{"push_id":1317494936,"size":855,"distinct_size":45,"ref":"refs/heads/SPARK-15962","head":"2ef6e3bdbd16c7f7b9ff006d48382e108ed37eef","before":"f19020f93180a08952429243e6ef48c1b2f694fb","commits":[{"sha":"26afb4ce4099e7942f8db1ead3817ed8fbf71ce3","author":{"name":"Narine Kokhlikyan","email":"320c8f16ec3d7c7efbde3043fd8d8758f3d71a34@slice.com"},"message":"[SPARK-16012][SPARKR] Implement gapplyCollect which will apply a R function on each group similar to gapply and collect the result back to R data.frame\n\n## What changes were proposed in this pull request?\ngapplyCollect() does gapply() on a SparkDataFrame and collect the result back to R. Compared to gapply() + collect(), gapplyCollect() offers performance optimization as well as programming convenience, as no schema is needed to be provided.\n\nThis is similar to dapplyCollect().\n\n## How was this patch tested?\nAdded test cases for gapplyCollect similar to dapplyCollect\n\nAuthor: Narine Kokhlikyan <narine@slice.com>\n\nCloses #13760 from NarineK/gapplyCollect.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/26afb4ce4099e7942f8db1ead3817ed8fbf71ce3"},{"sha":"e4fa58c43ce2bf8d76bffb0d9dc1132f8d0eae6a","author":{"name":"Sun Rui","email":"6f09da93fe840a44f8b7d461e5cd331e1e929387@gmail.com"},"message":"[SPARK-16299][SPARKR] Capture errors from R workers in daemon.R to avoid deletion of R session temporary directory.\n\n## What changes were proposed in this pull request?\nCapture errors from R workers in daemon.R to avoid deletion of R session temporary directory. See detailed description at https://issues.apache.org/jira/browse/SPARK-16299\n\n## How was this patch tested?\nSparkR unit tests.\n\nAuthor: Sun Rui <sunrui2016@gmail.com>\n\nCloses #13975 from sun-rui/SPARK-16299.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/e4fa58c43ce2bf8d76bffb0d9dc1132f8d0eae6a"},{"sha":"d601894c0494d415e7f330e02168c43a2dacfb02","author":{"name":"Reynold Xin","email":"9a371236ace08d82aa86864cade97762c12d4503@databricks.com"},"message":"[SPARK-16335][SQL] Structured streaming should fail if source directory does not exist\n\n## What changes were proposed in this pull request?\nIn structured streaming, Spark does not report errors when the specified directory does not exist. This is a behavior different from the batch mode. This patch changes the behavior to fail if the directory does not exist (when the path is not a glob pattern).\n\n## How was this patch tested?\nUpdated unit tests to reflect the new behavior.\n\nAuthor: Reynold Xin <rxin@databricks.com>\n\nCloses #14002 from rxin/SPARK-16335.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/d601894c0494d415e7f330e02168c43a2dacfb02"},{"sha":"d17e5f2f123eecd5a7a1d87f5ce75a0fc44552b4","author":{"name":"Dongjoon Hyun","email":"1bf3a3c57610939bd1558dffb564906c959abecb@apache.org"},"message":"[SPARK-16233][R][TEST] ORC test should be enabled only when HiveContext is available.\n\n## What changes were proposed in this pull request?\n\nORC test should be enabled only when HiveContext is available.\n\n## How was this patch tested?\n\nManual.\n```\n$ R/run-tests.sh\n...\n1. create DataFrame from RDD (test_sparkSQL.R#200) - Hive is not build with SparkSQL, skipped\n\n2. test HiveContext (test_sparkSQL.R#1021) - Hive is not build with SparkSQL, skipped\n\n3. read/write ORC files (test_sparkSQL.R#1728) - Hive is not build with SparkSQL, skipped\n\n4. enableHiveSupport on SparkSession (test_sparkSQL.R#2448) - Hive is not build with SparkSQL, skipped\n\n5. sparkJars tag in SparkContext (test_Windows.R#21) - This test is only for Windows, skipped\n\nDONE ===========================================================================\nTests passed.