Building real-time applications with Amazon ElastiCache - ADB204 - Anaheim AWS Summit

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Building real-time applications with
Amazon ElastiCache
Madelyn Olson
Software development engineer
AWS
A D B 2 0 4

Real-time apps need low latency and high throughput
Users: 1 million+
Data volume: TB
Locality: Global
Performance: Microseconds
Request rate: Millions
Access: Mobile,
Internet of Things (IoT),
Microservices
Scale: Up-out-in
Economics: Pay as you go
Developer access: Open APIs
Social mediaRide hailing Media streaming Social mediaRide hailing Media streaming Gaming
leaderboards

Redis and Memcached—Top two in-memory data store*
* https://db-engines.com/en/ranking, https://insights.stackoverflow.com/survey/2018/

About Redis
Simple
• Commands
• Client support
Fast
• In-memory
• <1ms
Powerful
• Various data types
• Atomic operations
• LRU/LFU/TTL
Scalable
• High availability
• Backups
• Cluster

About Memcached
Multi-threaded
• Easy to scale
Fast
• In-memory
• < 1ms
Established
• Launched in 2003
Simple
• LRU cache
• Key-value

Difficult
to scale
Difficult
to manage and
secure
Hard to make
highly available
Self-managing Redis and Memcached is challenging
Expensive
Online scaling can be error
prone, replication
performance needs to be
monitored
Manage hardware provisioning,
setup, configuration, backups, and
compliance (security, encryption,
patching)
Invest in people,
processes, hardware,
and software
Need to implement fast
error detection and
remediation

ElastiCache for Redis and Memcached
Fully-managed, open-sourcecompatible, in-memorydata storein the cloud
Extreme performance
In-memory data store and cache for
sub-millisecond response times
Platform tuning and optimization
(e.g., R5/M5)
Fully managed
AWS manages all hardware and software
setup, configuration, monitoring
Easily scalable
Read scaling with replicas
Write and memory scaling with sharding
(up to 250 nodes/170 TB of data)
OSS-compatible
Redis 5 support
Memcached 1.5 support
Client compatible
Reliable
Multi-AZ
Deep monitoring
Automatic failover
Secure and compliant
Amazon Virtual Private Cloud
HIPAA, PCI, FedRAMP
Encryption at-rest and in-transit
Authentication

When to use which ElastiCache databases
ElastiCache Redis (Default) ElastiCache Memcached
Primary use case In-memory database and cache Cache
Data model In-memory key-value In-memory key-value
Open source compatibility Redis Memcached
Cache databases compatibility All databases All databases
Cache size 155.17 TiB 12.7 TiB
Data structures
Strings, lists, sets, sorted sets, hashes, bit arrays,
HyperLogLog
Strings, objects
Commands 200+ Get, set, stats, add, replace, flush all
High availability and failover Yes No
Eviction policies
Advanced eviction policies
no eviction, allkeys-lru, volatile-lru, allkeys-random,
volatile-random, volatile-ttl
Least recently used (LRU)

Grab is Southeast Asia’s
largest ride-hailing
service with 45 million
downloads and 2.5
million daily rides
Challenge: Average response
time of the API layer is < 40 ms
Solution: ElastiCache for Redis
as a caching layer on Amazon
Relational Database Service
(Amazon RDS) for MySQL
provides sub-millisecond
latency even in times of peak
traffic of hundreds of thousands
of cab requests per minute

Use cases
Gaming
leaderboards
Chat apps
Caching
Session
store
Machine
learning
Real-time
analytics
Media
streaming
Message
queues
Geospatial

Challenge: Retrieving data from disk-
based databases and applications is
slow. Workarounds such as specialized
hardware, proprietary in-memory
databases, and data reduction are
expensive, have high lock-in, don’t
scale, and are difficult to manage.
Solution: Use Redis, an open source,
in-memory data store and cache, to
store frequently used data in-memory
for microsecond latency and high
throughput.
Use cases: Database query caching,
persistent session caching, and full
web-page caching.

Clients
Cache
reads/writes
DB
reads/writes
✓ Better performance – Microsecond speed
✓ Cost effective
✓ Higher throughput – ~50 million RPS
Caching

Monitoring
CacheMisses/CacheHits:
Successful retrievals
Reclaimed: Number of keys that
have expired
Evictions: Keys removed due to
memory limits

Challenge: Need low-latency and high-
concurrency data stores to process fast-
moving ephemeral session data such as
authentication tokens, cookies, and
session state.
Solution: Use Redis as an in-memory
key-value store and cache with
appropriate key expiration strategies to
store and manage ephemeral session
data for microsecond responses.
Apps: Web and mobile apps.
Session store

Challenge: Leaderboards need low-
latency and high-concurrency data
stores to process changing scores and
update ranks in real-time.
data store with data structures such as
sorted sets and ranked lists that ensure
uniqueness of elements while
maintaining lists sorted by scores with
microsecond latency.
Apps: Gaming leaderboards, sales
leaderboards.

