Hadoop to spark_v2

From Hadoop to Spark

Introduction Hadoop and Spark Comparison


HI, I’m Sujee Maniyam

•  Founder / Principal @ ElephantScale •  Consulting & Training in Big Data •  Spark / Hadoop / NoSQL /

Data Science

•  Author –  “Hadoop illuminated” open source book –  “HBase Design Patterns”

•  Open Source contributor: github.com/sujee

•  [email protected]

•  www.ElephantScale.com

(c) ElephantScale.com 2015

Spark Training available!

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Webinar Audience

u  I am already using Hadoop, Should I go to Spark?

u  I am thinking about Hadoop, should I skip Hadoop and go to Spark ?

(c) ElephantScale.com 2015 3

Webinar Outline

u  Intro: what is Hadoop and what is Spark?

u  Capabilities and advantages of Spark & Hadoop u  Best use cases for Spark / Hadoop

u  From Hadoop to Spark – how to?

Webinar: From Hadoop to Spark (c) ElephantScale.com 2015 4

Introduction



Hadoop in 20 Seconds

u  ‘The Original’ Big data platform

u  Very well field tested

u  Scales to peta-bytes of data

u  Enables analytics at massive scale


Hadoop Eco System

Batch Real Time


Hadoop Ecosystem – by function

u  HDFS – provides distributed storage

u  Map Reduce – Provides distributed computing

u  Pig – High level MapReduce

u  Hive – SQL layer over Hadoop

u  HBase – NoSQL storage for real-time queries


Hadoop Extended Eco-System


Source : hortonworks

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Hadoop : Use Cases

u  Two modes : Batch & Real Time u  Batch use case

– Analytics at large scale (Terra bytes to peta bytes scale) – Analytics times can be minutes / hours.

Depends on •  Size of data being analyzed •  And type of query

– Examples: •  Large ETL work loads •  “Analyze clickstream data and calculate top page visits” •  “Combine purchase data and click-data and figure out discounts to

apply”


Hadoop Use Cases

u  Real Time Use Cases do not rely on Map Reduce u  Instead we use HBase

– A real-time NoSQL datastore built on Hadoop u  Example : Tracking Sensor data

– Store data from millions of sensor – Could be billions of data points –  “Find latest reading from a sensor” – This query must be done in

real time (in milli-seconds) u  “Needle in HayStack” scenarios

– We look for one / few records within billions


Hadoop Reference Architecture (Example)


Source : hortonworks

Data Spectrum


Big Data Analytics Evolution (v1)

u  Decision times : batch ( hours / days) u  Use cases:

– Modeling – ETL – Reporting


Moving Towards Fast Data (v2)

u  Decision time : (near) real time – seconds (or milli seconds)

u  Use Cases – Alerts (medical / security) – Fraud detection


Current Big Data Processing Challenges u  Processing needs outpacing 1st generation tools u  Beyond Batch

– Not every one has terra-bytes of data to process – Small – Medium data sets (few hundred gigs) are more prevalent – Data may not be on disk

•  In memory •  Coming via streaming channels

u  MapReduce (MR)’s limitations – Batch processing doesn't fit all needs – Not effective for ‘iterative programming’ (machine learning

algorithms ..etc) – High latency for streaming needs

u  Spark is a 2nd generation tool addressing these needs 16 (c) ElephantScale.com 2015

What is Spark?

u  Open source cluster computing engine – Very fast: In-memory ops 100x faster than MR

•  On-disk ops 10x faster than MR – General purpose: MR, SQL, streaming, machine learning,

analytics – Compatible: Runs over Hadoop, Mesos, Yarn, standalone

•  Works with HDFS, S3, Cassandra, HBase, … – Easier to code: Word count in 2 lines

u  Spark's roots: – Came out of Berkeley AMP Lab – Now top-level Apache project

– Version 1.5 released in Sept 2015

“First Big Data platform to integrate batch, streaming and interactive computations in a unified framework” – stratio.com