\n```\n\nAuthor: Dongjoon Hyun <dongjoon@apache.org>\n\nCloses #14019 from dongjoon-hyun/SPARK-16233.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/d17e5f2f123eecd5a7a1d87f5ce75a0fc44552b4"},{"sha":"bad0f7dbba2eda149ee4fc5810674d971d17874a","author":{"name":"peng.zhang","email":"4a87c4e0a9a4a2759bb9bbc540b0b30a0b026a09@xiaomi.com"},"message":"[SPARK-16095][YARN] Yarn cluster mode should report correct state to SparkLauncher\n\n## What changes were proposed in this pull request?\nYarn cluster mode should return correct state for SparkLauncher\n\n## How was this patch tested?\nunit test\n\nAuthor: peng.zhang <peng.zhang@xiaomi.com>\n\nCloses #13962 from renozhang/SPARK-16095-spark-launcher-wrong-state.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/bad0f7dbba2eda149ee4fc5810674d971d17874a"},{"sha":"192d1f9cf3463d050b87422939448f2acf86acc9","author":{"name":"WeichenXu","email":"7a440c883b23bc43b7a6fdb423cbfd8e75175a83@outlook.com"},"message":"[GRAPHX][EXAMPLES] move graphx test data directory and update graphx document\n\n## What changes were proposed in this pull request?\n\nThere are two test data files used for graphx examples existing in directory \"graphx/data\"\nI move it into \"data/\" directory because the \"graphx\" directory is used for code files and other test data files (such as mllib, streaming test data) are all in there.\n\nI also update the graphx document where reference the data files which I move place.\n\n## How was this patch tested?\n\nN/A\n\nAuthor: WeichenXu <WeichenXu123@outlook.com>\n\nCloses #14010 from WeichenXu123/move_graphx_data_dir.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/192d1f9cf3463d050b87422939448f2acf86acc9"},{"sha":"0bd7cd18bc4d535b0c4499913f6747b3f6315ac2","author":{"name":"WeichenXu","email":"7a440c883b23bc43b7a6fdb423cbfd8e75175a83@outlook.com"},"message":"[SPARK-16345][DOCUMENTATION][EXAMPLES][GRAPHX] Extract graphx programming guide example snippets from source files instead of hard code them\n\n## What changes were proposed in this pull request?\n\nI extract 6 example programs from GraphX programming guide and replace them with\n`include_example` label.\n\nThe 6 example programs are:\n- AggregateMessagesExample.scala\n- SSSPExample.scala\n- TriangleCountingExample.scala\n- ConnectedComponentsExample.scala\n- ComprehensiveExample.scala\n- PageRankExample.scala\n\nAll the example code can run using\n`bin/run-example graphx.EXAMPLE_NAME`\n\n## How was this patch tested?\n\nManual.\n\nAuthor: WeichenXu <WeichenXu123@outlook.com>\n\nCloses #14015 from WeichenXu123/graphx_example_plugin.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/0bd7cd18bc4d535b0c4499913f6747b3f6315ac2"},{"sha":"3000b4b29f9165f436f186a8c1ba818e24f90615","author":{"name":"Dongjoon Hyun","email":"1bf3a3c57610939bd1558dffb564906c959abecb@apache.org"},"message":"[MINOR][BUILD] Fix Java linter errors\n\n## What changes were proposed in this pull request?\n\nThis PR fixes the minor Java linter errors like the following.\n```\n-    public int read(char cbuf[], int off, int len) throws IOException {\n+    public int read(char[] cbuf, int off, int len) throws IOException {\n```\n\n## How was this patch tested?\n\nManual.\n```\n$ build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install\n$ dev/lint-java\nUsing `mvn` from path: /usr/local/bin/mvn\nCheckstyle checks passed.\n```\n\nAuthor: Dongjoon Hyun <dongjoon@apache.org>\n\nCloses #14017 from dongjoon-hyun/minor_build_java_linter_error.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/3000b4b29f9165f436f186a8c1ba818e24f90615"},{"sha":"ea990f96930066c36055734d4f17eaf8e496eb3f","author":{"name":"gatorsmile","email":"ea6589371ad7a107e4268706d7d8403e3ba585bb@gmail.com"},"message":"[SPARK-16329][SQL] Star Expansion over Table Containing No Column\n\n#### What changes were proposed in this pull request?