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1 © Copyright 2013 Pivotal. All rights reserved. Disclaimer The views and opinions shared in this presentation are the speakers own, and are not official or un-official positions or statements on behalf of Pivotal Software Inc..

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Introduction to Apache Spark v1.0 with a slight focus toward Enterprise Architecture.

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Page 1: Spark forplainoldjavageeks svforum_20140724

1 © Copyright 2013 Pivotal. All rights reserved.

Disclaimer

The views and opinions shared in this presentation are the speakers own, and are not official or un-official positions or statements on behalf of Pivotal Software Inc..

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2 © Copyright 2013 Pivotal. All rights reserved.

Abstract Apache Spark is one of the most exciting and talked about ASF projects today, but how should enterprise architects view it, and what type of impact might it have on our platforms? 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, deployment topologies, and possible uses in enterprise. Concepts will be illustrated with a variety of demos covering: the programming model, the development experience, “realistic” infrastructure simulation with local virtual deployments, and Spark cluster monitoring tools.

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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 community.

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4 © Copyright 2013 Pivotal. All rights reserved. 4 © Copyright 2013 Pivotal. All rights reserved.

Intro to Apache Spark A primer for POJGs (Plain Old Java Geeks)

Scott Deeg: Sr. Field Engineer at Pivotal Software [email protected]

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What we’re talking about �  Intro: Agenda, it’s all about ME!

�  What is Spark, and what does it have to do with BigData/Hadoop?

�  Spark Programming Model –  Demo: interactive shell

�  Related Projects

�  Deployment Topologies

�  Internals: Execution, Shuffles, Tasks, Stages –  Demo: The algorithm matters, looking at a cluster

�  Relevant details from 1.0 launch

�  Q/A

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Who Am I?

A Plain Old Java Guy

�  Java since 1996, Symantec Visual Café 1.0

� Random consulting around Si Valley

� Hacker on Java based BPM product for 10 years

�  Joined VMW 2009 when they acquired SpringSource

� Rolled into Pivotal April 1 2013

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What Is Spark?

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What people have been asking me?

�  It’s one of those “in memory” things, right (Yes)

�  Is it “Big Data” (Yes)

�  Is it “Hadoop” (No)

�  JVM, Java, Scala (All)

�  Is it Real or just another shiny technology with a long, but ultimately small tail (?)

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Spark is … �  Distributed/Cluster Compute Execution Engine –  Came out of AMPLab project at UCB

�  Designed to run “batch” workloads on data in memory

�  Similar scalability and fault tolerance as Hadoop Map/Reduce –  Utilizes Lineage to reconstitute data instead of replication

�  Implementation of Resilient Distributed Dataset (RDD) in Scala

�  Programmatic interface via API or Interactive –  Scala, Java7/8, Python

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Spark is also … �  An ASF Top Level project

�  An active community of ~100-200 contributors across 25-35 companies –  More active than Hadoop MapReduce –  1000 people (the max) attended Spark Summit

�  An eco-system of domain specific tools –  Different models, but interoperable

�  Hadoop Compatible

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Spark is not …

� An OLTP data store

� A “permanent” data store

� Or an app cache

It’s also not Mature –  This is a good thing. Lots of room to grow.

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Berkley Data Analytics Stack (BDAS)

Support

� Batch

� Streaming

�  Interactive

Make it easy to compose them

https://amplab.cs.berkeley.edu/software/

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Short History �  2009 Started as research project at UCB

�  2010 Open Sourced

�  January 2011 AMPLab Created

�  October 2012 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

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Spark Philosophy

� Make life easy and productive for Data Scientists

� Provide well documented and expressive APIs

� Powerful Domain Specific Libraries

� Easy integration with storage systems

� Caching to avoid data movement (performance)

� Well defined releases, stable API

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Spark is not Hadoop, but is compatible

� Often better than Hadoop –  M/R fine for “Data Parallel”, but awkward for some workloads –  Low latency dispatch, Iterative, Streaming

� Natively accesses Hadoop data

� Spark just another YARN job –  Utilizes current investments in Hadoop –  Brings Spark to the Data

�  It’s not OR … it’s AND!

