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In HiveTableScan.scala, ObjectInspector was created for all of the partition based records, which probably causes ClassCastException if the object inspector is not identical among table & partitions. This is the follow up with: #1408 #1390 I've run a micro benchmark in my local with 15000000 records totally, and got the result as below: With This Patch | Partition-Based Table | Non-Partition-Based Table ------------ | ------------- | ------------- No | 1927 ms | 1885 ms Yes | 1541 ms | 1524 ms It showed this patch will also improve the performance. PS: the benchmark code is also attached. (thanks liancheng ) ``` package org.apache.spark.sql.hive import org.apache.spark.SparkContext import org.apache.spark.SparkConf import org.apache.spark.sql._ object HiveTableScanPrepare extends App { case class Record(key: String, value: String) val sparkContext = new SparkContext( new SparkConf() .setMaster("local") .setAppName(getClass.getSimpleName.stripSuffix("$"))) val hiveContext = new LocalHiveContext(sparkContext) val rdd = sparkContext.parallelize((1 to 3000000).map(i => Record(s"$i", s"val_$i"))) import hiveContext._ hql("SHOW TABLES") hql("DROP TABLE if exists part_scan_test") hql("DROP TABLE if exists scan_test") hql("DROP TABLE if exists records") rdd.registerAsTable("records") hql("""CREATE TABLE part_scan_test (key STRING, value STRING) PARTITIONED BY (part1 string, part2 STRING) | ROW FORMAT SERDE | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' | STORED AS RCFILE """.stripMargin) hql("""CREATE TABLE scan_test (key STRING, value STRING) | ROW FORMAT SERDE | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' | STORED AS RCFILE """.stripMargin) for (part1 <- 2000 until 2001) { for (part2 <- 1 to 5) { hql(s"""from records | insert into table part_scan_test PARTITION (part1='$part1', part2='2010-01-$part2') | select key, value """.stripMargin) hql(s"""from records | insert into table scan_test select key, value """.stripMargin) } } } object HiveTableScanTest extends App { val sparkContext = new SparkContext( new SparkConf() .setMaster("local") .setAppName(getClass.getSimpleName.stripSuffix("$"))) val hiveContext = new LocalHiveContext(sparkContext) import hiveContext._ hql("SHOW TABLES") val part_scan_test = hql("select key, value from part_scan_test") val scan_test = hql("select key, value from scan_test") val r_part_scan_test = (0 to 5).map(i => benchmark(part_scan_test)) val r_scan_test = (0 to 5).map(i => benchmark(scan_test)) println("Scanning Partition-Based Table") r_part_scan_test.foreach(printResult) println("Scanning Non-Partition-Based Table") r_scan_test.foreach(printResult) def printResult(result: (Long, Long)) { println(s"Duration: ${result._1} ms Result: ${result._2}") } def benchmark(srdd: SchemaRDD) = { val begin = System.currentTimeMillis() val result = srdd.count() val end = System.currentTimeMillis() ((end - begin), result) } } ``` Author: Cheng Hao <hao.cheng@intel.com> Closes #1439 from chenghao-intel/hadoop_table_scan and squashes the following commits: 888968f [Cheng Hao] Fix issues in code style 27540ba [Cheng Hao] Fix the TableScan Bug while partition serde differs 40a24a7 [Cheng Hao] Add Unit Test
…rror in UnitTests
Floating point math is not exact, and most floating-point numbers end up being slightly imprecise due to rounding errors.
Simple values like 0.1 cannot be precisely represented using binary floating point numbers, and the limited precision of floating point numbers means that slight changes in the order of operations or the precision of intermediates can change the result.
That means that comparing two floats to see if they are equal is usually not what we want. As long as this imprecision stays small, it can usually be ignored.
Based on discussion in the community, we have implemented two different APIs for relative tolerance, and absolute tolerance. It makes sense that test writers should know which one they need depending on their circumstances.
Developers also need to explicitly specify the eps, and there is no default value which will sometimes cause confusion.
When comparing against zero using relative tolerance, a exception will be raised to warn users that it's meaningless.
For relative tolerance, users can now write
assert(23.1 ~== 23.52 relTol 0.02)
assert(23.1 ~== 22.74 relTol 0.02)
assert(23.1 ~= 23.52 relTol 0.02)
assert(23.1 ~= 22.74 relTol 0.02)
assert(!(23.1 !~= 23.52 relTol 0.02))
assert(!(23.1 !~= 22.74 relTol 0.02))
// This will throw exception with the following message.
// "Did not expect 23.1 and 23.52 to be within 0.02 using relative tolerance."
assert(23.1 !~== 23.52 relTol 0.02)
// "Expected 23.1 and 22.34 to be within 0.02 using relative tolerance."
assert(23.1 ~== 22.34 relTol 0.02)
For absolute error,
assert(17.8 ~== 17.99 absTol 0.2)
assert(17.8 ~== 17.61 absTol 0.2)
assert(17.8 ~= 17.99 absTol 0.2)
assert(17.8 ~= 17.61 absTol 0.2)
assert(!(17.8 !~= 17.99 absTol 0.2))
assert(!(17.8 !~= 17.61 absTol 0.2))
// This will throw exception with the following message.
// "Did not expect 17.8 and 17.99 to be within 0.2 using absolute error."
assert(17.8 !~== 17.99 absTol 0.2)
// "Expected 17.8 and 17.59 to be within 0.2 using absolute error."
assert(17.8 ~== 17.59 absTol 0.2)
Authors:
DB Tsai <dbtsaialpinenow.com>
Marek Kolodziej <marekalpinenow.com>
Author: DB Tsai <dbtsai@alpinenow.com>
Closes #1425 from dbtsai/SPARK-2479_comparing_floating_point and squashes the following commits:
8c7cbcc [DB Tsai] Alpine Data Labs
… fix) JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410) Another try for #1399 & #1600. Those two PR breaks Jenkins builds because we made a separate profile `hive-thriftserver` in sub-project `assembly`, but the `hive-thriftserver` module is defined outside the `hive-thriftserver` profile. Thus every time a pull request that doesn't touch SQL code will also execute test suites defined in `hive-thriftserver`, but tests fail because related .class files are not included in the assembly jar. In the most recent commit, module `hive-thriftserver` is moved into its own profile to fix this problem. All previous commits are squashed for clarity. Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #1620 from liancheng/jdbc-with-maven-fix and squashes the following commits: 629988e [Cheng Lian] Moved hive-thriftserver module definition into its own profile ec3c7a7 [Cheng Lian] Cherry picked the Hive Thrift server
During rdd.take(n), JVM will close the socket if it had got enough data, the Python worker should keep silent in this case. In the same time, the worker should not print the trackback into stderr if it send the traceback to JVM successfully. Author: Davies Liu <davies.liu@gmail.com> Closes #1625 from davies/error and squashes the following commits: 4fbcc6d [Davies Liu] disable log4j during testing when exception is expected. cc14202 [Davies Liu] keep silent in worker if JVM close the socket
The pull request includes two changes: 1. Removes SortOrder introduced by SPARK-2125. The key ordering already includes the SortOrder information since an Ordering can be reverse. This is similar to Java's Comparator interface. Rarely does an API accept both a Comparator as well as a SortOrder. 2. Replaces the sortWith call in HashShuffleReader with an in-place quick sort. Author: Reynold Xin <rxin@apache.org> Closes #1631 from rxin/sortOrder and squashes the following commits: c9d37e1 [Reynold Xin] [SPARK-2726] and [SPARK-2727] Remove SortOrder and do in-place sort.
