## What changes were proposed in this pull request?
Upgrade snappy to 1.1.2.4 to improve snappy read/write performance.
## How was this patch tested?
Tested by running a job on the cluster and saw 7.5% cpu savings after this change.
Author: Sital Kedia <skedia@fb.com>
Closes#12096 from sitalkedia/snappyRelease.
### What changes were proposed in this pull request?
This PR removes the ANTLR3 based parser, and moves the new ANTLR4 based parser into the `org.apache.spark.sql.catalyst.parser package`.
### How was this patch tested?
Existing unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12071 from hvanhovell/SPARK-14211.
### What changes were proposed in this pull request?
The current ANTLR3 parser is quite complex to maintain and suffers from code blow-ups. This PR introduces a new parser that is based on ANTLR4.
This parser is based on the [Presto's SQL parser](https://github.com/facebook/presto/blob/master/presto-parser/src/main/antlr4/com/facebook/presto/sql/parser/SqlBase.g4). The current implementation can parse and create Catalyst and SQL plans. Large parts of the HiveQl DDL and some of the DML functionality is currently missing, the plan is to add this in follow-up PRs.
This PR is a work in progress, and work needs to be done in the following area's:
- [x] Error handling should be improved.
- [x] Documentation should be improved.
- [x] Multi-Insert needs to be tested.
- [ ] Naming and package locations.
### How was this patch tested?
Catalyst and SQL unit tests.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11557 from hvanhovell/ngParser.
This patch upgrades Py4J from 0.9.1 to 0.9.2 in order to include a patch which modifies Py4J to use the current thread's ContextClassLoader when performing reflection / class loading. This is necessary in order to fix [SPARK-5185](https://issues.apache.org/jira/browse/SPARK-5185), a longstanding issue affecting the use of `--jars` and `--packages` in PySpark.
In order to demonstrate that the fix works, I removed the workarounds which were added as part of [SPARK-6027](https://issues.apache.org/jira/browse/SPARK-6027) / #4779 and other patches.
Py4J diff: https://github.com/bartdag/py4j/compare/0.9.1...0.9.2
/cc zsxwing tdas davies brkyvz
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11687 from JoshRosen/py4j-0.9.2.
## What changes were proposed in this pull request?
Update snappy to 1.1.2.1 to pull in a single fix -- the OOM fix we already worked around.
Supersedes https://github.com/apache/spark/pull/11524
## How was this patch tested?
Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#11631 from srowen/SPARK-13663.
## What changes were proposed in this pull request?
Modifies the dependency declarations of the all the hive artifacts, to explicitly exclude the groovy-all JAR.
This stops the groovy classes *and everything else in that uber-JAR* from getting into spark-assembly JAR.
## How was this patch tested?
1. Pre-patch build was made: `mvn clean install -Pyarn,hive,hive-thriftserver`
1. spark-assembly expanded, observed to have the org.codehaus.groovy packages and JARs
1. A maven dependency tree was created `mvn dependency:tree -Pyarn,hive,hive-thriftserver -Dverbose > target/dependencies.txt`
1. This text file examined to confirm that groovy was being imported as a dependency of `org.spark-project.hive`
1. Patch applied
1. Repeated step1: clean build of project with ` -Pyarn,hive,hive-thriftserver` set
1. Examined created spark-assembly, verified no org.codehaus packages
1. Verified that the maven dependency tree no longer references groovy
Note also that the size of the assembly JAR was 181628646 bytes before this patch, 166318515 after —15MB smaller. That's a good metric of things being excluded
Author: Steve Loughran <stevel@hortonworks.com>
Closes#11449 from steveloughran/fixes/SPARK-13599-groovy-dependency.
It registers more Scala classes, including ListBuffer to support Kryo with FPGrowth.
See https://github.com/twitter/chill/releases for Chill's change log.
Author: mark800 <yky800@126.com>
Closes#11041 from mark800/master.
Phase 1: update plugin versions, test dependencies, some example and third-party versions
Author: Sean Owen <sowen@cloudera.com>
Closes#11206 from srowen/SPARK-13324.
This patch changes Spark's build to make Scala 2.11 the default Scala version. To be clear, this does not mean that Spark will stop supporting Scala 2.10: users will still be able to compile Spark for Scala 2.10 by following the instructions on the "Building Spark" page; however, it does mean that Scala 2.11 will be the default Scala version used by our CI builds (including pull request builds).
The Scala 2.11 compiler is faster than 2.10, so I think we'll be able to look forward to a slight speedup in our CI builds (it looks like it's about 2X faster for the Maven compile-only builds, for instance).
After this patch is merged, I'll update Jenkins to add new compile-only jobs to ensure that Scala 2.10 compilation doesn't break.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10608 from JoshRosen/SPARK-6363.
- Remove Akka dependency from core. Note: the streaming-akka project still uses Akka.
- Remove HttpFileServer
- Remove Akka configs from SparkConf and SSLOptions
- Rename `spark.akka.frameSize` to `spark.rpc.message.maxSize`. I think it's still worth to keep this config because using `DirectTaskResult` or `IndirectTaskResult` depends on it.
