changed from id to value, nullable is changed from false to true. res13: org.apache.spark.sql.types.StructType = StructType(StructField(value,LongType,true)) ``` SerializeFromObject and DeserializeToObject are inserted to wrap the Filter, and these two can possibly change the schema of Dataset. **After the change:** ``` scala> afterFilter.schema // schema is NOT changed. res47: org.apache.spark.sql.types.StructType = StructType(StructField(id,LongType,false)) ``` ## How was this patch tested? Unit test. Author: Sean Zhong <seanzhong@databricks.com> Closes #13529 from clockfly/spark-15632.
* [SPARK-15633][MINOR] Make package name for Java tests consistentReynold Xin2016-05-273-3/+3
| | | | | | | | | | | | ## What changes were proposed in this pull request? This is a simple patch that makes package names for Java 8 test suites consistent. I moved everything to test.org.apache.spark to we can test package private APIs properly. Also added "java8" as the package name so we can easily run all the tests related to Java 8. ## How was this patch tested? This is a test only change. Author: Reynold Xin <rxin@databricks.com> Closes #13364 from rxin/SPARK-15633.
* [SPARK-15031][EXAMPLE] Use SparkSession in examplesZheng RuiFeng2016-05-201-3/+3
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Use `SparkSession` according to [SPARK-15031](https://issues.apache.org/jira/browse/SPARK-15031) `MLLLIB` is not recommended to use now, so examples in `MLLIB` are ignored in this PR. `StreamingContext` can not be directly obtained from `SparkSession`, so example in `Streaming` are ignored too. cc andrewor14 ## How was this patch tested? manual tests with spark-submit Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13164 from zhengruifeng/use_sparksession_ii.
* [SPARK-11827][SQL] Adding java.math.BigInteger support in Java type ↵Kevin Yu2016-05-201-1/+10
| | | | | | | | | | inference for POJOs and Java collections Hello : Can you help check this PR? I am adding support for the java.math.BigInteger for java bean code path. I saw internally spark is converting the BigInteger to BigDecimal in ColumnType.scala and CatalystRowConverter.scala. I use the similar way and convert the BigInteger to the BigDecimal. . Author: Kevin Yu <qyu@us.ibm.com> Closes #10125 from kevinyu98/working_on_spark-11827.
* [SPARK-15171][SQL] Remove the references to deprecated method ↵Sean Zhong2016-05-182-5/+5
| | | | | | | | | | | | | | | | | dataset.registerTempTable ## What changes were proposed in this pull request? Update the unit test code, examples, and documents to remove calls to deprecated method `dataset.registerTempTable`. ## How was this patch tested? This PR only changes the unit test code, examples, and comments. It should be safe. This is a follow up of PR https://github.com/apache/spark/pull/12945 which was merged. Author: Sean Zhong <seanzhong@databricks.com> Closes #13098 from clockfly/spark-15171-remove-deprecation.
* [SPARK-14642][SQL] import org.apache.spark.sql.expressions._ breaks udf ↵Subhobrata Dey2016-05-101-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | under functions ## What changes were proposed in this pull request? PR fixes the import issue which breaks udf functions. The following code snippet throws an error ``` scala> import org.apache.spark.sql.functions._ import org.apache.spark.sql.functions._ scala> import org.apache.spark.sql.expressions._ import org.apache.spark.sql.expressions._ scala> udf((v: String) => v.stripSuffix("-abc")) <console>:30: error: No TypeTag available for String udf((v: String) => v.stripSuffix("-abc")) ``` This PR resolves the issue. ## How was this patch tested? patch tested with unit tests. (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: Subhobrata Dey <sbcd90@gmail.com> Closes #12458 from sbcd90/udfFuncBreak.
* [SPARK-15037][SQL][MLLIB] Use SparkSession instead of SQLContext in ↵Sandeep Singh2016-05-106-148/+133
| | | | | | | | | | | | | | | Scala/Java TestSuites ## What changes were proposed in this pull request? Use SparkSession instead of SQLContext in Scala/Java TestSuites as this PR already very big working Python TestSuites in a diff PR. ## How was this patch tested? Existing tests Author: Sandeep Singh <sandeep@techaddict.me> Closes #12907 from techaddict/SPARK-15037.
