spark-instrumented-optimizer/pom.xml

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<?xml version="1.0" encoding="UTF-8"?>
<!--
~ Licensed to the Apache Software Foundation (ASF) under one or more
~ contributor license agreements. See the NOTICE file distributed with
~ this work for additional information regarding copyright ownership.
~ The ASF licenses this file to You under the Apache License, Version 2.0
~ (the "License"); you may not use this file except in compliance with
~ the License. You may obtain a copy of the License at
~
~ http://www.apache.org/licenses/LICENSE-2.0
~
~ Unless required by applicable law or agreed to in writing, software
~ distributed under the License is distributed on an "AS IS" BASIS,
~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
~ See the License for the specific language governing permissions and
~ limitations under the License.
-->
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<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
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<parent>
<groupId>org.apache</groupId>
<artifactId>apache</artifactId>
<version>13</version>
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</parent>
<groupId>org.apache.spark</groupId>
<artifactId>spark-parent</artifactId>
<version>1.0.0-SNAPSHOT</version>
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<packaging>pom</packaging>
<name>Spark Project Parent POM</name>
<url>http://spark.apache.org/</url>
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<licenses>
<license>
<name>Apache 2.0 License</name>
<url>http://www.apache.org/licenses/LICENSE-2.0.html</url>
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<distribution>repo</distribution>
</license>
</licenses>
<scm>
<connection>scm:git:git@github.com:apache/spark.git</connection>
<developerConnection>scm:git:https://git-wip-us.apache.org/repos/asf/spark.git</developerConnection>
<url>scm:git:git@github.com:apache/spark.git</url>
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<tag>HEAD</tag>
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</scm>
<developers>
<developer>
<id>matei</id>
<name>Matei Zaharia</name>
<email>matei.zaharia@gmail.com</email>
<url>http://www.cs.berkeley.edu/~matei</url>
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<organization>Apache Software Foundation</organization>
<organizationUrl>http://spark.apache.org</organizationUrl>
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</developer>
</developers>
<issueManagement>
<system>JIRA</system>
<url>https://spark-project.atlassian.net/browse/SPARK</url>
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</issueManagement>
<prerequisites>
<maven>3.0.0</maven>
</prerequisites>
<mailingLists>
<mailingList>
<name>Dev Mailing List</name>
<post>dev@spark.apache.org</post>
<subscribe>dev-subscribe@spark.apache.org</subscribe>
<unsubscribe>dev-unsubscribe@spark.apache.org</unsubscribe>
</mailingList>
<mailingList>
<name>User Mailing List</name>
<post>user@spark.apache.org</post>
<subscribe>user-subscribe@spark.apache.org</subscribe>
<unsubscribe>user-unsubscribe@spark.apache.org</unsubscribe>
</mailingList>
<mailingList>
<name>Commits Mailing List</name>
<post>commits@spark.apache.org</post>
<subscribe>commits-subscribe@spark.apache.org</subscribe>
<unsubscribe>commits-unsubscribe@spark.apache.org</unsubscribe>
</mailingList>
</mailingLists>
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<modules>
<module>core</module>
<module>bagel</module>
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<module>graphx</module>
<module>mllib</module>
<module>tools</module>
<module>streaming</module>
<module>repl</module>
<module>assembly</module>
<module>external/twitter</module>
<module>external/kafka</module>
<module>external/flume</module>
<module>external/zeromq</module>
<module>external/mqtt</module>
<module>examples</module>
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</modules>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<java.version>1.6</java.version>
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<scala.version>2.10.3</scala.version>
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<scala.binary.version>2.10</scala.binary.version>
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<mesos.version>0.13.0</mesos.version>
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<akka.group>org.spark-project.akka</akka.group>
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<akka.version>2.2.3-shaded-protobuf</akka.version>
SPARK-1071: Tidy logging strategy and use of log4j Prompted by a recent thread on the mailing list, I tried and failed to see if Spark can be made independent of log4j. There are a few cases where control of the underlying logging is pretty useful, and to do that, you have to bind to a specific logger. Instead I propose some tidying that leaves Spark's use of log4j, but gets rid of warnings and should still enable downstream users to switch. The idea is to pipe everything (except log4j) through SLF4J, and have Spark use SLF4J directly when logging, and where Spark needs to output info (REPL and tests), bind from SLF4J to log4j. This leaves the same behavior in Spark. It means that downstream users who want to use something except log4j should: - Exclude dependencies on log4j, slf4j-log4j12 from Spark - Include dependency on log4j-over-slf4j - Include dependency on another logger X, and another slf4j-X - Recreate any log config that Spark does, that is needed, in the other logger's config That sounds about right. Here are the key changes: - Include the jcl-over-slf4j shim everywhere by depending on it in core. - Exclude dependencies on commons-logging from third-party libraries. - Include the jul-to-slf4j shim everywhere by depending on it in core. - Exclude slf4j-* dependencies from third-party libraries to prevent collision or warnings - Added missing slf4j-log4j12 binding to GraphX, Bagel module tests And minor/incidental changes: - Update to SLF4J 1.7.5, which happily matches Hadoop 2’s version and is a recommended update over 1.7.2 - (Remove a duplicate HBase dependency declaration in SparkBuild.scala) - (Remove a duplicate mockito dependency declaration that was causing warnings and bugging me) Author: Sean Owen <sowen@cloudera.com> Closes #570 from srowen/SPARK-1071 and squashes the following commits: 52eac9f [Sean Owen] Add slf4j-over-log4j12 dependency to core (non-test) and remove it from things that depend on core. 77a7fa9 [Sean Owen] SPARK-1071: Tidy logging strategy and use of log4j