In `reduce` and `aggregate`, the driver node spends linear time on the number of partitions. It becomes a bottleneck when there are many partitions and the data from each partition is big. SPARK-1485 (#506) tracks the progress of implementing AllReduce on Spark. I did several implementations including butterfly, reduce + broadcast, and treeReduce + broadcast. treeReduce + BT broadcast seems to be right way to go for Spark. Using binary tree may introduce some overhead in communication, because the driver still need to coordinate on data shuffling. In my experiments, n -> sqrt(n) -> 1 gives the best performance in general, which is why I set "depth = 2" in MLlib algorithms. But it certainly needs more testing. I left `treeReduce` and `treeAggregate` public for easy testing. Some numbers from a test on 32-node m3.2xlarge cluster. code: ~~~ import breeze.linalg._ import org.apache.log4j._ Logger.getRootLogger.setLevel(Level.OFF) for (n <- Seq(1, 10, 100, 1000, 10000, 100000, 1000000)) { val vv = sc.parallelize(0 until 1024, 1024).map(i => DenseVector.zeros[Double](n)) var start = System.nanoTime(); vv.treeReduce(_ + _, 2); println((System.nanoTime() - start) / 1e9) start = System.nanoTime(); vv.reduce(_ + _); println((System.nanoTime() - start) / 1e9) } ~~~ out: | n | treeReduce(,2) | reduce | |---|---------------------|-----------| | 10 | 0.215538731 | 0.204206899 | | 100 | 0.278405907 | 0.205732582 | | 1000 | 0.208972182 | 0.214298272 | | 10000 | 0.194792071 | 0.349353687 | | 100000 | 0.347683285 | 6.086671892 | | 1000000 | 2.589350682 | 66.572906702 | CC: @pwendell This is clearly more scalable than the default implementation. My question is whether we should use this implementation in `reduce` and `aggregate` or put them as separate methods. The concern is that users may use `reduce` and `aggregate` as collect, where having multiple stages doesn't reduce the data size. However, in this case, `collect` is more appropriate. Author: Xiangrui Meng <meng@databricks.com> Closes #1110 from mengxr/tree and squashes the following commits: c6cd267 [Xiangrui Meng] make depth default to 2 b04b96a [Xiangrui Meng] address comments 9bcc5d3 [Xiangrui Meng] add depth for readability 7495681 [Xiangrui Meng] fix compile error 142a857 [Xiangrui Meng] merge master d58a087 [Xiangrui Meng] move treeReduce and treeAggregate to mllib 8a2a59c [Xiangrui Meng] Merge branch 'master' into tree be6a88a [Xiangrui Meng] use treeAggregate in mllib 0f94490 [Xiangrui Meng] add docs eb71c33 [Xiangrui Meng] add treeReduce fe42a5e [Xiangrui Meng] add treeAggregate
… the current pu... ...sh model Currently Spark uses Flume's internal Avro Protocol to ingest data from Flume. If the executor running the receiver fails, it currently has to be restarted on the same node to be able to receive data. This commit adds a new Sink which can be deployed to a Flume agent. This sink can be polled by a new DStream that is also included in this commit. This model ensures that data can be pulled into Spark from Flume even if the receiver is restarted on a new node. This also allows the receiver to receive data on multiple threads for better performance. Author: Hari Shreedharan <harishreedharan@gmail.com> Author: Hari Shreedharan <hshreedharan@apache.org> Author: Tathagata Das <tathagata.das1565@gmail.com> Author: harishreedharan <hshreedharan@cloudera.com> Closes #807 from harishreedharan/master and squashes the following commits: e7f70a3 [Hari Shreedharan] Merge remote-tracking branch 'asf-git/master' 96cfb6f [Hari Shreedharan] Merge remote-tracking branch 'asf/master' e48d785 [Hari Shreedharan] Documenting flume-sink being ignored for Mima checks. 5f212ce [Hari Shreedharan] Ignore Spark Sink from mima. 981bf62 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' 7a1bc6e [Hari Shreedharan] Fix SparkBuild.scala a082eb3 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' 1f47364 [Hari Shreedharan] Minor fixes. 73d6f6d [Hari Shreedharan] Cleaned up tests a bit. Added some docs in multiple places. 65b76b4 [Hari Shreedharan] Fixing the unit test. e59cc20 [Hari Shreedharan] Use SparkFlumeEvent instead of the new type. Also, Flume Polling Receiver now uses the store(ArrayBuffer) method. f3c99d1 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' 3572180 [Hari Shreedharan] Adding a license header, making Jenkins happy. 799509f [Hari Shreedharan] Fix a compile issue. 3c5194c [Hari Shreedharan] Merge remote-tracking branch 'asf/master' d248d22 [harishreedharan] Merge pull request #1 from tdas/flume-polling 10b6214 [Tathagata Das] Changed public API, changed sink package, and added java unit test to make sure Java API is callable from Java. 1edc806 [Hari Shreedharan] SPARK-1729. Update logging in Spark Sink. 8c00289 [Hari Shreedharan] More debug messages 393bd94 [Hari Shreedharan] SPARK-1729. Use LinkedBlockingQueue instead of ArrayBuffer to keep track of connections. 120e2a1 [Hari Shreedharan] SPARK-1729. Some test changes and changes to utils classes. 9fd0da7 [Hari Shreedharan] SPARK-1729. Use foreach instead of map for all Options. 8136aa6 [Hari Shreedharan] Adding TransactionProcessor to map on returning batch of data 86aa274 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' 205034d [Hari Shreedharan] Merging master in 4b0c7fc [Hari Shreedharan] FLUME-1729. New Flume-Spark integration. bda01fc [Hari Shreedharan] FLUME-1729. Flume-Spark integration. 0d69604 [Hari Shreedharan] FLUME-1729. Better Flume-Spark integration. 3c23c18 [Hari Shreedharan] SPARK-1729. New Spark-Flume integration. 70bcc2a [Hari Shreedharan] SPARK-1729. New Flume-Spark integration. d6fa3aa [Hari Shreedharan] SPARK-1729. New Flume-Spark integration. e7da512 [Hari Shreedharan] SPARK-1729. Fixing import order 9741683 [Hari Shreedharan] SPARK-1729. Fixes based on review. c604a3c [Hari Shreedharan] SPARK-1729. Optimize imports. 0f10788 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model 87775aa [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model 8df37e4 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model 03d6c1c [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model 08176ad [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model d24d9d4 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model 6d6776a [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
1. there's no `hook_context.q` but a `hook_context_cs.q` in query folder 2. there's no `compute_stats_table.q` in query folder 3. there's no `having1.q` in query folder 4. `udf_E` and `udf_PI` appear twice in white list Author: Daoyuan <daoyuan.wang@intel.com> Closes #1634 from adrian-wang/testcases and squashes the following commits: d7482ce [Daoyuan] change some test lists
…es key twice JIRA: https://issues.apache.org/jira/browse/SPARK-2730 Author: Yin Huai <huai@cse.ohio-state.edu> Closes #1637 from yhuai/SPARK-2730 and squashes the following commits: 1a9f24e [Yin Huai] Remove unnecessary key evaluation.
Datetime and time in Python will be converted into java.util.Calendar after serialization, it will be converted into java.sql.Timestamp during inferSchema(). In javaToPython(), Timestamp will be converted into Calendar, then be converted into datetime in Python after pickling. Author: Davies Liu <davies.liu@gmail.com> Closes #1601 from davies/date and squashes the following commits: f0599b0 [Davies Liu] remove tests for sets and tuple in sql, fix list of list c9d607a [Davies Liu] convert datetype for runtime 709d40d [Davies Liu] remove brackets 96db384 [Davies Liu] support datetime type for SchemaRDD
…exact sample size Implemented stratified sampling that guarantees exact sample size using ScaRSR with two passes over the RDD for sampling without replacement and three passes for sampling with replacement. Author: Doris Xin <doris.s.xin@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #1025 from dorx/stratified and squashes the following commits: 245439e [Doris Xin] moved minSamplingRate to getUpperBound eaf5771 [Doris Xin] bug fixes. 17a381b [Doris Xin] fixed a merge issue and a failed unit ea7d27f [Doris Xin] merge master b223529 [Xiangrui Meng] use approx bounds for poisson fix poisson mean for waitlisting add unit tests for Java b3013a4 [Xiangrui Meng] move math3 back to test scope eecee5f [Doris Xin] Merge branch 'master' into stratified f4c21f3 [Doris Xin] Reviewer comments a10e68d [Doris Xin] style fix a2bf756 [Doris Xin] Merge branch 'master' into stratified 680b677 [Doris Xin] use mapPartitionWithIndex instead 9884a9f [Doris Xin] style fix bbfb8c9 [Doris Xin] Merge branch 'master' into stratified ee9d260 [Doris Xin] addressed reviewer comments 6b5b10b [Doris Xin] Merge branch 'master' into stratified 254e03c [Doris Xin] minor fixes and Java API. 4ad516b [Doris Xin] remove unused imports from PairRDDFunctions bd9dc6e [Doris Xin] unit bug and style violation fixed 1fe1cff [Doris Xin] Changed fractionByKey to a map to enable arg check 944a10c [Doris Xin] [SPARK-2145] Add lower bound on sampling rate 0214a76 [Doris Xin] cleanUp 90d94c0 [Doris Xin] merge master 9e74ab5 [Doris Xin] Separated out most of the logic in sampleByKey 7327611 [Doris Xin] merge master 50581fc [Doris Xin] added a TODO for logging in python 46f6c8c [Doris Xin] fixed the NPE caused by closures being cleaned before being passed into the aggregate function 7e1a481 [Doris Xin] changed the permission on SamplingUtil 1d413ce [Doris Xin] fixed checkstyle issues 9ee94ee [Doris Xin] [SPARK-2082] stratified sampling in PairRDDFunctions that guarantees exact sample size e3fd6a6 [Doris Xin] Merge branch 'master' into takeSample 7cab53a [Doris Xin] fixed import bug in rdd.py ffea61a [Doris Xin] SPARK-1939: Refactor takeSample method in RDD 1441977 [Doris Xin] SPARK-1939 Refactor takeSample method in RDD to use ScaSRS
… logical plans & sample usage. The idea is that every Catalyst logical plan gets hold of a Statistics class, the usage of which provides useful estimations on various statistics. See the implementations of `MetastoreRelation`. This patch also includes several usages of the estimation interface in the planner. For instance, we now use physical table sizes from the estimate interface to convert an equi-join to a broadcast join (when doing so is beneficial, as determined by a size threshold). Finally, there are a couple minor accompanying changes including: - Remove the not-in-use `BaseRelation`. - Make SparkLogicalPlan take a `SQLContext` in the second param list. Author: Zongheng Yang <zongheng.y@gmail.com> Closes #1238 from concretevitamin/estimates and squashes the following commits: 329071d [Zongheng Yang] Address review comments; turn config name from string to field in SQLConf. 8663e84 [Zongheng Yang] Use BigInt for stat; for logical leaves, by default throw an exception. 2f2fb89 [Zongheng Yang] Fix statistics for SparkLogicalPlan. 9951305 [Zongheng Yang] Remove childrenStats. 16fc60a [Zongheng Yang] Avoid calling statistics on plans if auto join conversion is disabled. 8bd2816 [Zongheng Yang] Add a note on performance of statistics. 6e594b8 [Zongheng Yang] Get size info from metastore for MetastoreRelation. 01b7a3e [Zongheng Yang] Update scaladoc for a field and move it to @param section. 549061c [Zongheng Yang] Remove numTuples in Statistics for now. 729a8e2 [Zongheng Yang] Update docs to be more explicit. 573e644 [Zongheng Yang] Remove singleton SQLConf and move back `settings` to the trait. 2d99eb5 [Zongheng Yang] {Cleanup, use synchronized in, enrich} StatisticsSuite. ca5b825 [Zongheng Yang] Inject SQLContext into SparkLogicalPlan, removing SQLConf mixin from it. 43d38a6 [Zongheng Yang] Revert optimization for BroadcastNestedLoopJoin (this fixes tests). 0ef9e5b [Zongheng Yang] Use multiplication instead of sum for default estimates. 4ef0d26 [Zongheng Yang] Make Statistics a case class. 3ba8f3e [Zongheng Yang] Add comment. e5bcf5b [Zongheng Yang] Fix optimization conditions & update scala docs to explain. 7d9216a [Zongheng Yang] Apply estimation to planning ShuffleHashJoin & BroadcastNestedLoopJoin. 73cde01 [Zongheng Yang] Move SQLConf back. Assign default sizeInBytes to SparkLogicalPlan. 73412be [Zongheng Yang] Move SQLConf to Catalyst & add default val for sizeInBytes. 7a60ab7 [Zongheng Yang] s/Estimates/Statistics, s/cardinality/numTuples. de3ae13 [Zongheng Yang] Add parquetAfter() properly in test. dcff9bd [Zongheng Yang] Cleanups. 84301a4 [Zongheng Yang] Refactors. 5bf5586 [Zongheng Yang] Typo. 56a8e6e [Zongheng Yang] Prototype impl of estimations for Catalyst logical plans.
