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* [SPARK-12926][SQL] SQLContext to display warning message when non-sql ↵Tejas Patil2016-01-281-3/+11
| | | | | | | | | | configs are being set Users unknowingly try to set core Spark configs in SQLContext but later realise that it didn't work. eg. sqlContext.sql("SET spark.shuffle.memoryFraction=0.4"). This PR adds a warning message when such operations are done. Author: Tejas Patil <tejasp@fb.com> Closes #10849 from tejasapatil/SPARK-12926.
* [SPARK-12818][SQL] Specialized integral and string types for Count-min SketchCheng Lian2016-01-283-35/+99
| | | | | | | | This PR is a follow-up of #10911. It adds specialized update methods for `CountMinSketch` so that we can avoid doing internal/external row format conversion in `DataFrame.countMinSketch()`. Author: Cheng Lian <lian@databricks.com> Closes #10968 from liancheng/cms-specialized.
* Provide same info as in spark-submit --helpJames Lohse2016-01-281-2/+3
| | | | | | | | this is stated for --packages and --repositories. Without stating it for --jars, people expect a standard java classpath to work, with expansion and using a different delimiter than a comma. Currently this is only state in the --help for spark-submit "Comma-separated list of local jars to include on the driver and executor classpaths." Author: James Lohse <jimlohse@users.noreply.github.com> Closes #10890 from jimlohse/patch-1.
* [SPARK-13045] [SQL] Remove ColumnVector.Struct in favor of ColumnarBatch.RowNong Li2016-01-273-120/+32
| | | | | | | | These two classes became identical as the implementation progressed. Author: Nong Li <nong@databricks.com> Closes #10952 from nongli/spark-13045.
* [HOTFIX] Fix Scala 2.11 compilationAndrew Or2016-01-271-1/+2
| | | | | | | | | | by explicitly marking annotated parameters as vals (SI-8813). Caused by #10835. Author: Andrew Or <andrew@databricks.com> Closes #10955 from andrewor14/fix-scala211.
* [SPARK-12865][SPARK-12866][SQL] Migrate SparkSQLParser/ExtendedHiveQlParser ↵Herman van Hovell2016-01-2716-226/+161
| | | | | | | | | | | | | | | | commands to new Parser This PR moves all the functionality provided by the SparkSQLParser/ExtendedHiveQlParser to the new Parser hierarchy (SparkQl/HiveQl). This also improves the current SET command parsing: the current implementation swallows ```set role ...``` and ```set autocommit ...``` commands, this PR respects these commands (and passes them on to Hive). This PR and https://github.com/apache/spark/pull/10723 end the use of Parser-Combinator parsers for SQL parsing. As a result we can also remove the ```AbstractSQLParser``` in Catalyst. The PR is marked WIP as long as it doesn't pass all tests. cc rxin viirya winningsix (this touches https://github.com/apache/spark/pull/10144) Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #10905 from hvanhovell/SPARK-12866.
* [SPARK-12938][SQL] DataFrame API for Bloom filterWenchen Fan2016-01-277-93/+306
| | | | | | | | | | This PR integrates Bloom filter from spark-sketch into DataFrame. This version resorts to RDD.aggregate for building the filter. A more performant UDAF version can be built in future follow-up PRs. This PR also add 2 specify `put` version(`putBinary` and `putLong`) into `BloomFilter`, which makes it easier to build a Bloom filter over a `DataFrame`. Author: Wenchen Fan <wenchen@databricks.com> Closes #10937 from cloud-fan/bloom-filter.
* [SPARK-13021][CORE] Fail fast when custom RDDs violate RDD.partition's API ↵Josh Rosen2016-01-272-0/+25
| | | | | | | | | | | | | | contract Spark's `Partition` and `RDD.partitions` APIs have a contract which requires custom implementations of `RDD.partitions` to ensure that for all `x`, `rdd.partitions(x).index == x`; in other words, the `index` reported by a repartition needs to match its position in the partitions array. If a custom RDD implementation violates this contract, then Spark has the potential to become stuck in an infinite recomputation loop when recomputing a subset of an RDD's partitions, since the tasks that are actually run will not correspond to the missing output partitions that triggered the recomputation. Here's a link to a notebook which demonstrates this problem: https://rawgit.com/JoshRosen/e520fb9a64c1c97ec985/raw/5e8a5aa8d2a18910a1607f0aa4190104adda3424/Violating%2520RDD.partitions%2520contract.html In order to guard against this infinite loop behavior, this patch modifies Spark so that it fails fast and refuses to compute RDDs' whose `partitions` violate the API contract. Author: Josh Rosen <joshrosen@databricks.com> Closes #10932 from JoshRosen/SPARK-13021.
