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Amazon Elastic MapReduce: Deep Dive and Best Practices Parviz Deyhim

November 13, 2013

Outline

Introduction to Amazon EMR

Amazon EMR Design Patterns

Amazon EMR Best Practices

Amazon Controlling Cost with EMR

Advanced Optimizations

Outline

Introduction to Amazon EMR

Amazon EMR Design Patterns

Amazon EMR Best Practices

Amazon Controlling Cost with EMR

Advanced Optimizations

What is EMR?

Map-Reduce engine Integrated with tools

Hadoop-as-a-service

Massively parallel

Cost effective AWS wrapper

Integrated to AWS services

HDFS

Amazon EMR

HDFS

Amazon EMR

Amazon S3 Amazon DynamoDB

HDFS

Analytics languages Data management

Amazon EMR

Amazon S3 Amazon DynamoDB

HDFS

Analytics languages Data management

Amazon EMR Amazon RDS

Amazon S3 Amazon DynamoDB

HDFS

Analytics languages Data management

Amazon Redshift

Amazon EMR Amazon RDS

Amazon S3 Amazon DynamoDB

AWS Data Pipeline

Amazon EMR Introduction

• Launch clusters of any size in a matter of minutes

• Use variety of different instance sizes that match your workload

Amazon EMR Introduction

• Don’t get stuck with hardware

• Don’t deal with capacity planning

• Run multiple clusters with different sizes, specs and node types

Amazon EMR Introduction

• Integration with Spot market • 70-80% discount

Outline

Introduction to Amazon EMR

Amazon EMR Design Patterns

Amazon EMR Best Practices

Amazon Controlling Cost with EMR

Advanced Optimizations

Amazon EMR Design Patterns

Pattern #1: Transient vs. Alive Clusters

Pattern #2: Core Nodes and Task Nodes

Pattern #3: Amazon S3 as HDFS

Pattern #4: Amazon S3 & HDFS

Pattern #5: Elastic Clusters

Pattern #1: Transient vs. Alive Clusters

Pattern #1: Transient Clusters

• Cluster lives for the duration of the job

• Shut down the cluster when the job is done

• Data persist on Amazon S3

• Input & output Data on Amazon S3

Benefits of Transient Clusters 1. Control your cost

2. Minimum maintenance • Cluster goes away when job is done

3. Practice cloud architecture • Pay for what you use

• Data processing as a workflow

When to use Transient cluster?

If ( Data Load Time + Processing Time) * Number Of Jobs < 24 Use Transient Clusters Else Use Alive Clusters

When to use Transient cluster?

( 20min data load + 1 hour Processing time) * 10 jobs = 13 hours < 24 hour = Use Transient Clusters

Alive Clusters • Very similar to traditional Hadoop deployments

• Cluster stays around after the job is done

• Data persistence model:

• Amazon S3

• Amazon S3 Copy To HDFS

• HDFS and Amazon S3 as backup

Alive Clusters • Always keep data safe on Amazon S3 even if

you’re using HDFS for primary storage

• Get in the habit of shutting down your cluster and start a new one, once a week or month

• Design your data processing workflow to account for failure

• You can use workflow managements such as AWS Data Pipeline

Benefits of Alive Clusters • Ability to share data between multiple jobs

EMR

Amazon S3

HDFS HDFS

Amazon S3

EMR

EMR

Transient cluster Long running clusters

Benefit of Alive Clusters • Cost effective for repetitive jobs

EMR

pm pm

EMR

EMR

EMR

pm pm

EMR

When to use Alive cluster? If ( Data Load Time + Processing Time) * Number Of Jobs > 24 Use Alive Clusters Else Use Transient Clusters

When to use Alive cluster?

