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BÂLE BERNE BRUGG DUSSELDORF FRANCFORT S.M. FRIBOURG E.BR. GENÈVE
HAMBOURG COPENHAGUE LAUSANNE MUNICH STUTTGART VIENNE ZURICH
Real-Time Analytics with Apache
Cassandra and Apache Spark
Guido Schmutz
Guido Schmutz
• Working for Trivadis for more than 18 years
• Oracle ACE Director for Fusion Middleware and SOA
• Author of different books
• Consultant, Trainer Software Architect for Java, Oracle, SOA and
Big Data / Fast Data
• Technology Manager @ Trivadis
• More than 25 years of software development experience
• Contact: guido.schmutz@trivadis.com
• Blog: http://guidoschmutz.wordpress.com
• Twitter: gschmutz
Agenda
1. Introduction
2. Apache Spark
3. Apache Cassandra
4. Combining Spark & Cassandra
5. Summary
Big Data Definition (4 Vs)
+	Time	to	action	?	– Big	Data	+	Real-Time	=	Stream	Processing
Characteristics	of	Big	Data:	Its	Volume,	
Velocity	and	Variety	in	combination
What is Real-Time Analytics?
What is it? Why do we need
it?
How does it work?
• Collect real-time data
• Process data as it flows in
• Data in Motion over Data at
Rest
• Reports and Dashboard
access processed data
Time
Events RespondAnalyze
Short	time	to	
analyze	&	
respond	
§ Required	- for	new	business	models	
§ Desired	- for	competitive	advantage
Real Time Analytics Use Cases
• Algorithmic Trading
• Online Fraud Detection
• Geo Fencing
• Proximity/Location Tracking
• Intrusion detection systems
• Traffic Management
• Recommendations
• Churn detection
• Internet of Things (IoT) / Intelligence
Sensors
• Social Media/Data Analytics
• Gaming Data Feed
• …
Apache Spark
Motivation – Why Apache Spark?
Hadoop MapReduce: Data Sharing on Disk
Spark: Speed up processing by using Memory instead of Disks
map reduce . . .
Input
HDFS
read
HDFS
write
HDFS
read
HDFS
write
op1 op2
. . .
Input
Output
Output
Apache Spark
Apache Spark is a fast and general engine for large-scale data processing
• The hot trend in Big Data!
• Originally developed 2009 in UC Berkley’s AMPLab
• Based on 2007 Microsoft Dryad paper
• Written in Scala, supports Java, Python, SQL and R
• Can run programs up to 100x faster than Hadoop MapReduce in memory, or 10x
faster on disk
• One of the largest OSS communities in big data with over 200 contributors in 50+
organizations
• Open Sourced in 2010 – since 2014 part of Apache Software foundation
Apache Spark
Spark	SQL
(Batch	Processing)
Blink	DB
(Approximate
Querying)
Spark	Streaming
(Real-Time)
MLlib,	Spark	R
(Machine	
Learning)
GraphX
(Graph	Processing)
Spark	Core	API	and	Execution	Model
Spark
Standalone
MESOS YARN HDFS
Elastic
Search
NoSQL S3
Libraries
Core	Runtime
Cluster	Resource	Managers Data	Stores
Resilient Distributed Dataset (RDD)
Are
• Immutable
• Re-computable
• Fault tolerant
• Reusable
Have Transformations
• Produce new RDD
• Rich set of transformation available
• filter(), flatMap(), map(),
distinct(), groupBy(), union(),
join(), sortByKey(),
reduceByKey(), subtract(), ...
Have Actions
• Start cluster computing operations
• Rich set of action available
• collect(), count(), fold(),
reduce(), count(), …
RDD RDD
Input Source
• File
• Database
• Stream
• Collection
.count() ->	100
Data
Partitions RDD
Data
Partition	0
Partition	1
Partition	2
Partition	3
Partition	4
Partition	5
Partition	6
Partition	7
Partition	8
Partition	9
Server	1
Server	2
Server	3
Server	4
Server	5
Partitions RDD
Data
Partition	0
Partition	1
Partition	2
Partition	3
Partition	4
Partition	5
Partition	6
Partition	7
Partition	8
Partition	9
Server	1
Server	2
Server	3
Server	4
Server	5
Partitions RDD
Data
Partition	0
Partition	1
Partition	2
Partition	3
Partition	4
Partition	5
Partition	6
Partition	7
Partition	8
Partition	9
Server	2
Server	3
Server	4
Server	5
Stage 1 – reduceByKey()
Stage 1 – flatMap() + map()
Spark Workflow Input	HDFS	File
HadoopRDD
MappedRDD
ShuffledRDD
Text	File	Output
sc.hapoopFile()
map()
reduceByKey()
sc.saveAsTextFile()
Transformations
(Lazy)
Action	
(Execute	
Transformations)
Master
MappedRDD
P0
P1
P3
ShuffledRDD
P0
MappedRDD
flatMap()
DAG	
Scheduler
Spark Workflow HDFS	File	Input	1
HadoopRDD
FilteredRDD
MappedRDD
ShuffledRDD
HDFS	File	Output
HadoopRDD
MappedRDD
HDFS	File	Input	2
SparkContext.hadoopFile()
SparkContext.hadoopFile()filter()
map() map()
join()
SparkContext.saveAsHadoopFile()
Transformations
(Lazy)
Action	
(Execute	Transformations)
Spark Execution Model
Data	
Storage
Worker
Master
Executer
Executer
Server
Executer
Stage 1 – flatMap() + map()
Spark Execution Model
Data	
Storage
Worker
Master
Executer
Data	
Storage
Worker
Executer
Data	
Storage
Worker
Executer
RDD
P0
P1
P3
Narrow	TransformationMaster
filter()
map()
sample()
flatMap()
Data	
Storage
Worker
Executer
Stage 2 – reduceByKey()
Spark Execution Model
Data	
Storage
Worker
Executer
Data	
Storage
Worker
Executer
RDD
P0
Wide	Transformation
Master
join()
reduceByKey()
union()
groupByKey()
Shuffle	!
