Amazon Elasticache Deep Dive - March 2017 AWS Online Tech Talks

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Michael Labib, AWS Specialist Solutions ArchitectMarch 2017

Amazon ElastiCache Deep Dive: Best Practices, Usage Patterns and Caching Strategies

Amazon ElastiCache

©2017,AmazonWebServices,Inc.oritsAffiliates.Allrightsreserved.

Learning Objectives

§ Learn how to integrate Amazon ElastiCache in your workloads§ Understand the benefits of an In-Memory data store§ Learn how to apply various caching strategies in your

applications§ Hands on demonstration using Amazon ElastiCache

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In-Memory Key-Value NoSQL Store

High-performance

Redis and Memcached

Fully managed; Zero admin

Highly Available and Reliable

Hardened by AmazonAmazon

ElastiCache

Redis – The In-Memory Leader

Powerful ~200 commands + Lua scripting

In-memory data structure server

Utility data structuresstrings, lists, hashes, sets, sorted sets, bitmaps & HyperLogLogs

Simple

Atomic operationssupports transactions

Ridiculously fast!<1ms latency for most commands

Highly Availablereplication

Persistence

Open Source

Redis Data Structure Fundamentals

Redis Data Types - String

• Binary safe.• Can contain a max value of 512 MB.• Great for storing Counters, HTML, Images, JSON objects, etc.

valueKey

Key

Redis Data Types - Set

• A collection of unique unordered Strings values• Great for Deduplicating and Grouping related information• Can union, intersect and find differences between other SETS

value: 75 value: 1 value: 39 value: 63 value: 63

Duplicate!

value: 63

Key

Redis Data Types - Sorted Set

• A collection of unique Strings values ordered by score• Great for Deduplication, Grouping and Sorting related information• Get specific range and rank of elements based on score

value: mikescore: 50 score: 75

value: dan value: emmascore: 79

value: linascore: 123

value: lukescore: 350

Key

Redis Data Types - List

HEAD value 1 value 2 value 3 TAIL

• A collection of Strings stored in the order of their insertion• Push and Pop from head or tail of the list or insert at specific value position• Great for message queues and timelines

Key

Redis Data Types - Hashes

Field 1 value 1

• A collection of unordered fields and values • Great for representing objects• Ability to Add, GET, and DEL individual fields by Key

Field 2 value 2

Field 3 value 3

Field 4 value 4

Redis Data Types & More!

Run Lua scripts Geospatial Queries! Pub / Sub

Amazon ElastiCache

Amazon ElastiCache

Redis Multi-AZ with Automatic Failover

Open-Source Compatible

Fully Managed

Enhanced Redis Engine

Easy to Deploy, Use and Monitor

No Cross-AZ Data Transfer Costs

Extreme Performance at Cloud Scale

ElastiCache - Customer Value

Usage Patterns

CachingClients

AmazonElastiCache

AmazonDynamoDB

Cache Reads/Writes

DBReads/Writes

Elastic Load Balancing

AmazonEC2

AmazonRDS

ü BetterPerformance- MicrosecondsSpeed

ü CostEffective

ü HigherThroughput- ~20M/RPS

DBReads/Writes

AWSLambda

Caching# Write Through

def save_user(user_id, values): record = db.query("update users ... where id = ?", user_id, values)cache.set(user_id, record, 300) # TTLreturn record

# Lazy Load

def get_user(user_id): record = cache.get(user_id)if record is None:

record = db.query("select * from users where id = ?", user_id)cache.set(user_id, record, 300) # TTL

return record

# App code

save_user(17, {"name": “Big Mike"})user = get_user(17)

Amazon ElastiCache

Caching# Write Through

def save_user(user_id, values): record = db.query("update users ... where id = ?", user_id, values)cache.set(user_id, record, 300) # TTLreturn record

# Lazy Load

def get_user(user_id): record = cache.get(user_id)if record is None:

record = db.query("select * from users where id = ?", user_id)cache.set(user_id, record, 300) # TTL

return record

# App code

save_user(17, {"name": “Big Mike"})user = get_user(17)

Amazon ElastiCache

Write Through1. Updated DB 2. SET in Cache

Lazy Load1. GET from cache. 2. If MISS get from DB3. Then SET in Cache

1) Install php, apache php memcache cliente.g. yum install php apache php-pecl-memcache

2) Configure “php.ini”session.save_handler = memcachesession.save_path="tcp://node1:11211, tcp://node2:11211"

