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* [SPARK-16622][SQL] Fix NullPointerException when the returned value of the ↵Liang-Chi Hsieh2016-07-232-17/+65
| | | | | | | | | | | | | | | | called method in Invoke is null ## What changes were proposed in this pull request? Currently we don't check the value returned by called method in `Invoke`. When the returned value is null and is assigned to a variable of primitive type, `NullPointerException` will be thrown. ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #14259 from viirya/agg-empty-ds.
* [SPARK-16287][HOTFIX][BUILD][SQL] Fix annotation argument needs to be a constantJacek Laskowski2016-07-221-3/+3
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Build fix for [SPARK-16287][SQL] Implement str_to_map SQL function that has introduced this compilation error: ``` /Users/jacek/dev/oss/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala:402: error: annotation argument needs to be a constant; found: "_FUNC_(text[, pairDelim, keyValueDelim]) - Creates a map after splitting the text ".+("into key/value pairs using delimiters. ").+("Default delimiters are \',\' for pairDelim and \':\' for keyValueDelim.") "into key/value pairs using delimiters. " + ^ ``` ## How was this patch tested? Local build Author: Jacek Laskowski <jacek@japila.pl> Closes #14315 from jaceklaskowski/build-fix-complexTypeCreator.
* [SPARK-16556][SPARK-16559][SQL] Fix Two Bugs in Bucket Specificationgatorsmile2016-07-224-3/+55
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? **Issue 1: Silent Ignorance of Bucket Specification When Creating Table Using Schema Inference** When creating a data source table without explicit specification of schema or SELECT clause, we silently ignore the bucket specification (CLUSTERED BY... SORTED BY...) in [the code](https://github.com/apache/spark/blob/ce3b98bae28af72299722f56e4e4ef831f471ec0/sql/core/src/main/scala/org/apache/spark/sql/execution/command/createDataSourceTables.scala#L339-L354). For example, ```SQL CREATE TABLE jsonTable USING org.apache.spark.sql.json OPTIONS ( path '${tempDir.getCanonicalPath}' ) CLUSTERED BY (inexistentColumnA) SORTED BY (inexistentColumnB) INTO 2 BUCKETS ``` This PR captures it and issues an error message. **Issue 2: Got a run-time `java.lang.ArithmeticException` when num of buckets is set to zero.** For example, ```SQL CREATE TABLE t USING PARQUET OPTIONS (PATH '${path.toString}') CLUSTERED BY (a) SORTED BY (b) INTO 0 BUCKETS AS SELECT 1 AS a, 2 AS b ``` The exception we got is ``` ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 1.0 (TID 2) java.lang.ArithmeticException: / by zero ``` This PR captures the misuse and issues an appropriate error message. ### How was this patch tested? Added a test case in DDLSuite Author: gatorsmile <gatorsmile@gmail.com> Closes #14210 from gatorsmile/createTableWithoutSchema.
* [SPARK-16287][SQL] Implement str_to_map SQL functionSandeep Singh2016-07-225-3/+112
| | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds `str_to_map` SQL function in order to remove Hive fallback. ## How was this patch tested? Pass the Jenkins tests with newly added. Author: Sandeep Singh <sandeep@techaddict.me> Closes #13990 from techaddict/SPARK-16287.
* [SPARK-16334] Maintain single dictionary per row-batch in vectorized parquet ↵Sameer Agarwal2016-07-211-8/+13
| | | | | | | | | | | | | | | | reader ## What changes were proposed in this pull request? As part of the bugfix in https://github.com/apache/spark/pull/12279, if a row batch consist of both dictionary encoded and non-dictionary encoded pages, we explicitly decode the dictionary for the values that are already dictionary encoded. Currently we reset the dictionary while reading every page that can potentially cause ` java.lang.ArrayIndexOutOfBoundsException` while decoding older pages. This patch fixes the problem by maintaining a single dictionary per row-batch in vectorized parquet reader. ## How was this patch tested? Manual Tests against a number of hand-generated parquet files. Author: Sameer Agarwal <sameerag@cs.berkeley.edu> Closes #14225 from sameeragarwal/vectorized.
* [SPARK-16656][SQL] Try to make CreateTableAsSelectSuite more stableYin Huai2016-07-211-10/+15
| | | | | | | | | ## What changes were proposed in this pull request? https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/62593/testReport/junit/org.apache.spark.sql.sources/CreateTableAsSelectSuite/create_a_table__drop_it_and_create_another_one_with_the_same_name/ shows that `create a table, drop it and create another one with the same name` failed. But other runs were good. Seems it is a flaky test. This PR tries to make this test more stable. Author: Yin Huai <yhuai@databricks.com> Closes #14289 from yhuai/SPARK-16656.