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<slf4j.version>1.7.5</slf4j.version>
<log4j.version>1.2.17</log4j.version>
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<hadoop.version>1.0.4</hadoop.version>
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<protobuf.version>2.4.1</protobuf.version>
<yarn.version>0.23.7</yarn.version>
<hbase.version>0.94.6</hbase.version>
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<PermGen>64m</PermGen>
<MaxPermGen>512m</MaxPermGen>
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</properties>
<repositories>
<repository>
<id>maven-repo</id> <!-- This should be at top, it makes maven try the central repo first and then others and hence faster dep resolution -->
<name>Maven Repository</name>
<url>http://repo.maven.apache.org/maven2</url>
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<releases>
<enabled>true</enabled>
</releases>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
<repository>
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<id>jboss-repo</id>
<name>JBoss Repository</name>
<url>http://repository.jboss.org/nexus/content/repositories/releases</url>
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<releases>
<enabled>true</enabled>
</releases>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
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<repository>
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<id>cloudera-repo</id>
<name>Cloudera Repository</name>
<url>https://repository.cloudera.com/artifactory/cloudera-repos</url>
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</repository>
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</repositories>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.eclipse.jetty</groupId>
<artifactId>jetty-server</artifactId>
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<version>7.6.8.v20121106</version>
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</dependency>
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
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<version>14.0.1</version>
</dependency>
<dependency>
<groupId>com.google.code.findbugs</groupId>
<artifactId>jsr305</artifactId>
<version>1.3.9</version>
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</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>${slf4j.version}</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>${slf4j.version}</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>jul-to-slf4j</artifactId>
<version>${slf4j.version}</version>
</dependency>
SPARK-1071: Tidy logging strategy and use of log4j Prompted by a recent thread on the mailing list, I tried and failed to see if Spark can be made independent of log4j. There are a few cases where control of the underlying logging is pretty useful, and to do that, you have to bind to a specific logger. Instead I propose some tidying that leaves Spark's use of log4j, but gets rid of warnings and should still enable downstream users to switch. The idea is to pipe everything (except log4j) through SLF4J, and have Spark use SLF4J directly when logging, and where Spark needs to output info (REPL and tests), bind from SLF4J to log4j. This leaves the same behavior in Spark. It means that downstream users who want to use something except log4j should: - Exclude dependencies on log4j, slf4j-log4j12 from Spark - Include dependency on log4j-over-slf4j - Include dependency on another logger X, and another slf4j-X - Recreate any log config that Spark does, that is needed, in the other logger's config That sounds about right. Here are the key changes: - Include the jcl-over-slf4j shim everywhere by depending on it in core. - Exclude dependencies on commons-logging from third-party libraries. - Include the jul-to-slf4j shim everywhere by depending on it in core. - Exclude slf4j-* dependencies from third-party libraries to prevent collision or warnings - Added missing slf4j-log4j12 binding to GraphX, Bagel module tests And minor/incidental changes: - Update to SLF4J 1.7.5, which happily matches Hadoop 2’s version and is a recommended update over 1.7.2 - (Remove a duplicate HBase dependency declaration in SparkBuild.scala) - (Remove a duplicate mockito dependency declaration that was causing warnings and bugging me) Author: Sean Owen <sowen@cloudera.com> Closes #570 from srowen/SPARK-1071 and squashes the following commits: 52eac9f [Sean Owen] Add slf4j-over-log4j12 dependency to core (non-test) and remove it from things that depend on core. 77a7fa9 [Sean Owen] SPARK-1071: Tidy logging strategy and use of log4j
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<dependency>
<groupId>org.slf4j</groupId>
<artifactId>jcl-over-slf4j</artifactId>
<version>${slf4j.version}</version>
<!-- <scope>runtime</scope> --> <!-- more correct, but scalac 2.10.3 doesn't like it -->
</dependency>
<dependency>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
<version>${log4j.version}</version>
</dependency>
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<dependency>
<groupId>com.ning</groupId>