This commit exists to close the following pull requests on Github: Closes #740 (close requested by 'rxin') Closes #647 (close requested by 'rxin') Closes #1383 (close requested by 'rxin') Closes #1485 (close requested by 'pwendell') Closes #693 (close requested by 'rxin') Closes #478 (close requested by 'JoshRosen')
For queries like `... HAVING COUNT(*) > 9` the expression is always resolved since it contains no attributes. This was causing us to avoid doing the Having clause aggregation rewrite. Author: Michael Armbrust <michael@databricks.com> Closes #1640 from marmbrus/havingNoRef and squashes the following commits: 92d3901 [Michael Armbrust] Don't check resolved for having filters.
Author: Josh Rosen <joshrosen@apache.org> Closes #1626 from JoshRosen/SPARK-2305 and squashes the following commits: 03fb283 [Josh Rosen] Update Py4J to version 0.8.2.1.
Adds a new method for evaluating expressions using code that is generated though Scala reflection. This functionality is configured by the SQLConf option `spark.sql.codegen` and is currently turned off by default. Evaluation can be done in several specialized ways: - *Projection* - Given an input row, produce a new row from a set of expressions that define each column in terms of the input row. This can either produce a new Row object or perform the projection in-place on an existing Row (MutableProjection). - *Ordering* - Compares two rows based on a list of `SortOrder` expressions - *Condition* - Returns `true` or `false` given an input row. For each of the above operations there is both a Generated and Interpreted version. When generation for a given expression type is undefined, the code generator falls back on calling the `eval` function of the expression class. Even without custom code, there is still a potential speed up, as loops are unrolled and code can still be inlined by JIT. This PR also contains a new type of Aggregation operator, `GeneratedAggregate`, that performs aggregation by using generated `Projection` code. Currently the required expression rewriting only works for simple aggregations like `SUM` and `COUNT`. This functionality will be extended in a future PR. This PR also performs several clean ups that simplified the implementation: - The notion of `Binding` all expressions in a tree automatically before query execution has been removed. Instead it is the responsibly of an operator to provide the input schema when creating one of the specialized evaluators defined above. In cases when the standard eval method is going to be called, binding can still be done manually using `BindReferences`. There are a few reasons for this change: First, there were many operators where it just didn't work before. For example, operators with more than one child, and operators like aggregation that do significant rewriting of the expression. Second, the semantics of equality with `BoundReferences` are broken. Specifically, we have had a few bugs where partitioning breaks because of the binding. - A copy of the current `SQLContext` is automatically propagated to all `SparkPlan` nodes by the query planner. Before this was done ad-hoc for the nodes that needed this. However, this required a lot of boilerplate as one had to always remember to make it `transient` and also had to modify the `otherCopyArgs`. Author: Michael Armbrust <michael@databricks.com> Closes #993 from marmbrus/newCodeGen and squashes the following commits: 96ef82c [Michael Armbrust] Merge remote-tracking branch 'apache/master' into newCodeGen f34122d [Michael Armbrust] Merge remote-tracking branch 'apache/master' into newCodeGen 67b1c48 [Michael Armbrust] Use conf variable in SQLConf object 4bdc42c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen 41a40c9 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen de22aac [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen fed3634 [Michael Armbrust] Inspectors are not serializable. ef8d42b [Michael Armbrust] comments 533fdfd [Michael Armbrust] More logging of expression rewriting for GeneratedAggregate. 3cd773e [Michael Armbrust] Allow codegen for Generate. 64b2ee1 [Michael Armbrust] Implement copy 3587460 [Michael Armbrust] Drop unused string builder function. 9cce346 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen 1a61293 [Michael Armbrust] Address review comments. 0672e8a [Michael Armbrust] Address comments. 1ec2d6e [Michael Armbrust] Address comments 033abc6 [Michael Armbrust] off by default 4771fab [Michael Armbrust] Docs, more test coverage. d30fee2 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen d2ad5c5 [Michael Armbrust] Refactor putting SQLContext into SparkPlan. Fix ordering, other test cases. be2cd6b [Michael Armbrust] WIP: Remove old method for reference binding, more work on configuration. bc88ecd [Michael Armbrust] Style 6cc97ca [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen 4220f1e [Michael Armbrust] Better config, docs, etc. ca6cc6b [Michael Armbrust] WIP 9d67d85 [Michael Armbrust] Fix hive planner fc522d5 [Michael Armbrust] Hook generated aggregation in to the planner. e742640 [Michael Armbrust] Remove unneeded changes and code. 675e679 [Michael Armbrust] Upgrade paradise. 0093376 [Michael Armbrust] Comment / indenting cleanup. d81f998 [Michael Armbrust] include schema for binding. 0e889e8 [Michael Armbrust] Use typeOf instead tq f623ffd [Michael Armbrust] Quiet logging from test suite. efad14f [Michael Armbrust] Remove some half finished functions. 92e74a4 [Michael Armbrust] add overrides a2b5408 [Michael Armbrust] WIP: Code generation with scala reflection.
…nced As of Spark 1.0, RangePartitioner goes through data twice: once to compute the count and once to do sampling. As a result, to do sortByKey, Spark goes through data 3 times (once to count, once to sample, and once to sort). `RangePartitioner` should go through data only once, collecting samples from input partitions as well as counting. If the data is balanced, this should give us a good sketch. If we see big partitions, we re-sample from them in order to collect enough items. The downside is that we need to collect more from each partition in the first pass. An alternative solution is caching the intermediate result and decide whether to fetch the data after. Author: Xiangrui Meng <meng@databricks.com> Author: Reynold Xin <rxin@apache.org> Closes #1562 from mengxr/range-partitioner and squashes the following commits: 6cc2551 [Xiangrui Meng] change foreach to for eb39b08 [Xiangrui Meng] Merge branch 'master' into range-partitioner eb95dd8 [Xiangrui Meng] separate sketching and determining bounds impl c436d30 [Xiangrui Meng] fix binary metrics unit tests db58a55 [Xiangrui Meng] add unit tests a6e35d6 [Xiangrui Meng] minor update 60be09e [Xiangrui Meng] remove importance sampler 9ee9992 [Xiangrui Meng] update range partitioner to run only one job on roughly balanced data cc12f47 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into range-part 06ac2ec [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into range-part 17bcbf3 [Reynold Xin] Added seed. badf20d [Reynold Xin] Renamed the method. 6940010 [Reynold Xin] Reservoir sampling implementation.
The main thing was that spark configs were not propagated to the driver, and so applications that do not specify `master` or `appName` automatically failed. This PR fixes that and a couple of miscellaneous things that are related. One thing that may or may not be an issue is that the jars must be available on the driver node. In `standalone-cluster` mode, this effectively means these jars must be available on all the worker machines, since the driver is launched on one of them. The semantics here are not the same as `yarn-cluster` mode, where all the relevant jars are uploaded to a distributed cache automatically and shipped to the containers. This is probably not a concern, but still worth a mention. Author: Andrew Or <andrewor14@gmail.com> Closes #1538 from andrewor14/standalone-cluster and squashes the following commits: 8c11a0d [Andrew Or] Clean up imports / comments (minor) 2678d13 [Andrew Or] Handle extraJavaOpts properly 7660547 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-cluster 6f64a9b [Andrew Or] Revert changes in YARN 2f2908b [Andrew Or] Fix tests ed01491 [Andrew Or] Don't go overboard with escaping 8e105e1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-cluster b890949 [Andrew Or] Abstract usages of converting spark opts to java opts 79f63a3 [Andrew Or] Move sparkProps into javaOpts 78752f8 [Andrew Or] Fix tests 5a9c6c7 [Andrew Or] Fix line too long c141a00 [Andrew Or] Don't display "unknown app" on driver log pages d7e2728 [Andrew Or] Avoid deprecation warning in standalone Client 6ceb14f [Andrew Or] Allow relevant configs to propagate to standalone Driver 7f854bc [Andrew Or] Fix test 855256e [Andrew Or] Fix standalone-cluster mode fd9da51 [Andrew Or] Formatting changes (minor)
The current PR contains the following changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Users can create Rows. * Introduce `applySchema` to create a `SchemaRDD` by applying a `schema: StructType` to an `RDD[Row]`. * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`. * `ScalaReflection.typeOfObject` provides a way to infer the Catalyst data type based on an object. Also, we can compose `typeOfObject` with some custom logics to form a new function to infer the data type (for different use cases). * `JsonRDD` has been refactored to use changes introduced by this PR. * Add a field `containsNull` to `ArrayType`. So, we can explicitly mark if an `ArrayType` can contain null values. The default value of `containsNull` is `false`. New APIs are introduced in the sql package object and SQLContext. You can find the scaladoc at [sql package object](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.package) and [SQLContext](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.SQLContext). An example of using `applySchema` is shown below. ```scala import org.apache.spark.sql._ val sqlContext = new org.apache.spark.sql.SQLContext(sc) val schema = StructType( StructField("name", StringType, false) :: StructField("age", IntegerType, true) :: Nil) val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Row(p(0), p(1).trim.toInt)) val peopleSchemaRDD = sqlContext. applySchema(people, schema) peopleSchemaRDD.printSchema // root // |-- name: string (nullable = false) // |-- age: integer (nullable = true) peopleSchemaRDD.registerAsTable("people") sqlContext.sql("select name from people").collect.foreach(println) ``` I will add new contents to the SQL programming guide later. JIRA: https://issues.apache.org/jira/browse/SPARK-2179 Author: Yin Huai <huai@cse.ohio-state.edu> Closes #1346 from yhuai/dataTypeAndSchema and squashes the following commits: 1d45977 [Yin Huai] Clean up. a6e08b4 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema c712fbf [Yin Huai] Converts types of values based on defined schema. 4ceeb66 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema e5f8df5 [Yin Huai] Scaladoc. 122d1e7 [Yin Huai] Address comments. 03bfd95 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 2476ed0 [Yin Huai] Minor updates. ab71f21 [Yin Huai] Format. fc2bed1 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema bd40a33 [Yin Huai] Address comments. 991f860 [Yin Huai] Move "asJavaDataType" and "asScalaDataType" to DataTypeConversions.scala. 1cb35fe [Yin Huai] Add "valueContainsNull" to MapType. 3edb3ae [Yin Huai] Python doc. 692c0b9 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 1d93395 [Yin Huai] Python APIs. 246da96 [Yin Huai] Add java data type APIs to javadoc index. 1db9531 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema d48fc7b [Yin Huai] Minor updates. 