Munich March 2015 - Cassandra + Spark Overview

@chbatey

Christopher BateyTechnical Evangelist for Apache Cassandra

Cassandra Spark Integration

@chbatey

Who am I? What is DataStax?• Technical Evangelist for Apache Cassandra• Cassandra related questions: @chbatey• DataStax- Enterprise ready version of Cassandra- Spark integration- Solr integration- OpsCenter- Majority of Cassandra drivers

@chbatey

Agenda•Cassandra overview• Spark overview• Spark Cassandra connector•Cassandra Spark examples

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Cassandra

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Cassandra for Applications

APACHE

CASSANDRA

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Common use cases•Ordered data such as time series-Event stores-Financial transactions-Sensor data e.g IoT

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Common use cases•Ordered data such as time series-Event stores-Financial transactions-Sensor data e.g IoT•Non functional requirements:-Linear scalability-High throughout durable writes-Multi datacenter including active-active-Analytics without ETL

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Cassandra

Cassandra

• Distributed masterless database (Dynamo)• Column family data model (Google BigTable)

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Datacenter and rack aware

Europe

• Distributed master less database (Dynamo)• Column family data model (Google BigTable)• Multi data centre replication built in from the start

USA

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Cassandra

Online

• Distributed master less database (Dynamo)• Column family data model (Google BigTable)• Multi data centre replication built in from the start• Analytics with Apache SparkAnalytics

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Scalability & Performance• Scalability- No single point of failure- No special nodes that become the bottle neck- Work/data can be re-distributed• Operational Performance i.e single digit ms- Single node for query- Single disk seek per query

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Cassandra can not join or aggregate

Client

Where do I go for the max?

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But but…• Sometimes you don’t need a answers in milliseconds• Data models done wrong - how do I fix it?• New requirements for old data?• Ad-hoc operational queries• Reports and Analytics- Managers always want counts / maxs

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Apache Spark• 10x faster on disk,100x faster in memory than Hadoop

MR• Works out of the box on EMR• Fault Tolerant Distributed Datasets• Batch, iterative and streaming analysis• In Memory Storage and Disk • Integrates with Most File and Storage Options

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Components

Sharkor

Spark SQLStreaming ML

Spark (General execution engine)

Graph

Cassandra

Compatible

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Spark architecture

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org.apache.spark.rdd.RDD• Resilient Distributed Dataset (RDD)• Created through transformations on data (map,filter..) or other RDDs • Immutable• Partitioned• Reusable

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RDD Operations• Transformations - Similar to Scala collections API• Produce new RDDs • filter, flatmap, map, distinct, groupBy, union, zip, reduceByKey, subtract

• Actions• Require materialization of the records to generate a value• collect: Array[T], count, fold, reduce..

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Word count

val file: RDD[String] = sc.textFile("hdfs://...")

val counts: RDD[(String, Int)] = file.flatMap(line => line.split(" ")) .map(word => (word, 1)) .reduceByKey(_ + _) counts.saveAsTextFile("hdfs://...")

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Spark shell

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Spark Streaming

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Cassandra + Spark

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Spark Cassandra Connector• Loads data from Cassandra to Spark• Writes data from Spark to Cassandra• Implicit Type Conversions and Object Mapping• Implemented in Scala (offers a Java API)• Open Source • Exposes Cassandra Tables as Spark RDDs + Spark

DStreams

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Analytics Workload Isolation

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Deployment• Spark worker in each of the

Cassandra nodes• Partitions made up of LOCAL

cassandra data

S C

S C

S C

S C

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Example Time

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It is on Github

"org.apache.spark" %% "spark-core" % sparkVersion"org.apache.spark" %% "spark-streaming" % sparkVersion"org.apache.spark" %% "spark-sql" % sparkVersion"org.apache.spark" %% "spark-streaming-kafka" % sparkVersion"com.datastax.spark" % "spark-cassandra-connector_2.10" % connectorVersion