Data	
Storage
Worker
Executer
Data	
Storage
Worker
Executer
Batch vs. Real-Time Processing
Petabytes	of	Data
Gigabytes
Per	Second
Various Input Sources
Apache Kafka
distributed publish-subscribe messaging system
Designed for processing of real time activity stream data (logs, metrics collections,
social media streams, …)
Initially developed at LinkedIn, now part of Apache
Does not use JMS API and standards
Kafka maintains feeds of messages in topics Kafka Cluster
Consumer Consumer Consumer
Producer Producer Producer
Apache Kafka
Kafka Broker
Temperature
Processor
Temperature	Topic
Rainfall	Topic
1 2 3 4 5 6
Rainfall
Processor1 2 3 4 5 6
Weather
Station
Apache Kafka
Kafka Broker
Temperature
Processor
Temperature	Topic
Rainfall	Topic
1 2 3 4 5 6
Rainfall
Processor
Partition	0
1 2 3 4 5 6
Partition	0
1 2 3 4 5 6
Partition	1 Temperature
Processor
Weather
Station
Apache
Kafka
Kafka Broker
Temperature
Processor
Weather
Station
Temperature	Topic
Rainfall	Topic
Rainfall
Processor
P	0
Temperature
Processor
1 2 3 4 5
P	1 1 2 3 4 5
Kafka Broker
Temperature	Topic
Rainfall	Topic
P	0 1 2 3 4 5
P	1 1 2 3 4 5
P	0 1 2 3 4 5
P	0 1 2 3 4 5
Discretized Stream (DStream)
Kafka
Weather
Station
Weather
Station
Weather
Station
Discretized Stream (DStream)
Kafka
Weather
Station
Weather
Station
Weather
Station
Discretized Stream (DStream)
Kafka
Weather
Station
Weather
Station
Weather
Station
Discretized Stream (DStream)
Kafka
Weather
Station
Weather
Station
Weather
Station Discrete	by	time
Individual	Event
DStream =	RDD
Discretized Stream (DStream)
DStream DStream
X	Seconds
Transform
.countByValue()
.reduceByKey()
.join
.map
Discretized Stream (DStream)
time	1 time	2 time	3
message
time	n….
f(message 1)
RDD	@time	1
f(message 2)
f(message n)
….
message 1
RDD	@time	1
message 2
message n
….
result 1
result 2
result n
….
message message message
f(message 1)
RDD	@time	2
f(message 2)
f(message n)
….
message 1
RDD	@time	2
message 2
message n
….
result 1
result 2
result n
….
f(message 1)
RDD	@time	3
f(message 2)
f(message n)
….
message 1
RDD	@time	3
message 2
message n
….
result 1
result 2
result n
….
f(message 1)
RDD	@time	n
f(message 2)
f(message n)
….
message 1
RDD	@time	n
message 2
message n
….
result 1
result 2
result n
….
Input	Stream
Event	DStream
MappedDStream
map()
saveAsHadoopFiles()
Time	Increasing
DStreamTransformation	Lineage
Actions	Trigger	
Spark	Jobs
Adapted	from	Chris	Fregly: http://slidesha.re/11PP7FV
Apache Spark Streaming – Core concepts
Discretized Stream (DStream)
• Core Spark Streaming abstraction
• micro batches of RDD’s
• Operations similar to RDD
Input DStreams
• Represents the stream of raw data received
from streaming sources
• Data can be ingested from many sources:
Kafka, Kinesis, Flume, Twitter, ZeroMQ, TCP
Socket, Akka actors, etc.
• Custom Sources can be easily written for
custom data sources
Operations
• Same as Spark Core + Additional Stateful
transformations (window, reduceByWindow)
Apache Cassandra
Apache Cassandra
Apache Cassandra™ is a free
• Distributed…
• High performance…
• Extremely scalable…
• Fault tolerant (i.e. no single point of failure)…
post-relational database solution
Optimized for high write throughput
Apache Cassandra - History
Bigtable Dynamo
Motivation - Why NoSQL Databases?
aaa • Dynamo Paper (2007)
• How to build a data store that is
• Reliable
• Performant
• “Always On”
• Nothing new and shiny
• 24 other papers cited
• Evolutionary
Motivation - Why NoSQL Databases?
