Spark forspringdevs springone_final

Introduction to Apache Spark
Scott Deeg – Sr. Field Engineer, Pivotal
Unless otherwise indicated, these slides are © 2013-2014 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/

Who Am I?
A Plain Old Java Geek
• Came to Si Valley seeking fame and fortune in 1995 (still looking)
• Started working in Java Jan 1996, Symantec Visual Café 1.0
• Hacker on J2EE based BPM product for 10 years
• Joined VMware 2009 / Rolled into Pivotal April 1 2013
• Primarily pre-sales consulting for large/medium enterprises
sdeeg@pivotal.io
Random Facts: CalPoly SLO, Physics, Guitar/Lutherie, Arduino, 3yr old boy, 100 yr old house
(aka: Lots’O’work), spaces not tabs
2

Agenda
• What is Spark?
• Programming Model
• Produce ecosystem
• Spark and Spring
• A bit on Internals
(with demo’s along the way)
3

What people have been asking me about Spark
• It’s one of those in memory things, right (yes)
• Is it “Big Data” (yes)
• Is it Hadoop (no)
• JVM, Java, Scala (yes)
• Is it “Real” or just another shiny technology with a long, but
ultimately small tail (?)
4

What is Spark?
5

Official Definition
Apache Spark is a fast and general
engine for large scale data processing
6

Spark is …
• Distributed/Cluster Compute Engine
• A toolset for Data Scientists / Analysts
• Runs “batch” workloads in memory
• Hadoop Compatible
• Implementation of Resilient Distributed Dataset (RDD) in Scala
• Programmatic interface via API or Interactive
• Scala, Java7/8, Python
7

Spark is also …
• An ASF Top Level project http://spark.apache.org
• Came out of AMPLab project at UCB
• An active community
• ~100-200 contributors across 25-35 companies
• More active than Hadoop MapReduce
• 1000 people (max) attended Spark Summit 2014 in SF
• An eco-system of domain specific tools
• Different models, but interoperable
• Backed by a commercial entity: Databricks
8

Spark is not …
• An OLTP data store
• A permanent or stable data store
• An app cache
It’s also not Mature
• Lots of room to grow.
9

Short History
• 2009 Started as research project at UCB
• 2010 Open Sourced
• January 2011 AMPLab Created
• October 2012 version 0.6
• Java, Stand alone cluster, maven
• June 21 2013 Spark accepted into ASF Incubator
• Feb 27 2014 Spark becomes top level ASF project
• May 30 2014 Spark 1.0
• August 2014 1.0.2
10

Spark Team Goals
• Make life easy and productive for Data Scientists
• Provide well documented and expressive APIs
• Powerful Domain Specific Libraries
• Easy integration with common Big Data storage systems
• High Performance
• Well defined releases, stable API
11

Spark is not Hadoop, but is compatible
• Often better than Hadoop
• M/R fine for “Data Parallel”, but awkward for some workloads
• Low latency, Iterative, Streaming
• Natively accesses Hadoop data
• Spark is YAYJ (Yet Another YARN Job)
• Utilize current investments in Hadoop
• Brings Spark (closer) to the Data
• Similar scalability and fault tolerance characteristics as Hadoop
It’s not OR … it’s AND
12

Improvements over Map/Reduce
• Efficiency
• General Execution Graphs (not just map->reduce->store)
• In memory
• Useful for iterative processing
• Usability
• Rich APIs in Scala, Java, Python
• Interactive REPL
Can Spark be the R for Big Data?
13

Topologies
• Local in JVM or through REPL
• Great for dev
• Spark Cluster (master/slaves)
• Improving rapidly
• Cluster Resource Managers
• YARN
• MESOS
• (PaaS?)
14

Spark Programming Model

Core Spark Concept
In the Spark model a program is a set of transformations and
actions on a dataset with the following properties:
Resilient Distributed Dataset (RDD)
• Read Only Collection of Objects spread across a cluster
• RDDs are built through parallel transformations (map, filter, …)
• Results are generated by actions (reduce, collect, …)
• Automatically rebuilt on failure using lineage
• Controllable persistence (RAM, HDFS, etc.)
16