33c4fec [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema b9f3071 [Yin Huai] Java API for applySchema. 1c9f33c [Yin Huai] Java APIs for DataTypes and Row. 624765c [Yin Huai] Tests for applySchema. aa92e84 [Yin Huai] Update data type tests. 8da1a17 [Yin Huai] Add Row.fromSeq. 9c99bc0 [Yin Huai] Several minor updates. 1d9c13a [Yin Huai] Update applySchema API. 85e9b51 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema e495e4e [Yin Huai] More comments. 42d47a3 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema c3f4a02 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 2e58dbd [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema b8b7db4 [Yin Huai] 1. Move sql package object and package-info to sql-core. 2. Minor updates on APIs. 3. Update scala doc. 68525a2 [Yin Huai] Update JSON unit test. 3209108 [Yin Huai] Add unit tests. dcaf22f [Yin Huai] Add a field containsNull to ArrayType to indicate if an array can contain null values or not. If an ArrayType is constructed by "ArrayType(elementType)" (the existing constructor), the value of containsNull is false. 9168b83 [Yin Huai] Update comments. fc649d7 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema eca7d04 [Yin Huai] Add two apply methods which will be used to extract StructField(s) from a StructType. 949d6bb [Yin Huai] When creating a SchemaRDD for a JSON dataset, users can apply an existing schema. 7a6a7e5 [Yin Huai] Fix bug introduced by the change made on SQLContext.inferSchema. 43a45e1 [Yin Huai] Remove sql.util.package introduced in a previous commit. 0266761 [Yin Huai] Format 03eec4c [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 90460ac [Yin Huai] Infer the Catalyst data type from an object and cast a data value to the expected type. 3fa0df5 [Yin Huai] Provide easier ways to construct a StructType. 16be3e5 [Yin Huai] This commit contains three changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Introduce `createSchemaRDD` to create a `SchemaRDD` from an `RDD` with a provided schema (represented by a `StructType`) and a provided function to construct `Row`, * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
Author: Koert Kuipers <koert@tresata.com> Closes #735 from koertkuipers/feat-kryo-max-buffersize and squashes the following commits: 15f6d81 [Koert Kuipers] change default for spark.kryoserializer.buffer.max.mb to 64mb and add some documentation 1bcc22c [Koert Kuipers] Merge branch 'master' into feat-kryo-max-buffersize 0c9f8eb [Koert Kuipers] make default for kryo max buffer size 16MB 143ec4d [Koert Kuipers] test resizable buffer in kryo Output 0732445 [Koert Kuipers] support setting maxCapacity to something different than capacity in kryo Output
…Math.exp, Math.log In a few places in MLlib, an expression of the form `log(1.0 + p)` is evaluated. When p is so small that `1.0 + p == 1.0`, the result is 0.0. However the correct answer is very near `p`. This is why `Math.log1p` exists. Similarly for one instance of `exp(m) - 1` in GraphX; there's a special `Math.expm1` method. While the errors occur only for very small arguments, given their use in machine learning algorithms, this is entirely possible. Also note the related PR for Python: #1652 Author: Sean Owen <srowen@gmail.com> Closes #1659 from srowen/SPARK-2748 and squashes the following commits: c5926d4 [Sean Owen] Use log1p, expm1 for better precision for tiny arguments
…very task) This is a resubmission of #1452. It was reverted because it broke the build. Currently (as of Spark 1.0.1), Spark sends RDD object (which contains closures) using Akka along with the task itself to the executors. This is inefficient because all tasks in the same stage use the same RDD object, but we have to send RDD object multiple times to the executors. This is especially bad when a closure references some variable that is very large. The current design led to users having to explicitly broadcast large variables. The patch uses broadcast to send RDD objects and the closures to executors, and use Akka to only send a reference to the broadcast RDD/closure along with the partition specific information for the task. For those of you who know more about the internals, Spark already relies on broadcast to send the Hadoop JobConf every time it uses the Hadoop input, because the JobConf is large. The user-facing impact of the change include: 1. Users won't need to decide what to broadcast anymore, unless they would want to use a large object multiple times in different operations 2. Task size will get smaller, resulting in faster scheduling and higher task dispatch throughput. In addition, the change will simplify some internals of Spark, eliminating the need to maintain task caches and the complex logic to broadcast JobConf (which also led to a deadlock recently). A simple way to test this: ```scala val a = new Array[Byte](1000*1000); scala.util.Random.nextBytes(a); sc.parallelize(1 to 1000, 1000).map { x => a; x }.groupBy { x => a; x }.count ``` Numbers on 3 r3.8xlarge instances on EC2 ``` master branch: 5.648436068 s, 4.715361895 s, 5.360161877 s with this change: 3.416348793 s, 1.477846558 s, 1.553432156 s ``` Author: Reynold Xin <rxin@apache.org> Closes #1498 from rxin/broadcast-task and squashes the following commits: f7364db [Reynold Xin] Code review feedback. f8535dc [Reynold Xin] Fixed the style violation. 252238d [Reynold Xin] Serialize the final task closure as well as ShuffleDependency in taskBinary. 111007d [Reynold Xin] Fix broadcast tests. 797c247 [Reynold Xin] Properly send SparkListenerStageSubmitted and SparkListenerStageCompleted. bab1d8b [Reynold Xin] Check for NotSerializableException in submitMissingTasks. cf38450 [Reynold Xin] Use TorrentBroadcastFactory. 991c002 [Reynold Xin] Use HttpBroadcast. de779f8 [Reynold Xin] Fix TaskContextSuite. cc152fc [Reynold Xin] Don't cache the RDD broadcast variable. d256b45 [Reynold Xin] Fixed unit test failures. One more to go. cae0af3 [Reynold Xin] [SPARK-2521] Broadcast RDD object (instead of sending it along with every task).
… tests dev/run-tests use "git diff --dirstat master" to check whether sql is changed. However, --dirstat won't show sql if sql's change is negligible (e.g. 1k loc change in core, and only 1 loc change in hive). We should use "git diff --name-only master" instead. Author: Reynold Xin <rxin@apache.org> Closes #1656 from rxin/hiveTest and squashes the following commits: f5eab9f [Reynold Xin] [SPARK-2747] git diff --dirstat can miss sql changes and not run Hive tests.
This avoids basically doing 1 - 1, for example: ```python >>> from math import exp >>> margin = -40 >>> 1 - 1 / (1 + exp(margin)) 0.0 >>> exp(margin) / (1 + exp(margin)) 4.248354255291589e-18 >>> ``` Author: Naftali Harris <naftaliharris@gmail.com> Closes #1652 from naftaliharris/patch-2 and squashes the following commits: 0d55a9f [Naftali Harris] Avoid numerical instability
This PR is a updated version of (#557) to actually test sending and receiving data through Kafka, and fix previous flaky issues. @tdas, would you mind reviewing this PR? Thanks a lot. Author: jerryshao <saisai.shao@intel.com> Closes #1751 from jerryshao/kafka-unit-test and squashes the following commits: b6a505f [jerryshao] code refactor according to comments 5222330 [jerryshao] Change JavaKafkaStreamSuite to better test it 5525f10 [jerryshao] Fix flaky issue of Kafka real unit test 4559310 [jerryshao] Minor changes for Kafka unit test 860f649 [jerryshao] Minor style changes, and tests ignored due to flakiness 796d4ca [jerryshao] Add real Kafka streaming test
Add a config (spark.yarn.access.namenodes) to allow applications running on yarn to access other secure HDFS cluster. User just specifies the namenodes of the other clusters and we get Tokens for those and ship them with the spark application. Author: Thomas Graves <tgraves@apache.org> Closes #1159 from tgravescs/spark-1528 and squashes the following commits: ddbcd16 [Thomas Graves] review comments 0ac8501 [Thomas Graves] SPARK-1528 - add support for accessing remote HDFS
It was easier to combine these 2 jira since they touch many of the same places. This pr adds the following: - adds modify acls - adds admin acls (list of admins/users that get added to both view and modify acls) - modify Kill button on UI to take modify acls into account - changes config name of spark.ui.acls.enable to spark.acls.enable since I choose poorly in original name. We keep backwards compatibility so people can still use spark.ui.acls.enable. The acls should apply to any web ui as well as any CLI interfaces. - send view and modify acls information on to YARN so that YARN interfaces can use (yarn cli for killing applications for example). Author: Thomas Graves <tgraves@apache.org> Closes #1196 from tgravescs/SPARK-1890 and squashes the following commits: 8292eb1 [Thomas Graves] review comments b92ec89 [Thomas Graves] remove unneeded variable from applistener 4c765f4 [Thomas Graves] Add in admin acls 72eb0ac [Thomas Graves] Add modify acls
JIRA Issue: https://issues.apache.org/jira/browse/SPARK-2859 Kryo project has been migrated from googlecode to github, hence we need to update its URL in related docs such as tuning.md. Author: Guancheng (G.C.) Chen <chenguancheng@gmail.com> Closes #1782 from gchen/kryo-docs and squashes the following commits: b62543c [Guancheng (G.C.) Chen] update url of Kryo project
This patch adds support for giving accumulators user-visible names and displaying accumulator values in the web UI. This allows users to create custom counters that can display in the UI. The current approach displays both the accumulator deltas caused by each task and a "current" value of the accumulator totals for each stage, which gets update as tasks finish. Currently in Spark developers have been extending the `TaskMetrics` functionality to provide custom instrumentation for RDD's. This provides a potentially nicer alternative of going through the existing accumulator framework (actually `TaskMetrics` and accumulators are on an awkward collision course as we add more features to the former). The current patch demo's how we can use the feature to provide instrumentation for RDD input sizes. The nice thing about going through accumulators is that users can read the current value of the data being tracked in their programs. This could be useful to e.g. decide to short-circuit a Spark stage depending on how things are going.  Author: Patrick Wendell <pwendell@gmail.com> Closes #1309 from pwendell/metrics and squashes the following commits: 8815308 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into HEAD 93fbe0f [Patrick Wendell] Other minor fixes cc43f68 [Patrick Wendell] Updating unit tests c991b1b [Patrick Wendell] Moving some code into the Accumulators class 9a9ba3c [Patrick Wendell] More merge fixes c5ace9e [Patrick Wendell] More merge conflicts 1da15e3 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into metrics 9860c55 [Patrick Wendell] Potential solution to posting listener events 0bb0e33 [Patrick Wendell] Remove "display" variable and assume display = name.isDefined 0ec4ac7 [Patrick Wendell] Java API's e95bf69 [Patrick Wendell] Stash be97261 [Patrick Wendell] Style fix 8407308 [Patrick Wendell] Removing examples in Hadoop and RDD class 64d405f [Patrick Wendell] Adding missing file 5d8b156 [Patrick Wendell] Changes based on Kay's review. 9f18bad [Patrick Wendell] Minor style changes and tests 7a63abc [Patrick Wendell] Adding Json serialization and responding to Reynold's feedback ad85076 [Patrick Wendell] Example of using named accumulators for custom RDD metrics. 0b72660 [Patrick Wendell] Initial WIP example of supporing globally named accumulators.