* [SPARK-12660][SPARK-14967][SQL] Implement Except Distinct by Left Anti Joingatorsmile2016-04-291-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? Replaces a logical `Except` operator with a `Left-anti Join` operator. This way, we can take advantage of all the benefits of join implementations (e.g. managed memory, code generation, broadcast joins). ```SQL SELECT a1, a2 FROM Tab1 EXCEPT SELECT b1, b2 FROM Tab2 ==> SELECT DISTINCT a1, a2 FROM Tab1 LEFT ANTI JOIN Tab2 ON a1<=>b1 AND a2<=>b2 ``` Note: 1. This rule is only applicable to EXCEPT DISTINCT. Do not use it for EXCEPT ALL. 2. This rule has to be done after de-duplicating the attributes; otherwise, the enerated join conditions will be incorrect. This PR also corrects the existing behavior in Spark. Before this PR, the behavior is like ```SQL test("except") { val df_left = Seq(1, 2, 2, 3, 3, 4).toDF("id") val df_right = Seq(1, 3).toDF("id") checkAnswer( df_left.except(df_right), Row(2) :: Row(2) :: Row(4) :: Nil ) } ``` After this PR, the result is corrected. We strictly follow the SQL compliance of `Except Distinct`. #### How was this patch tested? Modified and added a few test cases to verify the optimization rule and the results of operators. Author: gatorsmile <gatorsmile@gmail.com> Closes #12736 from gatorsmile/exceptByAntiJoin.
* [SPARK-14372][SQL] Dataset.randomSplit() needs a Java versionRekha Joshi2016-04-111-0/+10
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1.Added method randomSplitAsList() in Dataset for java for https://issues.apache.org/jira/browse/SPARK-14372 ## How was this patch tested? TestSuite Author: Rekha Joshi <rekhajoshm@gmail.com> Author: Joshi <rekhajoshm@gmail.com> Closes #12184 from rekhajoshm/SPARK-14372.
* [SPARK-14451][SQL] Move encoder definition into Aggregator interfaceReynold Xin2016-04-091-4/+13
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? When we first introduced Aggregators, we required the user of Aggregators to (implicitly) specify the encoders. It would actually make more sense to have the encoders be specified by the implementation of Aggregators, since each implementation should have the most state about how to encode its own data type. Note that this simplifies the Java API because Java users no longer need to explicitly specify encoders for aggregators. ## How was this patch tested? Updated unit tests. Author: Reynold Xin <rxin@databricks.com> Closes #12231 from rxin/SPARK-14451.
* [SPARK-14436][SQL] Make JavaDatasetAggregatorSuiteBase public.Marcelo Vanzin2016-04-062-53/+83
| | | | | | | | | | Without this, unit tests that extend that class fail for me locally on maven, because JUnit tries to run methods in that class and gets an IllegalAccessError. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #12212 from vanzin/SPARK-14436.
* [SPARK-14359] Unit tests for java 8 lambda syntax with typed aggregatesEric Liang2016-04-051-41/+45
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Adds unit tests for java 8 lambda syntax with typed aggregates as a follow-up to #12168 ## How was this patch tested? Unit tests. Author: Eric Liang <ekl@databricks.com> Closes #12181 from ericl/sc-2794-2.
* [SPARK-14359] Create built-in functions for typed aggregates in JavaEric Liang2016-04-051-0/+49
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This adds the corresponding Java static functions for built-in typed aggregates already exposed in Scala. ## How was this patch tested? Unit tests. rxin Author: Eric Liang <ekl@databricks.com> Closes #12168 from ericl/sc-2794.
* [SPARK-14334] [SQL] add toLocalIterator for Dataset/DataFrameDavies Liu2016-04-041-0/+10
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? RDD.toLocalIterator() could be used to fetch one partition at a time to reduce the memory usage. Right now, for Dataset/Dataframe we have to use df.rdd.toLocalIterator, which is super slow also requires lots of memory (because of the Java serializer or even Kyro serializer). This PR introduce an optimized toLocalIterator for Dataset/DataFrame, which is much faster and requires much less memory. For a partition with 5 millions rows, `df.rdd.toIterator` took about 100 seconds, but df.toIterator took less than 7 seconds. For 10 millions row, rdd.toIterator will crash (not enough memory) with 4G heap, but df.toLocalIterator could finished in 12 seconds. The JDBC server has been updated to use DataFrame.toIterator. ## How was this patch tested? Existing tests. Author: Davies Liu <davies@databricks.com> Closes #12114 from davies/local_iterator.