( 20min data load + 1 hour Processing time) * 20 jobs = 26hours > 24 hour = Use Alive Clusters

Pattern #2: Core & Task nodes

Core Nodes

Master instance group

Amazon EMR cluster

Core instance group

HDFS HDFS

Run TaskTrackers (Compute) Run DataNode (HDFS)

Core Nodes

Master instance group

Amazon EMR cluster

Core instance group

HDFS HDFS

Can add core nodes

Core Nodes

Master instance group

Amazon EMR cluster

Core instance group

HDFS HDFS

Can add core nodes More HDFS space More CPU/mem

HDFS

Core Nodes

Master instance group

Core instance group

HDFS HDFS

Can’t remove core nodes because of HDFS

HDFS

Amazon EMR cluster

Amazon EMR Task Nodes

Master instance group

Task instance group Core instance group

HDFS HDFS

Run TaskTrackers No HDFS

Reads from core node HDFS

Amazon EMR cluster

Amazon EMR Task Nodes

Master instance group

Amazon EMR cluster

Task instance group Core instance group

HDFS HDFS

Can add task nodes

Amazon EMR Task Nodes

Master instance group

Amazon EMR cluster

Task instance group Core instance group

HDFS HDFS

More CPU power More memory

Amazon EMR Task Nodes

Master instance group

Amazon EMR cluster

Task instance group Core instance group

HDFS HDFS

You can remove task nodes

Amazon EMR Task Nodes

Master instance group

Amazon EMR cluster

Task instance group Core instance group

HDFS HDFS

You can remove task nodes

Tasknode Use-Case #1 • Speed up job processing using Spot

market

• Run task nodes on Spot market

• Get discount on hourly price

• Nodes can come and go without interruption to your cluster

Tasknode Use-Case #2 • When you need extra horse power

for a short amount of time

• Example: Need to pull large amount of data from Amazon S3

Example:

HS1 48TB HDFS

HS1 48TB HDFS

Amazon S3

Example: HS1 48TB HDFS

HS1 48TB HDFS

Amazon S3

m1.xl

m1.xl

m1.xl

m1.xl

m1.xl

m1.xl

Add Spot task nodes to load data from Amazon S3

Example: HS1 48TB HDFS

HS1 48TB HDFS Amazon S3

m1.xl

m1.xl

m1.xl

m1.xl

m1.xl

m1.xl

Remove after data load from Amazon S3

Pattern #3: Amazon S3 as HDFS

Amazon S3 as HDFS

• Use Amazon S3 as your permanent data store

• HDFS for temporary storage data between jobs

• No additional step to copy data to HDFS

Amazon EMR cluster

Task instance group Core instance group

HDFS

HDFS

Amazon S3

Benefits: Amazon S3 as HDFS • Ability to shut down your cluster

HUGE Benefit!!

• Use Amazon S3 as your durable storage

11 9s of durability

Benefits: Amazon S3 as HDFS • No need to scale HDFS

• Capacity

• Replication for durability

• Amazon S3 scales with your data

• Both in IOPs and data storage

Benefits: Amazon S3 as HDFS • Ability to share data between multiple clusters

• Hard to do with HDFS

Amazon S3

EMR

EMR

Benefits: Amazon S3 as HDFS • Take advantage of Amazon S3 features

• Amazon S3 ServerSideEncryption

• Amazon S3 LifeCyclePolicy

• Amazon S3 versioning to protect against corruption

• Build elastic clusters

• Add nodes to read from Amazon S3

• Remove nodes with data safe on Amazon S3

What About Data Locality? • Run your job in the same region as your

Amazon S3 bucket

• Amazon EMR nodes have high speed connectivity to Amazon S3

• If your job Is CPU/memory-bounded data, locality doesn’t make a difference

Anti-Pattern: Amazon S3 as HDFS

• Iterative workloads – If you’re processing the same dataset more than once

• Disk I/O intensive workloads

Pattern #4: Amazon S3 & HDFS

Amazon S3 & HDFS

1. Data persist on Amazon S3

Amazon S3 & HDFS

2. Launch Amazon EMR and copy data to HDFS with S3distcp

S3D

istC

p

Amazon S3 & HDFS

3. Start processing data on HDFS

S3D

istC

p

Benefits: Amazon S3 & HDFS • Better pattern for I/O-intensive workloads

• Amazon S3 benefits discussed previously applies

• Durability

• Scalability

• Cost

• Features: lifecycle policy, security

Pattern #5: Elastic Clusters

Amazon EMR Elastic Cluster (m) 1. Start cluster with certain number of nodes

Amazon EMR Elastic Cluster (m) 2. Monitor your cluster with Amazon CloudWatch

metrics • Map Tasks

Running

• Map Tasks Remaining

• Cluster Idle?

