Term

Meaning

Application

User program built on Spark. Consists of a driver program and executors on the cluster.

Application jar

A jar containing the user's Spark application. In some cases users will want to create an "uber jar" containing their application along with its dependencies. The user's jar should never include Hadoop or Spark libraries, however, these will be added at runtime.

Driver program

The process running the main() function of the application and creating the SparkContext

Cluster manager

An external service for acquiring resources on the cluster (e.g. standalone manager, Mesos, YARN)

Deploy mode

Distinguishes where the driver process runs. In "cluster" mode, the framework launches the driver inside of the cluster. In "client" mode, the submitter launches the driver outside of the cluster.

Worker node

Any node that can run application code in the cluster

Executor

A process launched for an application on a worker node, that runs tasks and keeps data in memory or disk storage across them. Each application has its own executors.

Task

A unit of work that will be sent to one executor

Job

A parallel computation consisting of multiple tasks that gets spawned in response to a Spark action (e.g. save, collect); you'll see this term used in the driver's logs.

Stage

Each job gets divided into smaller sets of tasks called stages that depend on each other (similar to the map and reduce stages in MapReduce); you'll see this term used in the driver's logs.

14/11/05 20:17:40 WARN thread.QueuedThreadPool: 1 threads could not be stopped

14/11/05 20:17:40 INFO thread.QueuedThreadPool: Couldn't stop Thread[qtp26737473-36 Acceptor0 SocketConnector@0.0.0.0:39213,5,main]

14/11/05 20:17:41 INFO thread.QueuedThreadPool:  at java.net.PlainSocketImpl.socketAccept(Native Method)

14/11/05 20:17:41 INFO thread.QueuedThreadPool:  at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:398)

14/11/05 20:17:41 INFO thread.QueuedThreadPool:  at java.net.ServerSocket.implAccept(ServerSocket.java:530)

14/11/05 20:17:41 INFO thread.QueuedThreadPool:  at java.net.ServerSocket.accept(ServerSocket.java:498)

14/11/05 20:17:41 INFO thread.QueuedThreadPool:  at org.eclipse.jetty.server.bio.SocketConnector.accept(SocketConnector.java:117)

14/11/05 20:17:41 INFO thread.QueuedThreadPool:  at org.eclipse.jetty.server.AbstractConnector$Acceptor.run(AbstractConnector.java:938)

14/11/05 20:17:41 INFO thread.QueuedThreadPool:  at org.eclipse.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:608)

14/11/05 20:17:41 INFO thread.QueuedThreadPool:  at org.eclipse.jetty.util.thread.QueuedThreadPool$3.run(QueuedThreadPool.java:543)

14/11/05 20:17:41 INFO thread.QueuedThreadPool:  at java.lang.Thread.run(Thread.java:745)

14/11/05 20:17:41 INFO network.ConnectionManager: Key not valid ? sun.nio.ch.SelectionKeyImpl@2cc51248

14/11/05 20:17:41 INFO spark.MapOutputTrackerMasterActor: MapOutputTrackerActor stopped!

14/11/05 20:17:41 INFO network.ConnectionManager: Removing SendingConnection to ConnectionManagerId(era,48234)

14/11/05 20:17:41 INFO network.ConnectionManager: Removing SendingConnection to ConnectionManagerId(era,52620)

14/11/05 20:17:42 INFO network.ConnectionManager: key already cancelled ? sun.nio.ch.SelectionKeyImpl@2cc51248

java.nio.channels.CancelledKeyException

at org.apache.spark.network.ConnectionManager.run(ConnectionManager.scala:386)

at org.apache.spark.network.ConnectionManager$$anon$4.run(ConnectionManager.scala:139)

14/11/05 20:17:42 INFO network.ConnectionManager: Key not valid ? sun.nio.ch.SelectionKeyImpl@69aaccdf

14/11/05 20:17:42 INFO network.ConnectionManager: key already cancelled ? sun.nio.ch.SelectionKeyImpl@69aaccdf

java.nio.channels.CancelledKeyException

at org.apache.spark.network.ConnectionManager.run(ConnectionManager.scala:386)

at org.apache.spark.network.ConnectionManager$$anon$4.run(ConnectionManager.scala:139)

hdfs dfs -mkdir -p /user/spark/share/lib

hadoop dfs -put /usr/lib/spark/assembly/lib/spark-assembly-1.1.0-cdh5.2.0-hadoop2.5.0-cdh5.2.0.jar /user/spark/share/lib/spark-assembly.jar

hadoop dfs -chmod -R 777 /user/spark/

在spark-env.sh中配置

export SPARK_JAR=hdfs://era:8020/user/spark/share/lib/spark-assembly.jar

export STANDALONE_SPARK_MASTER_HOST="192.168.128.131"

export SPARK_MASTER_IP=$STANDALONE_SPARK_MASTER_HOST

### Let's run everything with JVM runtime, instead of Scala

export SPARK_LAUNCH_WITH_SCALA=0

export SPARK_LIBRARY_PATH=${SPARK_HOME}/lib

export SCALA_LIBRARY_PATH=${SPARK_HOME}/lib

export SPARK_MASTER_WEBUI_PORT=18080

export SPARK_MASTER_IP="192.168.128.131"

export SPARK_MASTER_PORT=7077

export SPARK_WORKER_CORES=1

export SPARK_WORKER_MEMORY=100m

export SPARK_WORKER_PORT=7078

export SPARK_WORKER_INSTANCES=1

export SPARK_WORKER_WEBUI_PORT=18081

export SPARK_WORKER_DIR=/var/run/spark/work

export SPARK_LOG_DIR=/var/log/spark

export SPARK_PID_DIR='/var/run/spark/'

if [ -n "$HADOOP_HOME" ]; then

  export SPARK_LIBRARY_PATH=$SPARK_LIBRARY_PATH:${HADOOP_HOME}/lib/native

fi

export HADOOP_CONF_DIR=${HADOOP_CONF_DIR:-/etc/hadoop/conf}

Master URL

Meaning

local

Run Spark locally with one worker thread (i.e. no parallelism at all).