* [SPARK-14355][BUILD] Fix typos in Exception/Testcase/Comments and static ↵Dongjoon Hyun2016-04-031-5/+5
| | | | | | | | | | | | | | | | | | | | | analysis results ## What changes were proposed in this pull request? This PR contains the following 5 types of maintenance fix over 59 files (+94 lines, -93 lines). - Fix typos(exception/log strings, testcase name, comments) in 44 lines. - Fix lint-java errors (MaxLineLength) in 6 lines. (New codes after SPARK-14011) - Use diamond operators in 40 lines. (New codes after SPARK-13702) - Fix redundant semicolon in 5 lines. - Rename class `InferSchemaSuite` to `CSVInferSchemaSuite` in CSVInferSchemaSuite.scala. ## How was this patch tested? Manual and pass the Jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12139 from dongjoon-hyun/SPARK-14355.
* [SPARK-14285][SQL] Implement common type-safe aggregate functionsReynold Xin2016-04-012-54/+123
| | | | | | | | | | | | ## What changes were proposed in this pull request? In the Dataset API, it is fairly difficult for users to perform simple aggregations in a type-safe way at the moment because there are no aggregators that have been implemented. This pull request adds a few common aggregate functions in expressions.scala.typed package, and also creates the expressions.java.typed package without implementation. The java implementation should probably come as a separate pull request. One challenge there is to resolve the type difference between Scala primitive types and Java boxed types. ## How was this patch tested? Added unit tests for them. Author: Reynold Xin <rxin@databricks.com> Closes #12077 from rxin/SPARK-14285.
* [MINOR] Fix newly added java-lint errorsDongjoon Hyun2016-03-261-12/+16
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes some newly added java-lint errors(unused-imports, line-lengsth). ## How was this patch tested? Pass the Jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11968 from dongjoon-hyun/SPARK-14167.
* [SPARK-14073][STREAMING][TEST-MAVEN] Move flume back to SparkShixiong Zhu2016-03-251-8/+25
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR moves flume back to Spark as per the discussion in the dev mail-list. ## How was this patch tested? Existing Jenkins tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #11895 from zsxwing/move-flume-back.
* [SPARK-14145][SQL] Remove the untyped version of Dataset.groupByKeyReynold Xin2016-03-241-23/+0
| | | | | | | | | | | | ## What changes were proposed in this pull request? Dataset has two variants of groupByKey, one for untyped and the other for typed. It actually doesn't make as much sense to have an untyped API here, since apps that want to use untyped APIs should just use the groupBy "DataFrame" API. ## How was this patch tested? This patch removes a method, and removes the associated tests. Author: Reynold Xin <rxin@databricks.com> Closes #11949 from rxin/SPARK-14145.
* [SPARK-14088][SQL] Some Dataset API touch-upReynold Xin2016-03-221-2/+2
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1. Deprecated unionAll. It is pretty confusing to have both "union" and "unionAll" when the two do the same thing in Spark but are different in SQL. 2. Rename reduce in KeyValueGroupedDataset to reduceGroups so it is more consistent with rest of the functions in KeyValueGroupedDataset. Also makes it more obvious what "reduce" and "reduceGroups" mean. Previously it was confusing because it could be reducing a Dataset, or just reducing groups. 3. Added a "name" function, which is more natural to name columns than "as" for non-SQL users. 4. Remove "subtract" function since it is just an alias for "except". ## How was this patch tested? All changes should be covered by existing tests. Also added couple test cases to cover "name". Author: Reynold Xin <rxin@databricks.com> Closes #11908 from rxin/SPARK-14088.
* [SPARK-13401][SQL][TESTS] Fix SQL test warnings.Yong Tang2016-03-221-0/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This fix tries to fix several SQL test warnings under the sql/core/src/test directory. The fixed warnings includes "[unchecked]", "[rawtypes]", and "[varargs]". ## How was this patch tested? All existing tests passed. Author: Yong Tang <yong.tang.github@outlook.com> Closes #11857 from yongtang/SPARK-13401.
