This adds a new ShuffleManager based on sorting, as described in https://issues.apache.org/jira/browse/SPARK-2045. The bulk of the code is in an ExternalSorter class that is similar to ExternalAppendOnlyMap, but sorts key-value pairs by partition ID and can be used to create a single sorted file with a map task's output. (Longer-term I think this can take on the remaining functionality in ExternalAppendOnlyMap and replace it so we don't have code duplication.)
The main TODOs still left are:
- [x] enabling ExternalSorter to merge across spilled files
- [x] with an Ordering
- [x] without an Ordering, using the keys' hash codes
- [x] adding more tests (e.g. a version of our shuffle suite that runs on this)
- [x] rebasing on top of the size-tracking refactoring in #1165 when that is merged
- [x] disabling spilling if spark.shuffle.spill is set to false
Despite this though, this seems to work pretty well (running successfully in cases where the hash shuffle would OOM, such as 1000 reduce tasks on executors with only 1G memory), and it seems to be comparable in speed or faster than hash-based shuffle (it will create much fewer files for the OS to keep track of). So I'm posting it to get some early feedback.
After these TODOs are done, I'd also like to enable ExternalSorter to sort data within each partition by a key as well, which will allow us to use it to implement external spilling in reduce tasks in `sortByKey`.
Author: Matei Zaharia <matei@databricks.com>
Closes#1499 from mateiz/sort-based-shuffle and squashes the following commits:
bd841f9 [Matei Zaharia] Various review comments
d1c137fd [Matei Zaharia] Various review comments
a611159 [Matei Zaharia] Compile fixes due to rebase
62c56c8 [Matei Zaharia] Fix ShuffledRDD sometimes not returning Tuple2s.
f617432 [Matei Zaharia] Fix a failing test (seems to be due to change in SizeTracker logic)
9464d5f [Matei Zaharia] Simplify code and fix conflicts after latest rebase
0174149 [Matei Zaharia] Add cleanup behavior and cleanup tests for sort-based shuffle
eb4ee0d [Matei Zaharia] Remove customizable element type in ShuffledRDD
fa2e8db [Matei Zaharia] Allow nextBatchStream to be called after we're done looking at all streams
a34b352 [Matei Zaharia] Fix tracking of indices within a partition in SpillReader, and add test
03e1006 [Matei Zaharia] Add a SortShuffleSuite that runs ShuffleSuite with sort-based shuffle
3c7ff1f [Matei Zaharia] Obey the spark.shuffle.spill setting in ExternalSorter
ad65fbd [Matei Zaharia] Rebase on top of Aaron's Sorter change, and use Sorter in our buffer
44d2a93 [Matei Zaharia] Use estimateSize instead of atGrowThreshold to test collection sizes
5686f71 [Matei Zaharia] Optimize merging phase for in-memory only data:
5461cbb [Matei Zaharia] Review comments and more tests (e.g. tests with 1 element per partition)
e9ad356 [Matei Zaharia] Update ContextCleanerSuite to make sure shuffle cleanup tests use hash shuffle (since they were written for it)
c72362a [Matei Zaharia] Added bug fix and test for when iterators are empty
de1fb40 [Matei Zaharia] Make trait SizeTrackingCollection private[spark]
4988d16 [Matei Zaharia] tweak
c1b7572 [Matei Zaharia] Small optimization
ba7db7f [Matei Zaharia] Handle null keys in hash-based comparator, and add tests for collisions
ef4e397 [Matei Zaharia] Support for partial aggregation even without an Ordering
4b7a5ce [Matei Zaharia] More tests, and ability to sort data if a total ordering is given
e1f84be [Matei Zaharia] Fix disk block manager test
5a40a1c [Matei Zaharia] More tests
614f1b4 [Matei Zaharia] Add spill metrics to map tasks
cc52caf [Matei Zaharia] Add more error handling and tests for error cases
bbf359d [Matei Zaharia] More work
3a56341 [Matei Zaharia] More partial work towards sort-based shuffle
7a0895d [Matei Zaharia] Some more partial work towards sort-based shuffle
b615476 [Matei Zaharia] Scaffolding for sort-based shuffle
The Maven-based builds in the build matrix have been failing for a few days:
https://amplab.cs.berkeley.edu/jenkins/view/Spark/
On inspection, it looks like the Spark SQL Java tests don't compile:
https://amplab.cs.berkeley.edu/jenkins/view/Spark/job/Spark-Master-Maven-pre-YARN/hadoop.version=1.0.4,label=centos/244/consoleFull
I confirmed it by repeating the command vs master:
`mvn -Dhadoop.version=1.0.4 -Dlabel=centos -DskipTests clean package`
The problem is that this module doesn't depend on JUnit. In fact, none of the modules do, but `com.novocode:junit-interface` (the SBT-JUnit bridge) pulls it in, in most places. However this module doesn't depend on `com.novocode:junit-interface`
Adding the `junit:junit` dependency fixes the compile problem. In fact, the other modules with Java tests should probably depend on it explicitly instead of happening to get it via `com.novocode:junit-interface`, since that is a bit SBT/Scala-specific (and I am not even sure it's needed).
