Apache Spark Operations

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Spark OperationsKostas Sakellis

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Me

• Software Engineer at Cloudera•Contributor to Apache Spark•Before that, contributed to Cloudera Manager

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Building a proof of concept!

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Examplesc.textFile(“hdfs://data/u.item”, 4) .map(Movie(_)) .filter(_.month.equals(“Nov”)) .collect()

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Examplesc.textFile(“hdfs://data/u.item”, 4) .map(Movie(_)) .filter(_.month.equals(“Nov”)) .collect()

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Examplesc.textFile(“hdfs://data/u.item”, 4) .map(Movie(_)) .filter(_.month.equals(“Nov”)) .collect()

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Partitionssc.textFile(“hdfs://data/u.item”, 4) .map(Movie(_)) .filter(_.month.equals(“Nov”)) .collect()

HDFS

Partition 1

Partition 2

Partition 3

Partition 4

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RDDssc.textFile(“hdfs://data/u.item”, 4) .map(Movie(_)) .filter(_.month.equals(“Nov”)) .collect()

…RDD

HDFS

Partition 1

Partition 2

Partition 3

Partition 4

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RDDssc.textFile(“hdfs://data/u.item”, 4) .map(Movie(_)) .filter(_.month.equals(“Nov”)) .collect()

…RDD …RDD

HDFS

Partition 1

Partition 2

Partition 3

Partition 4

Partition 1

Partition 2

Partition 3

Partition 4

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RDDssc.textFile(“hdfs://data/u.item”, 4) .map(Movie(_)) .filter(_.month.equals(“Nov”)) .collect()

…RDD …RDD

HDFS

Partition 1

Partition 2

Partition 3

Partition 4

Partition 1

Partition 2

Partition 3

Partition 4

…RDD

Partition 1

Partition 2

Partition 3

Partition 4

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…RDD …RDD

RDDs

HDFS

Partition 1

Partition 2

Partition 3

Partition 4

sc.textFile(“hdfs://data/u.item”, 4) .map(Movie(_)) .filter(_.month.equals(“Nov”)) .collect()

Partition 1

Partition 2

Partition 3

Partition 4

…RDD

Partition 1

Partition 2

Partition 3

Partition 4

Collect

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…RDD …RDD

RDD Lineage

HDFS

Partition 1

Partition 2

Partition 3

Partition 4

sc.textFile(“hdfs://data/u.item”, 4) .map(Movie(_)) .filter(_.month.equals(“Nov”)) .collect()

Partition 1

Partition 2

Partition 3

Partition 4

…RDD

Partition 1

Partition 2

Partition 3

Partition 4

Collect

Lineage

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Task

…RDD …RDD

HDFS

Partition 1

Partition 2

Partition 3

Partition 4

Partition 1

Partition 2

Partition 3

Partition 4

…RDD

Partition 1

Partition 2

Partition 3

Partition 4

Collect

•A pipelined set of transformation on a single thread

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Spark Architecture

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Spark System Architecture

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Deployments

• Spark supports pluggable Cluster Managers• local, Standalone, YARN and Mesos

• In early 2014, CDH 4.x with Spark 0.9 only supported Standalone•CDH 5.x includes Spark on YARN support

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Standalone

Master

WorkerClient

Worker

Process

AppMaster

Process

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Standalone

•On cluster./sbin/start-master.sh./sbin/start-slave.sh <master-spark-URL>

• Submit jobspark-submit --master <master-spark-URL>

…

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Container

YARN Architecture

Resource Manager

Node Manager

Client

Node Manager

Container

Process

AppMaster

Container

Process

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Container

Spark on YARN Architecture

Resource Manager

Node Manager

Client

Node Manager

Container

Process

AppMaster

Container

Process

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Container

Spark on YARN Architecture

Resource Manager

Node Manager

Client

Node Manager

Container

Process

AppMaster

Container

Process

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Spark on YARN

• Submit jobspark-submit --master yarn-client …

•Cluster modespark-submit --master yarn-cluster …

• Spark shell only works in client mode!

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Customers often have shared infrastructure

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Multi-tenancy

•Cluster utilization is top metric•Target: 70-80% utilization

•Mixed workloads from mixed customers•We recommend YARN•Built in resource manager

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Underutilized Clusters

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Dynamic Allocation

• Spark applications scale the number of executors based on load•Removes need for: --num-executors• Idle executors get killed

• First supported in CDH 5.4• Ideal for:•Long ETL jobs with large shuffles• shell applications: hive and spark shell

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Dynamic Allocation Limitations

• Still required to specify cores•--num-cores

•Memory•--executor-memory• Includes JVM overhead•Need to do the math yourself

•Our customers still get it wrong!

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The Future of Dynamic Allocation

•Only “task size” needed: --task-size• Eliminates•--num-cores•--num-executors•--executor-memory

• Leads to better cluster utilization

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Security, now it’s getting serious.

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Authentication

•Kerberos – the necessary evil•Ubiquitous amongst other services•YARN, HDFS, Hive, HBase, etc.

• Spark utilizes delegation tokens

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Encryption

•Control plane• File distribution•Block Manager•User UI / REST API•Data-at-rest (shuffle files)

SPARK-6028 (Replace with netty)Replace with nettySpark 1.4SPARK-2750 (SSL)SPARK-5682

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Authorization

• Enterprises have sensitive data•Beyond HDFS file permissions•Partial access to data•Column level granularity

•Apache Sentry•HDFS-Sentry synchronization plugin

•Record Service•Column level security for Spark!

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