Streaming Analytics with Spark, Kafka, Cassandra, and Akka

Helena Edelson VP of Product Engineering @Tuplejump

• Committer / Contributor: Akka, FiloDB, Spark Cassandra Connector, Spring Integration

• VP of Product Engineering @Tuplejump

• Previously: Sr Cloud Engineer / Architect at VMware, CrowdStrike, DataStax and SpringSource

Who@helenaedelsongithub.com/helena

TuplejumpTuplejump Data Blender combines sophisticated data collection with machine learning and analytics, to understand the intention of the analyst, without disrupting workflow.

• Ingest streaming and static data from disparate data sources• Combine them into a unified, holistic view • Easily enable fast, flexible and advanced data analysis

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Tuplejump Open Source github.com/tuplejump

• FiloDB - distributed, versioned, columnar analytical db for modern streaming workloads

• Calliope - the first Spark-Cassandra integration • Stargate - Lucene indexer for Cassandra • SnackFS - HDFS-compatible file system for Cassandra

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What Will We Talk About• The Problem Domain • Example Use Case • Rethinking Architecture

– We don't have to look far to look back – Streaming – Revisiting the goal and the stack – Simplification

THE PROBLEM DOMAINDelivering Meaning From A Flood Of Data

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The Problem DomainNeed to build scalable, fault tolerant, distributed data processing systems that can handle massive amounts of data from disparate sources, with different data structures.

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TranslationHow to build adaptable, elegant systems for complex analytics and learning tasks to run as large-scale clustered dataflows

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How Much Data

Yottabyte = quadrillion gigabytes or septillion bytes

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We all have a lot of data • Terabytes • Petabytes...

https://en.wikipedia.org/wiki/Yottabyte

Delivering Meaning• Deliver meaning in sec/sub-sec latency • Disparate data sources & schemas • Billions of events per second • High-latency batch processing • Low-latency stream processing • Aggregation of historical from the stream

While We Monitor, Predict & Proactively Handle

• Massive event spikes • Bursty traffic • Fast producers / slow consumers • Network partitioning & Out of sync systems • DC down • Wait, we've DDOS'd ourselves from fast streams? • Autoscale issues

– When we scale down VMs how do we not lose data?

And stay within our AWS / Rackspace budget

EXAMPLE CASE: CYBER SECURITY

Hunting The Hunter

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• Track activities of international threat actor groups, nation-state, criminal or hactivist • Intrusion attempts • Actual breaches

• Profile adversary activity • Analysis to understand their motives, anticipate actions

and prevent damage

Adversary Profiling & Hunting

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• Machine events • Endpoint intrusion detection • Anomalies/indicators of attack or compromise

• Machine learning • Training models based on patterns from historical data • Predict potential threats • profiling for adversary Identification

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Stream Processing

Data Requirements & Description• Streaming event data

• Log messages • User activity records • System ops & metrics data

• Disparate data sources • Wildly differing data structures

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Massive Amounts Of Data

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• One machine can generate 2+ TB per day • Tracking millions of devices • 1 million writes per second - bursty • High % writes, lower % reads • TTL

RETHINKING ARCHITECTURE

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WE DON'T HAVE TO LOOK FAR TO LOOK BACK

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Rethinking Architecture

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Most batch analytics flow from several years ago looked like...

STREAMING & DATA SCIENCE

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Rethinking Architecture

StreamingI need fast access to historical data on the fly for predictive modeling with real time data from the stream.

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Not A Stream, A Flood• Data emitters

• Netflix: 1 - 2 million events per second at peak • 750 billion events per day

• LinkedIn: > 500 billion events per day • Data ingesters

• Netflix: 50 - 100 billion events per day • LinkedIn: 2.5 trillion events per day

• 1 Petabyte of streaming data23

Which Translates To• Do it fast • Do it cheap • Do it at scale

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Challenges• Code changes at runtime • Distributed Data Consistency • Ordering guarantees • Complex compute algorithms

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Oh, and don't lose data

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Strategies• Partition For Scale & Data Locality • Replicate For Resiliency • Share Nothing • Fault Tolerance • Asynchrony • Async Message Passing • Memory Management

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• Data lineage and reprocessing in runtime • Parallelism • Elastically Scale • Isolation • Location Transparency

AND THEN WE GREEKED OUT

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Rethinking Architecture

Lambda ArchitectureA data-processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream processing methods.