Author: Sean Owen <srowen@gmail.com>
Closes#1660 from srowen/SPARK-2749 and squashes the following commits:
858ff7c [Sean Owen] Add explicit junit dep to other modules with Java tests for robustness
9636794 [Sean Owen] Add junit dep so that Spark SQL Java tests compile
JIRA issue: https://issues.apache.org/jira/browse/SPARK-2024
This PR is a followup to #455 and adds capabilities for saving PySpark RDDs using SequenceFile or any Hadoop OutputFormats.
* Added RDD methods ```saveAsSequenceFile```, ```saveAsHadoopFile``` and ```saveAsHadoopDataset```, for both old and new MapReduce APIs.
* Default converter for converting common data types to Writables. Users may specify custom converters to convert to desired data types.
* No out-of-box support for reading/writing arrays, since ArrayWritable itself doesn't have a no-arg constructor for creating an empty instance upon reading. Users need to provide ArrayWritable subtypes. Custom converters for converting arrays to suitable ArrayWritable subtypes are also needed when writing. When reading, the default converter will convert any custom ArrayWritable subtypes to ```Object[]``` and they get pickled to Python tuples.
* Added HBase and Cassandra output examples to show how custom output formats and converters can be used.
cc MLnick mateiz ahirreddy pwendell
Author: Kan Zhang <kzhang@apache.org>
Closes#1338 from kanzhang/SPARK-2024 and squashes the following commits:
c01e3ef [Kan Zhang] [SPARK-2024] code formatting
6591e37 [Kan Zhang] [SPARK-2024] renaming pickled -> pickledRDD
d998ad6 [Kan Zhang] [SPARK-2024] refectoring to get method params below 10
57a7a5e [Kan Zhang] [SPARK-2024] correcting typo
75ca5bd [Kan Zhang] [SPARK-2024] Better type checking for batch serialized RDD
0bdec55 [Kan Zhang] [SPARK-2024] Refactoring newly added tests
9f39ff4 [Kan Zhang] [SPARK-2024] Adding 2 saveAsHadoopDataset tests
0c134f3 [Kan Zhang] [SPARK-2024] Test refactoring and adding couple unbatched cases
7a176df [Kan Zhang] [SPARK-2024] Add saveAsSequenceFile to PySpark
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).
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
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`.
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)
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.
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.
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
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
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.
Spark only transitively depends on the latter, based on the Hadoop version.
Author: Aaron Davidson <aaron@databricks.com>
Closes#1621 from aarondav/lang3 and squashes the following commits:
93c93bf [Aaron Davidson] Use commons-lang3 in SignalLogger rather than commons-lang
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
**Problem.** When caching, we currently unroll the entire RDD partition before making sure we have enough free memory. This is a common cause for OOMs especially when (1) the BlockManager has little free space left in memory, and (2) the partition is large.
**Solution.** We maintain a global memory pool of `M` bytes shared across all threads, similar to the way we currently manage memory for shuffle aggregation. Then, while we unroll each partition, periodically check if there is enough space to continue. If not, drop enough RDD blocks to ensure we have at least `M` bytes to work with, then try again. If we still don't have enough space to unroll the partition, give up and drop the block to disk directly if applicable.