• Avg. Jobs Failed

Amazon EMR Elastic Cluster (m) 3. Increase the number of nodes as you need

more capacity by manually calling the API

Amazon EMR Elastic Cluster (a) 1. Start your cluster with certain number of nodes

Amazon EMR Elastic Cluster (a) 2. Monitor cluster capacity with Amazon

CloudWatch metrics

• Map Tasks Running

• Map Tasks Remaining

• Cluster Idle?

• Avg. Jobs Failed

Amazon EMR Elastic Cluster (a) 3. Get HTTP Amazon SNS notification to a simple

app deployed on Elastic Beanstalk

Amazon EMR Elastic Cluster (a) 4. Your app calls the API to add nodes to your

cluster

API

Outline

Introduction to Amazon EMR

Amazon EMR Design Patterns

Amazon EMR Best Practices

Amazon Controlling Cost with EMR

Advanced Optimizations

Amazon EMR Nodes and Size • Use m1.smal, m1.large, c1.medium for

functional testing

• Use M1.xlarge and larger nodes for production workloads

Amazon EMR Nodes and Size • Use CC2 for memory and CPU intensive

jobs

• Use CC2/C1.xlarge for CPU intensive jobs

• Hs1 instances for HDFS workloads

Amazon EMR Nodes and Size • Hi1 and HS1 instances for disk I/O-

intensive workload

• CC2 instances are more cost effective than M2.4xlarge

• Prefer smaller cluster of larger nodes than larger cluster of smaller nodes

Holy Grail Question

How many nodes do I need?

Introduction to Hadoop Splits

• Depends on how much data you have

• And how fast you like your data to be processed

Introduction to Hadoop Splits

Before we understand Amazon EMR capacity planning, we need to understand Hadoop’s inner working of splits

Introduction to Hadoop Splits • Data gets broken up to splits (64MB or 128)

Data Splits

128MB

Introduction to Hadoop Splits • Splits get packaged into mappers

Data Splits Mappers

Introduction to Hadoop Splits

• Mappers get assigned to nodes for processing

Mappers Instances

Introduction to Hadoop Splits • More data = More splits = More mappers

Introduction to Hadoop Splits • More data = More splits = More mappers

Queue

Introduction to Hadoop Splits • Data mappers > cluster mapper capacity =

mappers wait for capacity = processing delay

Queue

Introduction to Hadoop Splits • More nodes = reduced queue size = faster

processing

Queue

Calculating the Number of Splits for Your Job

Uncompressed files: Hadoop splits a single file to multiple splits. Example: 128MB = 2 splits based on 64MB split size

Calculating the Number of Splits for Your Job

Compressed files:

1. Splittable compressions: same logic as un-

compressed files

128MB BZIP 64MB BZIP

Calculating the Number of Splits for Your Job

Compressed files:

2. Unsplittable compressions: the entire file is a

single split.

128MB GZ 128MB GZ

Calculating the Number of Splits for Your Job

Number of splits If data files have unsplittable compression # of splits = number of files Example: 10 GZ files = 10 mappers

Cluster Sizing Calculation

Just tell me how many nodes I need for my job!!

Cluster Sizing Calculation

1. Estimate the number of mappers your job requires.

Cluster Sizing Calculation

2. Pick an instance and note down the number of mappers it can run in parallel

M1.xlarge = 8 mappers in parallel

Cluster Sizing Calculation

3. We need to pick some sample data files to run a test workload. The number of sample files should be the same number from step #2.

