data science with spark on amazon emr - pop-up loft tel aviv

Analytics with Spark on EMRJonathan Fritz

Sr. Product Manager, AWS

Spark moves at interactive speed

join

filter

groupBy

Stage 3

Stage 1

Stage 2

A: B:

C: D: E:

F:

= cached partition= RDD

map

• Massively parallel

• Uses DAGs instead of map-

reduce for execution

• Minimizes I/O by storing data

in RDDs in memory

• Partitioning-aware to avoid

network-intensive shuffle

Spark components to match your use case

Spark speaks your language

Use DataFrames to easily interact with data

• Distributed

collection of data

organized in

columns

• An extension of the

existing RDD API

• Optimized for query

execution

Easily create DataFrames from many formats

RDD

Additional libraries for Spark SQL Data Sources

at spark-packages.org

Load data with the Spark SQL Data Sources API

Additional libraries at spark-packages.org

Sample DataFrame manipulations

Use DataFrames for machine learning

• Spark ML libraries

(replacing MLlib) use

DataFrames as

input/output for

models

• Create ML pipelines

with a variety of

distributed algorithms

Create DataFrames on streaming data

• Access data in Spark Streaming DStream

• Create SQLContext on the SparkContext used for Spark

Streaming application for ad hoc queries

• Incorporate DataFrame in Spark Streaming application

• Checkpointing streaming jobs

Spark Pipeline

Use R to interact with DataFrames

• SparkR package for using R to manipulate DataFrames

• Create SparkR applications or interactively use the SparkR

shell (no Zeppelin support yet - ZEPPELIN-156)

• Comparable performance to Python and Scala

DataFrames

Spark SQL

• Seamlessly mix SQL with Spark programs

• Uniform data access

• Hive compatibility – run Hive queries without

modifications using HiveContext

• Connect through JDBC/ODBC using the Spark

ThriftServer (coming soon natively in EMR)

Spark architecture

• SparkContext runs as a library in your program, one instance per Spark app.

• Cluster managers: Standalone, Mesos or YARN

• Accesses storage via Hadoop InputFormat API, and can use S3 with EMRFS, HBase, HDFS, and more

Your application

SparkContext

Local

threads

Cluster

manager

Worker Worker

HDFS or other storage

Spark

executor

Spark

executor

Amazon EMR runs Spark on YARN

• Dynamically share and centrally configure

the same pool of cluster resources across

engines

• Schedulers for categorizing, isolating, and

prioritizing workloads

• Choose the number of executors to use, or

allow YARN to choose (dynamic allocation)

• Kerberos authentication

Storage S3, HDFS

YARNCluster Resource Management

BatchMapReduce

In MemorySpark

ApplicationsPig, Hive, Cascading, Spark Streaming, Spark SQL

RDDs (and now DataFrames) and Fault

Tolerance

RDDs track the transformations used to build them

(their lineage) to recompute lost data

E.g:messages = textFile(...).filter(lambda s: s.contains(“ERROR”))

.map(lambda s: s.split(‘\t’)[2])

HadoopRDDpath = hdfs://…

FilteredRDDfunc = contains(...)

MappedRDDfunc = split(…)

lines = spark.textFile(“hdfs://...”)

errors = lines.filter(lambda s: s.startswith(“ERROR”))

messages = errors.map(lambda s: s.split(‘\t’)[2])

messages.cache()Block 1

Block 2

Block 3

Worker

Worker

Worker

Driver

messages.filter(lambda s: “foo” in s).count()

messages.filter(lambda s: “bar” in s).count()

. . .

tasks

results

Cache 1

Cache 2

Cache 3

Base RDD Transformed RDD

Action

Result: full-text search of Wikipedia in <1 sec

(vs 20 sec for on-disk data)

Result: scaled to 1 TB data in 5-7 sec

(vs 170 sec for on-disk data)

Caching RDDs can boost performance

Load error messages from a log into memory, then interactively search for patterns

RDD Persistence

• Caching or Persisting dataset in memory

• Methods

• cache()

• persist()

• Small RDD MEMORY_ONLY

• Big RDD MEMORY_ONLY_SER (CPU intensive)

• Don’t spill to disk

• Use replicated storage for faster recovery

Inside Spark Executor on YARN

Max Container size on node

YARN Container Controls the max sum of memory used by the container

yarn.nodemanager.resource.memory-mb

→

Default: 116 GConfig File: yarn-site.xml



Executor space Where Spark executor Runs

Executor Container

→



Executor Memory Overhead - Off heap memory (VM overheads, interned strings etc.)

