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* [SPARK-20439][SQL] Fix Catalog API listTables and getTable when failed to ↵Xiao Li2017-04-241-0/+8
| | | | | | | | | | | | | | | fetch table metadata ### What changes were proposed in this pull request? `spark.catalog.listTables` and `spark.catalog.getTable` does not work if we are unable to retrieve table metadata due to any reason (e.g., table serde class is not accessible or the table type is not accepted by Spark SQL). After this PR, the APIs still return the corresponding Table without the description and tableType) ### How was this patch tested? Added a test case Author: Xiao Li <gatorsmile@gmail.com> Closes #17730 from gatorsmile/listTables.
* [SPARK-20349][SQL] ListFunctions returns duplicate functions after using ↵Xiao Li2017-04-171-0/+17
| | | | | | | | | | | | | | | | persistent functions ### What changes were proposed in this pull request? The session catalog caches some persistent functions in the `FunctionRegistry`, so there can be duplicates. Our Catalog API `listFunctions` does not handle it. It would be better if `SessionCatalog` API can de-duplciate the records, instead of doing it by each API caller. In `FunctionRegistry`, our functions are identified by the unquoted string. Thus, this PR is try to parse it using our parser interface and then de-duplicate the names. ### How was this patch tested? Added test cases. Author: Xiao Li <gatorsmile@gmail.com> Closes #17646 from gatorsmile/showFunctions.
* [SPARK-20335][SQL] Children expressions of Hive UDF impacts the determinism ↵Xiao Li2017-04-163-1/+45
| | | | | | | | | | | | | | | | | | | | | | | | | | of Hive UDF ### What changes were proposed in this pull request? ```JAVA /** * Certain optimizations should not be applied if UDF is not deterministic. * Deterministic UDF returns same result each time it is invoked with a * particular input. This determinism just needs to hold within the context of * a query. * * return true if the UDF is deterministic */ boolean deterministic() default true; ``` Based on the definition of [UDFType](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/UDFType.java#L42-L50), when Hive UDF's children are non-deterministic, Hive UDF is also non-deterministic. ### How was this patch tested? Added test cases. Author: Xiao Li <gatorsmile@gmail.com> Closes #17635 from gatorsmile/udfDeterministic.
* [SPARK-20303][SQL] Rename createTempFunction to registerFunctionXiao Li2017-04-121-4/+6
| | | | | | | | | | | | | | ### What changes were proposed in this pull request? Session catalog API `createTempFunction` is being used by Hive build-in functions, persistent functions, and temporary functions. Thus, the name is confusing. This PR is to rename it by `registerFunction`. Also we can move construction of `FunctionBuilder` and `ExpressionInfo` into the new `registerFunction`, instead of duplicating the logics everywhere. In the next PRs, the remaining Function-related APIs also need cleanups. ### How was this patch tested? Existing test cases. Author: Xiao Li <gatorsmile@gmail.com> Closes #17615 from gatorsmile/cleanupCreateTempFunction.
* [SPARK-18692][BUILD][DOCS] Test Java 8 unidoc build on Jenkinshyukjinkwon2017-04-122-3/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable. There are several problems with it: - It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?". - > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up. (see joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627)) To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above. There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013 Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings. ## How was this patch tested? Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`. This was tested via manually adding `time.time()` as below: ```diff profiles_and_goals = build_profiles + sbt_goals print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ", " ".join(profiles_and_goals)) + import time + st = time.time() exec_sbt(profiles_and_goals) + print("Elapsed :[%s]" % str(time.time() - st)) ``` produces ``` ... ======================================================================== Building Unidoc API Documentation ======================================================================== ... [info] Main Java API documentation successful. ... Elapsed :[94.8746569157] ... Author: hyukjinkwon <gurwls223@gmail.com> Closes #17477 from HyukjinKwon/SPARK-18692.
* [SPARK-20156][CORE][SQL][STREAMING][MLLIB] Java String toLowerCase "Turkish ↵Sean Owen2017-04-105-11/+12
| | | | | | | | | | | | | | | | | | locale bug" causes Spark problems ## What changes were proposed in this pull request? Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem"). The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods. ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #17527 from srowen/SPARK-20156.
* [SPARK-20260][MLLIB] String interpolation required for error messageVijay Ramesh2017-04-091-3/+3
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This error message doesn't get properly formatted because of a missing `s`. Currently the error looks like: ``` Caused by: java.lang.IllegalArgumentException: requirement failed: indices should be one-based and in ascending order; found current=$current, previous=$previous; line="$line" ``` (note the literal `$current` instead of the interpolated value) Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Vijay Ramesh <vramesh@demandbase.com> Closes #17572 from vijaykramesh/master.
* [SPARK-20245][SQL][MINOR] pass output to LogicalRelation directlyWenchen Fan2017-04-072-3/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently `LogicalRelation` has a `expectedOutputAttributes` parameter, which makes it hard to reason about what the actual output is. Like other leaf nodes, `LogicalRelation` should also take `output` as a parameter, to simplify the logic ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #17552 from cloud-fan/minor.
* [SPARK-20067][SQL] Unify and Clean Up Desc Commands Using Catalog InterfaceXiao Li2017-04-036-313/+32
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? This PR is to unify and clean up the outputs of `DESC EXTENDED/FORMATTED` and `SHOW TABLE EXTENDED` by moving the logics into the Catalog interface. The output formats are improved. We also add the missing attributes. It impacts the DDL commands like `SHOW TABLE EXTENDED`, `DESC EXTENDED` and `DESC FORMATTED`. In addition, by following what we did in Dataset API `printSchema`, we can use `treeString` to show the schema in the more readable way. Below is the current way: ``` Schema: STRUCT<`a`: STRING (nullable = true), `b`: INT (nullable = true), `c`: STRING (nullable = true), `d`: STRING (nullable = true)> ``` After the change, it should look like ``` Schema: root |-- a: string (nullable = true) |-- b: integer (nullable = true) |-- c: string (nullable = true) |-- d: string (nullable = true) ``` ### How was this patch tested? `describe.sql` and `show-tables.sql` Author: Xiao Li <gatorsmile@gmail.com> Closes #17394 from gatorsmile/descFollowUp.
* [SPARK-20194] Add support for partition pruning to in-memory catalogAdrian Ionescu2017-04-031-8/+0
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch implements `listPartitionsByFilter()` for `InMemoryCatalog` and thus resolves an outstanding TODO causing the `PruneFileSourcePartitions` optimizer rule not to apply when "spark.sql.catalogImplementation" is set to "in-memory" (which is the default). The change is straightforward: it extracts the code for further filtering of the list of partitions returned by the metastore's `getPartitionsByFilter()` out from `HiveExternalCatalog` into `ExternalCatalogUtils` and calls this new function from `InMemoryCatalog` on the whole list of partitions. Now that this method is implemented we can always pass the `CatalogTable` to the `DataSource` in `FindDataSourceTable`, so that the latter is resolved to a relation with a `CatalogFileIndex`, which is what the `PruneFileSourcePartitions` rule matches for. ## How was this patch tested? Ran existing tests and added new test for `listPartitionsByFilter` in `ExternalCatalogSuite`, which is subclassed by both `InMemoryCatalogSuite` and `HiveExternalCatalogSuite`. Author: Adrian Ionescu <adrian@databricks.com> Closes #17510 from adrian-ionescu/InMemoryCatalog.