**New configurations.**
- `spark.storage.bufferFraction` - the value of `M` as a fraction of the storage memory. (default: 0.2)
- `spark.storage.safetyFraction` - a margin of safety in case size estimation is slightly off. This is the equivalent of the existing `spark.shuffle.safetyFraction`. (default 0.9)
For more detail, see the [design document](https://issues.apache.org/jira/secure/attachment/12651793/spark-1777-design-doc.pdf). Tests pending for performance and memory usage patterns.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1165 from andrewor14/them-rdd-memories and squashes the following commits:
e77f451 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
c7c8832 [Andrew Or] Simplify logic + update a few comments
269d07b [Andrew Or] Very minor changes to tests
6645a8a [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
b7e165c [Andrew Or] Add new tests for unrolling blocks
f12916d [Andrew Or] Slightly clean up tests
71672a7 [Andrew Or] Update unrollSafely tests
369ad07 [Andrew Or] Correct ensureFreeSpace and requestMemory behavior
f4d035c [Andrew Or] Allow one thread to unroll multiple blocks
a66fbd2 [Andrew Or] Rename a few things + update comments
68730b3 [Andrew Or] Fix weird scalatest behavior
e40c60d [Andrew Or] Fix MIMA excludes
ff77aa1 [Andrew Or] Fix tests
1a43c06 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
b9a6eee [Andrew Or] Simplify locking behavior on unrollMemoryMap
ed6cda4 [Andrew Or] Formatting fix (super minor)
f9ff82e [Andrew Or] putValues -> putIterator + putArray
beb368f [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
8448c9b [Andrew Or] Fix tests
a49ba4d [Andrew Or] Do not expose unroll memory check period
69bc0a5 [Andrew Or] Always synchronize on putLock before unrollMemoryMap
3f5a083 [Andrew Or] Simplify signature of ensureFreeSpace
dce55c8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
8288228 [Andrew Or] Synchronize put and unroll properly
4f18a3d [Andrew Or] bufferFraction -> unrollFraction
28edfa3 [Andrew Or] Update a few comments / log messages
728323b [Andrew Or] Do not synchronize every 1000 elements
5ab2329 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
129c441 [Andrew Or] Fix bug: Use toArray rather than array
9a65245 [Andrew Or] Update a few comments + minor control flow changes
57f8d85 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
abeae4f [Andrew Or] Add comment clarifying the MEMORY_AND_DISK case
3dd96aa [Andrew Or] AppendOnlyBuffer -> Vector (+ a few small changes)
f920531 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
0871835 [Andrew Or] Add an effective storage level interface to BlockManager
64e7d4c [Andrew Or] Add/modify a few comments (minor)
8af2f35 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
4f4834e [Andrew Or] Use original storage level for blocks dropped to disk
ecc8c2d [Andrew Or] Fix binary incompatibility
24185ea [Andrew Or] Avoid dropping a block back to disk if reading from disk
2b7ee66 [Andrew Or] Fix bug in SizeTracking*
9b9a273 [Andrew Or] Fix tests
20eb3e5 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
649bdb3 [Andrew Or] Document spark.storage.bufferFraction
a10b0e7 [Andrew Or] Add initial memory request threshold + rename a few things
e9c3cb0 [Andrew Or] cacheMemoryMap -> unrollMemoryMap
198e374 [Andrew Or] Unfold -> unroll
0d50155 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
d9d02a8 [Andrew Or] Remove unused param in unfoldSafely
ec728d8 [Andrew Or] Add tests for safe unfolding of blocks
22b2209 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
078eb83 [Andrew Or] Add check for hasNext in PrimitiveVector.iterator
0871535 [Andrew Or] Fix tests in BlockManagerSuite
d68f31e [Andrew Or] Safely unfold blocks for all memory puts
5961f50 [Andrew Or] Fix tests
195abd7 [Andrew Or] Refactor: move unfold logic to MemoryStore
1e82d00 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
3ce413e [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
d5dd3b4 [Andrew Or] Free buffer memory in finally
ea02eec [Andrew Or] Fix tests
b8e1d9c [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
a8704c1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
e1b8b25 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
87aa75c [Andrew Or] Fix mima excludes again (typo)
11eb921 [Andrew Or] Clarify comment (minor)
50cae44 [Andrew Or] Remove now duplicate mima exclude
7de5ef9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
df47265 [Andrew Or] Fix binary incompatibility
6d05a81 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
f94f5af [Andrew Or] Update a few comments (minor)
776aec9 [Andrew Or] Prevent OOM if a single RDD partition is too large
bbd3eea [Andrew Or] Fix CacheManagerSuite to use Array
97ea499 [Andrew Or] Change BlockManager interface to use Arrays
c12f093 [Andrew Or] Add SizeTrackingAppendOnlyBuffer and tests
(This is a replacement of #1399, trying to fix potential `HiveThriftServer2` port collision between parallel builds. Please refer to [these comments](https://github.com/apache/spark/pull/1399#issuecomment-50212572) for details.)
JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)
Merging the Hive Thrift/JDBC server from [branch-1.0-jdbc](https://github.com/apache/spark/tree/branch-1.0-jdbc).
Thanks chenghao-intel for his initial contribution of the Spark SQL CLI.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1600 from liancheng/jdbc and squashes the following commits:
ac4618b [Cheng Lian] Uses random port for HiveThriftServer2 to avoid collision with parallel builds
090beea [Cheng Lian] Revert changes related to SPARK-2678, decided to move them to another PR
21c6cf4 [Cheng Lian] Updated Spark SQL programming guide docs
fe0af31 [Cheng Lian] Reordered spark-submit options in spark-shell[.cmd]
199e3fb [Cheng Lian] Disabled MIMA for hive-thriftserver
1083e9d [Cheng Lian] Fixed failed test suites
7db82a1 [Cheng Lian] Fixed spark-submit application options handling logic
9cc0f06 [Cheng Lian] Starts beeline with spark-submit
cfcf461 [Cheng Lian] Updated documents and build scripts for the newly added hive-thriftserver profile
061880f [Cheng Lian] Addressed all comments by @pwendell
7755062 [Cheng Lian] Adapts test suites to spark-submit settings
40bafef [Cheng Lian] Fixed more license header issues
e214aab [Cheng Lian] Added missing license headers
b8905ba [Cheng Lian] Fixed minor issues in spark-sql and start-thriftserver.sh
f975d22 [Cheng Lian] Updated docs for Hive compatibility and Shark migration guide draft
3ad4e75 [Cheng Lian] Starts spark-sql shell with spark-submit
a5310d1 [Cheng Lian] Make HiveThriftServer2 play well with spark-submit
61f39f4 [Cheng Lian] Starts Hive Thrift server via spark-submit
2c4c539 [Cheng Lian] Cherry picked the Hive Thrift server
This will decrease object allocation from the "update" closure used in map.changeValue.