* [SPARK-12895][SPARK-12896] Migrate TaskMetrics to accumulatorsAndrew Or2016-01-2770-1141/+3012
| | | | | | | | | | | | | | | | The high level idea is that instead of having the executors send both accumulator updates and TaskMetrics, we should have them send only accumulator updates. This eliminates the need to maintain both code paths since one can be implemented in terms of the other. This effort is split into two parts: **SPARK-12895: Implement TaskMetrics using accumulators.** TaskMetrics is basically just a bunch of accumulable fields. This patch makes TaskMetrics a syntactic wrapper around a collection of accumulators so we don't need to send TaskMetrics from the executors to the driver. **SPARK-12896: Send only accumulator updates to the driver.** Now that TaskMetrics are expressed in terms of accumulators, we can capture all TaskMetrics values if we just send accumulator updates from the executors to the driver. This completes the parent issue SPARK-10620. While an effort has been made to preserve as much of the public API as possible, there were a few known breaking DeveloperApi changes that would be very awkward to maintain. I will gather the full list shortly and post it here. Note: This was once part of #10717. This patch is split out into its own patch from there to make it easier for others to review. Other smaller pieces of already been merged into master. Author: Andrew Or <andrew@databricks.com> Closes #10835 from andrewor14/task-metrics-use-accums.
* [SPARK-10847][SQL][PYSPARK] Pyspark - DataFrame - Optional Metadata with ↵Jason Lee2016-01-272-1/+13
| | | | | | | | | | `None` triggers cryptic failure The error message is now changed from "Do not support type class scala.Tuple2." to "Do not support type class org.json4s.JsonAST$JNull$" to be more informative about what is not supported. Also, StructType metadata now handles JNull correctly, i.e., {'a': None}. test_metadata_null is added to tests.py to show the fix works. Author: Jason Lee <cjlee@us.ibm.com> Closes #8969 from jasoncl/SPARK-10847.
* [SPARK-13023][PROJECT INFRA] Fix handling of root module in modules_to_test()Josh Rosen2016-01-271-5/+5
| | | | | | | | There's a minor bug in how we handle the `root` module in the `modules_to_test()` function in `dev/run-tests.py`: since `root` now depends on `build` (since every test needs to run on any build test), we now need to check for the presence of root in `modules_to_test` instead of `changed_modules`. Author: Josh Rosen <joshrosen@databricks.com> Closes #10933 from JoshRosen/build-module-fix.
* [SPARK-1680][DOCS] Explain environment variables for running on YARN in ↵Andrew2016-01-271-0/+2
| | | | | | | | | | cluster mode JIRA 1680 added a property called spark.yarn.appMasterEnv. This PR draws users' attention to this special case by adding an explanation in configuration.html#environment-variables Author: Andrew <weiner.andrew.j@gmail.com> Closes #10869 from weineran/branch-yarn-docs.
* [SPARK-12983][CORE][DOC] Correct metrics.properties.templateBenFradet2016-01-271-34/+37
| | | | | | | | There are some typos or plain unintelligible sentences in the metrics template. Author: BenFradet <benjamin.fradet@gmail.com> Closes #10902 from BenFradet/SPARK-12983.
* [SPARK-12780] Inconsistency returning value of ML python models' propertiesXusen Yin2016-01-261-2/+3
| | | | | | | | https://issues.apache.org/jira/browse/SPARK-12780 Author: Xusen Yin <yinxusen@gmail.com> Closes #10724 from yinxusen/SPARK-12780.