* [SPARK-16632][SQL] Revert PR #14272: Respect Hive schema when merging ↵Cheng Lian2016-07-212-57/+0
| | | | | | | | | | | | | | | | | | parquet schema ## What changes were proposed in this pull request? PR #14278 is a more general and simpler fix for SPARK-16632 than PR #14272. After merging #14278, we no longer need changes made in #14272. So here I revert them. This PR targets both master and branch-2.0. ## How was this patch tested? Existing tests. Author: Cheng Lian <lian@databricks.com> Closes #14300 from liancheng/revert-pr-14272.
* [SPARK-16640][SQL] Add codegen for Elt functionLiang-Chi Hsieh2016-07-211-1/+24
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Elt function doesn't support codegen execution now. We should add the support. ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #14277 from viirya/elt-codegen.
* [SPARK-16632][SQL] Use Spark requested schema to guide vectorized Parquet ↵Cheng Lian2016-07-212-1/+28
| | | | | | | | | | | | | | | | | | | | reader initialization ## What changes were proposed in this pull request? In `SpecificParquetRecordReaderBase`, which is used by the vectorized Parquet reader, we convert the Parquet requested schema into a Spark schema to guide column reader initialization. However, the Parquet requested schema is tailored from the schema of the physical file being scanned, and may have inaccurate type information due to bugs of other systems (e.g. HIVE-14294). On the other hand, we already set the real Spark requested schema into Hadoop configuration in [`ParquetFileFormat`][1]. This PR simply reads out this schema to replace the converted one. ## How was this patch tested? New test case added in `ParquetQuerySuite`. [1]: https://github.com/apache/spark/blob/v2.0.0-rc5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala#L292-L294 Author: Cheng Lian <lian@databricks.com> Closes #14278 from liancheng/spark-16632-simpler-fix.
* [SPARK-16226][SQL] Weaken JDBC isolation level to avoid locking when writing ↵Sean Owen2016-07-212-14/+46
| | | | | | | | | | | | | | | | partitions ## What changes were proposed in this pull request? Saving partitions to JDBC in transaction can use a weaker transaction isolation level to reduce locking. Use better method to check if transactions are supported. ## How was this patch tested? Existing Jenkins tests. Author: Sean Owen <sowen@cloudera.com> Closes #14054 from srowen/SPARK-16226.
* [SPARK-16644][SQL] Aggregate should not propagate constraints containing ↵Wenchen Fan2016-07-203-4/+25
| | | | | | | | | | | | | | | | | aggregate expressions ## What changes were proposed in this pull request? aggregate expressions can only be executed inside `Aggregate`, if we propagate it up with constraints, the parent operator can not execute it and will fail at runtime. ## How was this patch tested? new test in SQLQuerySuite Author: Wenchen Fan <wenchen@databricks.com> Author: Yin Huai <yhuai@databricks.com> Closes #14281 from cloud-fan/bug.
* [SPARK-16272][CORE] Allow config values to reference conf, env, system props.Marcelo Vanzin2016-07-201-6/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | This allows configuration to be more flexible, for example, when the cluster does not have a homogeneous configuration (e.g. packages are installed on different paths in different nodes). By allowing one to reference the environment from the conf, it becomes possible to work around those in certain cases. As part of the implementation, ConfigEntry now keeps track of all "known" configs (i.e. those created through the use of ConfigBuilder), since that list is used by the resolution code. This duplicates some code in SQLConf, which could potentially be merged with this now. It will also make it simpler to implement some missing features such as filtering which configs show up in the UI or in event logs - which are not part of this change. Another change is in the way ConfigEntry reads config data; it now takes a string map and a function that reads env variables, so that it can be called both from SparkConf and SQLConf. This makes it so both places follow the same read path, instead of having to replicate certain logic in SQLConf. There are still a couple of methods in SQLConf that peek into fields of ConfigEntry directly, though. Tested via unit tests, and by using the new variable expansion functionality in a shell session with a custom spark.sql.hive.metastore.jars value. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #14022 from vanzin/SPARK-16272.