Author: Matei Zaharia <matei@databricks.com>
Closes#1607 from mateiz/spark-2684 and squashes the following commits:
b7d89e6 [Matei Zaharia] Add insertAll for Iterables too, and fix some code style
561fc97 [Matei Zaharia] Update ExternalAppendOnlyMap to take an iterator as input
Author: Matei Zaharia <matei@databricks.com>
Closes#1593 from mateiz/spark-2680 and squashes the following commits:
3c949c4 [Matei Zaharia] Lower spark.shuffle.memoryFraction to 0.2 by default
Similar to SPARK-1034, the problem was that Py4J didn’t cope well with the fake ClassTags used in the Java API. It doesn’t look like there’s any reason why PythonRDD needs to take a ClassTag, since it just ignores the type of the previous RDD, so I removed the type parameter and we no longer pass ClassTags from Python.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1605 from JoshRosen/spark-2601 and squashes the following commits:
b68e118 [Josh Rosen] Fix Py4J error when transforming pickleFiles [SPARK-2601]
handleMessageExecutor, handleReadWriteExecutor, and handleConnectExecutor are not marked as daemon and not named. I think there exists some condition in which Spark programs won't terminate because of this.
Stack dump attached in https://issues.apache.org/jira/browse/SPARK-2704
Author: Reynold Xin <rxin@apache.org>
Closes#1604 from rxin/daemon and squashes the following commits:
98d6a6c [Reynold Xin] [SPARK-2704] Name threads in ConnectionManager and mark them as daemon.
Added emptyRDD method to Java API with tests.
Author: bpaulin <bob@bobpaulin.com>
Closes#1597 from bobpaulin/SPARK-2279 and squashes the following commits:
5ad57c2 [bpaulin] [SPARK-2279] Added emptyRDD method to Java API
The current default value of spark.serializer.objectStreamReset is 10,000.
When trying to re-partition (e.g., to 64 partitions) a large file (e.g., 500MB), containing 1MB records, the serializer will cache 10000 x 1MB x 64 ~= 640 GB which will cause out of memory errors.
This patch sets the default value to a more reasonable default value (100).
Author: Hossein <hossein@databricks.com>
Closes#1595 from falaki/objectStreamReset and squashes the following commits:
650a935 [Hossein] Updated documentation
1aa0df8 [Hossein] Reduce default value of spark.serializer.objectStreamReset
Author: Josh Rosen <joshrosen@apache.org>
Closes#1596 from JoshRosen/spark-1458 and squashes the following commits:
fdbb0bf [Josh Rosen] Add SparkContext.version to Python & Java [SPARK-1458]
This is part of the scheduler cleanup/refactoring effort to make the scheduler code easier to maintain.
@kayousterhout @markhamstra please take a look ...
Author: Reynold Xin <rxin@apache.org>
Closes#1561 from rxin/dagSchedulerHashMaps and squashes the following commits:
1c44e15 [Reynold Xin] Clear pending tasks in submitMissingTasks.
620a0d1 [Reynold Xin] Use filterKeys.
5b54404 [Reynold Xin] Code review feedback.
c1e9a1c [Reynold Xin] Removed some HashMaps from DAGScheduler by storing information in Stage.
This reverts commit 06dc0d2c6b.
#1399 is making Jenkins fail. We should investigate and put this back after its passing tests.
Author: Michael Armbrust <michael@databricks.com>
Closes#1594 from marmbrus/revertJDBC and squashes the following commits:
59748da [Michael Armbrust] Revert "[SPARK-2410][SQL] Merging Hive Thrift/JDBC server"
Due to problems with when we update runningStages (in DAGScheduler.scala)
and how we decide to send a SparkListenerStageCompleted message to
SparkListeners, sometimes stages can be shown as "running" in the UI forever
(even after they have failed). This issue can manifest when stages are
resubmitted with 0 tasks, or when the DAGScheduler catches non-serializable
tasks. The problem also resulted in a (small) memory leak in the DAGScheduler,
where stages can stay in runningStages forever. This commit fixes
that problem and adds a unit test.