• Google Big Table (2006)
• Richer data model
• 1 key and lot’s of values
• Fast sequential access
• 38 other papers cited
Motivation - Why NoSQL Databases?
• Cassandra Paper (2008)
• Distributed features of Dynamo
• Data Model and storage from BigTable
• February 2010 graduated to a top-level Apache
Project
Apache Cassandra – More than one server
All nodes participate in a cluster
Shared nothing
Add or remove as needed
More capacity? Add more servers
Node is a basic unit inside a cluster
Each node owns a range of partitions
Consistent Hashing
Node	1
Node	2
Node	3
Node	4
[26-50]
[0-25]
[51-75]
[76-100] [0-25]
[0-25]
[26-50]
[26-50]
[51-75]
[51-75]
[76-100]
[76-100]
Apache Cassandra – Fully Replicated
Client writes local
Data syncs across WAN
Replication per Data Center
Node	1
Node	2
Node	3
Node	4
Node	1
Node	2
Node	3
Node	4
West						East
Client
Apache Cassandra
What is Cassandra NOT?
• A Data Ocean
• A Data Lake
• A Data Pond
• An In-Memory Database
• A Key-Value Store
• Not for Data Warehousing
What are good use cases?
• Product Catalog / Playlists
• Personalization (Ads, Recommendations)
• Fraud Detection
• Time Series (Finance, Smart Meter)
• IoT / Sensor Data
• Graph / Network data
How Cassandra stores data
• Model brought from Google Bigtable
• Row Key and a lot of columns
• Column names sorted (UTF8, Int, Timestamp, etc.)
Column	Name … Column Name
Column	Value Column	Value
Timestamp Timestamp
TTL TTL
Row	Key
1 2	Billion
Billion	of	Rows
Combining Spark & Cassandra
Spark and Cassandra Architecture – Great Combo
Good	at	analyzing	a	huge	amount	
of	data
Good	at	storing	a	huge	amount	of	
data
Spark and Cassandra Architecture
Spark	Streaming
(Near	Real-Time)
SparkSQL
(Structured	Data)
MLlib
(Machine	Learning)
GraphX
(Graph	Analysis)
Spark and Cassandra Architecture
Spark	Connector
Weather
Station
Spark	Streaming
(Near	Real-Time)
SparkSQL
(Structured	Data)
MLlib
(Machine	Learning)
GraphX
(Graph	Analysis)
Weather
Station
Weather
Station
Weather
Station
Weather
Station
Spark and Cassandra Architecture
• Single Node running Cassandra
• Spark Worker is really small
• Spark Master lives outside a
node
• Spark Worker starts Spark
Executer in separate JVM
• Node local
Worker
Master
Executer
Executer
Server
Executer
Spark and Cassandra Architecture
Worker
Worker
Worker
Master
Worker
• Each node runs Spark and
Cassandra
• Spark Master can make
decisions based on Token
Ranges
• Spark likes to work on small
partitions of data across a
large cluster
• Cassandra likes to spread out
data in a large cluster
0-25
26-50
51-75
76-100
Will	only	 have	
to	analyze	25%	
of	data!
Spark and Cassandra Architecture
Master
0-25
26-50
51-75
76-100
Worker
Worker
WorkerWorker
0-25
26-50
51-75
76-100
Transactional Analytics
Cassandra and Spark
Cassandra Cassandra	&	Spark
Joins	and	Unions No Yes
Transformations Limited Yes
Outside	Data	Integration No Yes
Aggregations Limited Yes
Summary
Summary
Kafka
• Topics store information broken into
partitions
• Brokers store partitions
• Partitions are replicated for data
resilience
Cassandra
• Goals of Apache Cassandra are all
about staying online and performant
• Best for applications close to your users
• Partitions are similar data grouped by a
partition key
Spark
• Replacement for Hadoop Map Reduce
• In memory
• More operations than just Map and Reduce
• Makes data analysis easier
• Spark Streaming can take a variety of sources
Spark + Cassandra
• Cassandra acts as the storage layer for Spark
• Deploy in a mixed cluster configuration
• Spark executors access Cassandra using the
DataStax connector
Lambda Architecture with Spark/Cassandra
Data
Collection
(Analytical)	Batch	Data	Processing
Batch
compute
Result	StoreData
Sources
Channel
Data
Access
Reports
Service
Analytic
Tools
Alerting
Tools
Social
(Analytical)	Real-Time	Data	Processing
Stream/Event	Processing
Batch
compute
Messaging
Result	Store
Query
Engine
Result	Store
Computed	
Information
Raw	Data	
(Reservoir)
Lambda Architecture with Spark/Cassandra
Data
Collection
(Analytical)	Batch	Data	Processing
Batch
compute
Result	StoreData
Sources
Channel
Data
Access
Reports
Service
Analytic
Tools
Alerting
Tools
Social
(Analytical)	Real-Time	Data	Processing
Stream/Event	Processing
Batch
compute
Messaging
Result	Store
Query
Engine
Result	Store
Computed	
Information
Raw	Data	
(Reservoir)
Guido Schmutz
Technology Manager
guido.schmutz@trivadis.com

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