* [SPARK-20160][SQL] Move ParquetConversions and OrcConversions Out Of ↵Xiao Li2017-04-012-4/+5
| | | | | | | | | | | | | | | | HiveSessionCatalog ### What changes were proposed in this pull request? `ParquetConversions` and `OrcConversions` should be treated as regular `Analyzer` rules. It is not reasonable to be part of `HiveSessionCatalog`. This PR also combines two rules `ParquetConversions` and `OrcConversions` to build a new rule `RelationConversions `. After moving these two rules out of HiveSessionCatalog, the next step is to clean up, rename and move `HiveMetastoreCatalog` because it is not related to the hive package any more. ### How was this patch tested? The existing test cases Author: Xiao Li <gatorsmile@gmail.com> Closes #17484 from gatorsmile/cleanup.
* [SPARK-20009][SQL] Support DDL strings for defining schema in ↵Takeshi Yamamuro2017-03-291-1/+1
| | | | | | | | | | | | | | functions.from_json ## What changes were proposed in this pull request? This pr added `StructType.fromDDL` to convert a DDL format string into `StructType` for defining schemas in `functions.from_json`. ## How was this patch tested? Added tests in `JsonFunctionsSuite`. Author: Takeshi Yamamuro <yamamuro@apache.org> Closes #17406 from maropu/SPARK-20009.
* [SPARK-20048][SQL] Cloning SessionState does not clone query execution listenersKunal Khamar2017-03-291-1/+1
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Bugfix from [SPARK-19540.](https://github.com/apache/spark/pull/16826) Cloning SessionState does not clone query execution listeners, so cloned session is unable to listen to events on queries. ## How was this patch tested? - Unit test Author: Kunal Khamar <kkhamar@outlook.com> Closes #17379 from kunalkhamar/clone-bugfix.
* [SPARK-20126][SQL] Remove HiveSessionStateHerman van Hovell2017-03-284-18/+22
| | | | | | | | | | | | ## What changes were proposed in this pull request? Commit https://github.com/apache/spark/commit/ea361165e1ddce4d8aa0242ae3e878d7b39f1de2 moved most of the logic from the SessionState classes into an accompanying builder. This makes the existence of the `HiveSessionState` redundant. This PR removes the `HiveSessionState`. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #17457 from hvanhovell/SPARK-20126.
* [SPARK-20100][SQL] Refactor SessionState initializationHerman van Hovell2017-03-281-112/+0
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The current SessionState initialization code path is quite complex. A part of the creation is done in the SessionState companion objects, a part of the creation is one inside the SessionState class, and a part is done by passing functions. This PR refactors this code path, and consolidates SessionState initialization into a builder class. This SessionState will not do any initialization and just becomes a place holder for the various Spark SQL internals. This also lays the ground work for two future improvements: 1. This provides us with a start for removing the `HiveSessionState`. Removing the `HiveSessionState` would also require us to move resource loading into a separate class, and to (re)move metadata hive. 2. This makes it easier to customize the Spark Session. Currently you will need to create a custom version of the builder. I have added hooks to facilitate this. A future step will be to create a semi stable API on top of this. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #17433 from hvanhovell/SPARK-20100.
* [SPARK-19261][SQL] Alter add columns for Hive serde and some datasource tablesXin Wu2017-03-211-1/+99
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Support` ALTER TABLE ADD COLUMNS (...) `syntax for Hive serde and some datasource tables. In this PR, we consider a few aspects: 1. View is not supported for `ALTER ADD COLUMNS` 2. Since tables created in SparkSQL with Hive DDL syntax will populate table properties with schema information, we need make sure the consistency of the schema before and after ALTER operation in order for future use. 3. For embedded-schema type of format, such as `parquet`, we need to make sure that the predicate on the newly-added columns can be evaluated properly, or pushed down properly. In case of the data file does not have the columns for the newly-added columns, such predicates should return as if the column values are NULLs. 4. For datasource table, this feature does not support the following: 4.1 TEXT format, since there is only one default column `value` is inferred for text format data. 4.2 ORC format, since SparkSQL native ORC reader does not support the difference between user-specified-schema and inferred schema from ORC files. 4.3 Third party datasource types that implements RelationProvider, including the built-in JDBC format, since different implementations by the vendors may have different ways to dealing with schema. 4.4 Other datasource types, such as `parquet`, `json`, `csv`, `hive` are supported. 5. Column names being added can not be duplicate of any existing data column or partition column names. Case sensitivity is taken into consideration according to the sql configuration. 6. This feature also supports In-Memory catalog, while Hive support is turned off. ## How was this patch tested? Add new test cases Author: Xin Wu <xinwu@us.ibm.com> Closes #16626 from xwu0226/alter_add_columns.
* [SPARK-19912][SQL] String literals should be escaped for Hive metastore ↵Dongjoon Hyun2017-03-212-0/+21
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | partition pruning ## What changes were proposed in this pull request? Since current `HiveShim`'s `convertFilters` does not escape the string literals. There exists the following correctness issues. This PR aims to return the correct result and also shows the more clear exception message. **BEFORE** ```scala scala> Seq((1, "p1", "q1"), (2, "p1\" and q=\"q1", "q2")).toDF("a", "p", "q").write.partitionBy("p", "q").saveAsTable("t1") scala> spark.table("t1").filter($"p" === "p1\" and q=\"q1").select($"a").show +---+ | a| +---+ +---+ scala> spark.table("t1").filter($"p" === "'\"").select($"a").show java.lang.RuntimeException: Caught Hive MetaException attempting to get partition metadata by filter from ... ``` **AFTER** ```scala scala> spark.table("t1").filter($"p" === "p1\" and q=\"q1").select($"a").show +---+ | a| +---+ | 2| +---+ scala> spark.table("t1").filter($"p" === "'\"").select($"a").show java.lang.UnsupportedOperationException: Partition filter cannot have both `"` and `'` characters ``` ## How was this patch tested? Pass the Jenkins test with new test cases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #17266 from dongjoon-hyun/SPARK-19912.
* [SPARK-19990][TEST] Use the database after Hive's current Database is droppedXiao Li2017-03-191-7/+9
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? This PR is to fix the following test failure in maven and the PR https://github.com/apache/spark/pull/15363. > org.apache.spark.sql.hive.orc.OrcSourceSuite SPARK-19459/SPARK-18220: read char/varchar column written by Hive The[ test history](https://spark-tests.appspot.com/test-details?suite_name=org.apache.spark.sql.hive.orc.OrcSourceSuite&test_name=SPARK-19459%2FSPARK-18220%3A+read+char%2Fvarchar+column+written+by+Hive) shows all the maven builds failed this test case with the same error message. ``` FAILED: SemanticException [Error 10072]: Database does not exist: db2 org.apache.spark.sql.execution.QueryExecutionException: FAILED: SemanticException [Error 10072]: Database does not exist: db2 at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$runHive$1.apply(HiveClientImpl.scala:637) at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$runHive$1.apply(HiveClientImpl.scala:621) at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$withHiveState$1.apply(HiveClientImpl.scala:288) at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:229) at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:228) at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:271) at org.apache.spark.sql.hive.client.HiveClientImpl.runHive(HiveClientImpl.scala:621) at org.apache.spark.sql.hive.client.HiveClientImpl.runSqlHive(HiveClientImpl.scala:611) at org.apache.spark.sql.hive.orc.OrcSuite$$anonfun$7.apply$mcV$sp(OrcSourceSuite.scala:160) at org.apache.spark.sql.hive.orc.OrcSuite$$anonfun$7.apply(OrcSourceSuite.scala:155) at org.apache.spark.sql.hive.orc.OrcSuite$$anonfun$7.apply(OrcSourceSuite.scala:155) at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22) at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85) at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104) at org.scalatest.Transformer.apply(Transformer.scala:22) at org.scalatest.Transformer.apply(Transformer.scala:20) at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166) at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:68) at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163) at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) ``` ### How was this patch tested? N/A Author: Xiao Li <gatorsmile@gmail.com> Closes #17344 from gatorsmile/testtest.