Thanks tsudukim for helping to look into this issue!
cc markhamstra rxin
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#1566 from kayousterhout/dag_fix and squashes the following commits:
217d74b [Kay Ousterhout] [SPARK-1726] [SPARK-2567] Eliminate zombie stages in UI.
This patch adds a sort flag into ShuffleDependecy and moves sort into hash shuffle implementation.
Moving sort into shuffle implementation can give space for other shuffle implementations (like sort-based shuffle) to better optimize sort through shuffle.
Author: jerryshao <saisai.shao@intel.com>
Closes#1210 from jerryshao/SPARK-2125 and squashes the following commits:
2feaf7b [jerryshao] revert MimaExcludes
ceddf75 [jerryshao] add MimaExeclude
f674ff4 [jerryshao] Add missing Scope restriction
b9fe0dd [jerryshao] Fix some style issues according to comments
ef6b729 [jerryshao] Change sort flag into Option
3f6eeed [jerryshao] Fix issues related to unit test
2f552a5 [jerryshao] Minor changes about naming and order
c92a281 [jerryshao] Move sort into shuffle implementations
JIRA issue:
- Main: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)
- Related: [SPARK-2678](https://issues.apache.org/jira/browse/SPARK-2678)
Cherry picked the Hive Thrift/JDBC server from [branch-1.0-jdbc](https://github.com/apache/spark/tree/branch-1.0-jdbc).
(Thanks chenghao-intel for his initial contribution of the Spark SQL CLI.)
TODO
- [x] Use `spark-submit` to launch the server, the CLI and beeline
- [x] Migration guideline draft for Shark users
----
Hit by a bug in `SparkSubmitArguments` while working on this PR: all application options that are recognized by `SparkSubmitArguments` are stolen as `SparkSubmit` options. For example:
```bash
$ spark-submit --class org.apache.hive.beeline.BeeLine spark-internal --help
```
This actually shows usage information of `SparkSubmit` rather than `BeeLine`.
~~Fixed this bug here since the `spark-internal` related stuff also touches `SparkSubmitArguments` and I'd like to avoid conflict.~~
**UPDATE** The bug mentioned above is now tracked by [SPARK-2678](https://issues.apache.org/jira/browse/SPARK-2678). Decided to revert changes to this bug since it involves more subtle considerations and worth a separate PR.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1399 from liancheng/thriftserver and squashes the following commits:
090beea [Cheng Lian] Revert changes related to SPARK-2678, decided to move them to another PR
21c6cf4 [Cheng Lian] Updated Spark SQL programming guide docs
fe0af31 [Cheng Lian] Reordered spark-submit options in spark-shell[.cmd]
199e3fb [Cheng Lian] Disabled MIMA for hive-thriftserver
1083e9d [Cheng Lian] Fixed failed test suites
7db82a1 [Cheng Lian] Fixed spark-submit application options handling logic
9cc0f06 [Cheng Lian] Starts beeline with spark-submit
cfcf461 [Cheng Lian] Updated documents and build scripts for the newly added hive-thriftserver profile
061880f [Cheng Lian] Addressed all comments by @pwendell
7755062 [Cheng Lian] Adapts test suites to spark-submit settings
40bafef [Cheng Lian] Fixed more license header issues
e214aab [Cheng Lian] Added missing license headers
b8905ba [Cheng Lian] Fixed minor issues in spark-sql and start-thriftserver.sh
f975d22 [Cheng Lian] Updated docs for Hive compatibility and Shark migration guide draft
3ad4e75 [Cheng Lian] Starts spark-sql shell with spark-submit
a5310d1 [Cheng Lian] Make HiveThriftServer2 play well with spark-submit
61f39f4 [Cheng Lian] Starts Hive Thrift server via spark-submit
2c4c539 [Cheng Lian] Cherry picked the Hive Thrift server
Renaming `short` to `shortForm` and `long` to `longForm`.
JIRA: https://issues.apache.org/jira/browse/SPARK-2683
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1585 from yhuai/SPARK-2683 and squashes the following commits:
5ddb843 [Yin Huai] "short" and "long" are Java keyworks. In order to generate javadoc, renaming "short" to "shortForm" and "long" to "longForm".
Author: Reynold Xin <rxin@apache.org>
Closes#1583 from rxin/closureClean and squashes the following commits:
8982fe6 [Reynold Xin] [SPARK-2529] Clean closures in foreach and foreachPartition.
JIRA: https://issues.apache.org/jira/browse/SPARK-2657
Our current code uses ArrayBuffers for each group of values in groupBy, as well as for the key's elements in CoGroupedRDD. ArrayBuffers have a lot of overhead if there are few values in them, which is likely to happen in cases such as join. In particular, they have a pointer to an Object[] of size 16 by default, which is 24 bytes for the array header + 128 for the pointers in there, plus at least 32 for the ArrayBuffer data structure. This patch replaces the per-group buffers with a CompactBuffer class that can store up to 2 elements more efficiently (in fields of itself) and acts like an ArrayBuffer beyond that. For a key's elements in CoGroupedRDD, we use an Array of CompactBuffers instead of an ArrayBuffer of ArrayBuffers.