* [SPARK-12967][NETTY] Avoid NettyRpc error message during sparkContext shutdownNishkam Ravi2016-01-264-5/+32
| | | | | | | | | | | If there's an RPC issue while sparkContext is alive but stopped (which would happen only when executing SparkContext.stop), log a warning instead. This is a common occurrence. vanzin Author: Nishkam Ravi <nishkamravi@gmail.com> Author: nishkamravi2 <nishkamravi@gmail.com> Closes #10881 from nishkamravi2/master_netty.
* [SPARK-12728][SQL] Integrates SQL generation with native viewCheng Lian2016-01-266-95/+200
| | | | | | | | | | | | | | | | | | | | | | | This PR is a follow-up of PR #10541. It integrates the newly introduced SQL generation feature with native view to make native view canonical. In this PR, a new SQL option `spark.sql.nativeView.canonical` is added. When this option and `spark.sql.nativeView` are both `true`, Spark SQL tries to handle `CREATE VIEW` DDL statements using SQL query strings generated from view definition logical plans. If we failed to map the plan to SQL, we fallback to the original native view approach. One important issue this PR fixes is that, now we can use CTE when defining a view. Originally, when native view is turned on, we wrap the view definition text with an extra `SELECT`. However, HiveQL parser doesn't allow CTE appearing as a subquery. Namely, something like this is disallowed: ```sql SELECT n FROM ( WITH w AS (SELECT 1 AS n) SELECT * FROM w ) v ``` This PR fixes this issue because the extra `SELECT` is no longer needed (also, CTE expressions are inlined as subqueries during analysis phase, thus there won't be CTE expressions in the generated SQL query string). Author: Cheng Lian <lian@databricks.com> Author: Yin Huai <yhuai@databricks.com> Closes #10733 from liancheng/spark-12728.integrate-sql-gen-with-native-view.
* [SPARK-12935][SQL] DataFrame API for Count-Min SketchCheng Lian2016-01-267-37/+205
| | | | | | | | This PR integrates Count-Min Sketch from spark-sketch into DataFrame. This version resorts to `RDD.aggregate` for building the sketch. A more performant UDAF version can be built in future follow-up PRs. Author: Cheng Lian <lian@databricks.com> Closes #10911 from liancheng/cms-df-api.
* [SPARK-12903][SPARKR] Add covar_samp and covar_pop for SparkRYanbo Liang2016-01-265-2/+73
| | | | | | | | | | | Add ```covar_samp``` and ```covar_pop``` for SparkR. Should we also provide ```cov``` alias for ```covar_samp```? There is ```cov``` implementation at stats.R which masks ```stats::cov``` already, but may bring to breaking API change. cc sun-rui felixcheung shivaram Author: Yanbo Liang <ybliang8@gmail.com> Closes #10829 from yanboliang/spark-12903.
* [SPARK-7780][MLLIB] intercept in logisticregressionwith lbfgs should not be ↵Holden Karau2016-01-266-28/+179
| | | | | | | | | | | | | | | regularized The intercept in Logistic Regression represents a prior on categories which should not be regularized. In MLlib, the regularization is handled through Updater, and the Updater penalizes all the components without excluding the intercept which resulting poor training accuracy with regularization. The new implementation in ML framework handles this properly, and we should call the implementation in ML from MLlib since majority of users are still using MLlib api. Note that both of them are doing feature scalings to improve the convergence, and the only difference is ML version doesn't regularize the intercept. As a result, when lambda is zero, they will converge to the same solution. Previously partially reviewed at https://github.com/apache/spark/pull/6386#issuecomment-168781424 re-opening for dbtsai to review. Author: Holden Karau <holden@us.ibm.com> Author: Holden Karau <holden@pigscanfly.ca> Closes #10788 from holdenk/SPARK-7780-intercept-in-logisticregressionwithLBFGS-should-not-be-regularized.