Cluster Sizing Calculation

4. Run an Amazon EMR cluster with a single core node and process your sample files from #3. Note down the amount of time taken to process your sample files.

Cluster Sizing Calculation

Total Mappers * Time To Process Sample Files

Instance Mapper Capacity * Desired Processing Time

Estimated Number Of Nodes:

Example: Cluster Sizing Calculation

1. Estimate the number of mappers your job requires

150

2. Pick an instance and note down the number of mappers it can run in parallel

m1.xlarge with 8 mapper capacity per instance

Example: Cluster Sizing Calculation

3. We need to pick some sample data files to run a test workload. The number of sample files should be the same number from step #2.

8 files selected for our sample test

Example: Cluster Sizing Calculation

4. Run an Amazon EMR cluster with a single core

node and process your sample files from #3. Note down the amount of time taken to process your sample files.

3 min to process 8 files

Cluster Sizing Calculation

Total Mappers For Your Job * Time To Process Sample Files

Per Instance Mapper Capacity * Desired Processing Time

Estimated number of nodes:

150 * 3 min 8 * 5 min

= 11 m1.xlarge

File Size Best Practices

• Avoid small files at all costs

• Anything smaller than 100MB

• Each mapper is a single JVM

• CPU time required to spawn JVMs/mappers

File Size Best Practices

Mappers take 2 sec to spawn up and be ready

for processing

10TB of 100mgB = 100,000 mappers * 2Sec =

total of 55 hours mapper CPU setup time

File Size Best Practices

Mappers take 2 sec to spawn up and be ready

for processing

10TB of 1000MB = 10,000 mappers * 2Sec =

total of 5 hours mapper CPU setup time

File Size on Amazon S3: Best Practices

• What’s the best Amazon S3 file size for

Hadoop?

About 1-2GB

• Why?

File Size on Amazon S3: Best Practices

• Life of mapper should not be less than 60 sec

• Single mapper can get 10MB-15MB/s speed to Amazon S3

≈ 60sec * 15MB 1GB

Holy Grail Question

What if I have small file issues?

Dealing with Small Files

• Use S3DistCP to combine smaller files together

• S3DistCP takes a pattern and target file to combine smaller input files to larger ones

Dealing with Small Files

Example: ./elastic-mapreduce --jobflow j-3GY8JC4179IOK --jar \

/home/hadoop/lib/emr-s3distcp-1.0.jar \

--args '--src,s3://myawsbucket/cf,\ --dest,hdfs:///local,\

--groupBy,.*XABCD12345678.([0-9]+-[0-9]+-[0-9]+-[0-9]+).*,\ --targetSize,128,\

Compressions

• Compress as much as you can

• Compress Amazon S3 input data files

– Reduces cost

– Speed up Amazon S3->mapper data transfer time

Compressions • Always Compress Data Files On Amazon S3

• Reduces Storage Cost

• Reduces Bandwidth Between Amazon S3 and Amazon EMR

• Speeds Up Your Job

Compressions • Compress Mappers and Reducer Output

• Reduces Disk IO

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

Compressions

• If You Are Time Sensitive, Faster Compressions Are A Better Choice

• If You Have Large Amount Of Data, Use Space Efficient Compressions

• If You Don’t Care, Pick GZIP

Change Compression Type • You May Decide To Change Compression Type

• Use S3DistCP to change the compression types of your files

• Example: ./elastic-mapreduce --jobflow j-3GY8JC4179IOK --jar \

/home/hadoop/lib/emr-s3distcp-1.0.jar \

--args '--src,s3://myawsbucket/cf,\

--dest,hdfs:///local,\

--outputCodec,lzo’

Outline

Introduction to Amazon EMR

Amazon EMR Design Patterns

Amazon EMR Best Practices

Amazon Controlling Cost with EMR

Advanced Optimizations

Architecting for cost • AWS pricing models:

– On-demand: Pay as you go model.