* [SPARK-19945][SQL] add test suite for SessionCatalog with HiveExternalCatalogwindpiger2017-03-161-0/+40
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently `SessionCatalogSuite` is only for `InMemoryCatalog`, there is no suite for `HiveExternalCatalog`. And there are some ddl function is not proper to test in `ExternalCatalogSuite`, because some logic are not full implement in `ExternalCatalog`, these ddl functions are full implement in `SessionCatalog`(e.g. merge the same logic from `ExternalCatalog` up to `SessionCatalog` ). It is better to test it in `SessionCatalogSuite` for this situation. So we should add a test suite for `SessionCatalog` with `HiveExternalCatalog` The main change is that in `SessionCatalogSuite` add two functions: `withBasicCatalog` and `withEmptyCatalog` And replace the code like `val catalog = new SessionCatalog(newBasicCatalog)` with above two functions ## How was this patch tested? add `HiveExternalSessionCatalogSuite` Author: windpiger <songjun@outlook.com> Closes #17287 from windpiger/sessioncatalogsuit.
* [SPARK-18112][SQL] Support reading data from Hive 2.1 metastoreXiao Li2017-03-151-4/+15
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? This PR is to support reading data from Hive 2.1 metastore. Need to update shim class because of the Hive API changes caused by the following three Hive JIRAs: - [HIVE-12730 MetadataUpdater: provide a mechanism to edit the basic statistics of a table (or a partition)](https://issues.apache.org/jira/browse/HIVE-12730) - [Hive-13341 Stats state is not captured correctly: differentiate load table and create table](https://issues.apache.org/jira/browse/HIVE-13341) - [HIVE-13622 WriteSet tracking optimizations](https://issues.apache.org/jira/browse/HIVE-13622) There are three new fields added to Hive APIs. - `boolean hasFollowingStatsTask`. We always set it to `false`. This is to keep the existing behavior unchanged (starting from 0.13), no matter which Hive metastore client version users choose. If we set it to `true`, the basic table statistics is not collected by Hive. For example, ```SQL CREATE TABLE tbl AS SELECT 1 AS a ``` When setting `hasFollowingStatsTask ` to `false`, the table properties is like ``` Properties: [numFiles=1, transient_lastDdlTime=1489513927, totalSize=2] ``` When setting `hasFollowingStatsTask ` to `true`, the table properties is like ``` Properties: [transient_lastDdlTime=1489513563] ``` - `AcidUtils.Operation operation`. Obviously, we do not support ACID. Thus, we set it to `AcidUtils.Operation.NOT_ACID`. - `EnvironmentContext environmentContext`. So far, this is always set to `null`. This was introduced for supporting DDL `alter table s update statistics set ('numRows'='NaN')`. Using this DDL, users can specify the statistics. So far, our Spark SQL does not need it, because we use different table properties to store our generated statistics values. However, when Spark SQL issues ALTER TABLE DDL statements, Hive metastore always automatically invalidate the Hive-generated statistics. In the follow-up PR, we can fix it by explicitly adding a property to `environmentContext`. ```JAVA putToProperties(StatsSetupConst.STATS_GENERATED, StatsSetupConst.USER) ``` Another alternative is to set `DO_NOT_UPDATE_STATS`to `TRUE`. See the Hive JIRA: https://issues.apache.org/jira/browse/HIVE-15653. We will not address it in this PR. ### How was this patch tested? Added test cases to VersionsSuite.scala Author: Xiao Li <gatorsmile@gmail.com> Closes #17232 from gatorsmile/Hive21.
* [SPARK-19887][SQL] dynamic partition keys can be null or empty stringWenchen Fan2017-03-151-1/+23
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When dynamic partition value is null or empty string, we should write the data to a directory like `a=__HIVE_DEFAULT_PARTITION__`, when we read the data back, we should respect this special directory name and treat it as null. This is the same behavior of impala, see https://issues.apache.org/jira/browse/IMPALA-252 ## How was this patch tested? new regression test Author: Wenchen Fan <wenchen@databricks.com> Closes #17277 from cloud-fan/partition.
* [SPARK-19921][SQL][TEST] Enable end-to-end testing using different Hive ↵Xiao Li2017-03-141-36/+49
| | | | | | | | | | | | | | | | | metastore versions. ### What changes were proposed in this pull request? To improve the quality of our Spark SQL in different Hive metastore versions, this PR is to enable end-to-end testing using different versions. This PR allows the test cases in sql/hive to pass the existing Hive client to create a SparkSession. - Since Derby does not allow concurrent connections, the pre-built Hive clients use different database from the TestHive's built-in 1.2.1 client. - Since our test cases in sql/hive only can create a single Spark context in the same JVM, the newly created SparkSession share the same spark context with the existing TestHive's corresponding SparkSession. ### How was this patch tested? Fixed the existing test cases. Author: Xiao Li <gatorsmile@gmail.com> Closes #17260 from gatorsmile/versionSuite.