* [SPARK-12854][SQL] Implement complex types support in ColumnarBatchNong Li2016-01-2616-90/+1671
| | | | | | | | | | | | | | | | | | | | | | | | | | This patch adds support for complex types for ColumnarBatch. ColumnarBatch supports structs and arrays. There is a simple mapping between the richer catalyst types to these two. Strings are treated as an array of bytes. ColumnarBatch will contain a column for each node of the schema. Non-complex schemas consists of just leaf nodes. Structs represent an internal node with one child for each field. Arrays are internal nodes with one child. Structs just contain nullability. Arrays contain offsets and lengths into the child array. This structure is able to handle arbitrary nesting. It has the key property that we maintain columnar throughout and that primitive types are only stored in the leaf nodes and contiguous across rows. For example, if the schema is ``` array<array<int>> ``` There are three columns in the schema. The internal nodes each have one children. The leaf node contains all the int data stored consecutively. As part of this, this patch adds append APIs in addition to the Put APIs (e.g. putLong(rowid, v) vs appendLong(v)). These APIs are necessary when the batch contains variable length elements. The vectors are not fixed length and will grow as necessary. This should make the usage a lot simpler for the writer. Author: Nong Li <nong@databricks.com> Closes #10820 from nongli/spark-12854.
* [SPARK-11622][MLLIB] Make LibSVMRelation extends HadoopFsRelation and…Jeff Zhang2016-01-263-16/+113
| | | | | | | | | | | | … Add LibSVMOutputWriter The behavior of LibSVMRelation is not changed except adding LibSVMOutputWriter * Partition is still not supported * Multiple input paths is not supported Author: Jeff Zhang <zjffdu@apache.org> Closes #9595 from zjffdu/SPARK-11622.
* [SPARK-12614][CORE] Don't throw non fatal exception from askShixiong Zhu2016-01-261-25/+29
| | | | | | | | Right now RpcEndpointRef.ask may throw exception in some corner cases, such as calling ask after stopping RpcEnv. It's better to avoid throwing exception from RpcEndpointRef.ask. We can send the exception to the future for `ask`. Author: Shixiong Zhu <shixiong@databricks.com> Closes #10568 from zsxwing/send-ask-fail.
* [SPARK-10509][PYSPARK] Reduce excessive param boiler plate codeHolden Karau2016-01-2612-317/+43
| | | | | | | | The current python ml params require cut-and-pasting the param setup and description between the class & ```__init__``` methods. Remove this possible case of errors & simplify use of custom params by adding a ```_copy_new_parent``` method to param so as to avoid cut and pasting (and cut and pasting at different indentation levels urgh). Author: Holden Karau <holden@us.ibm.com> Closes #10216 from holdenk/SPARK-10509-excessive-param-boiler-plate-code.
* [SPARK-12993][PYSPARK] Remove usage of ADD_FILES in pysparkJeff Zhang2016-01-261-10/+1
| | | | | | | | environment variable ADD_FILES is created for adding python files on spark context to be distributed to executors (SPARK-865), this is deprecated now. User are encouraged to use --py-files for adding python files. Author: Jeff Zhang <zjffdu@apache.org> Closes #10913 from zjffdu/SPARK-12993.
* [SQL] Minor Scaladoc format fixCheng Lian2016-01-261-4/+4
| | | | | | | | Otherwise the `^` character is always marked as error in IntelliJ since it represents an unclosed superscript markup tag. Author: Cheng Lian <lian@databricks.com> Closes #10926 from liancheng/agg-doc-fix.
* [SPARK-8725][PROJECT-INFRA] Test modules in topologically-sorted order in ↵Josh Rosen2016-01-264-18/+162
| | | | | | | | | | | | | | dev/run-tests This patch improves our `dev/run-tests` script to test modules in a topologically-sorted order based on modules' dependencies. This will help to ensure that bugs in upstream projects are not misattributed to downstream projects because those projects' tests were the first ones to exhibit the failure Topological sorting is also useful for shortening the feedback loop when testing pull requests: if I make a change in SQL then the SQL tests should run before MLlib, not after. In addition, this patch also updates our test module definitions to split `sql` into `catalyst`, `sql`, and `hive` in order to allow more tests to be skipped when changing only `hive/` files. Author: Josh Rosen <joshrosen@databricks.com> Closes #10885 from JoshRosen/SPARK-8725.