* [SPARK-16344][SQL] Decoding Parquet array of struct with a single field ↵Cheng Lian2016-07-206-47/+73
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | named "element" ## What changes were proposed in this pull request? Due to backward-compatibility reasons, the following Parquet schema is ambiguous: ``` optional group f (LIST) { repeated group list { optional group element { optional int32 element; } } } ``` According to the parquet-format spec, when interpreted as a standard 3-level layout, this type is equivalent to the following SQL type: ``` ARRAY<STRUCT<element: INT>> ``` However, when interpreted as a legacy 2-level layout, it's equivalent to ``` ARRAY<STRUCT<element: STRUCT<element: INT>>> ``` Historically, to disambiguate these cases, we employed two methods: - `ParquetSchemaConverter.isElementType()` Used to disambiguate the above cases while converting Parquet types to Spark types. - `ParquetRowConverter.isElementType()` Used to disambiguate the above cases while instantiating row converters that convert Parquet records to Spark rows. Unfortunately, these two methods make different decision about the above problematic Parquet type, and caused SPARK-16344. `ParquetRowConverter.isElementType()` is necessary for Spark 1.4 and earlier versions because Parquet requested schemata are directly converted from Spark schemata in these versions. The converted Parquet schemata may be incompatible with actual schemata of the underlying physical files when the files are written by a system/library that uses a schema conversion scheme that is different from Spark when writing Parquet LIST and MAP fields. In Spark 1.5, Parquet requested schemata are always properly tailored from schemata of physical files to be read. Thus `ParquetRowConverter.isElementType()` is no longer necessary. This PR replaces this method with a simply yet accurate scheme: whenever an ambiguous Parquet type is hit, convert the type in question back to a Spark type using `ParquetSchemaConverter` and check whether it matches the corresponding Spark type. ## How was this patch tested? New test cases added in `ParquetHiveCompatibilitySuite` and `ParquetQuerySuite`. Author: Cheng Lian <lian@databricks.com> Closes #14014 from liancheng/spark-16344-for-master-and-2.0.
* [SPARK-16634][SQL] Workaround JVM bug by moving some code out of ctor.Marcelo Vanzin2016-07-201-4/+11
| | | | | | | | | | | | | Some 1.7 JVMs have a bug that is triggered by certain Scala-generated bytecode. GenericArrayData suffers from that and fails to load in certain JVMs. Moving the offending code out of the constructor and into a helper method avoids the issue. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #14271 from vanzin/SPARK-16634.
* [SPARK-16632][SQL] Respect Hive schema when merging parquet schema.Marcelo Vanzin2016-07-202-0/+57
| | | | | | | | | | | | | | | | | | | | | | | When Hive (or at least certain versions of Hive) creates parquet files containing tinyint or smallint columns, it stores them as int32, but doesn't annotate the parquet field as containing the corresponding int8 / int16 data. When Spark reads those files using the vectorized reader, it follows the parquet schema for these fields, but when actually reading the data it tries to use the type fetched from the metastore, and then fails because data has been loaded into the wrong fields in OnHeapColumnVector. So instead of blindly trusting the parquet schema, check whether the Catalyst-provided schema disagrees with it, and adjust the types so that the necessary metadata is present when loading the data into the ColumnVector instance. Tested with unit tests and with tests that create byte / short columns in Hive and try to read them from Spark. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #14272 from vanzin/SPARK-16632.
* [SPARK-15705][SQL] Change the default value of ↵Yin Huai2016-07-191-1/+2
| | | | | | | | | | | spark.sql.hive.convertMetastoreOrc to false. ## What changes were proposed in this pull request? In 2.0, we add a new logic to convert HiveTableScan on ORC tables to Spark's native code path. However, during this conversion, we drop the original metastore schema (https://issues.apache.org/jira/browse/SPARK-15705). Because of this regression, I am changing the default value of `spark.sql.hive.convertMetastoreOrc` to false. Author: Yin Huai <yhuai@databricks.com> Closes #14267 from yhuai/SPARK-15705-changeDefaultValue.
* [SPARK-16602][SQL] `Nvl` function should support numeric-string casesDongjoon Hyun2016-07-193-2/+17
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `Nvl` function should support numeric-straing cases like Hive/Spark1.6. Currently, `Nvl` finds the tightest common types among numeric types. This PR extends that to consider `String` type, too. ```scala - TypeCoercion.findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { dtype => + TypeCoercion.findTightestCommonTypeToString(left.dataType, right.dataType).map { dtype => ``` **Before** ```scala scala> sql("select nvl('0', 1)").collect() org.apache.spark.sql.AnalysisException: cannot resolve `nvl("0", 1)` due to data type mismatch: input to function coalesce should all be the same type, but it's [string, int]; line 1 pos 7 ``` **After** ```scala scala> sql("select nvl('0', 1)").collect() res0: Array[org.apache.spark.sql.Row] = Array([0]) ``` ## How was this patch tested? Pass the Jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14251 from dongjoon-hyun/SPARK-16602.