3) Configure “php.d/memcache.ini”memcache.hash_strategy = consistentmemcache.allow_failover = 1memcache.session_redundancy=3*

4) Restart httpd

5) Begin using Session Data:Auto Scaling group

For situations where you need an external session store

• Especially needed when using ASGs• Cache is optimal for high-volume

reads

PHP ExampleSession Caching

https://github.com/mikelabib/elasticache-memcached-php-demo

IoT Device Data

AWSIoT

AWSIoT Device Amazon

EC2AWS

Lambda

Hot Data

AmazonDynamoDB

Longer Retention

Data Lake

Amazon S3

Amazon Glacier

Cold Data

AmazonKinesis

Firehose

AmazonElastiCache

Lambda Trigger for IoT Rulevar redis = require("redis");exports.handler = function(event, context) {

client = redis.createClient("redis://your-redis-endpoint:6379");multi = client.multi();

multi.zadd("SensorData", date, event.deviceId);multi.hmset(event.deviceId, "temperature", event.temperature,

"deviceIP", event.deviceIP, "humidity", event.humidity,"awsRequestId", context.awsRequestId);

multi.exec(function (err, replies) {if (err) {

console.log('error updating event: ' + err);context.fail('error updating event: ' + err);

} else { console.log('updated event ' + replies);context.succeed(replies); client.quit();

} });

}

AWSLambda

Amazon ElastiCache

AWS IoT

Lambda Trigger for IoT Rulevar redis = require("redis");exports.handler = function(event, context) {

client = redis.createClient("redis://your-redis-endpoint:6379");multi = client.multi();

multi.zadd("SensorData", date, event.deviceId);multi.hmset(event.deviceId, "temperature", event.temperature,

"deviceIP", event.deviceIP, "humidity", event.humidity,"awsRequestId", context.awsRequestId);

multi.exec(function (err, replies) {if (err) {

console.log('error updating event: ' + err);context.fail('error updating event: ' + err);

} else { console.log('updated event ' + replies);context.succeed(replies); client.quit();

} });

}

AWSLambda

Amazon ElastiCache

AWS IoT

Transaction block start

SET• Sorted Set• Hash

Transaction block endhttps://github.com/mikelabib/IoT-Sensor-Data-and-Amazon-ElastiCache

Streaming Data

AmazonEC2

AWSLambda

Amazon Kinesis Streams

AmazonDynamoDB

Hot Data

Longer Retention

AmazonElastiCache

Data Sources

AmazonKinesisAnalytics

AWSLambda

AmazonKinesisStreams

AmazonKinesisStreams

Data Sources

AmazonElastiCache

De-duplicate, Aggregate, Sort, Enrich, etc.

cleansed stream

Streaming Data Enrichment

Streaming Data Analytics

Data Sources

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Amazon Kinesis Streams

AmazonEMR

(Spark Streaming)

Amazon S3

AmazonEC2

Amazon Redshift

Spark Redis Connector

Data Lake

AmazonElastiCache

ElastiCache Redis with cluster mode

Features• Horizontal Scale of up to 3.5 TiB per cluster

• Up to 20 million reads per second

• Up to 4.5 million writes per second

• Enhanced Redis Engine within ElastiCache

• Up to 4x times failover than with Redis 2.8

• Cluster-level Backup and Restore

• Fully Supported by AWS CloudFormation

• Ability to resize your cluster

• Available in all AWS Regions

Redis 3.2 Support

AmazonElastiCache

Scaling with Redis Cluster

Redis Cluster – Automatic Client-Side Sharding

S5

S1

S2

S4 S3Client

• 16384 hash slots per Cluster• Slot for a key is CRC16 modulo {key}

• Slots are distributed across the Clusterinto Shards

• Developers must use a Redis cluster client!• Clients are redirected to the correct shard• Smart clients store a map

Shard S1 = slots 0 – 3276Shard S2 = slots 3277 – 6553Shard S3 = slots 6554 – 9829Shard S4 = slots 9830 – 13106Shard S5 = slots 13107 - 16383