* [SPARK-12952] EMLDAOptimizer initialize() should return EMLDAOptimizer other ↵Xusen Yin2016-01-261-1/+3
| | | | | | | | | | than its parent class https://issues.apache.org/jira/browse/SPARK-12952 Author: Xusen Yin <yinxusen@gmail.com> Closes #10863 from yinxusen/SPARK-12952.
* [SPARK-11923][ML] Python API for ml.feature.ChiSqSelectorXusen Yin2016-01-261-1/+97
| | | | | | | | https://issues.apache.org/jira/browse/SPARK-11923 Author: Xusen Yin <yinxusen@gmail.com> Closes #10186 from yinxusen/SPARK-11923.
* [SPARK-7799][STREAMING][DOCUMENT] Add the linking and deploying instructions ↵Shixiong Zhu2016-01-261-37/+44
| | | | | | | | | | | | for streaming-akka project Since `actorStream` is an external project, we should add the linking and deploying instructions for it. A follow up PR of #10744 Author: Shixiong Zhu <shixiong@databricks.com> Closes #10856 from zsxwing/akka-link-instruction.
* [SPARK-12682][SQL] Add support for (optionally) not storing tables in hive ↵Sameer Agarwal2016-01-262-0/+39
| | | | | | | | | | metadata format This PR adds a new table option (`skip_hive_metadata`) that'd allow the user to skip storing the table metadata in hive metadata format. While this could be useful in general, the specific use-case for this change is that Hive doesn't handle wide schemas well (see https://issues.apache.org/jira/browse/SPARK-12682 and https://issues.apache.org/jira/browse/SPARK-6024) which in turn prevents such tables from being queried in SparkSQL. Author: Sameer Agarwal <sameer@databricks.com> Closes #10826 from sameeragarwal/skip-hive-metadata.
* [SPARK-10911] Executors should System.exit on clean shutdown.zhuol2016-01-261-0/+1
| | | | | | | | Call system.exit explicitly to make sure non-daemon user threads terminate. Without this, user applications might live forever if the cluster manager does not appropriately kill them. E.g., YARN had this bug: HADOOP-12441. Author: zhuol <zhuol@yahoo-inc.com> Closes #9946 from zhuoliu/10911.
* [SPARK-3369][CORE][STREAMING] Java mapPartitions Iterator->Iterable is ↵Sean Owen2016-01-2630-110/+146
| | | | | | | | | | | | inconsistent with Scala's Iterator->Iterator Fix Java function API methods for flatMap and mapPartitions to require producing only an Iterator, not Iterable. Also fix DStream.flatMap to require a function producing TraversableOnce only, not Traversable. CC rxin pwendell for API change; tdas since it also touches streaming. Author: Sean Owen <sowen@cloudera.com> Closes #10413 from srowen/SPARK-3369.
* [SPARK-12961][CORE] Prevent snappy-java memory leakLiang-Chi Hsieh2016-01-261-6/+14
| | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-12961 To prevent memory leak in snappy-java, just call the method once and cache the result. After the library releases new version, we can remove this object. JoshRosen Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #10875 from viirya/prevent-snappy-memory-leak.
* [SPARK-12937][SQL] bloom filter serializationWenchen Fan2016-01-266-44/+159
| | | | | | | | | | This PR adds serialization support for BloomFilter. A version number is added to version the serialized binary format. Author: Wenchen Fan <wenchen@databricks.com> Closes #10920 from cloud-fan/bloom-filter.
* [SQL][MINOR] A few minor tweaks to CSV reader.Reynold Xin2016-01-262-14/+9
| | | | | | | | This pull request simply fixes a few minor coding style issues in csv, as I was reviewing the change post-hoc. Author: Reynold Xin <rxin@databricks.com> Closes #10919 from rxin/csv-minor.
* [SPARK-10086][MLLIB][STREAMING][PYSPARK] ignore StreamingKMeans test in ↵Xiangrui Meng2016-01-251-0/+1
| | | | | | | | | | | | | | PySpark for now I saw several failures from recent PR builds, e.g., https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/50015/consoleFull. This PR marks the test as ignored and we will fix the flakyness in SPARK-10086. gliptak Do you know why the test failure didn't show up in the Jenkins "Test Result"? cc: jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #10909 from mengxr/SPARK-10086.