* [SPARK-16535][BUILD] In pom.xml, remove groupId which is redundant ↵Xin Ren2016-07-194-4/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | definition and inherited from the parent https://issues.apache.org/jira/browse/SPARK-16535 ## What changes were proposed in this pull request? When I scan through the pom.xml of sub projects, I found this warning as below and attached screenshot ``` Definition of groupId is redundant, because it's inherited from the parent ``` ![screen shot 2016-07-13 at 3 13 11 pm](https://cloud.githubusercontent.com/assets/3925641/16823121/744f893e-4916-11e6-8a52-042f83b9db4e.png) I've tried to remove some of the lines with groupId definition, and the build on my local machine is still ok. ``` <groupId>org.apache.spark</groupId> ``` As I just find now `<maven.version>3.3.9</maven.version>` is being used in Spark 2.x, and Maven-3 supports versionless parent elements: Maven 3 will remove the need to specify the parent version in sub modules. THIS is great (in Maven 3.1). ref: http://stackoverflow.com/questions/3157240/maven-3-worth-it/3166762#3166762 ## How was this patch tested? I've tested by re-building the project, and build succeeded. Author: Xin Ren <iamshrek@126.com> Closes #14189 from keypointt/SPARK-16535.
* [SPARK-16615][SQL] Expose sqlContext in SparkSessionReynold Xin2016-07-181-1/+3
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch removes the private[spark] qualifier for SparkSession.sqlContext, as discussed in http://apache-spark-developers-list.1001551.n3.nabble.com/Re-transtition-SQLContext-to-SparkSession-td18342.html ## How was this patch tested? N/A - this is a visibility change. Author: Reynold Xin <rxin@databricks.com> Closes #14252 from rxin/SPARK-16615.
* [HOTFIX] Fix Scala 2.10 compilationReynold Xin2016-07-181-2/+2
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* [SPARK-16590][SQL] Improve LogicalPlanToSQLSuite to check generated SQL directlyDongjoon Hyun2016-07-18103-153/+820
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR improves `LogicalPlanToSQLSuite` to check the generated SQL directly by **structure**. So far, `LogicalPlanToSQLSuite` relies on `checkHiveQl` to ensure the **successful SQL generation** and **answer equality**. However, it does not guarantee the generated SQL is the same or will not be changed unnoticeably. ## How was this patch tested? Pass the Jenkins. This is only a testsuite change. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14235 from dongjoon-hyun/SPARK-16590.
* [SPARK-16515][SQL] set default record reader and writer for script ↵Daoyuan Wang2016-07-183-5/+45
| | | | | | | | | | | | | | | | | transformation ## What changes were proposed in this pull request? In ScriptInputOutputSchema, we read default RecordReader and RecordWriter from conf. Since Spark 2.0 has deleted those config keys from hive conf, we have to set default reader/writer class name by ourselves. Otherwise we will get None for LazySimpleSerde, the data written would not be able to read by script. The test case added worked fine with previous version of Spark, but would fail now. ## How was this patch tested? added a test case in SQLQuerySuite. Closes #14169 Author: Daoyuan Wang <daoyuan.wang@intel.com> Author: Yin Huai <yhuai@databricks.com> Closes #14249 from yhuai/scriptTransformation.
* [SPARK-16351][SQL] Avoid per-record type dispatch in JSON when writinghyukjinkwon2016-07-184-67/+163
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, `JacksonGenerator.apply` is doing type-based dispatch for each row to write appropriate values. It might not have to be done like this because the schema is already kept. So, appropriate writers can be created first according to the schema once, and then apply them to each row. This approach is similar with `CatalystWriteSupport`. This PR corrects `JacksonGenerator` so that it creates all writers for the schema once and then applies them to each row rather than type dispatching for every row. Benchmark was proceeded with the codes below: ```scala test("Benchmark for JSON writer") { val N = 500 << 8 val row = """{"struct":{"field1": true, "field2": 92233720368547758070}, "structWithArrayFields":{"field1":[4, 5, 6], "field2":["str1", "str2"]}, "arrayOfString":["str1", "str2"], "arrayOfInteger":[1, 2147483647, -2147483648], "arrayOfLong":[21474836470, 9223372036854775807, -9223372036854775808], "arrayOfBigInteger":[922337203685477580700, -922337203685477580800], "arrayOfDouble":[1.2, 1.7976931348623157E308, 4.9E-324, 2.2250738585072014E-308], "arrayOfBoolean":[true, false, true], "arrayOfNull":[null, null, null, null], "arrayOfStruct":[{"field1": true, "field2": "str1"}, {"field1": false}, {"field3": null}], "arrayOfArray1":[[1, 2, 3], ["str1", "str2"]], "arrayOfArray2":[[1, 2, 3], [1.1, 2.1, 3.1]] }""" val df = spark.sqlContext.read.json(spark.sparkContext.parallelize(List.fill(N)(row))) val benchmark = new Benchmark("JSON writer", N) benchmark.addCase("writing JSON file", 10) { _ => withTempPath { path => df.write.format("json").save(path.getCanonicalPath) } } benchmark.run() } ``` This produced the results below - **Before** ``` JSON writer: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ writing JSON file 1675 / 1767 0.1 13087.5 1.0X ``` - **After** ``` JSON writer: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ writing JSON file 1597 / 1686 0.1 12477.1 1.0X ``` In addition, I ran this benchmark 10 times for each and calculated the average elapsed time as below: | **Before** | **After**| |---------------|------------| |17478ms |16669ms | It seems roughly ~5% is improved. ## How was this patch tested? Existing tests should cover this. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14028 from HyukjinKwon/SPARK-16351.