– Spot: Market place. Bid for instances and get a discount

– Reserved Instance: upfront payment (for 1 or 3 year) for reduction in overall monthly payment

Reserved Instances use-case For alive and long-running clusters

Reserved Instances use-case For ad-hoc and unpredictable workloads, use medium utilization

Reserved Instances use-case

For unpredictable workloads, use Spot or on-demand pricing

Outline

Introduction to Amazon EMR

Amazon EMR Design Patterns

Amazon EMR Best Practices

Amazon Controlling Cost with EMR

Advanced Optimizations

Adv. Optimizations (Stage 1)

• The best optimization is to structure your data (i.e., smart data partitioning)

• Efficient data structuring= limit the amount of data being processed by Hadoop= faster jobs

Adv. Optimizations (Stage 1) • Hadoop is a batch processing framework

• Data processing time = an hour to days

• Not a great use-case for shorter jobs

• Other frameworks may be a better fit – Twitter Storm

– Spark

– Amazon Redshift, etc.

Adv. Optimizations (Stage 1) • Amazon EMR team has done a great deal of

optimization already

• For smaller clusters, Amazon EMR configuration optimization won’t buy you much

– Remember you’re paying for the full hour cost of an instance

Adv. Optimizations (Stage 1)

Best Optimization??

Adv. Optimizations (Stage 1)

Add more nodes

Adv. Optimizations (Stage 2)

• Monitor your cluster using Ganglia

• Amazon EMR has Ganglia bootstrap action

Adv. Optimizations (Stage 2)

• Monitor and look for bottlenecks – Memory

– CPU

– Disk IO

– Network IO

Adv. Optimizations Run Job

Adv. Optimizations Run Job

Find Bottlenecks

Ganglia CPU Disk Memory

Adv. Optimizations Run Job

Find Bottlenecks

Address Bottleneck

Ganglia CPU Disk Memory Fine Tune

Change Algo

Network IO • Most important metric to watch for if using

Amazon S3 for storage

• Goal: Drive as much network IO as possible from a single instance

Network IO • Larger instances can drive > 600Mbps

• Cluster computes can drive 1Gbps -2 Gbps

• Optimize to get more out of your instance throughput – Add more mappers?

Network IO • If you’re using Amazon S3 with Amazon

EMR, monitor Ganglia and watch network throughput.

• Your goal is to maximize your NIC throughput by having enough mappers per node

Network IO, Example

Low network utilization Increase number of mappers if possible to drive more traffic

CPU • Watch for CPU utilization of your clusters

• If >50% idle, increase # of mapper/reducer per instance

– Reduces the number of nodes and reduces cost

Example Adv. Optimizations (Stage 2)

What potential optimization do you see in this graph?

Example Adv. Optimizations (Stage 2)

40% CPU idle. Maybe add more mappers?

Disk IO • Limit the amount of disk IO

• Can increase mapper/reducer memory

• Compress data anywhere you can

• Monitor cluster and pay attention to HDFS bytes written metrics

• One play to pay attention to is mapper/reducer disk spill

Disk IO • Mapper has in memory buffer

mapper memory buffer

mapper

Disk IO • When memory gets full, data spills to disk

mapper memory buffer data spills to disk

mapper

Disk IO • If you see mapper/reducer excessive spill to disk,

increase buffer memory per mapper

• Excessive spill when ratio of “MAPPER_SPILLED_RECORDS” and “MAPPER_OUTPUT_RECORDS” is more than 1

Disk IO Example:

MAPPER_SPILLED_RECORDS = 221200123

MAPPER_OUTPUT_RECORDS = 101200123

Disk IO • Increase mapper buffer memory by increasing

“io.sort.mb”

<property><name>io.sort.mb<name><value>200</value></property>

• Same logic applies to reducers

Disk IO • Monitor disk IO using Ganglia

• Look out for disk IO wait

Disk IO • Monitor disk IO using Ganglia

• Look out for disk IO wait

Remember! Run Job

Find Bottlenecks

Address Bottleneck

Ganglia CPU Disk Memory Fine Tune

Change Algo

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