* [SPARK-14063][SQL] SQLContext.range should return Dataset[java.lang.Long]Reynold Xin2016-03-221-2/+2
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch changed the return type for SQLContext.range from `Dataset[Long]` (Scala primitive) to `Dataset[java.lang.Long]` (Java boxed long). Previously, SPARK-13894 changed the return type of range from `Dataset[Row]` to `Dataset[Long]`. The problem is that due to https://issues.scala-lang.org/browse/SI-4388, Scala compiles primitive types in generics into just Object, i.e. range at bytecode level now just returns `Dataset[Object]`. This is really bad for Java users because they are losing type safety and also need to add a type cast every time they use range. Talked to Jason Zaugg from Lightbend (Typesafe) who suggested the best approach is to return `Dataset[java.lang.Long]`. The downside is that when Scala users want to explicitly type a closure used on the dataset returned by range, they would need to use `java.lang.Long` instead of the Scala `Long`. ## How was this patch tested? The signature change should be covered by existing unit tests and API tests. I also added a new test case in DatasetSuite for range. Author: Reynold Xin <rxin@databricks.com> Closes #11880 from rxin/SPARK-14063.
* [SPARK-14011][CORE][SQL] Enable `LineLength` Java checkstyle ruleDongjoon Hyun2016-03-212-7/+9
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? [Spark Coding Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) has 100-character limit on lines, but it's disabled for Java since 11/09/15. This PR enables **LineLength** checkstyle again. To help that, this also introduces **RedundantImport** and **RedundantModifier**, too. The following is the diff on `checkstyle.xml`. ```xml - <!-- TODO: 11/09/15 disabled - the lengths are currently > 100 in many places --> - <!-- <module name="LineLength"> <property name="max" value="100"/> <property name="ignorePattern" value="^package.*|^import.*|a href|href|http://|https://|ftp://"/> </module> - --> <module name="NoLineWrap"/> <module name="EmptyBlock"> <property name="option" value="TEXT"/> -167,5 +164,7 </module> <module name="CommentsIndentation"/> <module name="UnusedImports"/> + <module name="RedundantImport"/> + <module name="RedundantModifier"/> ``` ## How was this patch tested? Currently, `lint-java` is disabled in Jenkins. It needs a manual test. After passing the Jenkins tests, `dev/lint-java` should passes locally. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11831 from dongjoon-hyun/SPARK-14011.
* [SPARK-13897][SQL] RelationalGroupedDataset and KeyValueGroupedDatasetReynold Xin2016-03-191-4/+4
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Previously, Dataset.groupBy returns a GroupedData, and Dataset.groupByKey returns a GroupedDataset. The naming is very similar, and unfortunately does not convey the real differences between the two. Assume we are grouping by some keys (K). groupByKey is a key-value style group by, in which the schema of the returned dataset is a tuple of just two fields: key and value. groupBy, on the other hand, is a relational style group by, in which the schema of the returned dataset is flattened and contain |K| + |V| fields. This pull request also removes the experimental tag from RelationalGroupedDataset. It has been with DataFrame since 1.3, and we have enough confidence now to stabilize it. ## How was this patch tested? This is a rename to improve API understandability. Should be covered by all existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #11841 from rxin/SPARK-13897.
* [SPARK-13894][SQL] SqlContext.range return type from DataFrame to DataSetCheng Hao2016-03-161-2/+2
| | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-13894 Change the return type of the `SQLContext.range` API from `DataFrame` to `Dataset`. ## How was this patch tested? No additional unit test required. Author: Cheng Hao <hao.cheng@intel.com> Closes #11730 from chenghao-intel/range.
* [SPARK-13895][SQL] DataFrameReader.text should return Dataset[String]Reynold Xin2016-03-151-4/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch changes DataFrameReader.text()'s return type from DataFrame to Dataset[String]. Closes #11731. ## How was this patch tested? Updated existing integration tests to reflect the change. Author: Reynold Xin <rxin@databricks.com> Closes #11739 from rxin/SPARK-13895.