* [SPARK-16588][SQL] Deprecate monotonicallyIncreasingId in Scala/JavaReynold Xin2016-07-172-7/+7
| | | | | | This patch deprecates monotonicallyIncreasingId in Scala/Java, as done in Python. This patch was originally written by HyukjinKwon. Closes #14236.
* [SPARK-16584][SQL] Move regexp unit tests to RegexpExpressionsSuiteReynold Xin2016-07-162-164/+194
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch moves regexp related unit tests from StringExpressionsSuite to RegexpExpressionsSuite to match the file name for regexp expressions. ## How was this patch tested? This is a test only change. Author: Reynold Xin <rxin@databricks.com> Closes #14230 from rxin/SPARK-16584.
* [SPARK-16582][SQL] Explicitly define isNull = false for non-nullable expressionsSameer Agarwal2016-07-161-0/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch is just a slightly safer way to fix the issue we encountered in https://github.com/apache/spark/pull/14168 should this pattern re-occur at other places in the code. ## How was this patch tested? Existing tests. Also, I manually tested that it fixes the problem in SPARK-16514 without having the proposed change in https://github.com/apache/spark/pull/14168 Author: Sameer Agarwal <sameerag@cs.berkeley.edu> Closes #14227 from sameeragarwal/codegen.
* [SPARK-16557][SQL] Remove stale doc in sql/README.mdReynold Xin2016-07-141-74/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? Most of the documentation in https://github.com/apache/spark/blob/master/sql/README.md is stale. It would be useful to keep the list of projects to explain what's going on, and everything else should be removed. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes #14211 from rxin/SPARK-16557.
* [SPARK-16528][SQL] Fix NPE problem in HiveClientImplJacek Lewandowski2016-07-141-6/+8
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? There are some calls to methods or fields (getParameters, properties) which are then passed to Java/Scala collection converters. Unfortunately those fields can be null in some cases and then the conversions throws NPE. We fix it by wrapping calls to those fields and methods with option and then do the conversion. ## How was this patch tested? Manually tested with a custom Hive metastore. Author: Jacek Lewandowski <lewandowski.jacek@gmail.com> Closes #14200 from jacek-lewandowski/SPARK-16528.
* [SPARK-16529][SQL][TEST] `withTempDatabase` should set `default` database ↵Dongjoon Hyun2016-07-151-1/+7
| | | | | | | | | | | | | | | | | | | | | | | | | | | | before dropping ## What changes were proposed in this pull request? `SQLTestUtils.withTempDatabase` is a frequently used test harness to setup a temporary table and clean up finally. This issue improves like the following for usability. ```scala - try f(dbName) finally spark.sql(s"DROP DATABASE $dbName CASCADE") + try f(dbName) finally { + if (spark.catalog.currentDatabase == dbName) { + spark.sql(s"USE ${DEFAULT_DATABASE}") + } + spark.sql(s"DROP DATABASE $dbName CASCADE") + } ``` In case of forgetting to reset the databaes, `withTempDatabase` will not raise Exception. ## How was this patch tested? This improves test harness. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14184 from dongjoon-hyun/SPARK-16529.