\nStar expansion over a table containing zero column does not work since 1.6. However, it works in Spark 1.5.1. This PR is to fix the issue in the master branch.\n\nFor example,\n```scala\nval rddNoCols = sqlContext.sparkContext.parallelize(1 to 10).map(_ => Row.empty)\nval dfNoCols = sqlContext.createDataFrame(rddNoCols, StructType(Seq.empty))\ndfNoCols.registerTempTable(\"temp_table_no_cols\")\nsqlContext.sql(\"select * from temp_table_no_cols\").show\n```\n\nWithout the fix, users will get the following the exception:\n```\njava.lang.IllegalArgumentException: requirement failed\n        at scala.Predef$.require(Predef.scala:221)\n        at org.apache.spark.sql.catalyst.analysis.UnresolvedStar.expand(unresolved.scala:199)\n```\n\n#### How was this patch tested?\nTests are added\n\nAuthor: gatorsmile <gatorsmile@gmail.com>\n\nCloses #14007 from gatorsmile/starExpansionTableWithZeroColumn.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/ea990f96930066c36055734d4f17eaf8e496eb3f"},{"sha":"54b27c1797fcd32b3f3e9d44e1a149ae396a61e6","author":{"name":"Dongjoon Hyun","email":"1bf3a3c57610939bd1558dffb564906c959abecb@apache.org"},"message":"[SPARK-16278][SPARK-16279][SQL] Implement map_keys/map_values SQL functions\n\n## What changes were proposed in this pull request?\n\nThis PR adds `map_keys` and `map_values` SQL functions in order to remove Hive fallback.\n\n## How was this patch tested?\n\nPass the Jenkins tests including new testcases.\n\nAuthor: Dongjoon Hyun <dongjoon@apache.org>\n\nCloses #13967 from dongjoon-hyun/SPARK-16278.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/54b27c1797fcd32b3f3e9d44e1a149ae396a61e6"},{"sha":"88134e736829f5f93a82879c08cb191f175ff8af","author":{"name":"Dongjoon Hyun","email":"1bf3a3c57610939bd1558dffb564906c959abecb@apache.org"},"message":"[SPARK-16288][SQL] Implement inline table generating function\n\n## What changes were proposed in this pull request?\n\nThis PR implements `inline` table generating function.\n\n## How was this patch tested?\n\nPass the Jenkins tests with new testcase.\n\nAuthor: Dongjoon Hyun <dongjoon@apache.org>\n\nCloses #13976 from dongjoon-hyun/SPARK-16288.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/88134e736829f5f93a82879c08cb191f175ff8af"},{"sha":"8cdb81fa8264085b1bc04638b649b681ae871843","author":{"name":"Koert Kuipers","email":"30c5d590de963572ca7afacba7113ee6d21838f7@tresata.com"},"message":"[SPARK-15204][SQL] improve nullability inference for Aggregator\n\n## What changes were proposed in this pull request?\n\nTypedAggregateExpression sets nullable based on the schema of the outputEncoder\n\n## How was this patch tested?\n\nAdd test in DatasetAggregatorSuite\n\nAuthor: Koert Kuipers <koert@tresata.com>\n\nCloses #13532 from koertkuipers/feat-aggregator-nullable.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/8cdb81fa8264085b1bc04638b649b681ae871843"},{"sha":"26283339786f38c50722a7488d0bca8573b9c352","author":{"name":"gatorsmile","email":"ea6589371ad7a107e4268706d7d8403e3ba585bb@gmail.com"},"message":"[SPARK-16358][SQL] Remove InsertIntoHiveTable From Logical Plan\n\n#### What changes were proposed in this pull request?\nLogicalPlan `InsertIntoHiveTable` is useless. Thus, we can remove it from the code base.\n\n#### How was this patch tested?\nThe existing test cases\n\nAuthor: gatorsmile <gatorsmile@gmail.com>\n\nCloses #14037 from gatorsmile/InsertIntoHiveTable.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/26283339786f38c50722a7488d0bca8573b9c352"},{"sha":"a539b724c1d407083cb87abfa06d8bf213501057","author":{"name":"wm624@hotmail.com","email":"b6c6222622ee4c4512cfd0b24fc74f0642b6e399@hotmail.com"},"message":"[SPARK-16260][ML][EXAMPLE] PySpark ML Example Improvements and Cleanup\n\n## What changes were proposed in this pull request?