* [SPARK-13823][CORE][STREAMING][SQL] Always specify Charset in String <-> ↵Sean Owen2016-03-131-2/+0
| | | | | | | | | | | | | | | | | | | | byte[] conversions (and remaining Coverity items) ## What changes were proposed in this pull request? - Fixes calls to `new String(byte[])` or `String.getBytes()` that rely on platform default encoding, to use UTF-8 - Same for `InputStreamReader` and `OutputStreamWriter` constructors - Standardizes on UTF-8 everywhere - Standardizes specifying the encoding with `StandardCharsets.UTF-8`, not the Guava constant or "UTF-8" (which means handling `UnuspportedEncodingException`) - (also addresses the other remaining Coverity scan issues, which are pretty trivial; these are separated into commit https://github.com/srowen/spark/commit/1deecd8d9ca986d8adb1a42d315890ce5349d29c ) ## How was this patch tested? Jenkins tests Author: Sean Owen <sowen@cloudera.com> Closes #11657 from srowen/SPARK-13823.
* [SPARK-13841][SQL] Removes Dataset.collectRows()/takeRows()Cheng Lian2016-03-132-21/+22
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes two methods, `collectRows()` and `takeRows()`, from `Dataset[T]`. These methods were added in PR #11443, and were later considered not useful. ## How was this patch tested? Existing tests should do the work. Author: Cheng Lian <lian@databricks.com> Closes #11678 from liancheng/remove-collect-rows-and-take-rows.
* [SPARK-13244][SQL] Migrates DataFrame to DatasetCheng Lian2016-03-104-47/+47
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR unifies DataFrame and Dataset by migrating existing DataFrame operations to Dataset and make `DataFrame` a type alias of `Dataset[Row]`. Most Scala code changes are source compatible, but Java API is broken as Java knows nothing about Scala type alias (mostly replacing `DataFrame` with `Dataset<Row>`). There are several noticeable API changes related to those returning arrays: 1. `collect`/`take` - Old APIs in class `DataFrame`: ```scala def collect(): Array[Row] def take(n: Int): Array[Row] ``` - New APIs in class `Dataset[T]`: ```scala def collect(): Array[T] def take(n: Int): Array[T] def collectRows(): Array[Row] def takeRows(n: Int): Array[Row] ``` Two specialized methods `collectRows` and `takeRows` are added because Java doesn't support returning generic arrays. Thus, for example, `DataFrame.collect(): Array[T]` actually returns `Object` instead of `Array<T>` from Java side. Normally, Java users may fall back to `collectAsList` and `takeAsList`. The two new specialized versions are added to avoid performance regression in ML related code (but maybe I'm wrong and they are not necessary here). 1. `randomSplit` - Old APIs in class `DataFrame`: ```scala def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame] def randomSplit(weights: Array[Double]): Array[DataFrame] ``` - New APIs in class `Dataset[T]`: ```scala def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]] def randomSplit(weights: Array[Double]): Array[Dataset[T]] ``` Similar problem as above, but hasn't been addressed for Java API yet. We can probably add `randomSplitAsList` to fix this one. 1. `groupBy` Some original `DataFrame.groupBy` methods have conflicting signature with original `Dataset.groupBy` methods. To distinguish these two, typed `Dataset.groupBy` methods are renamed to `groupByKey`. Other noticeable changes: 1. Dataset always do eager analysis now We used to support disabling DataFrame eager analysis to help reporting partially analyzed malformed logical plan on analysis failure. However, Dataset encoders requires eager analysi during Dataset construction. To preserve the error reporting feature, `AnalysisException` now takes an extra `Option[LogicalPlan]` argument to hold the partially analyzed plan, so that we can check the plan tree when reporting test failures. This plan is passed by `QueryExecution.assertAnalyzed`. ## How was this patch tested? Existing tests do the work. ## TODO - [ ] Fix all tests - [ ] Re-enable MiMA check - [ ] Update ScalaDoc (`since`, `group`, and example code) Author: Cheng Lian <lian@databricks.com> Author: Yin Huai <yhuai@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Author: Cheng Lian <liancheng@users.noreply.github.com> Closes #11443 from liancheng/ds-to-df.
* [SPARK-13702][CORE][SQL][MLLIB] Use diamond operator for generic instance ↵Dongjoon Hyun2016-03-092-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | creation in Java code. ## What changes were proposed in this pull request? In order to make `docs/examples` (and other related code) more simple/readable/user-friendly, this PR replaces existing codes like the followings by using `diamond` operator. ``` - final ArrayList<Product2<Object, Object>> dataToWrite = - new ArrayList<Product2<Object, Object>>(); + final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<>(); ``` Java 7 or higher supports **diamond** operator which replaces the type arguments required to invoke the constructor of a generic class with an empty set of type parameters (<>). Currently, Spark Java code use mixed usage of this. ## How was this patch tested? Manual. Pass the existing tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11541 from dongjoon-hyun/SPARK-13702.