* [SPARK-16543][SQL] Rename the columns of `SHOW PARTITION/COLUMNS` commandsDongjoon Hyun2016-07-141-4/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR changes the name of columns returned by `SHOW PARTITION` and `SHOW COLUMNS` commands. Currently, both commands uses `result` as a column name. **Comparison: Column Name** Command|Spark(Before)|Spark(After)|Hive ----------|--------------|------------|----- SHOW PARTITIONS|result|partition|partition SHOW COLUMNS|result|col_name|field Note that Spark/Hive uses `col_name` in `DESC TABLES`. So, this PR chooses `col_name` for consistency among Spark commands. **Before** ```scala scala> sql("show partitions p").show() +------+ |result| +------+ | b=2| +------+ scala> sql("show columns in p").show() +------+ |result| +------+ | a| | b| +------+ ``` **After** ```scala scala> sql("show partitions p").show +---------+ |partition| +---------+ | b=2| +---------+ scala> sql("show columns in p").show +--------+ |col_name| +--------+ | a| | b| +--------+ ``` ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14199 from dongjoon-hyun/SPARK-16543.
* [SPARK-16530][SQL][TRIVIAL] Wrong Parser Keyword in ALTER TABLE CHANGE COLUMNgatorsmile2016-07-141-1/+1
| | | | | | | | | | | | | | #### What changes were proposed in this pull request? Based on the [Hive SQL syntax](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-ChangeColumnName/Type/Position/Comment), the command to change column name/type/position/comment is `ALTER TABLE CHANGE COLUMN`. However, in our .g4 file, it is `ALTER TABLE CHANGE COLUMNS`. Because it is the last optional keyword, it does not take any effect. Thus, I put the issue as a Trivial level. cc hvanhovell #### How was this patch tested? Existing test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #14186 from gatorsmile/changeColumns.
* [SPARK-16448] RemoveAliasOnlyProject should not remove alias with metadataWenchen Fan2016-07-142-18/+108
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `Alias` with metadata is not a no-op and we should not strip it in `RemoveAliasOnlyProject` rule. This PR also did some improvement for this rule: 1. extend the semantic of `alias-only`. Now we allow the project list to be partially aliased. 2. add unit test for this rule. ## How was this patch tested? new `RemoveAliasOnlyProjectSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #14106 from cloud-fan/bug.
* [SPARK-16503] SparkSession should provide Spark versionLiwei Lin2016-07-131-1/+8
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch enables SparkSession to provide spark version. ## How was this patch tested? Manual test: ``` scala> sc.version res0: String = 2.1.0-SNAPSHOT scala> spark.version res1: String = 2.1.0-SNAPSHOT ``` ``` >>> sc.version u'2.1.0-SNAPSHOT' >>> spark.version u'2.1.0-SNAPSHOT' ``` Author: Liwei Lin <lwlin7@gmail.com> Closes #14165 from lw-lin/add-version.
* [SPARK-16482][SQL] Describe Table Command for Tables Requiring Runtime ↵gatorsmile2016-07-132-22/+22
| | | | | | | | | | | | | | | | | | Inferred Schema #### What changes were proposed in this pull request? If we create a table pointing to a parquet/json datasets without specifying the schema, describe table command does not show the schema at all. It only shows `# Schema of this table is inferred at runtime`. In 1.6, describe table does show the schema of such a table. ~~For data source tables, to infer the schema, we need to load the data source tables at runtime. Thus, this PR calls the function `lookupRelation`.~~ For data source tables, we infer the schema before table creation. Thus, this PR set the inferred schema as the table schema when table creation. #### How was this patch tested? Added test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #14148 from gatorsmile/describeSchema.
* [SPARK-16531][SQL][TEST] Remove timezone setting from ↵Burak Yavuz2016-07-131-10/+0
| | | | | | | | | | | | DataFrameTimeWindowingSuite ## What changes were proposed in this pull request? It's unnecessary. `QueryTest` already sets it. Author: Burak Yavuz <brkyvz@gmail.com> Closes #14170 from brkyvz/test-tz.
* [SPARK-16343][SQL] Improve the PushDownPredicate rule to pushdown predicates ↵蒋星博2016-07-142-19/+33
| | | | | | | | | | | | | | | | | | | | | | correctly in non-deterministic condition. ## What changes were proposed in this pull request? Currently our Optimizer may reorder the predicates to run them more efficient, but in non-deterministic condition, change the order between deterministic parts and non-deterministic parts may change the number of input rows. For example: ```SELECT a FROM t WHERE rand() < 0.1 AND a = 1``` And ```SELECT a FROM t WHERE a = 1 AND rand() < 0.1``` may call rand() for different times and therefore the output rows differ. This PR improved this condition by checking whether the predicate is placed before any non-deterministic predicates. ## How was this patch tested? Expanded related testcases in FilterPushdownSuite. Author: 蒋星博 <jiangxingbo@meituan.com> Closes #14012 from jiangxb1987/ppd.