Spark Illustrated

Spark Core

Spark SQL

Spark Streaming ML lib

Schema / sql Real Time

Machine Learning

Standalone YARN MESOS Cluster

managers

GraphX

Graph processing

HDFS S3 Cassandra ??? Data

Storage


Spark Core

u  Basic building blocks for distributed compute engine – Task schedulers and memory management – Fault recovery (recovers missing pieces on node failure) – Storage system interfaces

u  Defines Spark API and data model

u  Data Model: RDD (Resilient Distributed Dataset) – Distributed collection of items – Can be worked on in parallel – Easily created from many data sources (Any HDFS InputSource)

u  Spark API: Scala, Python, and Java – Compact API for working with RDD and interacting with Spark – Much easier to use than MapReduce API

Session 2: Introduction to Spark


Spark Components

u  Spark SQL: Structured data – Supports SQL and HQL (Hive Query Language) – Data sources include Hive tables, JSON, CSV, Parquet (1)

u  Spark Streaming: Live streams of data in real-time – Low latency, high throughput (1000s events / sec) – Log files, stock ticks, sensor data / IOT (Internet of Things) …

u  ML Lib: Machine Learning at scale – Classification/regression, collaborative filtering … – Model evaluation and data import

u  GraphX: Graph manipulation, graph-parallel computation – Social network friendships, link data, … – Graph manipulation and operations and common algorithms



Spark : 'Unified' Stack

u  Spark components support multiple programming models – Map reduce style batch processing – Streaming / real time processing – Querying via SQL – Machine learning

u  All modules are tightly integrated – Facilitates rich applications

u  Spark can be the only stack you need ! – No need to run multiple clusters (Hadoop cluster, Storm cluster,

etc.)



Hypo-meter J


Spark Job Trends


Hadoop and Spark Comparison


Going from Hadoop to Spark


Spark Benchmarks

Source : stratio.com


Spark Code / Activity

(c) ElephantScale.com

2015

Source : stratio.com

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Timeline : Hadoop & Spark


Hadoop Vs. Spark

Hadoop Spark

Source : http://www.kwigger.com/mit-skifte-til-mac/


Comparison With Hadoop

Hadoop Spark Distributed Storage + Distributed Compute

Distributed Compute Only

MapReduce framework Generalized computation Usually data on disk (HDFS) On disk / in memory Not ideal for iterative work Great at Iterative workloads

(machine learning ..etc) Batch process - Up 10x faster for data on disk

- Up to 100x faster for data in memory

Mostly Java Compact code Java, Python, Scala supported

No unified shell Shell for ad-hoc exploration


Spark Is Better Fit for Iterative Workloads


Spark Programming Model

u  More generic than MapReduce


Is Spark Replacing Hadoop?

u  Spark runs on Hadoop / YARN

u  Can access data in HDFS

u  Use YARN for clustering

u  Spark programming model is more flexible than MapReduce

u  Spark is really great if data fits in memory (few hundred gigs),

u  Spark is ‘storage agnostic’ (see next slide)


Spark & Pluggable Storage

Spark (compute engine)

HDFS Amazon S3 Cassandra ???


Spark & Hadoop

Use Case Hadoop Spark

Batch processing Hadoop’s MapReduce (Java, Pig, Hive)

Spark RDDs (java / scala / python)

SQL querying Hadoop : Hive Spark SQL

Stream Processing / Real Time processing

Storm Kafka

Spark Streaming

Machine Learning Mahout Spark ML Lib

Real time lookups HBase (NoSQL) No native Spark component. But Spark can query data in NoSQL stores


Hadoop + Yarn : OS for Distributed Compute

HDFS

YARN

Batch (mapreduce)

Streaming (storm, S4)

In-memory (spark)

Storage

Cluster Management

Applications

(or at least, that’s the idea)


Hadoop & Spark Future ???


