Spark + Cassandra

Carl Yeksigian

DataStax

Spark

-Fast large-scale data processing framework

-Focused on in-memory workloads

-Supports Java, Scala, and Python

-Integrated machine learning support (MLlib)

-Streaming support

-Simple developer API

Resilient Distributed Dataset (RDD)

-Presents a simple Collection API to the

developer

-Breaks full collection into partitions, which can

be operated on independently

-Knows how to recalculate itself if data is lost

-Abstracts how to complete a job from the tasks

RDD

RDD API

Partitions

-Partitions can be created so they are on the

same machine as the data

Uses for Spark with Cassandra

-Ad-hoc queries

-Joins, Unions across tables

-Rewriting tables

-Machine Learning

spark-cassandra-connector

DataStax OSS Projecthttps://github.com/datastax/spark-cassandra-connector

Spark Cassandra Connector

-Exposes Cassandra tables as RDDs

-Read from and write to Cassandra

-Data type mapping

-Scala and Java support

Spark + Bioinformatics

-ADAM is a bioinformatics project out of UC

Berkeley AMPLab

-Combines Spark + Parquet + Avrohttps://github.com/bigdatagenomics/adam

http://bdgenomics.org/

Simple Variant

case class Variant (

sampleid: String,

referencename: String,

location: Long,

allele: String)

create table adam.variants (

sampleid ascii,

referencename ascii,

location bigint,

allele ascii)

Connecting to Cassandra

import com.datastax.spark.connector._

// Spark connection options

val conf = new SparkConf(true)

.setMaster("spark://192.168.345.10:7077")

.setAppName("cassandra-demo")

.set("cassandra.connection.host", "192.168.345.10")

val sc = new SparkContext(conf)

Saving To Cassandra

val variants: RDD[VariantContext] = sc.adamVCFLoad(args(0))

variants.flatMap(getVariant)

.saveToCassandra("adam", "variants", AllColumns)

Querying Cassandra

val rdd = sc.cassandraTable("adam", "variants")

.map(r => (r.get[String]("allele"), 1L))

.reduceByKey(_ + _)

.map(r => (r._2, r._1))

.sortByKey(ascending = false)

rdd.collect()

.foreach(bc => println("%40s\t%d".format(bc._2, bc._1)))

Thanks

Acknowledgements:

Timothy Danford (AMPLab)

Matt Massie (AMPLab)

Frank Nothaft (AMPLab)

Jeff Hammerbacher (Cloudera/Mt Sinai)