* [SPARK-12834] Change ser/de of JavaArray and JavaListXusen Yin2016-01-251-1/+5
| | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-12834 We use `SerDe.dumps()` to serialize `JavaArray` and `JavaList` in `PythonMLLibAPI`, then deserialize them with `PickleSerializer` in Python side. However, there is no need to transform them in such an inefficient way. Instead of it, we can use type conversion to convert them, e.g. `list(JavaArray)` or `list(JavaList)`. What's more, there is an issue to Ser/De Scala Array as I said in https://issues.apache.org/jira/browse/SPARK-12780 Author: Xusen Yin <yinxusen@gmail.com> Closes #10772 from yinxusen/SPARK-12834.
* [SPARK-11922][PYSPARK][ML] Python api for ml.feature.quantile discretizerHolden Karau2016-01-251-4/+85
| | | | | | | | | | | Add Python API for ml.feature.QuantileDiscretizer. One open question: Do we want to do this stuff to re-use the java model, create a new model, or use a different wrapper around the java model. cc brkyvz & mengxr Author: Holden Karau <holden@us.ibm.com> Closes #10085 from holdenk/SPARK-11937-SPARK-11922-Python-API-for-ml.feature.QuantileDiscretizer.
* [SPARK-12934] use try-with-resources for streamstedyu2016-01-251-0/+2
| | | | | | | | liancheng please take a look Author: tedyu <yuzhihong@gmail.com> Closes #10906 from tedyu/master.
* [SPARK-12936][SQL] Initial bloom filter implementationWenchen Fan2016-01-255-0/+602
| | | | | | | | | | | | | This PR adds an initial implementation of bloom filter in the newly added sketch module. The implementation is based on the [`BloomFilter` class in guava](https://code.google.com/p/guava-libraries/source/browse/guava/src/com/google/common/hash/BloomFilter.java). Some difference from the design doc: * expose `bitSize` instead of `sizeInBytes` to user. * always need the `expectedInsertions` parameter when create bloom filter. Author: Wenchen Fan <wenchen@databricks.com> Closes #10883 from cloud-fan/bloom-filter.
* [SPARK-12879] [SQL] improve the unsafe row writing frameworkWenchen Fan2016-01-257-78/+258
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | As we begin to use unsafe row writing framework(`BufferHolder` and `UnsafeRowWriter`) in more and more places(`UnsafeProjection`, `UnsafeRowParquetRecordReader`, `GenerateColumnAccessor`, etc.), we should add more doc to it and make it easier to use. This PR abstract the technique used in `UnsafeRowParquetRecordReader`: avoid unnecessary operatition as more as possible. For example, do not always point the row to the buffer at the end, we only need to update the size of row. If all fields are of primitive type, we can even save the row size updating. Then we can apply this technique to more places easily. a local benchmark shows `UnsafeProjection` is up to 1.7x faster after this PR: **old version** ``` Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate ------------------------------------------------------------------------------- single long 2616.04 102.61 1.00 X single nullable long 3032.54 88.52 0.86 X primitive types 9121.05 29.43 0.29 X nullable primitive types 12410.60 21.63 0.21 X ``` **new version** ``` Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate ------------------------------------------------------------------------------- single long 1533.34 175.07 1.00 X single nullable long 2306.73 116.37 0.66 X primitive types 8403.93 31.94 0.18 X nullable primitive types 12448.39 21.56 0.12 X ``` For single non-nullable long(the best case), we can have about 1.7x speed up. Even it's nullable, we can still have 1.3x speed up. For other cases, it's not such a boost as the saved operations only take a little proportion of the whole process. The benchmark code is included in this PR. Author: Wenchen Fan <wenchen@databricks.com> Closes #10809 from cloud-fan/unsafe-projection.
* [SPARK-12934][SQL] Count-min sketch serializationCheng Lian2016-01-254-19/+213
| | | | | | | | | | This PR adds serialization support for `CountMinSketch`. A version number is added to version the serialized binary format. Author: Cheng Lian <lian@databricks.com> Closes #10893 from liancheng/cms-serialization.