Two Categories of Operations
• Transform
• Create from stable storage (hdfs, tachyon, etc.)
• Generate new RDDs from other RDD
• Lazy Operations that build a DAG
• Once Spark knows your transformations it can build a plan
• Action
• Return a result or write to storage
• Actions cause the DAG to execute
Ø map
Ø filter
Ø flatMap
Ø sample
Ø groupByKey
Ø reduceByKey
Ø union
Ø join
Ø sort
Ø count
Ø collect
Ø reduce
Ø lookup
Ø save
17

Demo
WordCount (of course)
val file = sc.textFile("hdfs://bfm1/…")
val words = file.flatMap(line => line.split(" "))
val wordOneMap = words.map(word => (word, 1))
val counts = wordOneMap.reduceByKey(_ + _)
counts.collect()
18

RDD Fault Tolerance
• RDDs maintain lineage information that can be used to
reconstruct lost partitions
cachedMsgs = textFile(...).filter(_.contains(“error”))
.map(_.split(‘t’)(2))
.cache()
HdfsRDD
path: hdfs://…
FilteredRDD
func: contains(...)
MappedRDD
func: split(…) CachedRDD
19
Source: http://spark.apache.org/

Optimizing Dataflow
20
Source: Aaron Davidson of Databricks

RDDs are Foundational
• General purpose enough to use to implement other programing
models
• SQL
• Streaming
• Machine Learning
• Graph
21

Spark Ecosystem

Spark SQL
• Models RDDs as relations
• SchemaRDD
• Replaces Shark
• Lighter weight version with no code from Hive
• Import/Export in different Storage formats
• Parquet, learn schema from existing Hive warehouse
JavaRDD<Person> people = ctx.textFile(“people.txt").map(…)
JavaSchemaRDD schemaPeople = sqlCtx.applySchema(people, Person.class);
schemaPeople.registerAsTable("people");
JavaSchemaRDD teens = sqlCtx.sql("SELECT name FROM people WHERE age >= 13");
23

Streaming
• Extend Spark to do large scale stream processing
• 100s of nodes with second scale end to end latency
• Simple, batch like API with RDDs
• Input is broken up into micro-batches that become RDDs
24
Image from http://spark.apache.org/

Streaming
• DStream is the primary construct
• Sources: HDFS, Flume, Kafka, Twitter, ZeroMQ, Custom
• Raw data needs to be replicated in-memory for FT
• Other features
• Window-based Transformations
• Arbitrary join of streams
JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, …);
JavaReceiverInputDStream<String> lines = ssc.socketTextStream(…)
JavaDStream<String> words = lines.flatMap(…)
JavaPairDStream<String, Integer> wordCounts = words.mapToPair(…)
25

MLbase (“Young Project”)
• Machine Learning toolset
• Library and higher level abstractions
• General tool in space is MatLab
• Difficult for end users to learn, debug, scale solutions
• Starting with MLlib
• Low level Distributed Machine Learning Library
• Many different Algorithms
• Classification, Regression, Collaborative Filtering, etc.
26

GraphX (alpha)
• Graph processing library
• Replaces Spark Bagel
• Graph Parallel not Data Parallel
• Reason in the context of neighbors
• GraphLab API
• Graph Creation => Algorithm => Post Processing
• Existing systems mainly deal with the Algorithm and not interactive
• Unify collection and graph models
27
Image from http://spark.apache.org/

Others
• Mesos
• Enable multiple frameworks to share same cluster resources
• Twitter is largest user: Over 6,000 servers
• Tachyon
• In-memory, fault tolerant file system that exposes HDFS
• Catalyst
• SQL Query Optimizer
28

Spark and Spring

Sample App: Rocket Telemetry
• Rockets generate data, and we want to understand it
• Batch processing to look for patterns across flights
• Streaming for watching it happen and alerting
• Boot, Java Config, MVC, etc.
WHY?
• Similar to Telematics
• Very important to Auto Insurance industry
• It’s my friends project
• It’s Real (model) rocket data!
30

Basics
• Spark’s a library, so just include it
• Some lib conflicts, but not much
• Logging loop
• Packaging not fun
• Have to exclude spark and hadoop clients IF they’re running on a cluster as
as they’re provided by the runtime
• mvn “shade” plugin, gradle being a pain
• Executable Boot jars don’t just run on the Spark cluster
31