* [SPARK-16439] Fix number formatting in SQL UIMaciej Brynski2016-07-131-1/+3
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Spark SQL UI display numbers greater than 1000 with u00A0 as grouping separator. Problem exists when server locale has no-breaking space as separator. (for example pl_PL) This patch turns off grouping and remove this separator. The problem starts with this PR. https://github.com/apache/spark/pull/12425/files#diff-803f475b01acfae1c5c96807c2ea9ddcR125 ## How was this patch tested? Manual UI tests. Screenshot attached. ![image](https://cloud.githubusercontent.com/assets/4006010/16749556/5cb5a372-47cb-11e6-9a95-67fd3f9d1c71.png) Author: Maciej Brynski <maciej.brynski@adpilot.pl> Closes #14142 from maver1ck/master.
* [MINOR] Fix Java style errors and remove unused importsXin Ren2016-07-132-3/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix Java style errors and remove unused imports, which are randomly found ## How was this patch tested? Tested on my local machine. Author: Xin Ren <iamshrek@126.com> Closes #14161 from keypointt/SPARK-16437.
* [SPARK-15889][STREAMING] Follow-up fix to erroneous condition in StreamTestSean Owen2016-07-131-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? A second form of AssertQuery now actually invokes the condition; avoids a build warning too ## How was this patch tested? Jenkins; running StreamTest Author: Sean Owen <sowen@cloudera.com> Closes #14133 from srowen/SPARK-15889.2.
* [SPARK-16514][SQL] Fix various regex codegen bugsEric Liang2016-07-122-15/+39
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? RegexExtract and RegexReplace currently crash on non-nullable input due use of a hard-coded local variable name (e.g. compiles fail with `java.lang.Exception: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 85, Column 26: Redefinition of local variable "m" `). This changes those variables to use fresh names, and also in a few other places. ## How was this patch tested? Unit tests. rxin Author: Eric Liang <ekl@databricks.com> Closes #14168 from ericl/sc-3906.
* [SPARK-16284][SQL] Implement reflect SQL functionpetermaxlee2016-07-136-25/+311
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch implements reflect SQL function, which can be used to invoke a Java method in SQL. Slightly different from Hive, this implementation requires the class name and the method name to be literals. This implementation also supports only a smaller number of data types, and requires the function to be static, as suggested by rxin in #13969. java_method is an alias for reflect, so this should also resolve SPARK-16277. ## How was this patch tested? Added expression unit tests and an end-to-end test. Author: petermaxlee <petermaxlee@gmail.com> Closes #14138 from petermaxlee/reflect-static.
* [SPARK-16119][SQL] Support PURGE option to drop table / partition.Marcelo Vanzin2016-07-1222-109/+268
| | | | | | | | | | | | | | | | | | | | | | | | | This option is used by Hive to directly delete the files instead of moving them to the trash. This is needed in certain configurations where moving the files does not work. For non-Hive tables and partitions, Spark already behaves as if the PURGE option was set, so there's no need to do anything. Hive support for PURGE was added in 0.14 (for tables) and 1.2 (for partitions), so the code reflects that: trying to use the option with older versions of Hive will cause an exception to be thrown. The change is a little noisier than I would like, because of the code to propagate the new flag through all the interfaces and implementations; the main changes are in the parser and in HiveShim, aside from the tests (DDLCommandSuite, VersionsSuite). Tested by running sql and catalyst unit tests, plus VersionsSuite which has been updated to test the version-specific behavior. I also ran an internal test suite that uses PURGE and would not pass previously. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #13831 from vanzin/SPARK-16119.
* [SPARK-16489][SQL] Guard against variable reuse mistakes in expression code ↵Reynold Xin2016-07-124-22/+68
| | | | | | | | | | | | | | | | generation ## What changes were proposed in this pull request? In code generation, it is incorrect for expressions to reuse variable names across different instances of itself. As an example, SPARK-16488 reports a bug in which pmod expression reuses variable name "r". This patch updates ExpressionEvalHelper test harness to always project two instances of the same expression, which will help us catch variable reuse problems in expression unit tests. This patch also fixes the bug in crc32 expression. ## How was this patch tested? This is a test harness change, but I also created a new test suite for testing the test harness. Author: Reynold Xin <rxin@databricks.com> Closes #14146 from rxin/SPARK-16489.
* [SPARK-15752][SQL] Optimize metadata only query that has an aggregate whose ↵Lianhui Wang2016-07-126-0/+408
| | | | | | | | | | | | | | | | children are deterministic project or filter operators. ## What changes were proposed in this pull request? when query only use metadata (example: partition key), it can return results based on metadata without scanning files. Hive did it in HIVE-1003. ## How was this patch tested? add unit tests Author: Lianhui Wang <lianhuiwang09@gmail.com> Author: Wenchen Fan <wenchen@databricks.com> Author: Lianhui Wang <lianhuiwang@users.noreply.github.com> Closes #13494 from lianhuiwang/metadata-only.