<artifactId>compress-lzf</artifactId>
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<version>1.0.0</version>
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</dependency>
<dependency>
<groupId>org.xerial.snappy</groupId>
<artifactId>snappy-java</artifactId>
<version>1.0.5</version>
</dependency>
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<dependency>
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<groupId>org.ow2.asm</groupId>
<artifactId>asm</artifactId>
<version>4.0</version>
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</dependency>
<dependency>
<groupId>com.clearspring.analytics</groupId>
<artifactId>stream</artifactId>
<version>2.4.0</version>
</dependency>
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<!-- In theory we need not directly depend on protobuf since Spark does not directly
use it. However, when building with Hadoop/YARN 2.2 Maven doesn't correctly bump
the protobuf version up from the one Mesos gives. For now we include this variable
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to explicitly bump the version when building with YARN. It would be nice to figure
out why Maven can't resolve this correctly (like SBT does). -->
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<dependency>
<groupId>com.google.protobuf</groupId>
<artifactId>protobuf-java</artifactId>
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<version>${protobuf.version}</version>
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</dependency>
<dependency>
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<groupId>com.twitter</groupId>
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<artifactId>chill_${scala.binary.version}</artifactId>
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<version>0.3.1</version>
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</dependency>
<dependency>
<groupId>com.twitter</groupId>
<artifactId>chill-java</artifactId>
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<version>0.3.1</version>
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</dependency>
<dependency>
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<groupId>${akka.group}</groupId>
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<artifactId>akka-actor_${scala.binary.version}</artifactId>
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<version>${akka.version}</version>
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<exclusions>
<exclusion>
<groupId>org.jboss.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
</exclusions>
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</dependency>
<dependency>
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<groupId>${akka.group}</groupId>
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<artifactId>akka-remote_${scala.binary.version}</artifactId>
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<version>${akka.version}</version>
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<exclusions>
<exclusion>
<groupId>org.jboss.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
</exclusions>
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</dependency>
<dependency>
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<groupId>${akka.group}</groupId>
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<artifactId>akka-slf4j_${scala.binary.version}</artifactId>
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<version>${akka.version}</version>
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<exclusions>
<exclusion>
<groupId>org.jboss.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
</exclusions>
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</dependency>
<dependency>
<groupId>${akka.group}</groupId>
<artifactId>akka-testkit_${scala.binary.version}</artifactId>
<version>${akka.version}</version>
<exclusions>
<exclusion>
<groupId>org.jboss.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
</exclusions>
</dependency>
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<dependency>
<groupId>it.unimi.dsi</groupId>
<artifactId>fastutil</artifactId>
<version>6.4.4</version>
</dependency>
<dependency>
<groupId>colt</groupId>
<artifactId>colt</artifactId>
<version>1.2.0</version>
</dependency>
<dependency>
<groupId>org.apache.mesos</groupId>
<artifactId>mesos</artifactId>
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<version>${mesos.version}</version>
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</dependency>
<dependency>
<groupId>io.netty</groupId>
<artifactId>netty-all</artifactId>
<version>4.0.17.Final</version>
</dependency>
<dependency>
<groupId>org.apache.derby</groupId>
<artifactId>derby</artifactId>
<version>10.4.2.0</version>
<scope>test</scope>
</dependency>
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<dependency>
<groupId>net.liftweb</groupId>
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<artifactId>lift-json_${scala.binary.version}</artifactId>
<version>2.5.1</version>
<!-- see also exclusion for json4s; this is necessary since it depends on
scala-library and scalap 2.10.0, but we use 2.10.3, and only override
scala-library -->
<exclusions>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scalap</artifactId>
</exclusion>
</exclusions>
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</dependency>
<dependency>
<groupId>com.codahale.metrics</groupId>
<artifactId>metrics-core</artifactId>
<version>3.0.0</version>
</dependency>
<dependency>
<groupId>com.codahale.metrics</groupId>
<artifactId>metrics-jvm</artifactId>
<version>3.0.0</version>
</dependency>
<dependency>
<groupId>com.codahale.metrics</groupId>
<artifactId>metrics-json</artifactId>
<version>3.0.0</version>
</dependency>
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<dependency>
<groupId>com.codahale.metrics</groupId>
<artifactId>metrics-ganglia</artifactId>