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Lambda ArchitectureA data-processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream processing methods.

• An approach • Coined by Nathan Marz • This was a huge stride forward

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31https://www.mapr.com/developercentral/lambda-architecture

Implementing Is Hard

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• Real-time pipeline backed by KV store for updates • Many moving parts - KV store, real time, batch • Running similar code in two places • Still ingesting data to Parquet/HDFS • Reconcile queries against two different places

Performance Tuning & Monitoring: on so many systems

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Also hard

Lambda ArchitectureAn immutable sequence of records is captured and fed into a batch system and a stream processing system in parallel.

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WAIT, DUAL SYSTEMS?

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Challenge Assumptions

Which Translates To• Performing analytical computations & queries in dual

systems • Implementing transformation logic twice • Duplicate Code • Spaghetti Architecture for Data Flows • One Busy Network

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Why Dual Systems?• Why is a separate batch system needed? • Why support code, machines and running services of

two analytics systems?

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Counter productive on some level?

YES

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• A unified system for streaming and batch • Real-time processing and reprocessing

• Code changes • Fault tolerance

http://radar.oreilly.com/2014/07/questioning-the-lambda-architecture.html - Jay Kreps

ANOTHER ASSUMPTION: ETL

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Challenge Assumptions

Extract, Transform, Load (ETL)

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"Designing and maintaining the ETL process is often considered one of the most difficult and resource-intensive portions of a data warehouse project."

http://docs.oracle.com/cd/B19306_01/server.102/b14223/ettover.htm

Extract, Transform, Load (ETL)

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ETL involves • Extraction of data from one system into another • Transforming it • Loading it into another system

Extract, Transform, Load (ETL)"Designing and maintaining the ETL process is often

considered one of the most difficult and resource-intensive portions of a data warehouse project."

http://docs.oracle.com/cd/B19306_01/server.102/b14223/ettover.htm

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Also unnecessarily redundant and often typeless

ETL

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• Each ETL step can introduce errors and risk • Can duplicate data after failover • Tools can cost millions of dollars • Decreases throughput • Increased complexity

ETL

• Writing intermediary files • Parsing and re-parsing plain text

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And let's duplicate the pattern over all our DataCenters

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These are not the solutions you're looking for

REVISITING THE GOAL & THE STACK

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Removing The 'E' in ETLThanks to technologies like Avro and Protobuf we don’t need the “E” in ETL. Instead of text dumps that you need to parse over multiple systems:

Scala & Avro (e.g.)• Can work with binary data that remains strongly typed• A return to strong typing in the big data ecosystem

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Removing The 'L' in ETLIf data collection is backed by a distributed messaging system (e.g. Kafka) you can do real-time fanout of the ingested data to all consumers. No need to batch "load".

• From there each consumer can do their own transformations

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#NoMoreGreekLetterArchitectures

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NoETL

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Strategy Technologies

Scalable Infrastructure / Elastic Spark, Cassandra, Kafka

Partition For Scale, Network Topology Aware Cassandra, Spark, Kafka, Akka Cluster

Replicate For Resiliency Spark,Cassandra, Akka Cluster all hash the node ring

Share Nothing, Masterless Cassandra, Akka Cluster both Dynamo style

Fault Tolerance / No Single Point of Failure Spark, Cassandra, Kafka

Replay From Any Point Of Failure Spark, Cassandra, Kafka, Akka + Akka Persistence

Failure Detection Cassandra, Spark, Akka, Kafka

Consensus & Gossip Cassandra & Akka Cluster

Parallelism Spark, Cassandra, Kafka, Akka

Asynchronous Data Passing Kafka, Akka, Spark

Fast, Low Latency, Data Locality Cassandra, Spark, Kafka

Location Transparency Akka, Spark, Cassandra, Kafka

My Nerdy Chart

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SMACK• Scala/Spark • Mesos • Akka • Cassandra • Kafka

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Spark Streaming

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Spark Streaming• One runtime for streaming and batch processing

• Join streaming and static data sets • No code duplication • Easy, flexible data ingestion from disparate sources to

disparate sinks • Easy to reconcile queries against multiple sources • Easy integration of KV durable storage

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How do I merge historical data with data in the stream?