There are some changes throughout the code to deal with CoGroupedRDD returning Array instead. We can also decide not to do that but CoGroupedRDD is a `DeveloperAPI` so I think it's okay to change it here.
Author: Matei Zaharia <matei@databricks.com>
Closes#1555 from mateiz/compact-groupby and squashes the following commits:
845a356 [Matei Zaharia] Lower initial size of CompactBuffer's vector to 8
07621a7 [Matei Zaharia] Review comments
0c1cd12 [Matei Zaharia] Don't use varargs in CompactBuffer.apply
bdc8a39 [Matei Zaharia] Small tweak to +=, and typos
f61f040 [Matei Zaharia] Fix line lengths
59da88b0 [Matei Zaharia] Fix line lengths
197cde8 [Matei Zaharia] Make CompactBuffer extend Seq to make its toSeq more efficient
775110f [Matei Zaharia] Change CoGroupedRDD to give (K, Array[Iterable[_]]) to avoid wrappers
9b4c6e8 [Matei Zaharia] Use CompactBuffer in CoGroupedRDD
ed577ab [Matei Zaharia] Use CompactBuffer in groupByKey
10f0de1 [Matei Zaharia] A CompactBuffer that's more memory-efficient than ArrayBuffer for small buffers
exact sample size not supported for now.
Author: Doris Xin <doris.s.xin@gmail.com>
Closes#1554 from dorx/pystratified and squashes the following commits:
4ba927a [Doris Xin] use rel diff (+- 50%) instead of abs diff (+- 50)
bdc3f8b [Doris Xin] updated unit to check sample holistically
7713c7b [Doris Xin] Python version of stratified sampling
During aggregation in Python worker, if the memory usage is above spark.executor.memory, it will do disk spilling aggregation.
It will split the aggregation into multiple stage, in each stage, it will partition the aggregated data by hash and dump them into disks. After all the data are aggregated, it will merge all the stages together (partition by partition).
Author: Davies Liu <davies.liu@gmail.com>
Closes#1460 from davies/spill and squashes the following commits:
cad91bf [Davies Liu] call gc.collect() after data.clear() to release memory as much as possible.
37d71f7 [Davies Liu] balance the partitions
902f036 [Davies Liu] add shuffle.py into run-tests
dcf03a9 [Davies Liu] fix memory_info() of psutil
67e6eba [Davies Liu] comment for MAX_TOTAL_PARTITIONS
f6bd5d6 [Davies Liu] rollback next_limit() again, the performance difference is huge:
e74b785 [Davies Liu] fix code style and change next_limit to memory_limit
400be01 [Davies Liu] address all the comments
6178844 [Davies Liu] refactor and improve docs
fdd0a49 [Davies Liu] add long doc string for ExternalMerger
1a97ce4 [Davies Liu] limit used memory and size of objects in partitionBy()
e6cc7f9 [Davies Liu] Merge branch 'master' into spill
3652583 [Davies Liu] address comments
e78a0a0 [Davies Liu] fix style
24cec6a [Davies Liu] get local directory by SPARK_LOCAL_DIR
57ee7ef [Davies Liu] update docs
286aaff [Davies Liu] let spilled aggregation in Python configurable
e9a40f6 [Davies Liu] recursive merger
6edbd1f [Davies Liu] Hash based disk spilling aggregation
Author: Neville Li <neville@spotify.com>
Closes#1188 from nevillelyh/neville/ui and squashes the following commits:
d3ac425 [Neville Li] SPARK-2250: show persisted RDD in stage UI
f075db9 [Neville Li] SPARK-2035: show call stack even when description is available
...rce manager UI
Use the event logger directory to provide a direct link to finished
application UI in yarn resourcemanager UI.