* [SPARK-19924][SQL] Handle InvocationTargetException for all Hive ShimXiao Li2017-03-141-7/+6
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Since we are using shim for most Hive metastore APIs, the exceptions thrown by the underlying method of Method.invoke() are wrapped by `InvocationTargetException`. Instead of doing it one by one, we should handle all of them in the `withClient`. If any of them is missing, the error message could looks unfriendly. For example, below is an example for dropping tables. ``` Expected exception org.apache.spark.sql.AnalysisException to be thrown, but java.lang.reflect.InvocationTargetException was thrown. ScalaTestFailureLocation: org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14 at (ExternalCatalogSuite.scala:193) org.scalatest.exceptions.TestFailedException: Expected exception org.apache.spark.sql.AnalysisException to be thrown, but java.lang.reflect.InvocationTargetException was thrown. at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:496) at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555) at org.scalatest.Assertions$class.intercept(Assertions.scala:1004) at org.scalatest.FunSuite.intercept(FunSuite.scala:1555) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply$mcV$sp(ExternalCatalogSuite.scala:193) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183) at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22) at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85) at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104) at org.scalatest.Transformer.apply(Transformer.scala:22) at org.scalatest.Transformer.apply(Transformer.scala:20) at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166) at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:68) at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163) at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306) at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(ExternalCatalogSuite.scala:40) at org.scalatest.BeforeAndAfterEach$class.runTest(BeforeAndAfterEach.scala:255) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite.runTest(ExternalCatalogSuite.scala:40) at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208) at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208) at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:413) at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:401) at scala.collection.immutable.List.foreach(List.scala:381) at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401) at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:396) at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:483) at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:208) at org.scalatest.FunSuite.runTests(FunSuite.scala:1555) at org.scalatest.Suite$class.run(Suite.scala:1424) at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1555) at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212) at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212) at org.scalatest.SuperEngine.runImpl(Engine.scala:545) at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:212) at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:31) at org.scalatest.BeforeAndAfterAll$class.liftedTree1$1(BeforeAndAfterAll.scala:257) at org.scalatest.BeforeAndAfterAll$class.run(BeforeAndAfterAll.scala:256) at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:31) at org.scalatest.tools.SuiteRunner.run(SuiteRunner.scala:55) at org.scalatest.tools.Runner$$anonfun$doRunRunRunDaDoRunRun$3.apply(Runner.scala:2563) at org.scalatest.tools.Runner$$anonfun$doRunRunRunDaDoRunRun$3.apply(Runner.scala:2557) at scala.collection.immutable.List.foreach(List.scala:381) at org.scalatest.tools.Runner$.doRunRunRunDaDoRunRun(Runner.scala:2557) at org.scalatest.tools.Runner$$anonfun$runOptionallyWithPassFailReporter$2.apply(Runner.scala:1044) at org.scalatest.tools.Runner$$anonfun$runOptionallyWithPassFailReporter$2.apply(Runner.scala:1043) at org.scalatest.tools.Runner$.withClassLoaderAndDispatchReporter(Runner.scala:2722) at org.scalatest.tools.Runner$.runOptionallyWithPassFailReporter(Runner.scala:1043) at org.scalatest.tools.Runner$.run(Runner.scala:883) at org.scalatest.tools.Runner.run(Runner.scala) at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.runScalaTest2(ScalaTestRunner.java:138) at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.main(ScalaTestRunner.java:28) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at com.intellij.rt.execution.application.AppMain.main(AppMain.java:147) Caused by: java.lang.reflect.InvocationTargetException at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.apache.spark.sql.hive.client.Shim_v0_14.dropTable(HiveShim.scala:736) at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$dropTable$1.apply$mcV$sp(HiveClientImpl.scala:451) at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$dropTable$1.apply(HiveClientImpl.scala:451) at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$dropTable$1.apply(HiveClientImpl.scala:451) at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$withHiveState$1.apply(HiveClientImpl.scala:287) at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:228) at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:227) at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:270) at org.apache.spark.sql.hive.client.HiveClientImpl.dropTable(HiveClientImpl.scala:450) at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$dropTable$1.apply$mcV$sp(HiveExternalCatalog.scala:456) at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$dropTable$1.apply(HiveExternalCatalog.scala:454) at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$dropTable$1.apply(HiveExternalCatalog.scala:454) at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:94) at org.apache.spark.sql.hive.HiveExternalCatalog.dropTable(HiveExternalCatalog.scala:454) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14$$anonfun$apply$mcV$sp$8.apply$mcV$sp(ExternalCatalogSuite.scala:194) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14$$anonfun$apply$mcV$sp$8.apply(ExternalCatalogSuite.scala:194) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14$$anonfun$apply$mcV$sp$8.apply(ExternalCatalogSuite.scala:194) at org.scalatest.Assertions$class.intercept(Assertions.scala:997) ... 57 more Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: NoSuchObjectException(message:db2.unknown_table table not found) at org.apache.hadoop.hive.ql.metadata.Hive.dropTable(Hive.java:1038) ... 79 more Caused by: NoSuchObjectException(message:db2.unknown_table table not found) at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.get_table_core(HiveMetaStore.java:1808) at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.get_table(HiveMetaStore.java:1778) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.apache.hadoop.hive.metastore.RetryingHMSHandler.invoke(RetryingHMSHandler.java:107) at com.sun.proxy.$Proxy10.get_table(Unknown Source) at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.getTable(HiveMetaStoreClient.java:1208) at org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.getTable(SessionHiveMetaStoreClient.java:131) at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.dropTable(HiveMetaStoreClient.java:952) at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.dropTable(HiveMetaStoreClient.java:904) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.invoke(RetryingMetaStoreClient.java:156) at com.sun.proxy.$Proxy11.dropTable(Unknown Source) at org.apache.hadoop.hive.ql.metadata.Hive.dropTable(Hive.java:1035) ... 79 more ``` After unwrapping the exception, the message is like ``` org.apache.hadoop.hive.ql.metadata.HiveException: NoSuchObjectException(message:db2.unknown_table table not found); org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: NoSuchObjectException(message:db2.unknown_table table not found); at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:100) at org.apache.spark.sql.hive.HiveExternalCatalog.dropTable(HiveExternalCatalog.scala:460) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply$mcV$sp(ExternalCatalogSuite.scala:193) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183) at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22) ... ``` ### How was this patch tested? Covered by the existing test case in `test("drop table when database/table does not exist")` in `ExternalCatalogSuite`. Author: Xiao Li <gatorsmile@gmail.com> Closes #17265 from gatorsmile/InvocationTargetException.
* [SPARK-19723][SQL] create datasource table with an non-existent location ↵windpiger2017-03-101-63/+48
| | | | | | | | | | | | | | | | | | | | | | | should work ## What changes were proposed in this pull request? This JIRA is a follow up work after [SPARK-19583](https://issues.apache.org/jira/browse/SPARK-19583) As we discussed in that [PR](https://github.com/apache/spark/pull/16938) The following DDL for datasource table with an non-existent location should work: ``` CREATE TABLE ... (PARTITIONED BY ...) LOCATION path ``` Currently it will throw exception that path not exists for datasource table for datasource table ## How was this patch tested? unit test added Author: windpiger <songjun@outlook.com> Closes #17055 from windpiger/CTDataSourcePathNotExists.
* [SPARK-19905][SQL] Bring back Dataset.inputFiles for Hive SerDe tablesCheng Lian2017-03-101-0/+11
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `Dataset.inputFiles` works by matching `FileRelation`s in the query plan. In Spark 2.1, Hive SerDe tables are represented by `MetastoreRelation`, which inherits from `FileRelation`. However, in Spark 2.2, Hive SerDe tables are now represented by `CatalogRelation`, which doesn't inherit from `FileRelation` anymore, due to the unification of Hive SerDe tables and data source tables. This change breaks `Dataset.inputFiles` for Hive SerDe tables. This PR tries to fix this issue by explicitly matching `CatalogRelation`s that are Hive SerDe tables in `Dataset.inputFiles`. Note that we can't make `CatalogRelation` inherit from `FileRelation` since not all `CatalogRelation`s are file based (e.g., JDBC data source tables). ## How was this patch tested? New test case added in `HiveDDLSuite`. Author: Cheng Lian <lian@databricks.com> Closes #17247 from liancheng/spark-19905-hive-table-input-files.
* [SPARK-19611][SQL] Preserve metastore field order when merging inferred schemaBudde2017-03-101-0/+21
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The ```HiveMetastoreCatalog.mergeWithMetastoreSchema()``` method added in #16944 may not preserve the same field order as the metastore schema in some cases, which can cause queries to fail. This change ensures that the metastore field order is preserved. ## How was this patch tested? A test for ensuring that metastore order is preserved was added to ```HiveSchemaInferenceSuite.``` The particular failure usecase from #16944 was tested manually as well. Author: Budde <budde@amazon.com> Closes #17249 from budde/PreserveMetastoreFieldOrder.