- Update comments and docs
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10854 from zsxwing/remove-akka.
This pull request removes the external block store API. This is rarely used, and the file system interface is actually a better, more standard way to interact with external storage systems.
There are some other things to remove also, as pointed out by JoshRosen. We will do those as follow-up pull requests.
Author: Reynold Xin <rxin@databricks.com>
Closes#10752 from rxin/remove-offheap.
CSV is the most common data format in the "small data" world. It is often the first format people want to try when they see Spark on a single node. Having to rely on a 3rd party component for this leads to poor user experience for new users. This PR merges the popular spark-csv data source package (https://github.com/databricks/spark-csv) with SparkSQL.
This is a first PR to bring the functionality to spark 2.0 master. We will complete items outlines in the design document (see JIRA attachment) in follow up pull requests.
Author: Hossein <hossein@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#10766 from rxin/csv.
- [x] Upgrade Py4J to 0.9.1
- [x] SPARK-12657: Revert SPARK-12617
- [x] SPARK-12658: Revert SPARK-12511
- Still keep the change that only reading checkpoint once. This is a manual change and worth to take a look carefully. bfd4b5c040
- [x] Verify no leak any more after reverting our workarounds
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10692 from zsxwing/py4j-0.9.1.
The current Spark Streaming kinesis connector references a quite old version 1.9.40 of the AWS Java SDK (1.10.40 is current). Numerous AWS features including Kinesis Firehose are unavailable in 1.9. Those two versions of the AWS SDK in turn require conflicting versions of Jackson (2.4.4 and 2.5.3 respectively) such that one cannot include the current AWS SDK in a project that also uses the Spark Streaming Kinesis ASL.
Author: BrianLondon <brian@seatgeek.com>
Closes#10256 from BrianLondon/master.
This is a hotfix for a build bug introduced by the Netty exclusion changes in #10672. We can't exclude `io.netty:netty` because Akka depends on it. There's not a direct conflict between `io.netty:netty` and `io.netty:netty-all`, because the former puts classes in the `org.jboss.netty` namespace while the latter uses the `io.netty` namespace. However, there still is a conflict between `org.jboss.netty:netty` and `io.netty:netty`, so we need to continue to exclude the JBoss version of that artifact.
While the diff here looks somewhat large, note that this is only a revert of a some of the changes from #10672. You can see the net changes in pom.xml at 3119206b71...5211ab8 (diff-600376dffeb79835ede4a0b285078036)
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10693 from JoshRosen/netty-hotfix.
Netty classes are published under multiple artifacts with different names, so our build needs to exclude the `io.netty:netty` and `org.jboss.netty:netty` versions of the Netty artifact. However, our existing exclusions were incomplete, leading to situations where duplicate Netty classes would wind up on the classpath and cause compile errors (or worse).
This patch fixes the exclusion issue by adding more exclusions and uses Maven Enforcer's [banned dependencies](https://maven.apache.org/enforcer/enforcer-rules/bannedDependencies.html) rule to prevent these classes from accidentally being reintroduced. I also updated `dev/test-dependencies.sh` to run `mvn validate` so that the enforcer rules can run as part of pull request builds.
/cc rxin srowen pwendell. I'd like to backport at least the exclusion portion of this fix to `branch-1.5` in order to fix the documentation publishing job, which fails nondeterministically due to incompatible versions of Netty classes taking precedence on the compile-time classpath.
Author: Josh Rosen <rosenville@gmail.com>
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10672 from JoshRosen/enforce-netty-exclusions.
This PR moves a major part of the new SQL parser to Catalyst. This is a prelude to start using this parser for all of our SQL parsing. The following key changes have been made:
The ANTLR Parser & Supporting classes have been moved to the Catalyst project. They are now part of the ```org.apache.spark.sql.catalyst.parser``` package. These classes contained quite a bit of code that was originally from the Hive project, I have added aknowledgements whenever this applied. All Hive dependencies have been factored out. I have also taken this chance to clean-up the ```ASTNode``` class, and to improve the error handling.
The HiveQl object that provides the functionality to convert an AST into a LogicalPlan has been refactored into three different classes, one for every SQL sub-project:
- ```CatalystQl```: This implements Query and Expression parsing functionality.
- ```SparkQl```: This is a subclass of CatalystQL and provides SQL/Core only functionality such as Explain and Describe.
- ```HiveQl```: This is a subclass of ```SparkQl``` and this adds Hive-only functionality to the parser such as Analyze, Drop, Views, CTAS & Transforms. This class still depends on Hive.
cc rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10583 from hvanhovell/SPARK-12575.
There are a couple of places in the `dev/run-tests-*.py` scripts which deal with Hadoop profiles, but the set of profiles that they handle does not include all Hadoop profiles defined in our POM. Similarly, the `hadoop-2.2` and `hadoop-2.6` profiles were missing from `dev/deps`.
This patch updates these scripts to include all four Hadoop profiles defined in our POM.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10565 from JoshRosen/add-missing-hadoop-profiles-in-test-scripts.