<version>3.0.0</version>
</dependency>
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<dependency>
<groupId>com.codahale.metrics</groupId>
<artifactId>metrics-graphite</artifactId>
<version>3.0.0</version>
</dependency>
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<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-compiler</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>jline</artifactId>
<version>${scala.version}</version>
</dependency>
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<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
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<dependency>
<groupId>org.scalatest</groupId>
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<artifactId>scalatest_${scala.binary.version}</artifactId>
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<version>1.9.1</version>
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<scope>test</scope>
</dependency>
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<dependency>
<groupId>commons-io</groupId>
<artifactId>commons-io</artifactId>
<version>2.4</version>
</dependency>
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<dependency>
<groupId>org.easymock</groupId>
<artifactId>easymock</artifactId>
<version>3.1</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.mockito</groupId>
<artifactId>mockito-all</artifactId>
<version>1.8.5</version>
<scope>test</scope>
</dependency>
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<dependency>
<groupId>org.scalacheck</groupId>
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<artifactId>scalacheck_${scala.binary.version}</artifactId>
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<version>1.10.0</version>
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<scope>test</scope>
</dependency>
<dependency>
<groupId>com.novocode</groupId>
<artifactId>junit-interface</artifactId>
<version>0.10</version>
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<scope>test</scope>
</dependency>
Standalone Scheduler fault tolerance using ZooKeeper This patch implements full distributed fault tolerance for standalone scheduler Masters. There is only one master Leader at a time, which is actively serving scheduling requests. If this Leader crashes, another master will eventually be elected, reconstruct the state from the first Master, and continue serving scheduling requests. Leader election is performed using the ZooKeeper leader election pattern. We try to minimize the use of ZooKeeper and the assumptions about ZooKeeper's behavior, so there is a layer of retries and session monitoring on top of the ZooKeeper client. Master failover follows directly from the single-node Master recovery via the file system (patch 194ba4b8), save that the Master state is stored in ZooKeeper instead. Configuration: By default, no recovery mechanism is enabled (spark.deploy.recoveryMode = NONE). By setting spark.deploy.recoveryMode to ZOOKEEPER and setting spark.deploy.zookeeper.url to an appropriate ZooKeeper URL, ZooKeeper recovery mode is enabled. By setting spark.deploy.recoveryMode to FILESYSTEM and setting spark.deploy.recoveryDirectory to an appropriate directory accessible by the Master, we will keep the behavior of from 194ba4b8. Additionally, places where a Master could be specificied by a spark:// url can now take comma-delimited lists to specify backup masters. Note that this is only used for registration of NEW Workers and application Clients. Once a Worker or Client has registered with the Master Leader, it is "in the system" and will never need to register again. Forthcoming: Documentation, tests (! - only ad hoc testing has been performed so far) I do not intend for this commit to be merged until tests are added, but this patch should still be mostly reviewable until then.
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<dependency>
<groupId>org.apache.curator</groupId>
<artifactId>curator-recipes</artifactId>
<version>2.4.0</version>
Standalone Scheduler fault tolerance using ZooKeeper This patch implements full distributed fault tolerance for standalone scheduler Masters. There is only one master Leader at a time, which is actively serving scheduling requests. If this Leader crashes, another master will eventually be elected, reconstruct the state from the first Master, and continue serving scheduling requests. Leader election is performed using the ZooKeeper leader election pattern. We try to minimize the use of ZooKeeper and the assumptions about ZooKeeper's behavior, so there is a layer of retries and session monitoring on top of the ZooKeeper client. Master failover follows directly from the single-node Master recovery via the file system (patch 194ba4b8), save that the Master state is stored in ZooKeeper instead. Configuration: By default, no recovery mechanism is enabled (spark.deploy.recoveryMode = NONE). By setting spark.deploy.recoveryMode to ZOOKEEPER and setting spark.deploy.zookeeper.url to an appropriate ZooKeeper URL, ZooKeeper recovery mode is enabled. By setting spark.deploy.recoveryMode to FILESYSTEM and setting spark.deploy.recoveryDirectory to an appropriate directory accessible by the Master, we will keep the behavior of from 194ba4b8. Additionally, places where a Master could be specificied by a spark:// url can now take comma-delimited lists to specify backup masters. Note that this is only used for registration of NEW Workers and application Clients. Once a Worker or Client has registered with the Master Leader, it is "in the system" and will never need to register again. Forthcoming: Documentation, tests (! - only ad hoc testing has been performed so far) I do not intend for this commit to be merged until tests are added, but this patch should still be mostly reviewable until then.