* [SPARK-19611][SQL] Introduce configurable table schema inferenceBudde2017-03-091-0/+305
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## Summary of changes Add a new configuration option that allows Spark SQL to infer a case-sensitive schema from a Hive Metastore table's data files when a case-sensitive schema can't be read from the table properties. - Add spark.sql.hive.caseSensitiveInferenceMode param to SQLConf - Add schemaPreservesCase field to CatalogTable (set to false when schema can't successfully be read from Hive table props) - Perform schema inference in HiveMetastoreCatalog if schemaPreservesCase is false, depending on spark.sql.hive.caseSensitiveInferenceMode - Add alterTableSchema() method to the ExternalCatalog interface - Add HiveSchemaInferenceSuite tests - Refactor and move ParquetFileForamt.meregeMetastoreParquetSchema() as HiveMetastoreCatalog.mergeWithMetastoreSchema - Move schema merging tests from ParquetSchemaSuite to HiveSchemaInferenceSuite [JIRA for this change](https://issues.apache.org/jira/browse/SPARK-19611) ## How was this patch tested? The tests in ```HiveSchemaInferenceSuite``` should verify that schema inference is working as expected. ```ExternalCatalogSuite``` has also been extended to cover the new ```alterTableSchema()``` API. Author: Budde <budde@amazon.com> Closes #16944 from budde/SPARK-19611.
* [SPARK-12334][SQL][PYSPARK] Support read from multiple input paths for orc ↵Jeff Zhang2017-03-091-0/+9
| | | | | | | | | | | | file in DataFrameReader.orc Beside the issue in spark api, also fix 2 minor issues in pyspark - support read from multiple input paths for orc - support read from multiple input paths for text Author: Jeff Zhang <zjffdu@apache.org> Closes #10307 from zjffdu/SPARK-12334.
* [SPARK-19763][SQL] qualified external datasource table location stored in ↵windpiger2017-03-093-9/+5
| | | | | | | | | | | | | | | | | | | | | | | | | | | catalog ## What changes were proposed in this pull request? If we create a external datasource table with a non-qualified location , we should qualified it to store in catalog. ``` CREATE TABLE t(a string) USING parquet LOCATION '/path/xx' CREATE TABLE t1(a string, b string) USING parquet PARTITIONED BY(b) LOCATION '/path/xx' ``` when we get the table from catalog, the location should be qualified, e.g.'file:/path/xxx' ## How was this patch tested? unit test added Author: windpiger <songjun@outlook.com> Closes #17095 from windpiger/tablepathQualified.
* [SPARK-19235][SQL][TESTS] Enable Test Cases in DDLSuite with Hive MetastoreXiao Li2017-03-081-82/+75
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? So far, the test cases in DDLSuites only verify the behaviors of InMemoryCatalog. That means, they do not cover the scenarios using HiveExternalCatalog. Thus, we need to improve the existing test suite to run these cases using Hive metastore. When porting these test cases, a bug of `SET LOCATION` is found. `path` is not set when the location is changed. After this PR, a few changes are made, as summarized below, - `DDLSuite` becomes an abstract class. Both `InMemoryCatalogedDDLSuite` and `HiveCatalogedDDLSuite` extend it. `InMemoryCatalogedDDLSuite` is using `InMemoryCatalog`. `HiveCatalogedDDLSuite` is using `HiveExternalCatalog`. - `InMemoryCatalogedDDLSuite` contains all the existing test cases in `DDLSuite`. - `HiveCatalogedDDLSuite` contains a subset of `DDLSuite`. The following test cases are excluded: 1. The following test cases only make sense for `InMemoryCatalog`: ``` test("desc table for parquet data source table using in-memory catalog") test("create a managed Hive source table") { test("create an external Hive source table") test("Create Hive Table As Select") ``` 2. The following test cases are unable to be ported because we are unable to alter table provider when using Hive metastore. In the future PRs we need to improve the test cases so that altering table provider is not needed: ``` test("alter table: set location (datasource table)") test("alter table: set properties (datasource table)") test("alter table: unset properties (datasource table)") test("alter table: set serde (datasource table)") test("alter table: set serde partition (datasource table)") test("alter table: change column (datasource table)") test("alter table: add partition (datasource table)") test("alter table: drop partition (datasource table)") test("alter table: rename partition (datasource table)") test("drop table - data source table") ``` **TODO** : in the future PRs, we need to remove `HiveDDLSuite` and move the test cases to either `DDLSuite`, `InMemoryCatalogedDDLSuite` or `HiveCatalogedDDLSuite`. ### How was this patch tested? N/A Author: Xiao Li <gatorsmile@gmail.com> Author: gatorsmile <gatorsmile@gmail.com> Closes #16592 from gatorsmile/refactorDDLSuite.
* [SPARK-19540][SQL] Add ability to clone SparkSession wherein cloned session ↵Kunal Khamar2017-03-082-0/+153
| | | | | | | | | | | | | | | | has an identical copy of the SessionState Forking a newSession() from SparkSession currently makes a new SparkSession that does not retain SessionState (i.e. temporary tables, SQL config, registered functions etc.) This change adds a method cloneSession() which creates a new SparkSession with a copy of the parent's SessionState. Subsequent changes to base session are not propagated to cloned session, clone is independent after creation. If the base is changed after clone has been created, say user registers new UDF, then the new UDF will not be available inside the clone. Same goes for configs and temp tables. Unit tests Author: Kunal Khamar <kkhamar@outlook.com> Author: Shixiong Zhu <shixiong@databricks.com> Closes #16826 from kunalkhamar/fork-sparksession.
* [SPARK-19864][SQL][TEST] provide a makeQualifiedPath functions to optimize ↵windpiger2017-03-085-38/+18
| | | | | | | | | | | | | | | some code ## What changes were proposed in this pull request? Currently there are lots of places to make the path qualified, it is better to provide a function to do this, then the code will be more simple. ## How was this patch tested? N/A Author: windpiger <songjun@outlook.com> Closes #17204 from windpiger/addQualifiledPathUtil.
* [SPARK-19865][SQL] remove the view identifier in SubqueryAliasjiangxingbo2017-03-082-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Since we have a `View` node now, we can remove the view identifier in `SubqueryAlias`, which was used to indicate a view node before. ## How was this patch tested? Update the related test cases. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #17210 from jiangxb1987/SubqueryAlias.
* [SPARK-19765][SPARK-18549][SQL] UNCACHE TABLE should un-cache all cached ↵Wenchen Fan2017-03-072-5/+3
| | | | | | | | | | | | | | | | | | | | | plans that refer to this table ## What changes were proposed in this pull request? When un-cache a table, we should not only remove the cache entry for this table, but also un-cache any other cached plans that refer to this table. This PR also includes some refactors: 1. use `java.util.LinkedList` to store the cache entries, so that it's safer to remove elements while iterating 2. rename `invalidateCache` to `recacheByPlan`, which is more obvious about what it does. ## How was this patch tested? new regression test Author: Wenchen Fan <wenchen@databricks.com> Closes #17097 from cloud-fan/cache.
* [SPARK-19832][SQL] DynamicPartitionWriteTask get partitionPath should escape ↵windpiger2017-03-061-1/+34
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | the partition name ## What changes were proposed in this pull request? Currently in DynamicPartitionWriteTask, when we get the paritionPath of a parition, we just escape the partition value, not escape the partition name. this will cause some problems for some special partition name situation, for example : 1) if the partition name contains '%' etc, there will be two partition path created in the filesytem, one is for escaped path like '/path/a%25b=1', another is for unescaped path like '/path/a%b=1'. and the data inserted stored in unescaped path, while the show partitions table will return 'a%25b=1' which the partition name is escaped. So here it is not consist. And I think the data should be stored in the escaped path in filesystem, which Hive2.0.0 also have the same action. 2) if the partition name contains ':', there will throw exception that new Path("/path","a:b"), this is illegal which has a colon in the relative path. ``` java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: a:b at org.apache.hadoop.fs.Path.initialize(Path.java:205) at org.apache.hadoop.fs.Path.<init>(Path.java:171) at org.apache.hadoop.fs.Path.<init>(Path.java:88) ... 48 elided Caused by: java.net.URISyntaxException: Relative path in absolute URI: a:b at java.net.URI.checkPath(URI.java:1823) at java.net.URI.<init>(URI.java:745) at org.apache.hadoop.fs.Path.initialize(Path.java:202) ... 50 more ``` ## How was this patch tested? unit test added Author: windpiger <songjun@outlook.com> Closes #17173 from windpiger/fixDatasourceSpecialCharPartitionName.