Demo
Show us some code already!
32
32

Spark and Spring XD
• Two different problems in Enterprise data
• Primary data pipeline(s)
• 24/7/365 rock solid
• Operations oriented
• Well defined transformations and routing rules with long term deployment
• Data analysis
• Batch and realtime aspects
• Transformation and processing exploration
• Frequently short term deployment
• Should not impact stability or operations of primary pipeline
33

Pretty Picture
Source Primary
Stream
Processing
Application
Stable Storage
(HDFS)
Batch
Analysis
Stream
Analysis
Operational
Data
(Redis,
Gem)
Sink
Transform / Filter
34
Source
Source

A bit on Internals

About this Sample
I can’t come up with a better example, so I use this one from Aaron
Davidson of Databricks. This is a summary from his slides, and my
notes from his talk at Spark Summit. All the images are from his
deck. For more detail I highly recommend:
http://spark-summit.org/2014/talk/a-deeper-understanding-of-spark-internals
36

Sample
37

What happens
• Create RDDs
• Pipeline operations as much of possible
• When a results doesn’t depend on other results, we can pipeline
• But, when data needs to be reorganized, no longer pipeline
• Stage is a merged operation
• Each stage gets a set of tasks
• Task is data and computation
38

RDDs and Stages
39

Tasks
40

Stages running
• Number of
partitions matter for
concurrency
• Rule of thumb is at
least 2x number of
cores
41

The Shuffle
• Redistributes data among partitions
• Hash keys into buckets
• Pull not push
• Writes to intermediate files to disk
• Becoming plugable
Ÿ Optimizations:
– Avoided when possible, if ”data is already properly" partitioned
– Partial aggregation reduces data movement
42

Other thought’s on Memory
• By default Spark (assumes it) owns 90% of the memory
• Partitions don’t have to fit in memory, but some things do
• EG: values for large sets in groupBy’s must fit in memory
• Shuffle memory is 20%
• If it goes over that, it’ll spill the data to disk
• Shuffle always writes to disk
• Turn on compression to keep objects serialized
• Saves space, but takes compute to serialize/de-serialize
43

This and That

Release cycle
• 1.0 Came out at end of May
• 1.X expected to be current for several years
• API Stability in 1.X for all non-Alpha projects
• Can recompile jobs, but hoping for binary compatibility
• Internal API are marked @DeveloperApi or @Experimental
• Plan (was?) for quarterly .X release cycle
• 2 mo dev / 1 mo QA
• 1.0.1 July, 1.0.2 August
45

Resources
Main spark page
• http://spark.apache.org/
An initial paper on Spark
• https://www.usenix.org/system/files/conference/nsdi12/nsdi12-final138.pdf
Demo code for this session
• https://github.com/SpringOne2GX-2014/SparkForSpring
46

Upcoming
• Blog post on executing Spring based Spark apps on clusters
(Spark native, YARN, and Mesos)
• Sample app with SpringXD as a source and Spark Streaming as
a processor
47

Thanks! J

Misc

Abstract
Apache Spark is one of the most exciting, active, and talked about
ASF projects today, but how should Spring developers and
enterprise architects view it? Is it the second coming of the Bean
spec, or just another shiny distraction? This talk will introduce Spark
and its core concepts, the ecosystem of services on top of it, types
of problems it can solve, similarities and differences from Hadoop,
integration with Spring XD, deployment topologies, and an
exploration of uses in enterprise. Concepts will be illustrated with
several demos covering: the programming model with Spring/Java8,
development experience, “realistic” infrastructure simulation with
local virtual deployments, and Spark cluster monitoring tools.
50

Bio
A self described Plain Old Java Geek, Scott Deeg began his journey with
Java in 1996 as a member of the Visual Café team at Symantec. From
there he worked primarily as a consultant and solution architect dealing
with enterprise Java applications. He joined Vmware in 2009 and is now a
part of the EMC/VMware spin out Pivotal where he continues to work with
large enterprises on their application platform and data needs. A big fan of
open source software and technology, he tries to occasionally get out of
the corporate world to talk about interesting things happening in the Java/
OSS community.
51

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