Availability Zone A

slots 0 - 5454 slots 5455 – 10909

Redis Cluster

Redis Cluster – Architecture

slots 10910 – 16363

Availability Zone B Availability Zone C

slots 5455 – 10909slots 5455 – 10909slots 0 - 5454 slots 0 - 5454

slots 10910 – 16363slots 10910 – 16363

Redis Cluster – Multi AZA cluster consists of 1 to 15 shards

example: 3 shard cluster,2 read replicas

Availability Zone A

slots 0 - 5454

Redis Cluster


slots 10910 – 16363


slots 5455 – 10909slots 5455 – 10909slots 0 - 5454 slots 0 - 5454

slots 10910 – 16363

Shard

ReplicaReplicaPrimary

Each shard has a Primary Node and up to 5 replica nodes

slots 5455 – 10909

slots 10910 – 16363

Availability Zone A

slots 0 - 5454 slots 5455 – 10909

Redis Cluster


slots 10910 – 16363


slots 5455 – 10909slots 5455 – 10909

Shard

ReplicaReplica Primary


slots 0 - 5454 slots 0 - 5454

slots 10910 – 16363slots 10910 – 16363

Availability Zone A

slots 0 - 5454

Redis Cluster


slots 10910 – 16363


slots 10910 – 16363slots 10910 – 16363

Shard

Replica PrimaryReplica


slots 5455 – 10909 slots 0 - 5454slots 5455 – 10909 slots 0 - 5454 slots 5455 – 10909

Redis Failure Scenarios

Availability Zone A

slots 0 - 5454 slots 5455 – 10909

Redis Cluster

slots 10910 – 16363


slots 5455 – 10909 slots 5455 – 10909slots 0 - 5454 slots 0 - 5454

slots 10910 – 16363 slots 10910 – 16363

Scenario 1: Single Primary Shard Failure

Availability Zone A

slots 0 - 5454 slots 5455 – 10909

Redis Cluster

Scenario 1: Single Primary Shard Failure

slots 10910 – 16363



slots 10910 – 16363

Mitigation: 1. Promote Read Replica Node (~15-30s)2. Repair Failed Node

slots 10910 – 16363

Availability Zone A

slots 0 - 5454 slots 5455 – 10909

Redis Cluster

Scenario 2: Majority of Primary Shards Fail

slots 10910 – 16363



slots 10910 – 16363slots 10910 – 16363

Availability Zone A

slots 0 - 5454 slots 5455 – 10909

Redis Cluster

slots 10910 – 16363



Mitigation: Redis enhancements on ElastiCache • Promote Read Replica Nodes• Repair Failed Nodes

slots 10910 – 16363slots 10910 – 16363

Scenario 2: Majority of Primary Shards Fail

How do I migrate from a non-clustered Redis environment to a clustered Redis environment on ElastiCache?

Migrating to a Cluster

1. Create new Cluster2. Make snapshot of old CacheCluster3. Restore snapshot to new Cluster4. Update Client5. Terminate old Cluster

S5

S1

S2

S4 S3Client

Old

< 3.2Client

How do change the number of Redis Shards I have allocated?

1. Create new Cluster2. Make snapshot of old CacheCluster3. Restore snapshot to new Cluster4. Terminate old Cluster

Resizing your Cluster

Architecting for Availability

• Upgrade to the latest engine version – 3.2.4• Use Newer Instance types when possible (e.g. M4 vs M3)• Set reserved-memory to 30% of total available memory• Swap usage should be zero or very low. Scale if not.• Put read-replicas in a different AZ from the primary• For important workloads use 2 read replicas per primary• Write to the primary, read from the read-replicas• Take snapshots from read-replicas to support your RPO• For Redis Cluster have odd number of shards.

Monitoring Your Cluster

Key ElastiCache CloudWatch Metrics

• CPUUtilization• Memcached – up to 90% ok• Redis – divide by cores (ex: 90% / 4 = 22.5%)

• SwapUsage low• CacheMisses / CacheHits Ratio low / stable• Evictions near zero

• Exception: Russian doll caching• CurrConnections stable

• Setup alarms with CloudWatch Metrics

• Whitepaper: http://bit.ly/elasticache-whitepaper

ElastiCache Modifiable Parameters• Maxclients: 65000 (unchangeable)

• Use connection pooling• timeout – Closes a connection after its been idle for a given interval• tcp-keepalive – Detects dead peers given an interval

• Databases: 16 (Default) for non-clustered mode• Logical partition

• Reserved-memory: 0 (Default) • Recommended

§ 50% of maxmemory to use before 2.8.22 § 30% after 2.8.22 – ElastiCache

• Maxmemory-policy:• The eviction policy for keys when maximum memory usage is reached • Possible values: volatile-lru, allkeys-lru, volatile-random, allkeys-random,

volatile-ttl, noeviction

Caching Strategies

Java App Java App

Web Server Web Server

Database CacheCustomers

AZ1 AZ2

Distributed Cache

Demo workload topology: Customer Data

CustomerDB Primary CustomerDB Standby

Caching Strategies

1. Cache Database SQL ResultSet (Row)

PRO When data retrieval logic is abstracted from the code consuming the ResultSet, caching the ROW can be extremely effective and can be implemented against any RDBMS.