* [SPARK-19257][SQL] location for table/partition/database should be java.net.URIwindpiger2017-03-069-39/+210
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently we treat the location of table/partition/database as URI string. It will be safer if we can make the type of location as java.net.URI. In this PR, there are following classes changes: **1. CatalogDatabase** ``` case class CatalogDatabase( name: String, description: String, locationUri: String, properties: Map[String, String]) ---> case class CatalogDatabase( name: String, description: String, locationUri: URI, properties: Map[String, String]) ``` **2. CatalogStorageFormat** ``` case class CatalogStorageFormat( locationUri: Option[String], inputFormat: Option[String], outputFormat: Option[String], serde: Option[String], compressed: Boolean, properties: Map[String, String]) ----> case class CatalogStorageFormat( locationUri: Option[URI], inputFormat: Option[String], outputFormat: Option[String], serde: Option[String], compressed: Boolean, properties: Map[String, String]) ``` Before and After this PR, it is transparent for user, there is no change that the user should concern. The `String` to `URI` just happened in SparkSQL internally. Here list some operation related location: **1. whitespace in the location** e.g. `/a/b c/d` For both table location and partition location, After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b c/d'` , then `DESC EXTENDED t ` show the location is `/a/b c/d`, and the real path in the FileSystem also show `/a/b c/d` **2. colon(:) in the location** e.g. `/a/b:c/d` For both table location and partition location, when `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b:c/d'` , **In linux file system** `DESC EXTENDED t ` show the location is `/a/b:c/d`, and the real path in the FileSystem also show `/a/b:c/d` **in HDFS** throw exception: `java.lang.IllegalArgumentException: Pathname /a/b:c/d from hdfs://iZbp1151s8hbnnwriekxdeZ:9000/a/b:c/d is not a valid DFS filename.` **while** After `INSERT INTO TABLE t PARTITION(a="a:b") SELECT 1` then `DESC EXTENDED t ` show the location is `/xxx/a=a%3Ab`, and the real path in the FileSystem also show `/xxx/a=a%3Ab` **3. percent sign(%) in the location** e.g. `/a/b%c/d` For both table location and partition location, After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b%c/d'` , then `DESC EXTENDED t ` show the location is `/a/b%c/d`, and the real path in the FileSystem also show `/a/b%c/d` **4. encoded(%25) in the location** e.g. `/a/b%25c/d` For both table location and partition location, After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b%25c/d'` , then `DESC EXTENDED t ` show the location is `/a/b%25c/d`, and the real path in the FileSystem also show `/a/b%25c/d` **while** After `INSERT INTO TABLE t PARTITION(a="%25") SELECT 1` then `DESC EXTENDED t ` show the location is `/xxx/a=%2525`, and the real path in the FileSystem also show `/xxx/a=%2525` **Additionally**, except the location, there are two other factors will affect the location of the table/partition. one is the table name which does not allowed to have special characters, and the other is `partition name` which have the same actions with `partition value`, and `partition name` with special character situation has add some testcase and resolve a bug in [PR](https://github.com/apache/spark/pull/17173) ### Summary: After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION path`, the path which we get from `DESC TABLE` and `real path in FileSystem` are all the same with the `CREATE TABLE` command(different filesystem has different action that allow what kind of special character to create the path, e.g. HDFS does not allow colon, but linux filesystem allow it ). `DataBase` also have the same logic with `CREATE TABLE` while if the `partition value` has some special character like `%` `:` `#` etc, then we will get the path with encoded `partition value` like `/xxx/a=A%25B` from `DESC TABLE` and `real path in FileSystem` In this PR, the core change code is using `new Path(str).toUri` and `new Path(uri).toString` which transfrom `str to uri `or `uri to str`. for example: ``` val str = '/a/b c/d' val uri = new Path(str).toUri --> '/a/b%20c/d' val strFromUri = new Path(uri).toString -> '/a/b c/d' ``` when we restore table/partition from metastore, or get the location from `CREATE TABLE` command, we can use it as above to change string to uri `new Path(str).toUri ` ## How was this patch tested? unit test added. The `current master branch` also `passed all the test cases` added in this PR by a litter change. https://github.com/apache/spark/pull/17149/files#diff-b7094baa12601424a5d19cb930e3402fR1764 here `toURI` -> `toString` when test in master branch. This can show that this PR is transparent for user. Author: windpiger <songjun@outlook.com> Closes #17149 from windpiger/changeStringToURI.
* [SPARK-13446][SQL] Support reading data from Hive 2.0.1 metastoreXiao Li2017-03-032-6/+15
| | | | | | | | | | | | | | | ### What changes were proposed in this pull request? This PR is to make Spark work with Hive 2.0's metastores. Compared with Hive 1.2, Hive 2.0's metastore has an API update due to removal of `HOLD_DDLTIME` in https://issues.apache.org/jira/browse/HIVE-12224. Based on the following Hive JIRA description, `HOLD_DDLTIME` should be removed from our internal API too. (https://github.com/apache/spark/pull/17063 was submitted for it): > This arcane feature was introduced long ago via HIVE-1394 It was broken as soon as it landed, HIVE-1442 and is thus useless. Fact that no one has fixed it since informs that its not really used by anyone. Better is to remove it so no one hits the bug of HIVE-1442 In the next PR, we will support 2.1.0 metastore, whose APIs were changed due to https://issues.apache.org/jira/browse/HIVE-12730. However, before that, we need a code cleanup for stats collection and setting. ### How was this patch tested? Added test cases to VersionsSuite.scala Author: Xiao Li <gatorsmile@gmail.com> Closes #17061 from gatorsmile/Hive2.
* [SPARK-18939][SQL] Timezone support in partition values.Takuya UESHIN2017-03-031-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is a follow-up pr of #16308 and #16750. This pr enables timezone support in partition values. We should use `timeZone` option introduced at #16750 to parse/format partition values of the `TimestampType`. For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT` which will be used for partition values, the values written by the default timezone option, which is `"GMT"` because the session local timezone is `"GMT"` here, are: ```scala scala> spark.conf.set("spark.sql.session.timeZone", "GMT") scala> val df = Seq((1, new java.sql.Timestamp(1451606400000L))).toDF("i", "ts") df: org.apache.spark.sql.DataFrame = [i: int, ts: timestamp] scala> df.show() +---+-------------------+ | i| ts| +---+-------------------+ | 1|2016-01-01 00:00:00| +---+-------------------+ scala> df.write.partitionBy("ts").save("/path/to/gmtpartition") ``` ```sh $ ls /path/to/gmtpartition/ _SUCCESS ts=2016-01-01 00%3A00%3A00 ``` whereas setting the option to `"PST"`, they are: ```scala scala> df.write.option("timeZone", "PST").partitionBy("ts").save("/path/to/pstpartition") ``` ```sh $ ls /path/to/pstpartition/ _SUCCESS ts=2015-12-31 16%3A00%3A00 ``` We can properly read the partition values if the session local timezone and the timezone of the partition values are the same: ```scala scala> spark.read.load("/path/to/gmtpartition").show() +---+-------------------+ | i| ts| +---+-------------------+ | 1|2016-01-01 00:00:00| +---+-------------------+ ``` And even if the timezones are different, we can properly read the values with setting corrent timezone option: ```scala // wrong result scala> spark.read.load("/path/to/pstpartition").show() +---+-------------------+ | i| ts| +---+-------------------+ | 1|2015-12-31 16:00:00| +---+-------------------+ // correct result scala> spark.read.option("timeZone", "PST").load("/path/to/pstpartition").show() +---+-------------------+ | i| ts| +---+-------------------+ | 1|2016-01-01 00:00:00| +---+-------------------+ ``` ## How was this patch tested? Existing tests and added some tests. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #17053 from ueshin/issues/SPARK-18939.