Author: Rahul Singhal <rahul.singhal@guavus.com>
Closes#1094 from rahulsinghaliitd/SPARK-2150 and squashes the following commits:
95f230c [Rahul Singhal] SPARK-2150: Provide direct link to finished application UI in yarn resource manager UI
...spark-submit
The PR allows invocations like
spark-submit --class org.MyClass --spark.shuffle.spill false myjar.jar
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1253 from sryza/sandy-spark-2310 and squashes the following commits:
1dc9855 [Sandy Ryza] More doc and cleanup
00edfb9 [Sandy Ryza] Review comments
91b244a [Sandy Ryza] Change format to --conf PROP=VALUE
8fabe77 [Sandy Ryza] SPARK-2310. Support arbitrary Spark properties on the command line with spark-submit
Author: GuoQiang Li <witgo@qq.com>
Closes#1511 from witgo/JsonProtocol and squashes the following commits:
2b6227f [GuoQiang Li] Fix NPE for JsonProtocol
Author: Ian O Connell <ioconnell@twitter.com>
Closes#1377 from ianoc/feature/SPARK-2102 and squashes the following commits:
5498566 [Ian O Connell] Docs update suggested by Patrick
20e8555 [Ian O Connell] Slight style change
f92c294 [Ian O Connell] Add docs for new KryoSerializer option
f3735c8 [Ian O Connell] Add using a kryo resource pool for the SqlSerializer
4e5c342 [Ian O Connell] Register the SparkConf for kryo, it gets swept into serialization
665805a [Ian O Connell] Add a spark.kryo.registrationRequired option for configuring the Kryo Serializer
Hi mridulm, I just think of this issue of [#1212](https://github.com/apache/spark/pull/1212): I added FakeRackUtil to hold the host -> rack mapping. It should be cleaned up after use so that it won't mess up with test cases others may add later.
Really sorry about this.
Author: Rui Li <rui.li@intel.com>
Closes#1454 from lirui-intel/SPARK-2277-fix-UT and squashes the following commits:
f8ea25c [Rui Li] SPARK-2277: clear host->rack info properly
Make spark's "local[N]" better.
In our company, we use "local[N]" in production. It works exellentlly. It's our best choice.
Author: woshilaiceshide <woshilaiceshide@qq.com>
Closes#1544 from woshilaiceshide/localX and squashes the following commits:
6c85154 [woshilaiceshide] [CORE] SPARK-2640: In "local[N]", free cores of the only executor should be touched by "spark.task.cpus" for every finish/start-up of tasks.
It's useful to know whether one thread is constantly spilling or multiple threads are spilling relatively infrequently. Right now everything looks a little jumbled and we can't tell which lines belong to the same thread. For instance:
```
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (194 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (198 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (198 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 10 MB to disk (197 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 9 MB to disk (45 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 23 MB to disk (198 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 38 MB to disk (25 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 161 MB to disk (25 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 0 MB to disk (199 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (166 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (199 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (200 times so far)
```
Author: Andrew Or <andrewor14@gmail.com>
Closes#1517 from andrewor14/external-log and squashes the following commits:
90e48bb [Andrew Or] Log thread ID when spilling
The name `preservesPartitioning` is ambiguous: 1) preserves the indices of partitions, 2) preserves the partitioner. The latter is correct and `preservesPartitioning` should really be called `preservesPartitioner` to avoid confusion. Unfortunately, this is already part of the API and we cannot change. We should be clear in the doc and fix wrong usages.
This PR
1. adds notes in `maPartitions*`,
2. makes `RDD.sample` preserve partitioner,
3. changes `preservesPartitioning` to false in `RDD.zip` because the keys of the first RDD are no longer the keys of the zipped RDD,
4. fixes some wrong usages in MLlib.
Author: Xiangrui Meng <meng@databricks.com>
Closes#1526 from mengxr/preserve-partitioner and squashes the following commits:
b361e65 [Xiangrui Meng] update doc based on pwendell's comments
3b1ba19 [Xiangrui Meng] update doc
357575c [Xiangrui Meng] fix unit test
20b4816 [Xiangrui Meng] Merge branch 'master' into preserve-partitioner
d1caa65 [Xiangrui Meng] add doc to explain preservesPartitioning fix wrong usage of preservesPartitioning make sample preserse partitioning
The issue is caused by #1112 .
Author: GuoQiang Li <witgo@qq.com>
Closes#1501 from witgo/webui_style and squashes the following commits:
4b34998 [GuoQiang Li] In some cases, pages display incorrect in WebUI
### Why and what?
Currently, the AppendOnlyMap performs an "in-place" sort by converting its array of [key, value, key, value] pairs into a an array of [(key, value), (key, value)] pairs. However, this causes us to allocate many Tuple2 objects, which come at a nontrivial overhead.
This patch adds a Sorter API, intended for in memory sorts, which simply ports the Android Timsort implementation (available under Apache v2) and abstracts the interface in a way which introduces no more than 1 virtual function invocation of overhead at each abstraction point.
Please compare our port of the Android Timsort sort with the original implementation: http://www.diffchecker.com/wiwrykcl
### Memory implications
An AppendOnlyMap contains N kv pairs, which results in roughly 2N elements within its underlying array. Each of these elements is 4 bytes wide in a [compressed OOPS](https://wikis.oracle.com/display/HotSpotInternals/CompressedOops) system, which is the default.
Today's approach immediately allocates N Tuple2 objects, which take up 24N bytes in total (exposed via YourKit), and undergoes a Java sort. The Java 6 version immediately copies the entire array (4N bytes here), while the Java 7 version has a worst-case allocation of half the array (2N bytes).
This results in a worst-case sorting overhead of 24N + 2N = 26N bytes (for Java 7).