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Boiler plateimport com.datastax.spark.connector.rdd._import org.apache.spark._import com.datastax.spark.connector._import com.datastax.spark.connector.cql._object BasicCassandraInteraction extends App { val conf = new SparkConf(true).set("spark.cassandra.connection.host", "127.0.0.1") val sc = new SparkContext("local[4]", "AppName", conf)

// cool stuff}

Cassandra Host

Spark master e.g spark://host:port

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Word Count + Save to Cassandra

val textFile: RDD[String] = sc.textFile("Spark-Readme.md") val words: RDD[String] = textFile.flatMap(line => line.split("\\s+")) val wordAndCount: RDD[(String, Int)] = words.map((_, 1)) val wordCounts: RDD[(String, Int)] = wordAndCount.reduceByKey(_ + _)println(wordCounts.first())wordCounts.saveToCassandra("test", "words", SomeColumns("word", "count"))

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Denormalised tableCREATE TABLE IF NOT EXISTS customer_events( customer_id text, time timestamp, id uuid,

event_type text, store_name text, store_type text, store_location text, staff_name text, staff_title text, PRIMARY KEY ((customer_id), time, id))

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Store it twiceCREATE TABLE IF NOT EXISTS customer_events(customer_id text, time timestamp, id uuid, event_type text, store_name text, store_type text, store_location text, staff_name text, staff_title text, PRIMARY KEY ((customer_id), time, id))

CREATE TABLE IF NOT EXISTS customer_events_by_staff( customer_id text, time timestamp, id uuid, event_type text, store_name text, store_type text, store_location text, staff_name text, staff_title text, PRIMARY KEY ((staff_name), time, id))

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My reaction a year ago

@chbatey

Too simple

val events_by_customer = sc.cassandraTable("test", “customer_events") events_by_customer.saveToCassandra("test", "customer_events_by_staff", SomeColumns("customer_id", "time", "id", "event_type", "staff_name", "staff_title", "store_location", "store_name", "store_type"))

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Aggregations with Spark SQLPartition Key Clustering Columns

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Now now…val cc = new CassandraSQLContext(sc) cc.setKeyspace("test")

val rdd: SchemaRDD = cc.sql("SELECT store_name, event_type, count(store_name) from customer_events GROUP BY store_name, event_type")

rdd.collect().foreach(println)

[SportsApp,WATCH_STREAM,1][SportsApp,LOGOUT,1][SportsApp,LOGIN,1][ChrisBatey.com,WATCH_MOVIE,1][ChrisBatey.com,LOGOUT,1][ChrisBatey.com,BUY_MOVIE,1][SportsApp,WATCH_MOVIE,2]

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Lamda architecture

http://lambda-architecture.net/

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Network word countCassandraConnector(conf).withSessionDo { session => session.execute("CREATE TABLE IF NOT EXISTS test.network_word_count(word text PRIMARY KEY, number int)") session.execute("CREATE TABLE IF NOT EXISTS test.network_word_count_raw(time timeuuid PRIMARY KEY, raw text)") } val ssc = new StreamingContext(conf, Seconds(5))val lines = ssc.socketTextStream("localhost", 9999) lines.map((UUIDs.timeBased(), _)).saveToCassandra("test", "network_word_count_raw") val words = lines.flatMap(_.split("\\s+")) val countOfOne = words.map((_, 1)) val reduced = countOfOne.reduceByKey(_ + _)reduced.saveToCassandra("test", "network_word_count")

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Summary• Cassandra is an operational database• Spark gives us the flexibility to do slower things- Schema migrations- Ad-hoc queries- Report generation• Spark streaming + Cassandra allow us to build online

analytical platforms

@chbatey

Thanks for listening• Follow me on twitter @chbatey• Cassandra + Fault tolerance posts a plenty: • http://christopher-batey.blogspot.co.uk/• Github for all examples: • https://github.com/chbatey/spark-sandbox• Cassandra resources: http://planetcassandra.org/• In London in April? http://www.eventbrite.com/e/cassandra-

day-london-2015-april-22nd-2015-tickets-15053026006?aff=CommunityLanding