* [SPARK-18726][SQL] resolveRelation for FileFormat DataSource don't need to ↵windpiger2017-03-021-0/+11
| | | | | | | | | | | | | | | | | | | | | | listFiles twice ## What changes were proposed in this pull request? Currently when we resolveRelation for a `FileFormat DataSource` without providing user schema, it will execute `listFiles` twice in `InMemoryFileIndex` during `resolveRelation`. This PR add a `FileStatusCache` for DataSource, this can avoid listFiles twice. But there is a bug in `InMemoryFileIndex` see: [SPARK-19748](https://github.com/apache/spark/pull/17079) [SPARK-19761](https://github.com/apache/spark/pull/17093), so this pr should be after SPARK-19748/ SPARK-19761. ## How was this patch tested? unit test added Author: windpiger <songjun@outlook.com> Closes #17081 from windpiger/resolveDataSourceScanFilesTwice.
* [SPARK-19583][SQL] CTAS for data source table with a created location should ↵windpiger2017-03-011-0/+99
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | succeed ## What changes were proposed in this pull request? ``` spark.sql( s""" |CREATE TABLE t |USING parquet |PARTITIONED BY(a, b) |LOCATION '$dir' |AS SELECT 3 as a, 4 as b, 1 as c, 2 as d """.stripMargin) ``` Failed with the error message: ``` path file:/private/var/folders/6r/15tqm8hn3ldb3rmbfqm1gf4c0000gn/T/spark-195cd513-428a-4df9-b196-87db0c73e772 already exists.; org.apache.spark.sql.AnalysisException: path file:/private/var/folders/6r/15tqm8hn3ldb3rmbfqm1gf4c0000gn/T/spark-195cd513-428a-4df9-b196-87db0c73e772 already exists.; at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:102) ``` while hive table is ok ,so we should fix it for datasource table. The reason is that the SaveMode check is put in `InsertIntoHadoopFsRelationCommand` , and the SaveMode check actually use `path`, this is fine when we use `DataFrameWriter.save()`, because this situation of SaveMode act on `path`. While when we use `CreateDataSourceAsSelectCommand`, the situation of SaveMode act on table, and we have already do SaveMode check in `CreateDataSourceAsSelectCommand` for table , so we should not do SaveMode check in the following logic in `InsertIntoHadoopFsRelationCommand` for path, this is redundant and wrong logic for `CreateDataSourceAsSelectCommand` After this PR, the following DDL will succeed, when the location has been created we will append it or overwrite it. ``` CREATE TABLE ... (PARTITIONED BY ...) LOCATION path AS SELECT ... ``` ## How was this patch tested? unit test added Author: windpiger <songjun@outlook.com> Closes #16938 from windpiger/CTASDataSourceWitLocation.
* [SPARK-19775][SQL] Remove an obsolete `partitionBy().insertInto()` test caseDongjoon Hyun2017-03-021-13/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This issue removes [a test case](https://github.com/apache/spark/blame/master/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala#L287-L298) which was introduced by [SPARK-14459](https://github.com/apache/spark/commit/652bbb1bf62722b08a062c7a2bf72019f85e179e) and was superseded by [SPARK-16033](https://github.com/apache/spark/blame/master/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala#L365-L371). Basically, we cannot use `partitionBy` and `insertInto` together. ```scala test("Reject partitioning that does not match table") { withSQLConf(("hive.exec.dynamic.partition.mode", "nonstrict")) { sql("CREATE TABLE partitioned (id bigint, data string) PARTITIONED BY (part string)") val data = (1 to 10).map(i => (i, s"data-$i", if ((i % 2) == 0) "even" else "odd")) .toDF("id", "data", "part") intercept[AnalysisException] { // cannot partition by 2 fields when there is only one in the table definition data.write.partitionBy("part", "data").insertInto("partitioned") } } } ``` ## How was this patch tested? This only removes a test case. Pass the existing Jenkins test. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #17106 from dongjoon-hyun/SPARK-19775.
* [SPARK-19463][SQL] refresh cache after the InsertIntoHadoopFsRelationCommandwindpiger2017-02-281-12/+2
| | | | | | | | | | | | | ## What changes were proposed in this pull request? If we first cache a DataSource table, then we insert some data into the table, we should refresh the data in the cache after the insert command. ## How was this patch tested? unit test added Author: windpiger <songjun@outlook.com> Closes #16809 from windpiger/refreshCacheAfterInsert.
* [SPARK-19678][SQL] remove MetastoreRelationWenchen Fan2017-02-288-189/+106
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? `MetastoreRelation` is used to represent table relation for hive tables, and provides some hive related information. We will resolve `SimpleCatalogRelation` to `MetastoreRelation` for hive tables, which is unnecessary as these 2 are the same essentially. This PR merges `SimpleCatalogRelation` and `MetastoreRelation` ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #17015 from cloud-fan/table-relation.
* [SPARK-15615][SQL][BUILD][FOLLOW-UP] Replace deprecated usage of ↵hyukjinkwon2017-02-274-22/+22
| | | | | | | | | | | | | | | | | | json(RDD[String]) API ## What changes were proposed in this pull request? This PR proposes to replace the deprecated `json(RDD[String])` usage to `json(Dataset[String])`. This currently produces so many warnings. ## How was this patch tested? Fixed tests. Author: hyukjinkwon <gurwls223@gmail.com> Closes #17071 from HyukjinKwon/SPARK-15615-followup.
* [SPARK-19735][SQL] Remove HOLD_DDLTIME from Catalog APIsXiao Li2017-02-241-4/+1
| | | | | | | | | | | | ### What changes were proposed in this pull request? As explained in Hive JIRA https://issues.apache.org/jira/browse/HIVE-12224, HOLD_DDLTIME was broken as soon as it landed. Hive 2.0 removes HOLD_DDLTIME from the API. In Spark SQL, we always set it to FALSE. Like Hive, we should also remove it from our Catalog APIs. ### How was this patch tested? N/A Author: Xiao Li <gatorsmile@gmail.com> Closes #17063 from gatorsmile/removalHoldDDLTime.