Note that this also documents spark.executorEnv.* which to me means its public. If we don't want that please speak up. Author: Thomas Graves <tgraves@apache.org> Closes #1512 from tgravescs/SPARK-1680 and squashes the following commits: 11525df [Thomas Graves] more doc changes 553bad0 [Thomas Graves] fix documentation 152bf7c [Thomas Graves] fix docs 5382326 [Thomas Graves] try fix docs 32f86a4 [Thomas Graves] use configs for specifying environment variables on YARN
It also moves the model to local in order to map `RDD[String]` to `RDD[Vector]`. Ishiihara Author: Xiangrui Meng <meng@databricks.com> Closes #1790 from mengxr/word2vec-fix and squashes the following commits: a87146c [Xiangrui Meng] add setters and make a default constructor e5c923b [Xiangrui Meng] fix random seed in word2vec; move model to local
…b) to 32KB. This can substantially reduce memory usage during shuffle. Author: Reynold Xin <rxin@apache.org> Closes #1781 from rxin/SPARK-2503-spark.shuffle.file.buffer.kb and squashes the following commits: 104b8d8 [Reynold Xin] [SPARK-2503] Lower shuffle output buffer (spark.shuffle.file.buffer.kb) to 32KB.
…t in pyspark's linear methods Related to Jira Issue: [SPARK-2550](https://issues.apache.org/jira/browse/SPARK-2550?jql=project%20%3D%20SPARK%20AND%20resolution%20%3D%20Unresolved%20AND%20priority%20%3D%20Major%20ORDER%20BY%20key%20DESC) Author: Michael Giannakopoulos <miccagiann@gmail.com> Closes #1775 from miccagiann/linearMethodsReg and squashes the following commits: cb774c3 [Michael Giannakopoulos] MiniBatchFraction added in related PythonMLLibAPI java stubs. 81fcbc6 [Michael Giannakopoulos] Fixing a typo-error. 8ad263e [Michael Giannakopoulos] Adding regularizer type and intercept parameters to LogisticRegressionWithSGD and SVMWithSGD.
I inquired on dev mailing list about the motivation for checking the jdbc statement instead of the connection in the close() logic of JdbcRDD. Ted Yu believes there essentially is none- it is a simple cut and paste issue. So here is the tiny fix to patch it. Author: Stephen Boesch <javadba> Author: Stephen Boesch <javadba@gmail.com> Closes #1792 from javadba/closejdbc and squashes the following commits: 363be4f [Stephen Boesch] SPARK-2869 - Fix tiny bug in JdbcRdd for closing jdbc connection (reformat with braces) 6518d36 [Stephen Boesch] SPARK-2689 Fix tiny bug in JdbcRdd for closing jdbc connection 3fb23ed [Stephen Boesch] SPARK-2689 Fix potential leak of connection/PreparedStatement in case of error in JdbcRDD 095b2c9 [Stephen Boesch] Fix tiny bug (likely copy and paste error) in closing jdbc connection
module spark-hive-thriftserver_2.10 and spark-hive_2.10 both named "Spark Project Hive" in pom.xml, so rename spark-hive-thriftserver_2.10 project name to "Spark Project Hive Thrift Server" Author: wangfei <wangfei1@huawei.com> Closes #1789 from scwf/patch-1 and squashes the following commits: ca1f5e9 [wangfei] [sql] rename module name of hive-thriftserver
…l buffer size and reducing memory allocation JIRA issue: [SPARK-2650](https://issues.apache.org/jira/browse/SPARK-2650) Please refer to [comments](https://issues.apache.org/jira/browse/SPARK-2650?focusedCommentId=14084397&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14084397) of SPARK-2650 for some other details. This PR adjusts the initial in-memory columnar buffer size to 1MB, same as the default value of Shark's `shark.column.partitionSize.mb` property when running in local mode. Will add Shark style partition size estimation in another PR. Also, before this PR, `NullableColumnBuilder` copies the whole buffer to add the null positions section, and then `CompressibleColumnBuilder` copies and compresses the buffer again, even if compression is disabled (`PassThrough` compression scheme is used to disable compression). In this PR the first buffer copy is eliminated to reduce memory consumption. Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #1769 from liancheng/spark-2650 and squashes the following commits: 88a042e [Cheng Lian] Fixed method visibility and removed dead code 001f2e5 [Cheng Lian] Try fixing SPARK-2650 by adjusting initial buffer size and reducing memory allocation
This PR aims to finalize accepted data value types in Python RDDs provided to Python `applySchema`. JIRA: https://issues.apache.org/jira/browse/SPARK-2854 Author: Yin Huai <huai@cse.ohio-state.edu> Closes #1793 from yhuai/SPARK-2854 and squashes the following commits: 32f0708 [Yin Huai] LongType only accepts long values. c2b23dd [Yin Huai] Do data type conversions based on the specified Spark SQL data type.
Minor refactoring to allow resolution either using a nodes input or output. Author: Michael Armbrust <michael@databricks.com> Closes #1795 from marmbrus/ordering and squashes the following commits: 237f580 [Michael Armbrust] style 74d833b [Michael Armbrust] newline 705d963 [Michael Armbrust] Add a rule for resolving ORDER BY expressions that reference attributes not present in the SELECT clause. 82cabda [Michael Armbrust] Generalize attribute resolution.
Scala 2.11 packages not available for the current version (3.2.6) Signed-off-by: Anand Avati <avatiredhat.com> Author: Anand Avati <avati@redhat.com> Closes #1702 from avati/SPARK-1812-json4s-jackson-3.2.10 and squashes the following commits: 7be8324 [Anand Avati] SPARK-1812: core - upgrade to json4s 3.2.10
copied from original JIRA (https://issues.apache.org/jira/browse/SPARK-2294): If an executor E is free, a task may be speculatively assigned to E when there are other tasks in the job that have not been launched (at all) yet. Similarly, a task without any locality preferences may be assigned to E when there was another NODE_LOCAL task that could have been scheduled. This happens because TaskSchedulerImpl calls TaskSetManager.resourceOffer (which in turn calls TaskSetManager.findTask) with increasing locality levels, beginning with PROCESS_LOCAL, followed by NODE_LOCAL, and so on until the highest currently allowed level. Now, supposed NODE_LOCAL is the highest currently allowed locality level. The first time findTask is called, it will be called with max level PROCESS_LOCAL; if it cannot find any PROCESS_LOCAL tasks, it will try to schedule tasks with no locality preferences or speculative tasks. As a result, speculative tasks or tasks with no preferences may be scheduled instead of NODE_LOCAL tasks. ---- I added an additional parameter in resourceOffer and findTask, maxLocality, indicating when we should consider the tasks without locality preference Author: CodingCat <zhunansjtu@gmail.com> Closes #1313 from CodingCat/SPARK-2294 and squashes the following commits: bf3f13b [CodingCat] rollback some forgotten changes 89f9bc0 [CodingCat] address matei's comments 18cae02 [CodingCat] add test case for node-local tasks 2ba6195 [CodingCat] fix failed test cases 87dd09e [CodingCat] fix style 9b9432f [CodingCat] remove hasNodeLocalOnlyTasks fdd1573 [CodingCat] fix failed test cases 941a4fd [CodingCat] see my shocked face.......... f600085 [CodingCat] remove hasNodeLocalOnlyTasks checking 0b8a46b [CodingCat] test whether hasNodeLocalOnlyTasks affect the results 73ceda8 [CodingCat] style fix b3a430b [CodingCat] remove fine granularity tracking for node-local only tasks f9a2ad8 [CodingCat] simplify the logic in TaskSchedulerImpl c8c1de4 [CodingCat] simplify the patch be652ed [CodingCat] avoid unnecessary delay when we only have nopref tasks dee9e22 [CodingCat] fix locality inversion bug in TaskManager by moving nopref branch