The Sorter does not require allocating any tuples, but since it uses Timsort, it may copy up to half the entire array in the worst case.
This results in a worst-case sorting overhead of 4N bytes.
Thus, we have reduced the worst-case overhead of the sort by roughly 22 bytes times the number of elements.
### Performance implications
As the destructiveSortedIterator is used for spilling in an ExternalAppendOnlyMap, the purpose of this patch is to provide stability by reducing memory usage rather than improve performance. However, because it implements Timsort, it also brings a substantial performance boost over our prior implementation.
Here are the results of a microbenchmark that sorted 25 million, randomly distributed (Float, Int) pairs. The Java Arrays.sort() tests were run **only on the keys**, and thus moved less data. Our current implementation is called "Tuple-sort using Arrays.sort()" while the new implementation is "KV-array using Sorter".
<table>
<tr><th>Test</th><th>First run (JDK6)</th><th>Average of 10 (JDK6)</th><th>First run (JDK7)</th><th>Average of 10 (JDK7)</th></tr>
<tr><td>primitive Arrays.sort()</td><td>3216 ms</td><td>1190 ms</td><td>2724 ms</td><td>131 ms (!!)</td></tr>
<tr><td>Arrays.sort()</td><td>18564 ms</td><td>2006 ms</td><td>13201 ms</td><td>878 ms</td></tr>
<tr><td>Tuple-sort using Arrays.sort()</td><td>31813 ms</td><td>3550 ms</td><td>20990 ms</td><td>1919 ms</td></tr>
<tr><td><b>KV-array using Sorter</b></td><td></td><td></td><td><b>15020 ms</b></td><td><b>834 ms</b></td></tr>
</table>
The results show that this Sorter performs exactly as expected (after the first run) -- it is as fast as the Java 7 Arrays.sort() (which shares the same algorithm), but is significantly faster than the Tuple-sort on Java 6 or 7.
In short, this patch should significantly improve performance for users running either Java 6 or 7.
Author: Aaron Davidson <aaron@databricks.com>
Closes#1502 from aarondav/sort and squashes the following commits:
652d936 [Aaron Davidson] Update license, move Sorter to java src
a7b5b1c [Aaron Davidson] fix licenses
5c0efaf [Aaron Davidson] Update tmpLength
ec395c8 [Aaron Davidson] Ignore benchmark (again) and fix docs
034bf10 [Aaron Davidson] Change to Apache v2 Timsort
b97296c [Aaron Davidson] Don't try to run benchmark on Jenkins + private[spark]
6307338 [Aaron Davidson] SPARK-2047: Introduce an in-mem Sorter, and use it to reduce mem usage
Changes RDD.toDebugString() to show hierarchy and shuffle transformations more clearly
New output:
```
(3) FlatMappedValuesRDD[325] at apply at Transformer.scala:22
| MappedValuesRDD[324] at apply at Transformer.scala:22
| CoGroupedRDD[323] at apply at Transformer.scala:22
+-(5) MappedRDD[320] at apply at Transformer.scala:22
| | MappedRDD[319] at apply at Transformer.scala:22
| | MappedValuesRDD[318] at apply at Transformer.scala:22
| | MapPartitionsRDD[317] at apply at Transformer.scala:22
| | ShuffledRDD[316] at apply at Transformer.scala:22
| +-(10) MappedRDD[315] at apply at Transformer.scala:22
| | ParallelCollectionRDD[314] at apply at Transformer.scala:22
+-(100) MappedRDD[322] at apply at Transformer.scala:22
| ParallelCollectionRDD[321] at apply at Transformer.scala:22
```
Author: Gregory Owen <greowen@gmail.com>
Closes#1364 from GregOwen/to-debug-string and squashes the following commits:
08f5c78 [Gregory Owen] toDebugString: prettier debug printing to show shuffles and joins more clearly
1603f7b [Gregory Owen] toDebugString: prettier debug printing to show shuffles and joins more clearly
As a result of shivaram's experience debugging long scheduler delay, I think we should improve the tooltip to point people in the right direction if scheduler delay is large.
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#1488 from kayousterhout/better_tooltips and squashes the following commits:
22176fd [Kay Ousterhout] Improve scheduler delay tooltip.
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1474 from sryza/sandy-spark-2564 and squashes the following commits:
35b8388 [Sandy Ryza] Fix compile error on upmerge
7b985fb [Sandy Ryza] Fix test compile error
43f79e6 [Sandy Ryza] SPARK-2564. ShuffleReadMetrics.totalBlocksRead is redundant
We should fix this in branch-1.0 as well.
Author: Reynold Xin <rxin@apache.org>
Closes#1500 from rxin/rangePartitioner and squashes the following commits:
c0a94f5 [Reynold Xin] [SPARK-2598] RangePartitioner's binary search does not use the given Ordering.