* [SPARK-16189][SQL] Add ExternalRDD logical plan for input with RDD to have a ↵Takuya UESHIN2016-07-125-20/+81
| | | | | | | | | | | | | | | | | | | chance to eliminate serialize/deserialize. ## What changes were proposed in this pull request? Currently the input `RDD` of `Dataset` is always serialized to `RDD[InternalRow]` prior to being as `Dataset`, but there is a case that we use `map` or `mapPartitions` just after converted to `Dataset`. In this case, serialize and then deserialize happens but it would not be needed. This pr adds `ExistingRDD` logical plan for input with `RDD` to have a chance to eliminate serialize/deserialize. ## How was this patch tested? Existing tests. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #13890 from ueshin/issues/SPARK-16189.
* [SPARK-16199][SQL] Add a method to list the referenced columns in data ↵petermaxlee2016-07-112-15/+145
| | | | | | | | | | | | | | | | | source Filter ## What changes were proposed in this pull request? It would be useful to support listing the columns that are referenced by a filter. This can help simplify data source planning, because with this we would be able to implement unhandledFilters method in HadoopFsRelation. This is based on rxin's patch (#13901) and adds unit tests. ## How was this patch tested? Added a new suite FiltersSuite. Author: petermaxlee <petermaxlee@gmail.com> Author: Reynold Xin <rxin@databricks.com> Closes #14120 from petermaxlee/SPARK-16199.
* [SPARK-12639][SQL] Mark Filters Fully Handled By Sources with *Russell Spitzer2016-07-113-11/+21
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In order to make it clear which filters are fully handled by the underlying datasource we will mark them with an *. This will give a clear visual queue to users that the filter is being treated differently by catalyst than filters which are just presented to the underlying DataSource. Examples from the FilteredScanSuite, in this example `c IN (...)` is handled by the source, `b < ...` is not ### Before ``` //SELECT a FROM oneToTenFiltered WHERE a + b > 9 AND b < 16 AND c IN ('bbbbbBBBBB', 'cccccCCCCC', 'dddddDDDDD', 'foo') == Physical Plan == Project [a#0] +- Filter (((a#0 + b#1) > 9) && (b#1 < 16)) +- Scan SimpleFilteredScan(1,10)[a#0,b#1] PushedFilters: [LessThan(b,16), In(c, [bbbbbBBBBB,cccccCCCCC,dddddDDDDD,foo]] ``` ### After ``` == Physical Plan == Project [a#0] +- Filter (((a#0 + b#1) > 9) && (b#1 < 16)) +- Scan SimpleFilteredScan(1,10)[a#0,b#1] PushedFilters: [LessThan(b,16), *In(c, [bbbbbBBBBB,cccccCCCCC,dddddDDDDD,foo]] ``` ## How was the this patch tested? Manually tested with the Spark Cassandra Connector, a source which fully handles underlying filters. Now fully handled filters appear with an * next to their names. I can add an automated test as well if requested Post 1.6.1 Tested by modifying the FilteredScanSuite to run explains. Author: Russell Spitzer <Russell.Spitzer@gmail.com> Closes #11317 from RussellSpitzer/SPARK-12639-Star.
* [SPARK-16488] Fix codegen variable namespace collision in pmod and partitionBySameer Agarwal2016-07-112-12/+27
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch fixes a variable namespace collision bug in pmod and partitionBy ## How was this patch tested? Regression test for one possible occurrence. A more general fix in `ExpressionEvalHelper.checkEvaluation` will be in a subsequent PR. Author: Sameer Agarwal <sameer@databricks.com> Closes #14144 from sameeragarwal/codegen-bug.
* [SPARK-16430][SQL][STREAMING] Fixed bug in the maxFilesPerTrigger in ↵Tathagata Das2016-07-112-5/+36
| | | | | | | | | | | | | | | | FileStreamSource ## What changes were proposed in this pull request? Incorrect list of files were being allocated to a batch. This caused a file to read multiple times in the multiple batches. ## How was this patch tested? Added unit tests Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #14143 from tdas/SPARK-16430-1.
* [SPARK-16433][SQL] Improve StreamingQuery.explain when no data arrivesShixiong Zhu2016-07-113-5/+5
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Display `No physical plan. Waiting for data.` instead of `N/A` for StreamingQuery.explain when no data arrives because `N/A` doesn't provide meaningful information. ## How was this patch tested? Existing unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #14100 from zsxwing/SPARK-16433.
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