#1751 caused maven builds to fail. ``` ~/Apache/spark(branch-1.1|✔) ➤ mvn -U -DskipTests clean install . . . [error] Apache/spark/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala:36: object NIOServerCnxnFactory is not a member of package org.apache.zookeeper.server [error] import org.apache.zookeeper.server.NIOServerCnxnFactory [error] ^ [error] Apache/spark/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala:199: not found: type NIOServerCnxnFactory [error] val factory = new NIOServerCnxnFactory() [error] ^ [error] two errors found [error] Compile failed at Aug 5, 2014 1:42:36 PM [0.503s] ``` The problem is how SBT and Maven resolves multiple versions of the same library, which in this case, is Zookeeper. Observing and comparing the dependency trees from Maven and SBT showed this. Spark depends on ZK 3.4.5 whereas Apache Kafka transitively depends on upon ZK 3.3.4. SBT decides to evict 3.3.4 and use the higher version 3.4.5. But Maven decides to stick to the closest (in the tree) dependent version of 3.3.4. And 3.3.4 does not have NIOServerCnxnFactory. The solution in this patch excludes zookeeper from the apache-kafka dependency in streaming-kafka module so that it just inherits zookeeper from Spark core. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #1797 from tdas/kafka-zk-fix and squashes the following commits: 94b3931 [Tathagata Das] Fixed zookeeper dependency of Kafka
The goal of this PR is to allow users of Spark to write tight firewall rules for their clusters. This is currently not possible because Spark uses random ports in many places, notably the communication between executors and drivers. The changes in this PR are based on top of ash211's changes in #1107. The list covered here may or may not be the complete set of port needed for Spark to operate perfectly. However, as of the latest commit there are no known sources of random ports (except in tests). I have not documented a few of the more obscure configs. My spark-env.sh looks like this: ``` export SPARK_MASTER_PORT=6060 export SPARK_WORKER_PORT=7070 export SPARK_MASTER_WEBUI_PORT=9090 export SPARK_WORKER_WEBUI_PORT=9091 ``` and my spark-defaults.conf looks like this: ``` spark.master spark://andrews-mbp:6060 spark.driver.port 5001 spark.fileserver.port 5011 spark.broadcast.port 5021 spark.replClassServer.port 5031 spark.blockManager.port 5041 spark.executor.port 5051 ``` Author: Andrew Or <andrewor14@gmail.com> Author: Andrew Ash <andrew@andrewash.com> Closes #1777 from andrewor14/configure-ports and squashes the following commits: 621267b [Andrew Or] Merge branch 'master' of github.com:apache/spark into configure-ports 8a6b820 [Andrew Or] Use a random UI port during tests 7da0493 [Andrew Or] Fix tests 523c30e [Andrew Or] Add test for isBindCollision b97b02a [Andrew Or] Minor fixes c22ad00 [Andrew Or] Merge branch 'master' of github.com:apache/spark into configure-ports 93d359f [Andrew Or] Executors connect to wrong port when collision occurs d502e5f [Andrew Or] Handle port collisions when creating Akka systems a2dd05c [Andrew Or] Patrick's comment nit 86461e2 [Andrew Or] Remove spark.executor.env.port and spark.standalone.client.port 1d2d5c6 [Andrew Or] Fix ports for standalone cluster mode cb3be88 [Andrew Or] Various doc fixes (broken link, format etc.) e837cde [Andrew Or] Remove outdated TODOs bfbab28 [Andrew Or] Merge branch 'master' of github.com:apache/spark into configure-ports de1b207 [Andrew Or] Update docs to reflect new ports b565079 [Andrew Or] Add spark.ports.maxRetries 2551eb2 [Andrew Or] Remove spark.worker.watcher.port 151327a [Andrew Or] Merge branch 'master' of github.com:apache/spark into configure-ports 9868358 [Andrew Or] Add a few miscellaneous ports 6016e77 [Andrew Or] Add spark.executor.port 8d836e6 [Andrew Or] Also document SPARK_{MASTER/WORKER}_WEBUI_PORT 4d9e6f3 [Andrew Or] Fix super subtle bug 3f8e51b [Andrew Or] Correct erroneous docs... e111d08 [Andrew Or] Add names for UI services 470f38c [Andrew Or] Special case non-"Address already in use" exceptions 1d7e408 [Andrew Or] Treat 0 ports specially + return correct ConnectionManager port ba32280 [Andrew Or] Minor fixes 6b550b0 [Andrew Or] Assorted fixes 73fbe89 [Andrew Or] Move start service logic to Utils ec676f4 [Andrew Or] Merge branch 'SPARK-2157' of github.com:ash211/spark into configure-ports 038a579 [Andrew Ash] Trust the server start function to report the port the service started on 7c5bdc4 [Andrew Ash] Fix style issue 0347aef [Andrew Ash] Unify port fallback logic to a single place 24a4c32 [Andrew Ash] Remove type on val to match surrounding style 9e4ad96 [Andrew Ash] Reformat for style checker 5d84e0e [Andrew Ash] Document new port configuration options 066dc7a [Andrew Ash] Fix up HttpServer port increments cad16da [Andrew Ash] Add fallover increment logic for HttpServer c5a0568 [Andrew Ash] Fix ConnectionManager to retry with increment b80d2fd [Andrew Ash] Make Spark's block manager port configurable 17c79bb [Andrew Ash] Add a configuration option for spark-shell's class server f34115d [Andrew Ash] SPARK-1176 Add port configuration for HttpBroadcast 49ee29b [Andrew Ash] SPARK-1174 Add port configuration for HttpFileServer 1c0981a [Andrew Ash] Make port in HttpServer configurable
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As described in [SPARK-2627](https://issues.apache.org/jira/browse/SPARK-2627), we'd like Python code to automatically be checked for PEP 8 compliance by Jenkins. This pull request aims to do that. Notes: * We may need to install [`pep8`](https://pypi.python.org/pypi/pep8) on the build server. * I'm expecting tests to fail now that PEP 8 compliance is being checked as part of the build. I'm fine with cleaning up any remaining PEP 8 violations as part of this pull request. * I did not understand why the RAT and scalastyle reports are saved to text files. I did the same for the PEP 8 check, but only so that the console output style can match those for the RAT and scalastyle checks. The PEP 8 report is removed right after the check is complete. * Updates to the ["Contributing to Spark"](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark) guide will be submitted elsewhere, as I don't believe that text is part of the Spark repo. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Author: nchammas <nicholas.chammas@gmail.com> Closes apache#1744 from nchammas/master and squashes the following commits: 274b238 [Nicholas Chammas] [SPARK-2627] [PySpark] minor indentation changes 983d963 [nchammas] Merge pull request #5 from apache/master 1db5314 [nchammas] Merge pull request #4 from apache/master 0e0245f [Nicholas Chammas] [SPARK-2627] undo erroneous whitespace fixes bf30942 [Nicholas Chammas] [SPARK-2627] PEP8: comment spacing 6db9a44 [nchammas] Merge pull request #3 from apache/master 7b4750e [Nicholas Chammas] merge upstream changes 91b7584 [Nicholas Chammas] [SPARK-2627] undo unnecessary line breaks 44e3e56 [Nicholas Chammas] [SPARK-2627] use tox.ini to exclude files b09fae2 [Nicholas Chammas] don't wrap comments unnecessarily bfb9f9f [Nicholas Chammas] [SPARK-2627] keep up with the PEP 8 fixes 9da347f [nchammas] Merge pull request #2 from apache/master aa5b4b5 [Nicholas Chammas] [SPARK-2627] follow Spark bash style for if blocks d0a83b9 [Nicholas Chammas] [SPARK-2627] check that pep8 downloaded fine dffb5dd [Nicholas Chammas] [SPARK-2627] download pep8 at runtime a1ce7ae [Nicholas Chammas] [SPARK-2627] space out test report sections 21da538 [Nicholas Chammas] [SPARK-2627] it's PEP 8, not PEP8 6f4900b [Nicholas Chammas] [SPARK-2627] more misc PEP 8 fixes fe57ed0 [Nicholas Chammas] removing merge conflict backups 9c01d4c [nchammas] Merge pull request #1 from apache/master 9a66cb0 [Nicholas Chammas] resolving merge conflicts a31ccc4 [Nicholas Chammas] [SPARK-2627] miscellaneous PEP 8 fixes beaa9ac [Nicholas Chammas] [SPARK-2627] fail check on non-zero status 723ed39 [Nicholas Chammas] always delete the report file 0541ebb [Nicholas Chammas] [SPARK-2627] call Python linter from run-tests 12440fa [Nicholas Chammas] [SPARK-2627] add Scala linter 61c07b9 [Nicholas Chammas] [SPARK-2627] add Python linter 75ad552 [Nicholas Chammas] make check output style consistent
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…iled stages (in UI) This is a HOTFIX for 1.1. Author: Reynold Xin <rxin@apache.org> Author: Kay Ousterhout <kayousterhout@gmail.com> Closes apache#2127 from rxin/SPARK-3224 and squashes the following commits: effb1ce [Reynold Xin] Move log message. 49282b3 [Reynold Xin] Kay's feedback. 3f01847 [Reynold Xin] Merge pull request #2 from kayousterhout/SPARK-3224 796d282 [Kay Ousterhout] Added unit test for SPARK-3224 3d3d356 [Reynold Xin] Remove map output loc even for repeated FetchFaileds. 1dd3eb5 [Reynold Xin] [SPARK-3224] FetchFailed reduce stages should only show up once in the failed stages UI.