Going from Hadoop to Spark

Introduction Hadoop and Spark Comparison Going from Hadoop to Spark


Why Move From Hadoop to Spark?

u  Spark is ‘easier’ than Hadoop

u  ‘friendlier’ for data scientists / analysts

–  Interactive shell

•  fast development cycles

•  adhoc exploration

u  API supports multiple languages

– Java, Scala, Python

u  Great for small (Gigs) to medium (100s of Gigs) data


Spark : ‘Unified’ Stack

u  Spark supports multiple programming models – Map reduce style batch processing – Streaming / real time processing – Querying via SQL – Machine learning

u  All modules are tightly integrated – Facilitates rich applications

u  Spark can be the only stack you need ! – No need to run multiple clusters

(Hadoop cluster, Storm cluster, … etc.)

Image: buymeposters.com


Migrating From Hadoop à Spark Functionality Hadoop Spark Distributed Storage -  HDFS

-  Cloud storage (Amazon S3)

-  HDFS -  Cloud storage

(Amazon S3) -  Distributed File

system (NFS / Ceph)

-  Distributed NoSQL (Cassandra)

-  Tachyon (in memory)

SQL querying Hive Spark SQL (Data frames)

ETL work flow Pig -  Spork : Pig on Spark

-  Mix of Spark SQL + RDD programming

Machine Learning Mahout ML Lib NoSQL DB HBase ???


Things to Consider When Moving From Hadoop to Spark

1.  Data size

2.  File System

3.  Analytics

A.  SQL

B.  ETL

C.  Machine Learning


Data Size : “You Don’t Have Big Data”


Data Size (T-shirt sizing)

Image credit : blog.trumpi.co.za

10 G + 100 G +

1 TB + 100 TB + PB +

< few G

Hadoop / Spark

Spark


Data Size

u  Lot of Spark adoption at SMALL – MEDIUM scale

– Good fit

– Data might fit in memory !!

u  Applications

–  Iterative workloads (Machine learning, etc.)

– Streaming


Decision : Data Size


Data Size

< 1 TB (Spark)

> 1 TB (Hadoop /

Spark)

Decision : File System


“What kind of file system do I need for Spark”

File System u  Hadoop = Storage + Compute u  Spark = Compute only u  Spark needs a distributed FS

u  File system choices for Spark – HDFS - Hadoop File System

•  Reliable •  Good performance (data locality) •  Field tested for PB of data

– S3 : Amazon •  Reliable cloud storage •  Huge scale

– NFS : Network File System (‘shared FS across machines)

– Tachyon (in memory - experimental)


Spark File Systems


File Systems For Spark

HDFS NFS Amazon S3

Data locality High (best)

Local enough None (ok)

Throughput High (best)

Medium (good)

Low (ok)

Latency Low (best)

Low High

Reliability Very High (replicated)

Low Very High

Cost Varies Varies $30 / TB / Month


File Systems Throughput Comparison

u  Data : 10G + (11.3 G)

u  Each file : ~1+ G ( x 10)

u  400 million records total

u  Partition size : 128 M

u  On HDFS & S3

u  Cluster :

– 8 Nodes on Amazon m3.xlarge (4 cpu , 15 G Mem, 40G SSD )

– Hadoop cluster , Horton Works HDP v2.2

– Spark : on same 8 nodes, stand-alone, v 1.2


HDFS Vs. S3 (lower is better)


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HDFS Vs. S3 (lower is better)


2015 52

HDFS Vs. S3 Conclusions

HDFS S3

Data locality à much higher throughput

Data is streamed à lower throughput

Need to maintain an Hadoop cluster No Hadoop cluster to maintain à convenient

Large data sets (TB + ) Good use case: -  Smallish data sets (few gigs) -  Load once and cache and re-use


Decision : File Systems


Already have Hadoop?

NO

HDFS

S3 NFS (Ceph)

Cassandra (real time)

YES use HDFS

Next Decision : SQL


“We use SQL heavily for data mining. We are using Hive / Impala on Hadoop. Is Spark right for us?”