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Join Streams With Static Dataval ssc = new StreamingContext(conf, Milliseconds(500)) ssc.checkpoint("checkpoint")

val staticData: RDD[(Int,String)] = ssc.sparkContext.textFile("whyAreWeParsingFiles.txt").flatMap(func) val stream: DStream[(Int,String)] = KafkaUtils.createStream(ssc, zkQuorum, group, Map(topic -> n)) .transform { events => events.join(staticData)) .saveToCassandra(keyspace,table)

ssc.start()

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Training Data

Feature Extraction

Model Training

Model Testing

Test Data

Your Data Extract Data To Analyze

Train your model to predict

Spark MLLib

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Spark Streaming & ML

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val context = new StreamingContext(conf, Milliseconds(500)) val model = KMeans.train(dataset, ...) // learn offline

val stream = KafkaUtils .createStream(ssc, zkQuorum, group,..) .map(event => model.predict(event.feature))

Apache MesosOpen-source cluster manager developed at UC Berkeley.

Abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to easily be built and run effectively.

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AkkaHigh performance concurrency framework for Scala and Java • Fault Tolerance • Asynchronous messaging and data processing • Parallelization • Location Transparency • Local / Remote Routing • Akka: Cluster / Persistence / Streams

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Akka ActorsA distribution and concurrency abstraction • Compute Isolation • Behavioral Context Switching • No Exposed Internal State • Event-based messaging • Easy parallelism • Configurable fault tolerance

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Akka Actor Hierarchy

http://www.slideshare.net/jboner/building-reactive-applications-with-akka-in-scala

import akka.actor._ class NodeGuardianActor(args...) extends Actor with SupervisorStrategy {

val temperature = context.actorOf( Props(new TemperatureActor(args)), "temperature")

val precipitation = context.actorOf( Props(new PrecipitationActor(args)), "precipitation")

override def preStart(): Unit = { /* lifecycle hook: init */ }

def receive : Actor.Receive = { case Initialized => context become initialized } def initialized : Actor.Receive = { case e: SomeEvent => someFunc(e) case e: OtherEvent => otherFunc(e) } }

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Apache Cassandra• Extremely Fast • Extremely Scalable • Multi-Region / Multi-Datacenter • Always On

• No single point of failure • Survive regional outages

• Easy to operate • Automatic & configurable replication 66

Apache Cassandra• Very flexible data modeling (collections, user defined

types) and changeable over time • Perfect for ingestion of real time / machine data • Huge community

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Spark Cassandra Connector

• NOSQL JOINS! • Write & Read data between Spark and Cassandra • Compatible with Spark 1.4 • Handles Data Locality for Speed • Implicit type conversions • Server-Side Filtering - SELECT, WHERE, etc. • Natural Timeseries Integration

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http://github.com/datastax/spark-cassandra-connector

KillrWeather

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http://github.com/killrweather/killrweather

A reference application showing how to easily integrate streaming and batch data processing with Apache Spark Streaming, Apache Cassandra, Apache Kafka and Akka for fast, streaming computations on time series data in asynchronous event-driven environments.

http://github.com/databricks/reference-apps/tree/master/timeseries/scala/timeseries-weather/src/main/scala/com/databricks/apps/weather

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• High Throughput Distributed Messaging • Decouples Data Pipelines • Handles Massive Data Load • Support Massive Number of Consumers • Distribution & partitioning across cluster nodes • Automatic recovery from broker failures