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Improvements over Map/Reduce

� Efficiency –  General Execution Graphs (not just map->reduce->store) –  In memory

� Usability –  Rich APIs in Scala, Java, Python –  Interactive

Can Spark be the R for Big Data?

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Spark Programming Model RDDs in (a little) Detail

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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.)

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Two Categories of Operations

� Transform –  Create from stable storage (hdfs, tachyon, etc.) –  Generate RDD from other RDD (map, filter, groupBy) –  Lazy Operations that build a DAG of Tasks –  Once Spark knows your transformations it can build a plan

� Action –  Return a result or write to storage (count, collect, save, etc.) –  Actions cause the DAG to execute

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Transformation and Actions

� Transformations –  map –  filter –  flatMap –  sample –  groupByKey –  reduceByKey –  union –  join –  sort

� Actions –  count –  collect –  reduce –  lookup –  save

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Demo 1

� WordCount (of course)

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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

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RDDs are Foundational

� General purpose enough to use to implement other programing models –  SQL –  Graph –  ML –  Streaming

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Related Projects Things that use Spark Core

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Spark SQL

� Lib in Spark Core that 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

� Takes columnar storage from Shark

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Spark 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

� Single semantics for both real time and high latency

� Other features –  Window-based Transformations –  Arbitrary join of streams

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Streaming (cont)

�  Input is broken up into Batches that become RDDs

� RDD’s are composed into DAGs to generate output

� Raw data is replicated in-memory for FT

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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

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MLbase

� 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.

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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

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Topologies

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Topologies

� Local –  Great for dev

� Spark Cluster (master/slaves) –  Improving rapidly

� Cluster Resource Managers –  YARN –  MESOS

�  (PaaS?)

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Data Science Platform

IMDG

Cluster Manager

RDD M/R

Application Platform

Stream Server

MPP

SQL

Data Lake / HDFS / Virtual Storage

App Data Platform

SQL Objects JSON GemFireXD

...ETC

Hadoop HDFS Isilon

App Dev / Ops

YARN Mesos

MLbase Streaming

Legacy Systems

Legacy

Data Scientists/Analysts Data Sources End Users

SparkSQL

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PHD

General Solution Pipeline

Streaming Ingest

GemFire (IMDB)

Machine data

Stream

message Source

RabbitMQ Transport

HDFS Sink

GemFire Tap

SQL

REST API

Analytics – Counters and

Gauges

Message Transformer

Analytics Taps

HDFS

Dashboard

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PHD

Where’s Spark?

Streaming Ingest

GemFire (IMDB)

Machine data

Stream

message Source

Transport

HDFS Sink

GemFire Tap

SQL

REST API

Analytics – Counters and

Gauges

Message Transformer

Analytics Taps

HDFS

Dashboard

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Demo 2

� My local dev/test environment

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How Spark Runs DAGs, shuffle’s, tasks, stages, etc.

(thanks to Aaron Davidson of Databricks)

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Sample

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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

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RDDs and Stages

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Tasks

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Stages running �  Number of

partitions matter for concurrency

�  Rule of thumb is at least 2x number of cores

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The Shuffle

� Redistributes data among partitions –  Hash keys into buckets –  Pull not push –  Writes to intermediate

files to disk –  Becoming plugable

� Optimizations: –  Avoided when possible, if ”data is already properly" partitioned –  Partial aggregation reduces data movement

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Other thought’s on Memory

� By default Spark 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

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Demo 3

� Compare algorithms

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Spark 1.0 (actually 1.0.1)

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Release cycle

� 1.0 Came out at end of May

� 1.X expected to be current for several years

� Quarterly release cycle –  2 mo dev / 1 mo QA –  Actual release is based on vote

� 1.1 due end of August

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1.0 Details �  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

�  Focus: Core Engine, Streaming, MLLib, SparkSQL –  History Server for Spark UI

▪  Driving development of instrumentation –  Job Submission Tool

▪  Don’t configure Context in code (eg: master)

�  SparkSQL

�  Java8 Lamdas –  No more writing closures as Classes

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Thanks!