SQL in Hadoop / Spark Hadoop Spark

Engine -  Hive (on Map Reduce or Tez on Hortonworks)

-  Impala (Cloudera)

-  Spark SQL using Dataframes

-  Hive context

Language HiveQL - HiveQL - RDD programming in Java / Python / Scala

Scale Terabytes / Petabytes Gigabytes / Terabytes / Petabytes

Inter operability Data stored in HDFS -  Hive tables

-  File system

Formats CSV, JSON, Parquet CSV, JSON, Parquet


Dataframes Vs. RDDs

u  RDDs have data u  DataFrames also have schema u  Dataframes Used to be called ‘schemaRDD’ u  Unified way to load / save data in multiple formats u  Provides high level operations

– Count / sum / average – Select columns & filter them

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// load json data df = sqlContext.read .format(“json”) .load(“/data/data.json”) // save as parquet (faster queries) df.write .format(“parquet”) .saveAsTable(“/data/datap/”)

Supported Formats

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Creating a DataFrame From JSON {"name": "John", "age": 35 } {"name": "Jane", "age": 40 } {"name": "Mike", "age": 20 } {"name": "Sue", "age": 52 }

(c) ElephantScale.com 2015 59 Session 6: Spark SQL

scala> val peopleDF = sqlContext.read.json("people.json") peopleDF: org.apache.spark.sql.DataFrame = [age: bigint, name: string] scala> peopleDF.printSchema() root |-- age: long(nullable = true) |-- name: string (nullable = true) scala> peopleDF.show() +---+----+ |age|name| +---+----+ | 35|John| | 40|Jane| | 20|Mike| | 52| Sue| +---+----+

Querying Using SQL

u  A DataFrame can be registered as a temporary table – You can then use SQL to query it, as shown below – This is handled similarly to DSL queries - building up an AST and

sending it to Catalyst

(c) ElephantScale.com 2015 60 Session 6: Spark SQL

scala> df.registerTempTable("people") scala> sqlContext.sql("select * from people").show() name age John 35 Jane 40 Mike 20 Sue 52 scala> sqlContext.sql("select * from people where age > 35").show() name age Jane 40 Sue 52

Going From Hive à Spark

u  Spark natively supports querying data stored in Hive tables! u  Handy to use in an existing Hadoop cluster !!

61 Session 6: Spark SQL

HIVE Hive> select customer_id, SUM(cost) as total from billing group by customer_id order by total DESC LIMIT 10;

SPARK val hiveCtx = new org.apache.spark.sql.hive.HiveContext(sc) val top10 = hiveCtx.sql( "select customer_id, SUM(cost) as total from billing group by customer_id order by total DESC LIMIT 10") top10.collect()


Spark SQL Vs. Hive


2015

Fast on same HDFS data !

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Spark SQL Vs. Hive


2015

Fast on same data on HDFS

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Decision : SQL

Using Hive?

Yes Spark Using HiveContext

NO Spark SQL with

Dataframes (c) ElephantScale.com 2015 64

Next Decision : ETL


“we do lot of ETL work on our Hadoop cluster. Using tools like Pig / Cascading Can we use Spark? “

ETL on Hadoop / Spark

ETL Hadoop Spark

ETL Tools Pig, Cascading, Oozie -  Native RDD programming (Scala, Java, Python)

-  Cascading?

Pig High level ETL workflow Spork : Pig on Spark

Cascading High level Spark-scalding

Cask Works Works


Data Transformation on Spark

u  Dataframes are great for high level manipulation of data – High level operations : Join / Union …etc – Joining / Merging disparate data sets – Can read and understand multitude of data formats (JSON /

Parquet ..etc) – Very easy to program

u  RDD APIs allow low level programming – Complex manipulations – Lookups – Supports multiple lanaguages (Java / Scala / Python)

u  High level libraries are emerging – Tresata – CASK


Decisions : ETL

Current ETL

Pig

Spork RDD API

Dataframes

Cascading

Cascading on Spark

RDD

Data frames

Java MapReduce

/ Custom

RDD

Dataframes


Decision : Machine Learning


Can we use Spark for Machine Learning?

YES

Machine Learning : Hadoop / Spark

Hadoop Spark

Tool Mahout MLLib

API Java Java / Scala / Python

Iterative Algorithms Slower Very fast (in memory)

In Memory processing No YES

Mahout runs on Hadoop or on Spark

New and young lib

Latest news! Mahout only accepts new code that runs on Spark

Mahout & MLLib on Spark Future? Many opinions


Decision : In Memory Process


How can we do in-memory processing using Spark?