* [SPARK-17078][SQL] Show stats when explainwangzhenhua2017-02-241-0/+13
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently we can only check the estimated stats in logical plans by debugging. We need to provide an easier and more efficient way for developers/users. In this pr, we add EXPLAIN COST command to show stats in the optimized logical plan. E.g. ``` spark-sql> EXPLAIN COST select count(1) from store_returns; ... == Optimized Logical Plan == Aggregate [count(1) AS count(1)#24L], Statistics(sizeInBytes=16.0 B, rowCount=1, isBroadcastable=false) +- Project, Statistics(sizeInBytes=4.3 GB, rowCount=5.76E+8, isBroadcastable=false) +- Relation[sr_returned_date_sk#3,sr_return_time_sk#4,sr_item_sk#5,sr_customer_sk#6,sr_cdemo_sk#7,sr_hdemo_sk#8,sr_addr_sk#9,sr_store_sk#10,sr_reason_sk#11,sr_ticket_number#12,sr_return_quantity#13,sr_return_amt#14,sr_return_tax#15,sr_return_amt_inc_tax#16,sr_fee#17,sr_return_ship_cost#18,sr_refunded_cash#19,sr_reversed_charge#20,sr_store_credit#21,sr_net_loss#22] parquet, Statistics(sizeInBytes=28.6 GB, rowCount=5.76E+8, isBroadcastable=false) ... ``` ## How was this patch tested? Add test cases. Author: wangzhenhua <wangzhenhua@huawei.com> Author: Zhenhua Wang <wzh_zju@163.com> Closes #16594 from wzhfy/showStats.
* [SPARK-19459] Support for nested char/varchar fields in ORCHerman van Hovell2017-02-231-4/+8
| | | | | | | | | | | | ## What changes were proposed in this pull request? This PR is a small follow-up on https://github.com/apache/spark/pull/16804. This PR also adds support for nested char/varchar fields in orc. ## How was this patch tested? I have added a regression test to the OrcSourceSuite. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #17030 from hvanhovell/SPARK-19459-follow-up.
* [SPARK-19670][SQL][TEST] Enable Bucketed Table Reading and Writing Testing ↵Xiao Li2017-02-214-799/+58
| | | | | | | | | | | | | | Without Hive Support ### What changes were proposed in this pull request? Bucketed table reading and writing does not need Hive support. We can move the test cases from `sql/hive` to `sql/core`. After this PR, we can improve the test case coverage. Bucket table reading and writing can be tested with and without Hive support. ### How was this patch tested? N/A Author: Xiao Li <gatorsmile@gmail.com> Closes #17004 from gatorsmile/mvTestCaseForBuckets.
* [SPARK-18922][TESTS] Fix new test failures on Windows due to path and ↵hyukjinkwon2017-02-201-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | resource not closed ## What changes were proposed in this pull request? This PR proposes to fix new test failures on WIndows as below: **Before** ``` KafkaRelationSuite: - test late binding start offsets *** FAILED *** (7 seconds, 679 milliseconds) Cause: java.nio.file.FileSystemException: C:\projects\spark\target\tmp\spark-4c4b0cd1-4cb7-4908-949d-1b0cc8addb50\topic-4-0\00000000000000000000.log -> C:\projects\spark\target\tmp\spark-4c4b0cd1-4cb7-4908-949d-1b0cc8addb50\topic-4-0\00000000000000000000.log.deleted: The process cannot access the file because it is being used by another process. KafkaSourceSuite: - deserialization of initial offset with Spark 2.1.0 *** FAILED *** (3 seconds, 542 milliseconds) java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-97ef64fc-ae61-4ce3-ac59-287fd38bd824 - deserialization of initial offset written by Spark 2.1.0 *** FAILED *** (60 milliseconds) java.nio.file.InvalidPathException: Illegal char <:> at index 2: /C:/projects/spark/external/kafka-0-10-sql/target/scala-2.11/test-classes/kafka-source-initial-offset-version-2.1.0.b HiveDDLSuite: - partitioned table should always put partition columns at the end of table schema *** FAILED *** (657 milliseconds) org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-f1b83d09-850a-4bba-8e43-a2a28dfaa757; DDLSuite: - create a data source table without schema *** FAILED *** (94 milliseconds) org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-a3f3c161-afae-4d6f-9182-e8642f77062b; - SET LOCATION for managed table *** FAILED *** (219 milliseconds) org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree: Exchange SinglePartit +- *HashAggregate(keys=[], functions=[partial_count(1)], output=[count#99367L]) +- *FileScan parquet default.tbl[] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/C:projectsspark arget mpspark-15be2f2f-4ea9-4c47-bfee-1b7b49363033], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<> - insert data to a data source table which has a not existed location should succeed *** FAILED *** (16 milliseconds) org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-34987671-e8d1-4624-ba5b-db1012e1246b; - insert into a data source table with no existed partition location should succeed *** FAILED *** (16 milliseconds) org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-4c6ccfbf-4091-4032-9fbc-3d40c58267d5; - read data from a data source table which has a not existed location should succeed *** FAILED *** (0 milliseconds) - read data from a data source table with no existed partition location should succeed *** FAILED *** (0 milliseconds) org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-6af39e37-abd1-44e8-ac68-e2dfcf67a2f3; InputOutputMetricsSuite: - output metrics on records written *** FAILED *** (0 milliseconds) java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-cd69ee77-88f2-4202-bed6-19c0ee05ef55\InputOutputMetricsSuite, expected: file:/// - output metrics on records written - new Hadoop API *** FAILED *** (16 milliseconds) java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-b69e8fcb-047b-4de8-9cdf-5f026efb6762\InputOutputMetricsSuite, expected: file:/// ``` **After** ``` KafkaRelationSuite: - test late binding start offsets !!! CANCELED !!! (62 milliseconds) KafkaSourceSuite: - deserialization of initial offset with Spark 2.1.0 (5 seconds, 341 milliseconds) - deserialization of initial offset written by Spark 2.1.0 (910 milliseconds) HiveDDLSuite: - partitioned table should always put partition columns at the end of table schema (2 seconds) DDLSuite: - create a data source table without schema (828 milliseconds) - SET LOCATION for managed table (406 milliseconds) - insert data to a data source table which has a not existed location should succeed (406 milliseconds) - insert into a data source table with no existed partition location should succeed (453 milliseconds) - read data from a data source table which has a not existed location should succeed (94 milliseconds) - read data from a data source table with no existed partition location should succeed (265 milliseconds) InputOutputMetricsSuite: - output metrics on records written (172 milliseconds) - output metrics on records written - new Hadoop API (297 milliseconds) ``` ## How was this patch tested? Fixed tests in `InputOutputMetricsSuite`, `KafkaRelationSuite`, `KafkaSourceSuite`, `DDLSuite.scala` and `HiveDDLSuite`. Manually tested via AppVeyor as below: `InputOutputMetricsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/ex8nvwa6tsh7rmto `KafkaRelationSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/h8dlcowew52y8ncw `KafkaSourceSuite`: https://ci.appveyor.com/project/spark-test/spark/build/634-20170219-windows-test/job/9ybgjl7yeubxcre4 `DDLSuite`: https://ci.appveyor.com/project/spark-test/spark/build/635-20170219-windows-test `HiveDDLSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/up6o9n47er087ltb Author: hyukjinkwon <gurwls223@gmail.com> Closes #16999 from HyukjinKwon/windows-fix.
* [SPARK-15453][SQL][FOLLOW-UP] FileSourceScanExec to extract `outputOrdering` ↵Xiao Li2017-02-201-92/+137
| | | | | | | | | | | | | | information ### What changes were proposed in this pull request? `outputOrdering` is also dependent on whether the bucket has more than one files. The test cases fail when we try to move them to sql/core. This PR is to fix the test cases introduced in https://github.com/apache/spark/pull/14864 and add a test case to verify [the related logics](https://github.com/tejasapatil/spark/blob/070c24994747c0479fb2520774ede27ff1cf8cac/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala#L197-L206). ### How was this patch tested? N/A Author: Xiao Li <gatorsmile@gmail.com> Closes #16994 from gatorsmile/bucketingTS.
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-29 00:40:51 +0000