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Type Coercion should support every type to have null value Author: Daoyuan Wang <daoyuan.wang@intel.com> Author: Michael Armbrust <michael@databricks.com> Closes apache#2246 from adrian-wang/spark3363-0 and squashes the following commits: c6241de [Daoyuan Wang] minor code clean 595b417 [Daoyuan Wang] Merge pull request #2 from marmbrus/pr/2246 832e640 [Michael Armbrust] reduce code duplication ef6f986 [Daoyuan Wang] make double boolean miss in jsonRDD compatibleType c619f0a [Daoyuan Wang] Type Coercion should support every type to have null value
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Testing arguments to `sbt` need to be passed as an array, not a single, long string. Fixes a bug introduced in apache#2420. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Closes apache#2437 from nchammas/selective-testing and squashes the following commits: a9f9c1c [Nicholas Chammas] fix printing of sbt test arguments cf57cbf [Nicholas Chammas] fix sbt test arguments e33b978 [Nicholas Chammas] Merge pull request #2 from apache/master 0b47ca4 [Nicholas Chammas] Merge branch 'master' of github.com:nchammas/spark 8051486 [Nicholas Chammas] Merge pull request #1 from apache/master 03180a4 [Nicholas Chammas] Merge branch 'master' of github.com:nchammas/spark d4c5f43 [Nicholas Chammas] Merge pull request #6 from apache/master
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This patch brings Python API for Streaming. This patch is based on work from @giwa Author: giwa <ugw.gi.world@gmail.com> Author: Ken Takagiwa <ken@Kens-MacBook-Pro.local> Author: Davies Liu <davies.liu@gmail.com> Author: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net> Author: Tathagata Das <tathagata.das1565@gmail.com> Author: Ken <ugw.gi.world@gmail.com> Author: Ken Takagiwa <ugw.gi.world@gmail.com> Author: Matthew Farrellee <matt@redhat.com> Closes apache#2538 from davies/streaming and squashes the following commits: 64561e4 [Davies Liu] fix tests 331ecce [Davies Liu] fix example 3e2492b [Davies Liu] change updateStateByKey() to easy API 182be73 [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming 02d0575 [Davies Liu] add wrapper for foreachRDD() bebeb4a [Davies Liu] address all comments 6db00da [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming 8380064 [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming 52c535b [Davies Liu] remove fix for sum() e108ec1 [Davies Liu] address comments 37fe06f [Davies Liu] use random port for callback server d05871e [Davies Liu] remove reuse of PythonRDD be5e5ff [Davies Liu] merge branch of env, make tests stable. 8071541 [Davies Liu] Merge branch 'env' into streaming c7bbbce [Davies Liu] fix sphinx docs 6bb9d91 [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming 4d0ea8b [Davies Liu] clear reference of SparkEnv after stop 54bd92b [Davies Liu] improve tests c2b31cb [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming 7a88f9f [Davies Liu] rollback RDD.setContext(), use textFileStream() to test checkpointing bd8a4c2 [Davies Liu] fix scala style 7797c70 [Davies Liu] refactor ff88bec [Davies Liu] rename RDDFunction to TransformFunction d328aca [Davies Liu] fix serializer in queueStream 6f0da2f [Davies Liu] recover from checkpoint fa7261b [Davies Liu] refactor a13ff34 [Davies Liu] address comments 8466916 [Davies Liu] support checkpoint 9a16bd1 [Davies Liu] change number of partitions during tests b98d63f [Davies Liu] change private[spark] to private[python] eed6e2a [Davies Liu] rollback not needed changes e00136b [Davies Liu] address comments 069a94c [Davies Liu] fix the number of partitions during window() 338580a [Davies Liu] change _first(), _take(), _collect() as private API 19797f9 [Davies Liu] clean up 6ebceca [Davies Liu] add more tests c40c52d [Davies Liu] change first(), take(n) to has the same behavior as RDD 98ac6c2 [Davies Liu] support ssc.transform() b983f0f [Davies Liu] address comments 847f9b9 [Davies Liu] add more docs, add first(), take() e059ca2 [Davies Liu] move check of window into Python fce0ef5 [Davies Liu] rafactor of foreachRDD() 7001b51 [Davies Liu] refactor of queueStream() 26ea396 [Davies Liu] refactor 74df565 [Davies Liu] fix print and docs b32774c [Davies Liu] move java_import into streaming 604323f [Davies Liu] enable streaming tests c499ba0 [Davies Liu] remove Time and Duration 3f0fb4b [Davies Liu] refactor fix tests c28f520 [Davies Liu] support updateStateByKey d357b70 [Davies Liu] support windowed dstream bd13026 [Davies Liu] fix examples eec401e [Davies Liu] refactor, combine TransformedRDD, fix reuse PythonRDD, fix union 9a57685 [Davies Liu] fix python style bd27874 [Davies Liu] fix scala style 7339be0 [Davies Liu] delete tests 7f53086 [Davies Liu] support transform(), refactor and cleanup df098fc [Davies Liu] Merge branch 'master' into giwa 550dfd9 [giwa] WIP fixing 1.1 merge 5cdb6fa [giwa] changed for SCCallSiteSync e685853 [giwa] meged with rebased 1.1 branch 2d32a74 [giwa] added some StreamingContextTestSuite 4a59e1e [giwa] WIP:added more test for StreamingContext 8ffdbf1 [giwa] added atexit to handle callback server d5f5fcb [giwa] added comment for StreamingContext.sparkContext 63c881a [giwa] added StreamingContext.sparkContext d39f102 [giwa] added StreamingContext.remember d542743 [giwa] clean up code 2fdf0de [Matthew Farrellee] Fix scalastyle errors c0a06bc [giwa] delete not implemented functions f385976 [giwa] delete inproper comments b0f2015 [giwa] added comment in dstream._test_output bebb3f3 [giwa] remove the last brank line fbed8da [giwa] revert pom.xml 8ed93af [giwa] fixed explanaiton 066ba90 [giwa] revert pom.xml fa4af88 [giwa] remove duplicated import 6ae3caa [giwa] revert pom.xml 7dc7391 [giwa] fixed typo 62dc7a3 [giwa] clean up exmples f04882c [giwa] clen up examples b171ec3 [giwa] fixed pep8 violation f198d14 [giwa] clean up code 3166d31 [giwa] clean up c00e091 [giwa] change test case not to use awaitTermination e80647e [giwa] adopted the latest compression way of python command 58e41ff [giwa] merge with master 455e5af [giwa] removed wasted print in DStream af336b7 [giwa] add comments ddd4ee1 [giwa] added TODO coments 99ce042 [giwa] added saveAsTextFiles and saveAsPickledFiles 2a06cdb [giwa] remove waste duplicated code c5ecfc1 [giwa] basic function test cases are passed 8dcda84 [giwa] all tests are passed if numSlice is 2 and the numver of each input is over 4 795b2cd [giwa] broke something 1e126bf [giwa] WIP: solved partitioned and None is not recognized f67cf57 [giwa] added mapValues and flatMapVaules WIP for glom and mapPartitions test 953deb0 [giwa] edited the comment to add more precise description af610d3 [giwa] removed unnesessary changes c1d546e [giwa] fixed PEP-008 violation 99410be [giwa] delete waste file b3b0362 [giwa] added basic operation test cases 9cde7c9 [giwa] WIP added test case bd3ba53 [giwa] WIP 5c04a5f [giwa] WIP: added PythonTestInputStream 019ef38 [giwa] WIP 1934726 [giwa] update comment 376e3ac [giwa] WIP 932372a [giwa] clean up dstream.py 0b09cff [giwa] added stop in StreamingContext 92e333e [giwa] implemented reduce and count function in Dstream 1b83354 [giwa] Removed the waste line 88f7506 [Ken Takagiwa] Kill py4j callback server properly 54b5358 [Ken Takagiwa] tried to restart callback server 4f07163 [Tathagata Das] Implemented DStream.foreachRDD in the Python API using Py4J callback server. fe02547 [Ken Takagiwa] remove waste file 2ad7bd3 [Ken Takagiwa] clean up codes 6197a11 [Ken Takagiwa] clean up code eb4bf48 [Ken Takagiwa] fix map function 98c2a00 [Ken Takagiwa] added count operation but this implementation need double check 58591d2 [Ken Takagiwa] reduceByKey is working 0df7111 [Ken Takagiwa] delete old file f485b1d [Ken Takagiwa] fied input of socketTextDStream dd6de81 [Ken Takagiwa] initial commit for socketTextStream 247fd74 [Ken Takagiwa] modified the code base on comment in tdas#10 4bcb318 [Ken Takagiwa] implementing transform function in Python 38adf95 [Ken Takagiwa] added reducedByKey not working yet 66fcfff [Ken Takagiwa] modify dstream.py to fix indent error 41886c2 [Ken Takagiwa] comment PythonDStream.PairwiseDStream 0b99bec [Ken] initial commit for pySparkStreaming c214199 [giwa] added testcase for combineByKey 5625bdc [giwa] added gorupByKey testcase 10ab87b [giwa] added sparkContext as input parameter in StreamingContext 10b5b04 [giwa] removed wasted print in DStream e54f986 [giwa] add comments 16aa64f [giwa] added TODO coments 74535d4 [giwa] added saveAsTextFiles and saveAsPickledFiles f76c182 [giwa] remove waste duplicated code 18c8723 [giwa] modified streaming test case to add coment 13fb44c [giwa] basic function test cases are passed 3000b2b [giwa] all tests are passed if numSlice is 2 and the numver of each input is over 4 ff14070 [giwa] broke something bcdec33 [giwa] WIP: solved partitioned and None is not recognized 270a9e1 [giwa] added mapValues and flatMapVaules WIP for glom and mapPartitions test bb10956 [giwa] edited the comment to add more precise description 253a863 [giwa] removed unnesessary changes 3d37822 [giwa] fixed PEP-008 violation f21cab3 [giwa] delete waste file 878bad7 [giwa] added basic operation test cases ce2acd2 [giwa] WIP added test case 9ad6855 [giwa] WIP 1df77f5 [giwa] WIP: added PythonTestInputStream 1523b66 [giwa] WIP 8a0fbbc [giwa] update comment fe648e3 [giwa] WIP 29c2bc5 [giwa] initial commit for testcase 4d40d63 [giwa] clean up dstream.py c462bb3 [giwa] added stop in StreamingContext d2c01ba [giwa] clean up examples 3c45cd2 [giwa] implemented reduce and count function in Dstream b349649 [giwa] Removed the waste line 3b498e1 [Ken Takagiwa] Kill py4j callback server properly 84a9668 [Ken Takagiwa] tried to restart callback server 9ab8952 [Tathagata Das] Added extra line. 05e991b [Tathagata Das] Added missing file b1d2a30 [Tathagata Das] Implemented DStream.foreachRDD in the Python API using Py4J callback server. 