Numbers Every One Should Know by Jeff Dean, Fellow @ Google

Operation Cost (in nano seconds)

L1 cache reference 0.5

Branch mispredict (cpu) 5

L2 cache reference 7

Mutex lock/unlock 100

Main memory reference 100

Compress 1K bytes with Zippy 10,000

Send 2K bytes over 1 Gbps network 20,000

Read 1 MB sequentially from memory 250,000 0.25 ms

Round trip within same datacenter 500,000 0.5 ms

Disk seek 10,000,000 10 ms

Read 1 MB sequentially from network 10,000,000 10 ms

Read 1 MB sequentially from disk 30,000,000 30 ms

Send packet CA->Netherlands->CA 150,000,000 150 ms


Spark Caching

u  Caching is pretty effective (small / medium data sets) u  Cached data can not be shared across applications

(each application executes in its own sandbox)


Caching Results

Cached!


Caching Results

Cached!


Sharing Cached Data

u  By default Spark applications can not share cached data – Running in isolation

u  1) ‘spark job server’ – Multiplexer – All requests are executed through same ‘context’ – Provides web-service interface

u  2) Tachyon – Distributed In-memory file system – Memory is the new disk! – Out of AMP lab , Berkeley – Early stages (very promising)


Spark Job Server


Spark Job Server

u  Open sourced from Ooyala u  ‘Spark as a Service’ – simple REST interface to launch jobs u  Sub-second latency ! u  Pre-load jars for even faster spinup u  Share cached RDDs across requests (NamedRDD) u  https://github.com/spark-jobserver/spark-jobserver


App1 : sharedCtx.saveRDD(“my cached rdd”, rdd1) App2: RDD rdd2 = sharedCtx.loadRDD (“my cached rdd”)

Tachyon + Spark


How to Get Spark?


Getting Spark


Running Hadoop?

NO Need HDFS?

YES Install HDFS + YARN + Spark

NO Environment ?

Production Spark + Mesos +

S3

Testing Spark (standalone)

+ NFS or S3

YES Install Spark on Hadoop cluster

Spark Cluster Setup 1 : Simple

u  Great for POCs / experimentation u  No dependencies u  Using Spark’s ‘stand alone’ manager


Spark Cluster Setup 2 : Production

u  Works well with Hadoop eco system (HDFS / Hive ..etc) u  Best way to adopt Spark on Hadoop u  Uses YARN as cluster manager


Spark Cluster Setup 3 : Production

u  Uses Mesos as cluster manager


Hadoop -> Spark Case Study


Use Case 1 : Moving to Cloud


Use Case 1 : Lessons Learned

u  Size – Small Hadoop cluster (8 nodes) – Smallish data : 50G – 300G – Data for processing : few Gigs per query

u  Good ! – Only one moving part 'spark' – No Hadoop cluster to maintain – S3 was a dependable storage (passive) – Query response time gone from minutes to seconds (b/c we went

from MR à Spark)

u  Not so good – We lost data locality of HDFS

(ok for small/medium data sets) (c) ElephantScale.com 2015 87

Use Case 2 : Persistent Caching in Spark

u  Can we improve latency in this setup? u  Caching will help u  How ever, in Spark cached data can not be shared across

applications L


Use Case 2 : Persistent Caching in Spark

u  Spark Job Server to rescue !


Final Thoughts

u  Already on Hadoop? – Try Spark side-by-side – Process some data in HDFS – Try Spark SQL for Hive tables

u  Contemplating Hadoop? – Try Spark (standalone) – Choose NFS or S3 file system

u  Take advantage of caching –  Iterative loads – Spark Job server – Tachyon


Thanks and questions?

Sujee Maniyam Founder / Principal @ ElephantScale

Expert Consulting + Training in Big Data technologies [email protected]

Elephantscale.com Sign up for upcoming trainings : ElephantScale.com/training


Technology

Hadoop to spark_v2