SPARK WITH COUCHBASETO ELECTRIFY YOUR DATA PROCESSING

Michael Nitschinger, Couchbase

What is Spark?

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Introduction

Apache Spark is a fast and general engine for large-scale data processing.

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More Facts Over 450 contributors, very active Apache Big Data

project. Huge public interest:

Source: http://www.google.com/trends/explore?hl=en-US#q=apache%20spark,%20apache%20hadoop&cmpt=q

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Community

Ecosystem growing fast Hadoop RDBMS NoSQL

Package Repository http://spark-packages.org/ Connectors Utility Libraries

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Components: Spark Core

Resilient Distributed DatasetsClusteringExecution

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Components: Spark SQL

Structured Data FramesDistributed querying with SQL

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Components: Spark Streaming

Fault-tolerant streaming applications

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Components: Spark MLib

Built-In Machine Learning Algorithms

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Components: Spark GraphX

Graph processing and graph-parallel computations

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How does it work? Resilient Distributed Datatypes paper:

https://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf

rdd1.join(rdd2) .groupBy(…) .filter(…)

RDD Objects

build DAG

agnostic to operators!

doesn’t know about stages

DAGScheduler

split graph into stages of tasks

submit each stage as ready

DAG

TaskScheduler

TaskSet

launch tasks via cluster manager

retry failed or straggling tasks

Clustermanager

Worker

execute tasks

store and serve blocks

Block manager

ThreadsTask

stagefailed

Why should you care?

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Spark Benefits

Linearly scalable to 1000+ worker nodes Simpler to use than Hadoop MR Only partial recompute on failure

For developers and data scientists machine learning R integration

Tight but not mandatory Hadoop integration Sources, Sinks Scheduler

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Spark vs Hadoop

Spark is RAM while Hadoop is mainly HDFS (disk) bound

Fully compatible with Hadoop Input/Output

Easier to develop against thanks to functional composition

Hadoop certainly more mature, but Spark ecosystem growing fast

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Ecosystem Flexibility

RDBMS

StreamsWeb APIs

DCPKVN1QLViews

BatchingData Archive

OLTP Data

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Infrastructure Consolidation

The Couchbase Spark Connector

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Couchbase Connector Spark Core

Automatic Cluster and Resource Management Creating and Persisting RDDs Java APIs in addition to Scala (planned before GA)

Spark SQL Easy JSON handling and querying Tight N1QL Integration (partially in dp2, fully planned before

GA)

Spark Streaming Persisting DStreams DCP source (partially in dp2, fully planned before GA)

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Facts Current Version: 1.0.0-dp2 Beta in July, GA in Q3 (tentative)

Code: https://github.com/couchbaselabs/couchbase-spark-connector

Docs until GA: https://github.com/couchbaselabs/couchbase-spark-connector/wiki

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Connection Management

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Connection Management

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Creating RDDs

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Persisting RDDs

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Spark SQL Integration

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Spark Streaming with DCP

Questions?

Thank you.