\n1). Remove unused import in Scala example;\n\n2). Move spark session import outside example off;\n\n3). Change parameter setting the same as Scala;\n\n4). Change comment to be consistent;\n\n5). Make sure that Scala and python using the same data set;\n\nI did one pass and fixed the above issues. There are missing examples in python, which might be added later.\n\nTODO: For some examples, there are comments on how to run examples; But there are many missing. We can add them later.\n\n## How was this patch tested?\n\n(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)\n\nManually test them\n\nAuthor: wm624@hotmail.com <wm624@hotmail.com>\n\nCloses #14021 from wangmiao1981/ann.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/a539b724c1d407083cb87abfa06d8bf213501057"},{"sha":"18fb57f58a04685823408f3a174a8722f155fd4d","author":{"name":"Sean Owen","email":"4c59f357315334980589bde1049040bf6de6532c@cloudera.com"},"message":"[MINOR][DOCS] Remove unused images; crush PNGs that could use it for good measure\n\n## What changes were proposed in this pull request?\n\nCoincidentally, I discovered that a couple images were unused in `docs/`, and then searched and found more, and then realized some PNGs were pretty big and could be crushed, and before I knew it, had done the same for the ASF site (not committed yet).\n\nNo functional change at all, just less superfluous image data.\n\n## How was this patch tested?\n\n`jekyll serve`\n\nAuthor: Sean Owen <sowen@cloudera.com>\n\nCloses #14029 from srowen/RemoveCompressImages.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/18fb57f58a04685823408f3a174a8722f155fd4d"},{"sha":"7dbffcdd6dc76b8e8d6a9cd6eeb24323a6b740c3","author":{"name":"Michael Allman","email":"844aface8de49b32585c3c744b570659f1739c56@videoamp.com"},"message":"[SPARK-16353][BUILD][DOC] Missing javadoc options for java unidoc\n\nLink to Jira issue: https://issues.apache.org/jira/browse/SPARK-16353\n\n## What changes were proposed in this pull request?\n\nThe javadoc options for the java unidoc generation are ignored when generating the java unidoc. For example, the generated `index.html` has the wrong HTML page title. This can be seen at http://spark.apache.org/docs/latest/api/java/index.html.\n\nI changed the relevant setting scope from `doc` to `(JavaUnidoc, unidoc)`.\n\n## How was this patch tested?\n\nI ran `docs/jekyll build` and verified that the java unidoc `index.html` has the correct HTML page title.\n\nAuthor: Michael Allman <michael@videoamp.com>\n\nCloses #14031 from mallman/spark-16353.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/7dbffcdd6dc76b8e8d6a9cd6eeb24323a6b740c3"},{"sha":"8f6cf00c697689174bcf522091e396b4fa5ef66d","author":{"name":"Michael Allman","email":"844aface8de49b32585c3c744b570659f1739c56@videoamp.com"},"message":"[SPARK-15968][SQL] Nonempty partitioned metastore tables are not cached\n\n(Please note this is a revision of PR #13686, which has been closed in favor of this PR.)\n\nThis PR addresses [SPARK-15968](https://issues.apache.org/jira/browse/SPARK-15968).\n\n## What changes were proposed in this pull request?\n\nThe `getCached` method of [HiveMetastoreCatalog](https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala) computes `pathsInMetastore` from the metastore relation's catalog table. This only returns the table base path, which is incomplete/inaccurate for a nonempty partitioned table. As a result, cached lookups on nonempty partitioned tables always miss.