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<exclusions>
<exclusion>
<groupId>org.jboss.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
<exclusions>
<exclusion>
<groupId>asm</groupId>
<artifactId>asm</artifactId>
</exclusion>
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<exclusion>
<groupId>org.jboss.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
SPARK-1071: Tidy logging strategy and use of log4j Prompted by a recent thread on the mailing list, I tried and failed to see if Spark can be made independent of log4j. There are a few cases where control of the underlying logging is pretty useful, and to do that, you have to bind to a specific logger. Instead I propose some tidying that leaves Spark's use of log4j, but gets rid of warnings and should still enable downstream users to switch. The idea is to pipe everything (except log4j) through SLF4J, and have Spark use SLF4J directly when logging, and where Spark needs to output info (REPL and tests), bind from SLF4J to log4j. This leaves the same behavior in Spark. It means that downstream users who want to use something except log4j should: - Exclude dependencies on log4j, slf4j-log4j12 from Spark - Include dependency on log4j-over-slf4j - Include dependency on another logger X, and another slf4j-X - Recreate any log config that Spark does, that is needed, in the other logger's config That sounds about right. Here are the key changes: - Include the jcl-over-slf4j shim everywhere by depending on it in core. - Exclude dependencies on commons-logging from third-party libraries. - Include the jul-to-slf4j shim everywhere by depending on it in core. - Exclude slf4j-* dependencies from third-party libraries to prevent collision or warnings - Added missing slf4j-log4j12 binding to GraphX, Bagel module tests And minor/incidental changes: - Update to SLF4J 1.7.5, which happily matches Hadoop 2’s version and is a recommended update over 1.7.2 - (Remove a duplicate HBase dependency declaration in SparkBuild.scala) - (Remove a duplicate mockito dependency declaration that was causing warnings and bugging me) Author: Sean Owen <sowen@cloudera.com> Closes #570 from srowen/SPARK-1071 and squashes the following commits: 52eac9f [Sean Owen] Add slf4j-over-log4j12 dependency to core (non-test) and remove it from things that depend on core. 77a7fa9 [Sean Owen] SPARK-1071: Tidy logging strategy and use of log4j
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<exclusion>
<groupId>commons-logging</groupId>
<artifactId>commons-logging</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.avro</groupId>
<artifactId>avro</artifactId>
<version>1.7.4</version>
<exclusions>
<exclusion>
<groupId>org.jboss.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
<exclusion>
<groupId>io.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>net.java.dev.jets3t</groupId>
<artifactId>jets3t</artifactId>
<version>0.7.1</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-yarn-api</artifactId>
<version>${yarn.version}</version>
<exclusions>
<exclusion>
<groupId>asm</groupId>
<artifactId>asm</artifactId>
</exclusion>
<exclusion>
<groupId>org.jboss.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-yarn-common</artifactId>
<version>${yarn.version}</version>
<exclusions>
<exclusion>
<groupId>asm</groupId>
<artifactId>asm</artifactId>
</exclusion>
<exclusion>
<groupId>org.jboss.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
</exclusions>
</dependency>
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<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-yarn-client</artifactId>
<version>${yarn.version}</version>
<exclusions>
<exclusion>
<groupId>asm</groupId>
<artifactId>asm</artifactId>
</exclusion>
<exclusion>
<groupId>org.jboss.netty</groupId>
<artifactId>netty</artifactId>
</exclusion>
</exclusions>
</dependency>
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</dependencies>
</dependencyManagement>
<build>
<pluginManagement>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-enforcer-plugin</artifactId>
<version>1.1.1</version>
<executions>
<execution>
<id>enforce-versions</id>
<goals>
<goal>enforce</goal>
</goals>
<configuration>
<rules>
<requireMavenVersion>
<version>3.0.0</version>
</requireMavenVersion>
<requireJavaVersion>
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<version>${java.version}</version>
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</requireJavaVersion>
</rules>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>build-helper-maven-plugin</artifactId>
<version>1.7</version>
</plugin>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.1.5</version>
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<executions>
<execution>