* [SPARK-12905][ML][PYSPARK] PCAModel return eigenvalues for PySparkYanbo Liang2016-01-252-0/+13
| | | | | | | | | | ```PCAModel``` can output ```explainedVariance``` at Python side. cc mengxr srowen Author: Yanbo Liang <ybliang8@gmail.com> Closes #10830 from yanboliang/spark-12905.
* [SPARK-12975][SQL] Throwing Exception when Bucketing Columns are part of ↵gatorsmile2016-01-253-3/+83
| | | | | | | | | | | | | | | | | | | | | | Partitioning Columns When users are using `partitionBy` and `bucketBy` at the same time, some bucketing columns might be part of partitioning columns. For example, ``` df.write .format(source) .partitionBy("i") .bucketBy(8, "i", "k") .saveAsTable("bucketed_table") ``` However, in the above case, adding column `i` into `bucketBy` is useless. It is just wasting extra CPU when reading or writing bucket tables. Thus, like Hive, we can issue an exception and let users do the change. Also added a test case for checking if the information of `sortBy` and `bucketBy` columns are correctly saved in the metastore table. Could you check if my understanding is correct? cloud-fan rxin marmbrus Thanks! Author: gatorsmile <gatorsmile@gmail.com> Closes #10891 from gatorsmile/commonKeysInPartitionByBucketBy.
* [SPARK-12901][SQL][HOT-FIX] Fix scala 2.11 compilation.Yin Huai2016-01-252-2/+2
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* [SPARK-12902] [SQL] visualization for generated operatorsDavies Liu2016-01-259-32/+104
| | | | | | | | | | | | | | This PR brings back visualization for generated operators, they looks like: ![sql](https://cloud.githubusercontent.com/assets/40902/12460920/0dc7956a-bf6b-11e5-9c3f-8389f452526e.png) ![stage](https://cloud.githubusercontent.com/assets/40902/12460923/11806ac4-bf6b-11e5-9c72-e84a62c5ea93.png) Note: SQL metrics are not supported right now, because they are very slow, will be supported once we have batch mode. Author: Davies Liu <davies@databricks.com> Closes #10828 from davies/viz_codegen.
* [SPARK-12149][WEB UI] Executor UI improvement suggestions - Color UIAlex Bozarth2016-01-257-20/+103
| | | | | | | | | | | | | Added color coding to the Executors page for Active Tasks, Failed Tasks, Completed Tasks and Task Time. Active Tasks is shaded blue with it's range based on percentage of total cores used. Failed Tasks is shaded red ranging over the first 10% of total tasks failed Completed Tasks is shaded green ranging over 10% of total tasks including failed and active tasks, but only when there are active or failed tasks on that executor. Task Time is shaded red when GC Time goes over 10% of total time with it's range directly corresponding to the percent of total time. Author: Alex Bozarth <ajbozart@us.ibm.com> Closes #10154 from ajbozarth/spark12149.
* Closes #10879Xiangrui Meng2016-01-250-0/+0
| | | | | | | | | Closes #9046 Closes #8532 Closes #10756 Closes #8960 Closes #10485 Closes #10467
* [SPARK-11965][ML][DOC] Update user guide for RFormula feature interactionsYanbo Liang2016-01-252-1/+40
| | | | | | | | Update user guide for RFormula feature interactions. Meanwhile we also update other new features such as supporting string label in Spark 1.6. Author: Yanbo Liang <ybliang8@gmail.com> Closes #10222 from yanboliang/spark-11965.
* [SPARK-12755][CORE] Stop the event logger before the DAG schedulerMichael Allman2016-01-251-6/+6
| | | | | | | | | | [SPARK-12755][CORE] Stop the event logger before the DAG scheduler to avoid a race condition where the standalone master attempts to build the app's history UI before the event log is stopped. This contribution is my original work, and I license this work to the Spark project under the project's open source license. Author: Michael Allman <michael@videoamp.com> Closes #10700 from mallman/stop_event_logger_first.
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