* [SPARK-13692][CORE][SQL] Fix trivial Coverity/Checkstyle defectsDongjoon Hyun2016-03-091-1/+7
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This issue fixes the following potential bugs and Java coding style detected by Coverity and Checkstyle. - Implement both null and type checking in equals functions. - Fix wrong type casting logic in SimpleJavaBean2.equals. - Add `implement Cloneable` to `UTF8String` and `SortedIterator`. - Remove dereferencing before null check in `AbstractBytesToBytesMapSuite`. - Fix coding style: Add '{}' to single `for` statement in mllib examples. - Remove unused imports in `ColumnarBatch` and `JavaKinesisStreamSuite`. - Remove unused fields in `ChunkFetchIntegrationSuite`. - Add `stop()` to prevent resource leak. Please note that the last two checkstyle errors exist on newly added commits after [SPARK-13583](https://issues.apache.org/jira/browse/SPARK-13583). ## How was this patch tested? manual via `./dev/lint-java` and Coverity site. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11530 from dongjoon-hyun/SPARK-13692.
* [SPARK-13423][WIP][CORE][SQL][STREAMING] Static analysis fixes for 2.xSean Owen2016-03-032-20/+17
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Make some cross-cutting code improvements according to static analysis. These are individually up for discussion since they exist in separate commits that can be reverted. The changes are broadly: - Inner class should be static - Mismatched hashCode/equals - Overflow in compareTo - Unchecked warnings - Misuse of assert, vs junit.assert - get(a) + getOrElse(b) -> getOrElse(a,b) - Array/String .size -> .length (occasionally, -> .isEmpty / .nonEmpty) to avoid implicit conversions - Dead code - tailrec - exists(_ == ) -> contains find + nonEmpty -> exists filter + size -> count - reduce(_+_) -> sum map + flatten -> map The most controversial may be .size -> .length simply because of its size. It is intended to avoid implicits that might be expensive in some places. ## How was the this patch tested? Existing Jenkins unit tests. Author: Sean Owen <sowen@cloudera.com> Closes #11292 from srowen/SPARK-13423.
* [SPARK-13101][SQL] nullability of array type element should not fail ↵Wenchen Fan2016-02-081-3/+1
| | | | | | | | | | analysis of encoder nullability should only be considered as an optimization rather than part of the type system, so instead of failing analysis for mismatch nullability, we should pass analysis and add runtime null check. Author: Wenchen Fan <wenchen@databricks.com> Closes #11035 from cloud-fan/ignore-nullability.
* [SPARK-12938][SQL] DataFrame API for Bloom filterWenchen Fan2016-01-271-0/+31
| | | | | | | | | | This PR integrates Bloom filter from spark-sketch into DataFrame. This version resorts to RDD.aggregate for building the filter. A more performant UDAF version can be built in future follow-up PRs. This PR also add 2 specify `put` version(`putBinary` and `putLong`) into `BloomFilter`, which makes it easier to build a Bloom filter over a `DataFrame`. Author: Wenchen Fan <wenchen@databricks.com> Closes #10937 from cloud-fan/bloom-filter.
* [SPARK-12935][SQL] DataFrame API for Count-Min SketchCheng Lian2016-01-261-1/+27
| | | | | | | | This PR integrates Count-Min Sketch from spark-sketch into DataFrame. This version resorts to `RDD.aggregate` for building the sketch. A more performant UDAF version can be built in future follow-up PRs. Author: Cheng Lian <lian@databricks.com> Closes #10911 from liancheng/cms-df-api.
* [SPARK-3369][CORE][STREAMING] Java mapPartitions Iterator->Iterable is ↵Sean Owen2016-01-261-14/+11
| | | | | | | | | | | | inconsistent with Scala's Iterator->Iterator Fix Java function API methods for flatMap and mapPartitions to require producing only an Iterator, not Iterable. Also fix DStream.flatMap to require a function producing TraversableOnce only, not Traversable. CC rxin pwendell for API change; tdas since it also touches streaming. Author: Sean Owen <sowen@cloudera.com> Closes #10413 from srowen/SPARK-3369.