\n\nRather than get `pathsInMetastore` from\n\n    metastoreRelation.catalogTable.storage.locationUri.toSeq\n\nI modified the `getCached` method to take a `pathsInMetastore` argument. Calls to this method pass in the paths computed from calls to the Hive metastore. This is how `getCached` was implemented in Spark 1.5:\n\nhttps://github.com/apache/spark/blob/e0c3212a9b42e3e704b070da4ac25b68c584427f/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala#L444.\n\nI also added a call in `InsertIntoHiveTable.scala` to invalidate the table from the SQL session catalog.\n\n## How was this patch tested?\n\nI've added a new unit test to `parquetSuites.scala`:\n\n    SPARK-15968: nonempty partitioned metastore Parquet table lookup should use cached relation\n\nNote that the only difference between this new test and the one above it in the file is that the new test populates its partitioned table with a single value, while the existing test leaves the table empty. This reveals a subtle, unexpected hole in test coverage present before this patch.\n\nNote I also modified a different but related unit test in `parquetSuites.scala`:\n\n    SPARK-15248: explicitly added partitions should be readable\n\nThis unit test asserts that Spark SQL should return data from a table partition which has been placed there outside a metastore query immediately after it is added. I changed the test so that, instead of adding the data as a parquet file saved in the partition's location, the data is added through a SQL `INSERT` query. I made this change because I could find no way to efficiently support partitioned table caching without failing that test.\n\nIn addition to my primary motivation, I can offer a few reasons I believe this is an acceptable weakening of that test. First, it still validates a fix for [SPARK-15248](https://issues.apache.org/jira/browse/SPARK-15248), the issue for which it was written. Second, the assertion made is stronger than that required for non-partitioned tables. If you write data to the storage location of a non-partitioned metastore table without using a proper SQL DML query, a subsequent call to show that data will not return it. I believe this is an intentional limitation put in place to make table caching feasible, but I'm only speculating.\n\nBuilding a large `HadoopFsRelation` requires `stat`-ing all of its data files. In our environment, where we have tables with 10's of thousands of partitions, the difference between using a cached relation versus a new one is a matter of seconds versus minutes. Caching partitioned table metadata vastly improves the usability of Spark SQL for these cases.\n\nThanks.\n\nAuthor: Michael Allman <michael@videoamp.com>\n\nCloses #13818 from mallman/spark-15968.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/8f6cf00c697689174bcf522091e396b4fa5ef66d"},{"sha":"7742d9f1584150befeb2f3d76cdbd4ea1f37c914","author":{"name":"hyukjinkwon","email":"4c114bf3676ebcdb1616a11ca478d9181f4025da@gmail.com"},"message":"[SPARK-15198][SQL] Support for pushing down filters for boolean types in ORC data source\n\n## What changes were proposed in this pull request?\n\nIt seems ORC supports all the types in  ([`PredicateLeaf.Type`](https://github.com/apache/hive/blob/e085b7e9bd059d91aaf013df0db4d71dca90ec6f/storage-api/src/java/org/apache/hadoop/hive/ql/io/sarg/PredicateLeaf.java#L50-L56)) which includes boolean types. So, this was tested first.\n\nThis PR adds the support for pushing filters down for `BooleanType` in ORC data source.\n\nThis PR also removes `OrcTableScan` class and the companion object, which is not used anymore.\n\n## How was this patch tested?\n\nUnittest in `OrcFilterSuite` and `OrcQuerySuite`.\n\nAuthor: hyukjinkwon <gurwls223@gmail.com>\n\nCloses #12972 from HyukjinKwon/SPARK-15198.