678e854 [Ken Takagiwa] remove waste file 0a8bbbb [Ken Takagiwa] clean up codes bab31c1 [Ken Takagiwa] clean up code 72b9738 [Ken Takagiwa] fix map function d3ee86a [Ken Takagiwa] added count operation but this implementation need double check 15feea9 [Ken Takagiwa] edit python sparkstreaming example 6f98e50 [Ken Takagiwa] reduceByKey is working c455c8d [Ken Takagiwa] added reducedByKey not working yet dc6995d [Ken Takagiwa] delete old file b31446a [Ken Takagiwa] fixed typo of network_workdcount.py ccfd214 [Ken Takagiwa] added doctest for pyspark.streaming.duration 0d1b954 [Ken Takagiwa] fied input of socketTextDStream f746109 [Ken Takagiwa] initial commit for socketTextStream bb7ccf3 [Ken Takagiwa] remove unused import in python 224fc5e [Ken Takagiwa] add empty line d2099d8 [Ken Takagiwa] sorted the import following Spark coding convention 5bac7ec [Ken Takagiwa] revert streaming/pom.xml e1df940 [Ken Takagiwa] revert pom.xml 494cae5 [Ken Takagiwa] remove not implemented DStream functions in python 17a74c6 [Ken Takagiwa] modified the code base on comment in tdas#10 1a0f065 [Ken Takagiwa] implementing transform function in Python d7b4d6f [Ken Takagiwa] added reducedByKey not working yet 87438e2 [Ken Takagiwa] modify dstream.py to fix indent error b406252 [Ken Takagiwa] comment PythonDStream.PairwiseDStream 454981d [Ken] initial commit for pySparkStreaming 150b94c [giwa] added some StreamingContextTestSuite f7bc8f9 [giwa] WIP:added more test for StreamingContext ee50c5a [giwa] added atexit to handle callback server fdc9125 [giwa] added comment for StreamingContext.sparkContext f5bfb70 [giwa] added StreamingContext.sparkContext da09768 [giwa] added StreamingContext.remember d68b568 [giwa] clean up code 4afa390 [giwa] clean up code 1fd6bc7 [Ken Takagiwa] Merge pull request #2 from mattf/giwa-master d9d59fe [Matthew Farrellee] Fix scalastyle errors 67473a9 [giwa] delete not implemented functions c97377c [giwa] delete inproper comments 2ea769e [giwa] added comment in dstream._test_output 3b27bd4 [giwa] remove the last brank line acfcaeb [giwa] revert pom.xml 93f7637 [giwa] fixed explanaiton 50fd6f9 [giwa] revert pom.xml 4f82c89 [giwa] remove duplicated import 9d1de23 [giwa] revert pom.xml 7339df2 [giwa] fixed typo 9c85e48 [giwa] clean up exmples 24f95db [giwa] clen up examples 0d30109 [giwa] fixed pep8 violation b7dab85 [giwa] improve test case 583e66d [giwa] move tests for streaming inside streaming directory 1d84142 [giwa] remove unimplement test f0ea311 [giwa] clean up code 171edeb [giwa] clean up 4dedd2d [giwa] change test case not to use awaitTermination 268a6a5 [giwa] Changed awaitTermination not to call awaitTermincation in Scala. Just use time.sleep instread 09a28bf [giwa] improve testcases 58150f5 [giwa] Changed the test case to focus the test operation 199e37f [giwa] adopted the latest compression way of python command 185fdbf [giwa] merge with master f1798c4 [giwa] merge with master e70f706 [giwa] added testcase for combineByKey e162822 [giwa] added gorupByKey testcase 97742fe [giwa] added sparkContext as input parameter in StreamingContext 14d4c0e [giwa] removed wasted print in DStream 6d8190a [giwa] add comments 4aa99e4 [giwa] added TODO coments e9fab72 [giwa] added saveAsTextFiles and saveAsPickledFiles 94f2b65 [giwa] remove waste duplicated code 580fbc2 [giwa] modified streaming test case to add coment 99e4bb3 [giwa] basic function test cases are passed 7051a84 [giwa] all tests are passed if numSlice is 2 and the numver of each input is over 4 35933e1 [giwa] broke something 9767712 [giwa] WIP: solved partitioned and None is not recognized 4f2d7e6 [giwa] added mapValues and flatMapVaules WIP for glom and mapPartitions test 33c0f94 [giwa] edited the comment to add more precise description 774f18d [giwa] removed unnesessary changes 3a671cc [giwa] remove export PYSPARK_PYTHON in spark submit 8efa266 [giwa] fixed PEP-008 violation fa75d71 [giwa] delete waste file 7f96294 [giwa] added basic operation test cases 3dda31a [giwa] WIP added test case 1f68b78 [giwa] WIP c05922c [giwa] WIP: added PythonTestInputStream 1fd12ae [giwa] WIP c880a33 [giwa] update comment 5d22c92 [giwa] WIP ea4b06b [giwa] initial commit for testcase 5a9b525 [giwa] clean up dstream.py 79c5809 [giwa] added stop in StreamingContext 189dcea [giwa] clean up examples b8d7d24 [giwa] implemented reduce and count function in Dstream b6468e6 [giwa] Removed the waste line b47b5fd [Ken Takagiwa] Kill py4j callback server properly 19ddcdd [Ken Takagiwa] tried to restart callback server c9fc124 [Tathagata Das] Added extra line. 4caae3f [Tathagata Das] Added missing file 4eff053 [Tathagata Das] Implemented DStream.foreachRDD in the Python API using Py4J callback server. 5e822d4 [Ken Takagiwa] remove waste file aeaf8a5 [Ken Takagiwa] clean up codes 9fa249b [Ken Takagiwa] clean up code 05459c6 [Ken Takagiwa] fix map function a9f4ecb [Ken Takagiwa] added count operation but this implementation need double check d1ee6ca [Ken Takagiwa] edit python sparkstreaming example 0b8b7d0 [Ken Takagiwa] reduceByKey is working d25d5cf [Ken Takagiwa] added reducedByKey not working yet 7f7c5d1 [Ken Takagiwa] delete old file 967dc26 [Ken Takagiwa] fixed typo of network_workdcount.py 57fb740 [Ken Takagiwa] added doctest for pyspark.streaming.duration 4b69fb1 [Ken Takagiwa] fied input of socketTextDStream 02f618a [Ken Takagiwa] initial commit for socketTextStream 4ce4058 [Ken Takagiwa] remove unused import in python 856d98e [Ken Takagiwa] add empty line 490e338 [Ken Takagiwa] sorted the import following Spark coding convention 5594bd4 [Ken Takagiwa] revert pom.xml 2adca84 [Ken Takagiwa] remove not implemented DStream functions in python e551e13 [Ken Takagiwa] add coment for hack why PYSPARK_PYTHON is needed in spark-submit 3758175 [Ken Takagiwa] add coment for hack why PYSPARK_PYTHON is needed in spark-submit c5518b4 [Ken Takagiwa] modified the code base on comment in tdas#10 dcf243f [Ken Takagiwa] implementing transform function in Python 9af03f4 [Ken Takagiwa] added reducedByKey not working yet 6e0d9c7 [Ken Takagiwa] modify dstream.py to fix indent error e497b9b [Ken Takagiwa] comment PythonDStream.PairwiseDStream 5c3a683 [Ken] initial commit for pySparkStreaming 665bfdb [giwa] added testcase for combineByKey a3d2379 [giwa] added gorupByKey testcase 636090a [giwa] added sparkContext as input parameter in StreamingContext e7ebb08 [giwa] removed wasted print in DStream d8b593b [giwa] add comments ea9c873 [giwa] added TODO coments 89ae38a [giwa] added saveAsTextFiles and saveAsPickledFiles e3033fc [giwa] remove waste duplicated code a14c7e1 [giwa] modified streaming test case to add coment 536def4 [giwa] basic function test cases are passed 2112638 [giwa] all tests are passed if numSlice is 2 and the numver of each input is over 4 080541a [giwa] broke something 0704b86 [giwa] WIP: solved partitioned and None is not recognized 90a6484 [giwa] added mapValues and flatMapVaules WIP for glom and mapPartitions test a65f302 [giwa] edited the comment to add more precise description bdde697 [giwa] removed unnesessary changes e8c7bfc [giwa] remove export PYSPARK_PYTHON in spark submit 3334169 [giwa] fixed PEP-008 violation db0a303 [giwa] delete waste file 2cfd3a0 [giwa] added basic operation test cases 90ae568 [giwa] WIP added test case a120d07 [giwa] WIP f671cdb [giwa] WIP: added PythonTestInputStream 56fae45 [giwa] WIP e35e101 [giwa] Merge branch 'master' into testcase ba5112d [giwa] update comment 28aa56d [giwa] WIP fb08559 [giwa] initial commit for testcase a613b85 [giwa] clean up dstream.py c40c0ef [giwa] added stop in StreamingContext 31e4260 [giwa] clean up examples d2127d6 [giwa] implemented reduce and count function in Dstream 48f7746 [giwa] Removed the waste line 0f83eaa [Ken Takagiwa] delete py4j 0.8.1 1679808 [Ken Takagiwa] Kill py4j callback server properly f96cd4e [Ken Takagiwa] tried to restart callback server fe86198 [Ken Takagiwa] add py4j 0.8.2.1 but server is not launched 1064fe0 [Ken Takagiwa] Merge branch 'master' of https://github.com/giwa/spark 28c6620 [Ken Takagiwa] Implemented DStream.foreachRDD in the Python API using Py4J callback server 85b0fe1 [Ken Takagiwa] Merge pull request #1 from tdas/python-foreach 54e2e8c [Tathagata Das] Added extra line. e185338 [Tathagata Das] Added missing file a778d4b [Tathagata Das] Implemented DStream.foreachRDD in the Python API using Py4J callback server. cc2092b [Ken Takagiwa] remove waste file d042ac6 [Ken Takagiwa] clean up codes 84a021f [Ken Takagiwa] clean up code bd20e17 [Ken Takagiwa] fix map function d01a125 [Ken Takagiwa] added count operation but this implementation need double check 7d05109 [Ken Takagiwa] merge with remote branch ae464e0 [Ken Takagiwa] edit python sparkstreaming example 04af046 [Ken Takagiwa] reduceByKey is working 3b6d7b0 [Ken Takagiwa] implementing transform function in Python 571d52d [Ken Takagiwa] added reducedByKey not working yet 5720979 [Ken Takagiwa] delete old file e604fcb [Ken Takagiwa] fixed typo of network_workdcount.py 4b7c08b [Ken Takagiwa] Merge branch 'master' of https://github.com/giwa/spark ce7d426 [Ken Takagiwa] added doctest for pyspark.streaming.duration a8c9fd5 [Ken Takagiwa] fixed for socketTextStream a61fa9e [Ken Takagiwa] fied input of socketTextDStream 1e84f41 [Ken Takagiwa] initial commit for socketTextStream 6d012f7 [Ken Takagiwa] remove unused import in python 25d30d5 [Ken Takagiwa] add empty line 6e0a64a [Ken Takagiwa] sorted the import following Spark coding convention fa4a7fc [Ken Takagiwa] revert streaming/pom.xml 8f8202b [Ken Takagiwa] revert streaming pom.xml c9d79dd [Ken Takagiwa] revert pom.xml 57e3e52 [Ken Takagiwa] remove not implemented DStream functions in python 0a516f5 [Ken Takagiwa] add coment for hack why PYSPARK_PYTHON is needed in spark-submit a7a0b5c [Ken Takagiwa] add coment for hack why PYSPARK_PYTHON is needed in spark-submit 72bfc66 [Ken Takagiwa] modified the code base on comment in tdas#10 69e9cd3 [Ken Takagiwa] implementing transform function in Python 94a0787 [Ken Takagiwa] added reducedByKey not working yet 88068cf [Ken Takagiwa] modify dstream.py to fix indent error 1367be5 [Ken Takagiwa] comment PythonDStream.PairwiseDStream eb2b3ba [Ken] Merge remote-tracking branch 'upstream/master' d8e51f9 [Ken] initial commit for pySparkStreaming
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Dec 24, 2014
update doc for WholeCombineFileRecordReader Author: Davies Liu <davies@databricks.com> Author: Josh Rosen <joshrosen@databricks.com> Closes apache#3301 from davies/fix_doc and squashes the following commits: 1d7422f [Davies Liu] Merge pull request #2 from JoshRosen/whole-text-file-cleanup dc3d21a [Josh Rosen] More genericization in ConfigurableCombineFileRecordReader. 95d13eb [Davies Liu] address comment bf800b9 [Davies Liu] update doc for WholeCombineFileRecordReader
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