𝑠𝑝𝑎𝑟𝑘. 𝑦𝑎𝑟𝑛. 𝑒𝑥𝑒𝑐𝑢𝑡𝑜𝑟.𝑚𝑒𝑚𝑜𝑟𝑦𝑂𝑣𝑒𝑟ℎ𝑒𝑎𝑑 = 𝑒𝑥𝑒𝑐𝑢𝑡𝑜𝑟𝑀𝑒𝑚𝑜𝑟𝑦 ∗ 0.10

Executor Container

Memory

Overhead

Config File: spark-default.conf



Spark executor memory - Amount of memory to use per executor process

spark.executor.memory

Executor Container

Memory

Overhead

Spark Executor Memory

Config File: spark-default.conf



Shuffle Memory Fraction – pre-Spark 1.6

Executor Container

Memory

Overhead


ShufflememoryFraction

Default: 0.2



Storage storage Fraction - pre-Spark 1.6

Executor Container

Memory

Overhead


ShufflememoryFraction

StoragememoryFraction

Default: 0.6



In Spark 1.6+, Spark automatically balances the amount of memory for execution

and cached data.

Executor Container

Memory

Overhead


Execution / Cache

Default: 0.6

Dynamic Allocation on YARN

Scaling up on executors

- Request when you want the job to complete faster

- Idle resources on cluster

- Exponential increase in executors over time

New default in EMR 4.4 (coming soon!)

Dynamic allocation setup

Property Value

Spark.dynamicAllocation.enabled true

Spark.shuffle.service.enabled true

spark.dynamicAllocation.minExecutors 5

spark.dynamicAllocation.maxExecutors 17

spark.dynamicAllocation.initalExecutors 0

sparkdynamicAllocation.executorIdleTime 60s

spark.dynamicAllocation.schedulerBacklogTimeout 5s

spark.dynamicAllocation.sustainedSchedulerBackl

ogTimeout

5s

Optional

Compress your input data set

• Always compress Data Files on Amazon S3

• Reduces storage cost

• Reduces bandwidth between Amazon S3 and Amazon

EMR, which can speed up bandwidth constrained jobs

Compressions

Compression Types:

– Some are fast BUT offer less space reduction

– Some are space efficient BUT Slower

– Some are splitable and some are not

Algorithm % Space

Remaining

Encoding

Speed

Decoding

Speed

GZIP 13% 21MB/s 118MB/s

LZO 20% 135MB/s 410MB/s

Snappy 22% 172MB/s 409MB/s

Data Serialization

• Data is serialized when cached or shuffled

Default: Java serializer

• Kyro serialization (10x faster than Java serialization)

• Does not support all Serializable types

• Register the class in advance

Usage: Set in SparkConf

conf.set("spark.serializer”,"org.apache.spark.serializer.KryoSerializer")

Running Spark on

Amazon EMR

Focus on deriving insights from your data

instead of manually configuring clusters

Easy to install and configure Spark

Secured

Spark submit, Oozie or use Zeppelin UI

Quickly addand remove capacity

Hourly, reserved, or EC2 Spot pricing

Use S3 to decouplecompute and storage

Launch the latest Spark version

Spark 1.6.0 is the current version on EMR.

< 3 week cadence with latest open source release

Create a fully configured cluster in minutes

AWS Management

Console

AWS Command Line

Interface (CLI)

Or use a AWS SDK directly with the Amazon EMR API

Or easily change your settings

Many storage layers to choose from

Amazon DynamoDB

EMR-DyanmoDB

connector

Amazon RDS

Amazon

Kinesis

Streaming data

connectorsJDBC Data Source

w/ Spark SQL

ElasticSearch

connector

Amazon Redshift

Spark-Redshift

connector

EMR File System

(EMRFS)

Amazon S3

Amazon EMR

Decouple compute and storage by using S3

as your data layer

HDFS

S3 is designed for 11

9’s of durability and is

massively scalable

EC2 Instance

Memory

Amazon S3

Amazon EMR

Amazon EMR

Amazon EMR

Easy to run your Spark workloads

Amazon EMR Step API

SSH to master node and use Spark

Submit, Oozie or Zeppelin

Submit a Spark

application

Amazon EMR

Secured Spark clusters

Encryption At-Rest

• HDFS transparent encryption (AES 256)

• Local disk encryption for temporary files using LUKS encryption

• EMRFS support for Amazon S3 client-side and server-side encryption

Encryption In-Flight

• Secure communication with SSL from S3 to EC2 (nodes of cluster)

• HDFS blocks encrypted in-transit when using HDFS encryption

• SASL encryption for Spark Shuffle

Permissions

• IAM roles, Kerberos, and IAM Users

Access

• VPC and Security Groups

Auditing

• AWS CloudTrailAmazon S3

Customer use cases

Some of our customers running Spark on EMR

Integration Pattern – ETL with Spark

Amazon EMRAmazon S3

HDFSRead

Unstructure

d Data

Write

Structured

Extract

Load from

HDFS

Store Output Data

Integration Pattern – Tumbling Window Reporting

Amazon EMR

Amazon

Kinesis

Streaming Input

HDFS

Tumbling/Fixed

Window

Aggregation

Periodic Output

Amazon Redshift

COPY from

EMR

Or checkpoint to S3 and use

the Lambda loader app

Zeppelin demo

Jonathan FritzSr. Product [email protected]

data science with spark on amazon emr - pop-up loft tel aviv

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