local[K]

Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).

local

Run Spark locally with as many worker threads as logical cores on your machine.

spark://HOST:PORT

Connect to the given Spark standalone cluster master. The port must be whichever one your master is configured to use, which is 7077 by default.

mesos://HOST:PORT

Connect to the given Mesos cluster. The port must be whichever one your is configured to use, which is 5050 by default. Or, for a Mesos cluster using ZooKeeper, use mesos://zk://....

yarn-client

Connect to a YARN cluster in client mode. The cluster location will be found based on the HADOOP_CONF_DIR variable.

yarn-cluster

Connect to a YARN cluster in cluster mode. The cluster location will be found based on HADOOP_CONF_DIR.

Transformation

Meaning

map(func)

Return a new distributed dataset formed by passing each element of the source through a function func.

filter(func)

Return a new dataset formed by selecting those elements of the source on which func returns true.

flatMap(func)

Similar to map, but each input item can be mapped to 0 or more output items (so func should return a Seq rather than a single item).

mapPartitions(func)

Similar to map, but runs separately on each partition (block) of the RDD, so func must be of type Iterator<T> => Iterator<U> when running on an RDD of type T.

mapPartitionsWithIndex(func)

Similar to mapPartitions, but also provides func with an integer value representing the index of the partition, so func must be of type (Int, Iterator<T>) => Iterator<U> when running on an RDD of type T.

sample(withReplacement,fraction, seed)

Sample a fraction fraction of the data, with or without replacement, using a given random number generator seed.

union(otherDataset)

Return a new dataset that contains the union of the elements in the source dataset and the argument.

intersection(otherDataset)

Return a new RDD that contains the intersection of elements in the source dataset and the argument.

distinct([numTasks]))

Return a new dataset that contains the distinct elements of the source dataset.

groupByKey([numTasks])

When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable<V>) pairs.

Note: If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using reduceByKey or combineByKey will yield much better performance.

Note: By default, the level of parallelism in the output depends on the number of partitions of the parent RDD. You can pass an optional numTasks argument to set a different number of tasks.

reduceByKey(func, [numTasks])

When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function func, which must be of type (V,V) => V. Like ingroupByKey, the number of reduce tasks is configurable through an optional second argument.

aggregateByKey(zeroValue)(seqOp, combOp, [numTasks])

When called on a dataset of (K, V) pairs, returns a dataset of (K, U) pairs where the values for each key are aggregated using the given combine functions and a neutral "zero" value. Allows an aggregated value type that is different than the input value type, while avoiding unnecessary allocations. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument.

sortByKey([ascending], [numTasks])

When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean ascending argument.

join(otherDataset, [numTasks])

When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. Outer joins are also supported through leftOuterJoin and rightOuterJoin.

cogroup(otherDataset, [numTasks])

When called on datasets of type (K, V) and (K, W), returns a dataset of (K, Iterable<V>, Iterable<W>) tuples. This operation is also called groupWith.

cartesian(otherDataset)

When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements).

pipe(command, [envVars])

Pipe each partition of the RDD through a shell command, e.g. a Perl or bash script. RDD elements are written to the process's stdin and lines output to its stdout are returned as an RDD of strings.

coalesce(numPartitions)

Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently after filtering down a large dataset.

repartition(numPartitions)

Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them. This always shuffles all data over the network.

mapToPair

JavaPairRDD ---> JavaPairRDD

JavaPairRDD<Integer, Integer> tc;

JavaPairRDD<Integer, Integer> edges = tc.mapToPair(

      new PairFunction<Tuple2<Integer, Integer>, Integer, Integer>() {

        @Override

        public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> e) {

          return new Tuple2<Integer, Integer>(e._2(), e._1());

        }

    });

Action

Meaning

reduce(func)

Aggregate the elements of the dataset using a function func (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel.

collect()

Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data.

count()

Return the number of elements in the dataset.

first()

Return the first element of the dataset (similar to take(1)).

take(n)

Return an array with the first n elements of the dataset. Note that this is currently not executed in parallel. Instead, the driver program computes all the elements.

takeSample(withReplacement,num, [seed])

Return an array with a random sample of num elements of the dataset, with or without replacement, optionally pre-specifying a random number generator seed.

takeOrdered(n, [ordering])

Return the first n elements of the RDD using either their natural order or a custom comparator.

saveAsTextFile(path)

Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file.

saveAsSequenceFile(path)

(Java and Scala)

Write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. This is available on RDDs of key-value pairs that either implement Hadoop's Writable interface. In Scala, it is also available on types that are implicitly convertible to Writable (Spark includes conversions for basic types like Int, Double, String, etc).

saveAsObjectFile(path)

(Java and Scala)

Write the elements of the dataset in a simple format using Java serialization, which can then be loaded usingSparkContext.objectFile().

countByKey()

Only available on RDDs of type (K, V). Returns a hashmap of (K, Int) pairs with the count of each key.

foreach(func)

Run a function func on each element of the dataset. This is usually done for side effects such as updating an accumulator variable (see below) or interacting with external storage systems.