* [SPARK-12616][SQL] Making Logical Operator `Union` Support Arbitrary Number ↵gatorsmile2016-01-201-2/+2
| | | | | | | | | | | | | | of Children The existing `Union` logical operator only supports two children. Thus, adding a new logical operator `Unions` which can have arbitrary number of children to replace the existing one. `Union` logical plan is a binary node. However, a typical use case for union is to union a very large number of input sources (DataFrames, RDDs, or files). It is not uncommon to union hundreds of thousands of files. In this case, our optimizer can become very slow due to the large number of logical unions. We should change the Union logical plan to support an arbitrary number of children, and add a single rule in the optimizer to collapse all adjacent `Unions` into a single `Unions`. Note that this problem doesn't exist in physical plan, because the physical `Unions` already supports arbitrary number of children. Author: gatorsmile <gatorsmile@gmail.com> Author: xiaoli <lixiao1983@gmail.com> Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local> Closes #10577 from gatorsmile/unionAllMultiChildren.
* [SPARK-12756][SQL] use hash expression in ExchangeWenchen Fan2016-01-132-16/+21
| | | | | | | | | | This PR makes bucketing and exchange share one common hash algorithm, so that we can guarantee the data distribution is same between shuffle and bucketed data source, which enables us to only shuffle one side when join a bucketed table and a normal one. This PR also fixes the tests that are broken by the new hash behaviour in shuffle. Author: Wenchen Fan <wenchen@databricks.com> Closes #10703 from cloud-fan/use-hash-expr-in-shuffle.
* [SPARK-12600][SQL] Remove deprecated methods in Spark SQLReynold Xin2016-01-041-2/+2
| | | | | | Author: Reynold Xin <rxin@databricks.com> Closes #10559 from rxin/remove-deprecated-sql.
* [SPARK-12371][SQL] Runtime nullability check for NewInstanceCheng Lian2015-12-221-1/+125
| | | | | | | | This PR adds a new expression `AssertNotNull` to ensure non-nullable fields of products and case classes don't receive null values at runtime. Author: Cheng Lian <lian@databricks.com> Closes #10331 from liancheng/dataset-nullability-check.
* [SPARK-12404][SQL] Ensure objects passed to StaticInvoke is SerializableKousuke Saruta2015-12-181-0/+52
| | | | | | | | | | | | | | | | | | | | | Now `StaticInvoke` receives `Any` as a object and `StaticInvoke` can be serialized but sometimes the object passed is not serializable. For example, following code raises Exception because `RowEncoder#extractorsFor` invoked indirectly makes `StaticInvoke`. ``` case class TimestampContainer(timestamp: java.sql.Timestamp) val rdd = sc.parallelize(1 to 2).map(_ => TimestampContainer(System.currentTimeMillis)) val df = rdd.toDF val ds = df.as[TimestampContainer] val rdd2 = ds.rdd <----------------- invokes extractorsFor indirectory ``` I'll add test cases. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Author: Michael Armbrust <michael@databricks.com> Closes #10357 from sarutak/SPARK-12404.
* [SPARK-12195][SQL] Adding BigDecimal, Date and Timestamp into Encodergatorsmile2015-12-081-0/+17
| | | | | | | | | | This PR is to add three more data types into Encoder, including `BigDecimal`, `Date` and `Timestamp`. marmbrus cloud-fan rxin Could you take a quick look at these three types? Not sure if it can be merged to 1.6. Thank you very much! Author: gatorsmile <gatorsmile@gmail.com> Closes #10188 from gatorsmile/dataTypesinEncoder.
* [SPARK-11954][SQL] Encoder for JavaBeansWenchen Fan2015-12-011-4/+170
| | | | | | | | | | | create java version of `constructorFor` and `extractorFor` in `JavaTypeInference` Author: Wenchen Fan <wenchen@databricks.com> This patch had conflicts when merged, resolved by Committer: Michael Armbrust <michael@databricks.com> Closes #9937 from cloud-fan/pojo.
* [SPARK-11967][SQL] Consistent use of varargs for multiple paths in ↵Reynold Xin2015-11-241-0/+23
| | | | | | | | | | | | DataFrameReader This patch makes it consistent to use varargs in all DataFrameReader methods, including Parquet, JSON, text, and the generic load function. Also added a few more API tests for the Java API. Author: Reynold Xin <rxin@databricks.com> Closes #9945 from rxin/SPARK-11967.
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