Spark Streaming & Kafkaval context = new StreamingContext(conf, Seconds(1))

val wordCount = KafkaUtils.createStream(context, ...) .flatMap(_.split(" ")) .map(x => (x, 1)) .reduceByKey(_ + _)

wordCount.saveToCassandra(ks,table) context.start() // start receiving and computing

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class KafkaStreamingActor(params: Map[String, String], ssc: StreamingContext) extends AggregationActor(settings: Settings) { import settings._ val kafkaStream = KafkaUtils.createStream[String, String, StringDecoder, StringDecoder]( ssc, params, Map(KafkaTopicRaw -> 1), StorageLevel.DISK_ONLY_2) .map(_._2.split(",")) .map(RawWeatherData(_)) kafkaStream.saveToCassandra(CassandraKeyspace, CassandraTableRaw)

/** RawWeatherData: wsid, year, month, day, oneHourPrecip */ kafkaStream.map(hour => (hour.wsid, hour.year, hour.month, hour.day, hour.oneHourPrecip)) .saveToCassandra(CassandraKeyspace, CassandraTableDailyPrecip)

/** Now the [[StreamingContext]] can be started. */ context.parent ! OutputStreamInitialized def receive : Actor.Receive = {…} }

Gets the partition key: Data LocalitySpark C* Connector feeds this to Spark

Cassandra Counter column in our schema,no expensive `reduceByKey` needed. Simply let C* do it: not expensive and fast.

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/** For a given weather station, calculates annual cumulative precip - or year to date. */class PrecipitationActor(ssc: StreamingContext, settings: WeatherSettings) extends AggregationActor { def receive : Actor.Receive = { case GetPrecipitation(wsid, year) => cumulative(wsid, year, sender) case GetTopKPrecipitation(wsid, year, k) => topK(wsid, year, k, sender) } /** Computes annual aggregation.Precipitation values are 1 hour deltas from the previous. */ def cumulative(wsid: String, year: Int, requester: ActorRef): Unit = ssc.cassandraTable[Double](keyspace, dailytable) .select("precipitation") .where("wsid = ? AND year = ?", wsid, year) .collectAsync() .map(AnnualPrecipitation(_, wsid, year)) pipeTo requester /** Returns the 10 highest temps for any station in the `year`. */ def topK(wsid: String, year: Int, k: Int, requester: ActorRef): Unit = { val toTopK = (aggregate: Seq[Double]) => TopKPrecipitation(wsid, year, ssc.sparkContext.parallelize(aggregate).top(k).toSeq) ssc.cassandraTable[Double](keyspace, dailytable) .select("precipitation") .where("wsid = ? AND year = ?", wsid, year) .collectAsync().map(toTopK) pipeTo requester }}

A New Approach• One Runtime: streaming, scheduled • Simplified architecture • Allows us to

• Write different types of applications • Write more type safe code • Write more reusable code

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Need daily analytics aggregate reports? Do it in the stream, save results in Cassandra for easy reporting as needed - with data locality not offered by S3.

FiloDBDistributed, columnar database designed to run very fast analytical queries

• Ingest streaming data from many streaming sources • Row-level, column-level operations and built in versioning

offer greater flexibility than file-based technologies • Currently based on Apache Cassandra & Spark • github.com/tuplejump/FiloDB

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FiloDB• Breakthrough performance levels for analytical queries

• Performance comparable to Parquet • One to two orders of magnitude faster than Spark on

Cassandra 2.x • Versioned - critical for reprocessing logic/code changes • Can simplify your infrastructure dramatically • Queries run in parallel in Spark for scale-out ad-hoc analysis • Space-saving techniques

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WRAPPING UP

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Architectyr?

79"This is a giant mess"

- Going Real-time - Data Collection and Stream Processing with Apache Kafka, Jay Kreps

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Simplified

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www.tuplejump.com

info@tuplejump.com@tuplejump

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@helenaedelsongithub.com/helenaslideshare.net/helenaedelson

THANK YOU!

I'm speaking at QCon SF on the broader topic of Streaming at Scale

http://qconsf.com/sf2015/track/streaming-data-scale

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