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/7742d9f1584150befeb2f3d76cdbd4ea1f37c914"},{"sha":"7f7eb3934ea258f2b163a87da06766bf5c7d443d","author":{"name":"Dongjoon Hyun","email":"1bf3a3c57610939bd1558dffb564906c959abecb@apache.org"},"message":"[SPARK-16360][SQL] Speed up SQL query performance by removing redundant `executePlan` call\n\n## What changes were proposed in this pull request?\n\nCurrently, there are a few reports about Spark 2.0 query performance regression for large queries.\n\nThis PR speeds up SQL query processing performance by removing redundant **consecutive `executePlan`** call in `Dataset.ofRows` function and `Dataset` instantiation. Specifically, this PR aims to reduce the overhead of SQL query execution plan generation, not real query execution. So, we can not see the result in the Spark Web UI. Please use the following query script. The result is **25.78 sec** -> **12.36 sec** as expected.\n\n**Sample Query**\n```scala\nval n = 4000\nval values = (1 to n).map(_.toString).mkString(\", \")\nval columns = (1 to n).map(\"column\" + _).mkString(\", \")\nval query =\n  s\"\"\"\n     |SELECT $columns\n     |FROM VALUES ($values) T($columns)\n     |WHERE 1=2 AND 1 IN ($columns)\n     |GROUP BY $columns\n     |ORDER BY $columns\n     |\"\"\".stripMargin\n\ndef time[R](block: => R): R = {\n  val t0 = System.nanoTime()\n  val result = block\n  println(\"Elapsed time: \" + ((System.nanoTime - t0) / 1e9) + \"s\")\n  result\n}\n```\n\n**Before**\n```scala\nscala> time(sql(query))\nElapsed time: 30.138142577s  // First query has a little overhead of initialization.\nres0: org.apache.spark.sql.DataFrame = [column1: int, column2: int ... 3998 more fields]\nscala> time(sql(query))\nElapsed time: 25.787751452s  // Let's compare this one.\nres1: org.apache.spark.sql.DataFrame = [column1: int, column2: int ... 3998 more fields]\n```\n\n**After**\n```scala\nscala> time(sql(query))\nElapsed time: 17.500279659s  // First query has a little overhead of initialization.\nres0: org.apache.spark.sql.DataFrame = [column1: int, column2: int ... 3998 more fields]\nscala> time(sql(query))\nElapsed time: 12.364812255s  // This shows the real difference. The speed up is about 2 times.\nres1: org.apache.spark.sql.DataFrame = [column1: int, column2: int ... 3998 more fields]\n```\n\n## How was this patch tested?\n\nManual by the above script.\n\nAuthor: Dongjoon Hyun <dongjoon@apache.org>\n\nCloses #14044 from dongjoon-hyun/SPARK-16360.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/7f7eb3934ea258f2b163a87da06766bf5c7d443d"},{"sha":"07d9c5327f050f9da611d5239f61ed73b36ce4e6","author":{"name":"hyukjinkwon","email":"4c114bf3676ebcdb1616a11ca478d9181f4025da@gmail.com"},"message":"[SPARK-9876][SQL][FOLLOWUP] Enable string and binary tests for Parquet predicate pushdown and replace deprecated fromByteArray.\n\n## What changes were proposed in this pull request?\n\nIt seems Parquet has been upgraded to 1.8.1 by https://github.com/apache/spark/pull/13280. So,  this PR enables string and binary predicate push down which was disabled due to [SPARK-11153](https://issues.apache.org/jira/browse/SPARK-11153) and [PARQUET-251](https://issues.apache.org/jira/browse/PARQUET-251) and cleans up some comments unremoved (I think by mistake).\n\nThis PR also replace the API, `fromByteArray()` deprecated in [PARQUET-251](https://issues.apache.org/jira/browse/PARQUET-251).\n\n## How was this patch tested?\n\nUnit tests in `ParquetFilters`\n\nAuthor: hyukjinkwon <gurwls223@gmail.com>\n\nCloses #13389 from HyukjinKwon/parquet-1.8-followup.","distinct":false,"url":"https://api.github.com/repos/kiszk/spark/commits/07d9c5327f050f9da611d5239f61ed73b36ce4e6"}]}
{"actor":{"display_login":"kiszk"}}