<id>scala-compile-first</id>
<phase>process-resources</phase>
<goals>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>scala-test-compile-first</id>
<phase>process-test-resources</phase>
<goals>
<goal>testCompile</goal>
</goals>
</execution>
<execution>
<id>attach-scaladocs</id>
<phase>verify</phase>
<goals>
<goal>doc-jar</goal>
</goals>
</execution>
</executions>
<configuration>
<scalaVersion>${scala.version}</scalaVersion>
<recompileMode>incremental</recompileMode>
<useZincServer>true</useZincServer>
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<args>
<arg>-unchecked</arg>
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<arg>-deprecation</arg>
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</args>
<jvmArgs>
<jvmArg>-Xms64m</jvmArg>
<jvmArg>-Xms1024m</jvmArg>
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<jvmArg>-Xmx1024m</jvmArg>
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<jvmArg>-XX:PermSize=${PermGen}</jvmArg>
<jvmArg>-XX:MaxPermSize=${MaxPermGen}</jvmArg>
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</jvmArgs>
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<javacArgs>
<javacArg>-source</javacArg>
<javacArg>${java.version}</javacArg>
<javacArg>-target</javacArg>
<javacArg>${java.version}</javacArg>
</javacArgs>
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</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
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<configuration>
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<source>${java.version}</source>
<target>${java.version}</target>
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<encoding>UTF-8</encoding>
<maxmem>1024m</maxmem>
<fork>true</fork>
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</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-surefire-plugin</artifactId>
<version>2.12.4</version>
<configuration>
<!-- Uses scalatest instead -->
<skipTests>true</skipTests>
</configuration>
</plugin>
<plugin>
<groupId>org.scalatest</groupId>
<artifactId>scalatest-maven-plugin</artifactId>
<version>1.0-RC2</version>
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<configuration>
<reportsDirectory>${project.build.directory}/surefire-reports</reportsDirectory>
<junitxml>.</junitxml>
<filereports>${project.build.directory}/SparkTestSuite.txt</filereports>
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<argLine>-Xms64m -Xmx3g</argLine>
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<stderr />
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</configuration>
<executions>
<execution>
<id>test</id>
<goals>
<goal>test</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-jar-plugin</artifactId>
<version>2.4</version>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-antrun-plugin</artifactId>
<version>1.7</version>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.0</version>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-source-plugin</artifactId>
<version>2.2.1</version>
<configuration>
<attach>true</attach>
</configuration>
<executions>
<execution>
<id>create-source-jar</id>
<goals>
<goal>jar-no-fork</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</pluginManagement>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-enforcer-plugin</artifactId>
</plugin>
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>build-helper-maven-plugin</artifactId>
<executions>
<execution>
<id>add-scala-sources</id>
<phase>generate-sources</phase>
<goals>
<goal>add-source</goal>
</goals>
<configuration>
<sources>
<source>src/main/scala</source>
</sources>
</configuration>
</execution>
<execution>
<id>add-scala-test-sources</id>
<phase>generate-test-sources</phase>
<goals>
<goal>add-test-source</goal>
</goals>
<configuration>
<sources>
<source>src/test/scala</source>
</sources>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-source-plugin</artifactId>
</plugin>
</plugins>
</build>
<profiles>
<profile>
<id>yarn-alpha</id>
<properties>
<hadoop.major.version>2</hadoop.major.version>
<!-- 0.23.* is same as 2.0.* - except hardened to run production jobs -->
<hadoop.version>0.23.7</hadoop.version>
<!--<hadoop.version>2.0.5-alpha</hadoop.version> -->
</properties>
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<modules>
<module>yarn</module>
</modules>
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</profile>
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<profile>
<id>yarn</id>
<properties>
<hadoop.major.version>2</hadoop.major.version>
<hadoop.version>2.2.0</hadoop.version>
<protobuf.version>2.5.0</protobuf.version>
</properties>
<modules>
<module>yarn</module>
</modules>
</profile>
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</profiles>
</project>