CONData retrieval still requires extracting values from the ROW and does not further simplify data access; It only reduces data retrieval latency.

SELECT * FROM x WHERE y

ID First_Name Last_Name City123 Michael Labib Chicago

ResultSet Object (ROW) Key: Query, Value: CRS as byte array

Caching Strategies

2. Cache database values into custom format in a Redis String



Key: 123, Value: firstNameString firstName = rs.getString(First_Name)

PROVery easy to implement. Cache any desired database fields and values into a Redis String. For example, store your retrieved data into a JSON object stored in a Redis String.

CONMinor. Application code will leverage different types of Objects when retrieving data (i.e. Redis data structures and database results when needed)

Caching Strategies

3. Cache serialized application object (e.g. Java Object )



Key: CUSTOMER_ID:123, Value: Customer Object as byte array

String firstName = rs.getString(First_Name);customer.setFirstName(firstName);

String lastName = rs.getString(Last_Name);customer.setLastName(lastName);

PRO Utilize application objects in their native structure and data state when serialized.

CONAdvanced application development use case.

Caching Strategies

4. Leverage advanced Redis Data Structures for cached data



Key: CUSTOMER_ID:123, Value: rsHash

String firstName = rs.getString(First_Name);rsHash.put(“firstName", firstName);

String lastName = rs.getString(Last_Name);rsHash.put(“lastName", lastName);

jedis.hmset(“CUSTOMER_ID:123", rsHash);

PRO In addition to reducing data retrieval latency, cache data into specific data structure that simplifies the data access pattern.

CONMinor. Application code will leverage different types of objects when retrieving data (i.e. Redis data structures and database results when needed)

Demo Steps: Database Setup

• Create CustomerDB with Database of choice

• Load sample data• Create sample or use mock tool https://www.mockaroo.com/

• Set appropriate security groups to grant access to EC2 SGs hosting your applications• Enable port 3306 for your IP to use DB editor for convenience

CREATE table Customer (CUSTOMER_ID INT,FIRST_NAME VARCHAR(50),LAST_NAME VARCHAR(50),EMAIL VARCHAR(50),GENDER VARCHAR(50),CITY VARCHAR(50),STATE VARCHAR(50),ADDRESS VARCHAR(50),COUNTRY VARCHAR(50) );

INSERT into Customer (CUSTOMER_ID, FIRST_NAME, LAST_NAME, EMAIL, GENDER, CITY, STATE, ADDRESS, COUNTRY) VALUES (1, 'Maria', 'Rodriguez', '[email protected]', 'Female', 'Dallas', 'Texas', '63 8th Circle', 'United States');

Demo Steps: ElastiCache & EC2 Setup

• Create ElastiCache for Redis Cluster

• Test connectivity from EC2

• Set appropriate security groups to grant access to EC2 SGs hosting your applications• Install Redis Client on the EC2 instances to command line Redis access

wget http://download.redis.io/redis-stable.tar.gztar xvzf redis-stable.tar.gzcd redis-stablemake && make install

src/redis-cli -h your-elasticache-redis-endpoint -p 6379

Demo: Example Java application

uses

uses

implementsimplements

<<interface>>

<<interface>>DAO Pattern

uses

<<interface>>

uses

Demo: Data retrieval logic

Data retrieval details abstracted from consuming applications

1. Check cache first for requested data2. If not available, retrieve the data from origin database3. Cache any data not available previously set in cache for 5

minutes4. Return results

https://github.com/mikelabib/amazon-elasticache-redis-caching-demosDemo

Summary

§ Caching your data can greatly improve your data retrieval speeds

§ There are various strategies you can implement to cache your data, use the one that meets your needs

§ Caching your data can also greatly reduce your database costs when architecting for scale

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Technology

Amazon Elasticache Deep Dive - March 2017 AWS Online Tech Talks