Redis data type—Sorted sets
• A set of unique items that all have a corresponding score
• Similar to an index
• Fast lookup and ranges based on the score
• Based on a probabilistic skip list with set
• Primitive add and remove with: ZADD and ZREM
• Scanning and searching with: ZRANGE and ZRANGEBYSCORE

Leaderboard example—Sorted set
Populating a leaderboard
> zadd leaderboard 556 Andy
> zadd leaderboard 819 Barry
> zadd leaderboard 105 Carl
> zadd leaderboard 1312 Derek
Updating a leaderboard
> zadd leaderboard 1502 Carl
> zrevrange leaderboard 0 -1
1) "Carl"
2) "Derek"
3) "Barry"
4) "Andy"
Querying a leaderboard
> zrevrange leaderboard 0 -1
1) "Derek"
2) "Barry"
3) "Andy"
4) "Carl"
> zrevrank leaderboard Barry
1
Score
Name
Start
index
End
index

Challenge: Need a fast data store to
process fast-moving streaming data in
real-time.
data store with versatile data structures
such as streams, lists, and sets to
ingest, process, and analyze real-time
data with sub-millisecond latency.
Apps: Social media, ad targeting,
personalization, IoT, and time-series
data analytics.
Real-time streaming and analytics store

Streaming data access
Hot data
Longer
retention
Data sources
1

Native streaming data type—Redis streams
New data type (Redis 5.0)
Push and pop values like list; supports lookup by ID
Implemented with Rax
A Radix library tree optimized for fast lookups and
range queries
Consumer groups
Multiple producers and consumers can interact
with the same queue
Start
End
Consumer 1
Consumer 2
Consumer A
Consumer B

Redis streams example
> XADD locstream * device1 123456 latitude 48.858372 longitude 2.294481
1549057272661-0
> XREAD STREAMS locstream 0
1) 1) "locstream"
2) 1) 1) 1549057272661-0
2) ...
> XREAD STREAMS locstream 1549057272661-0
(nil)
Autogenerate ID Properties
Last ID processed
Same ID

Challenge: Chat and messaging apps
require a low-latency and high-
throughput communication channel
over which a client can send messages
that are redistributed to other
participants.
store and cache with Redis Streams and
pub/sub features to build high-
performance chat and messaging apps.
Apps: Chat rooms, comment streams,
machine-to-machine communication.

Redis pub/sub
• Messages are categorized into channels
• Subscribers can subscribe to multiple patterns or channels
• Publishers publish to a given channel
• Messages are not stored anywhere
• Clients must be connected to receive
• Two main commands: PUBLISH and SUBSCRIBE

Chat and messaging example—Pub/sub
> psubscribe sports:*
Reading messages...
1) "psubscribe"
2) "sports:*"
3) (integer) 1
> publish sports:patriots "Goooo team!"
(integer) 1
1) "pmessage"
2) "sports:*"
3) "sports:patriots"
4) "Goooo team!"

Challenge: Need low-latency and
high-concurrency data stores to process
fast-moving location data in real-time.
data store with purpose-built data
structures such as sorted sets,
encoding/decoding of location data,
and APIs for location-specific indexing,
searching, and sorting.
Apps: Ride-hailing, dating, social
media, gaming, e-commerce.
Geospatial apps

Geospatial enrichment
Frontend
application
Restaurant
geospatial
data
Friend
relationship
graph
Query friend
recommendations
Query
nearby
recommended
restaurants
Query nearby
restaurants
> GEORADIUS restaurants 47.6062 122.3321 10 mi
1) "Starbucks"
2) "McDonalds"
3) "The Best Burger Place"
Lat/Long
Distance

Peloton is the leading
in-home fitness
company
Challenge: Leaderboard
requires elasticity, low latency,
and real-time processing to
deliver customizable rider data
for thousands of users riding
together from the comfort of
their homes.
as an in-memory data store
provides scale and performance
to process streaming data for
on-demand, live leaderboard.

Expedia is a leader in
the $1 trillion travel
industry, with an
extensive portfolio of
brands
Challenge: Need in-memory
data store and cache for real-
time analytics application that
processes ~200 million
messages daily.
as an in-memory data store and
cache to speed up data ingest
and analytics.

Challenge: Large surge event for a new feature
launch on Fortnite required scaling Redis clusters.
Solution: ElastiCache for Redis with online
cluster resizing to dynamically scale out and scale
in. Migrated to optimized R5 instances for higher
throughput, superior performance, and managing
traffic spikes.
Epic Games—ElastiCache to manage surge events

Challenge:
Scale the business to the next level with three times data
growth every year and petabytes of data stored. Need to quickly
process and analyze 50 GB of data daily.
Solution:
Airbnb adopted purpose-built databases for different needs.
Session state: ElastiCache for in-memory store for
sub-millisecond session access.

Software and Internet Financial Ser vicesRide-Hailing Gaming
Media and Entertainment Social Media Telecommunications Travel
Industrials and Ser vices Logistics and Operations Publishing Other
ElastiCache customers

Redis page
aws.amazon.com/redis
ElastiCache for Redis page
aws.amazon.com/elasticache/redis
ElastiCache for Memcached page
https://aws.amazon.com/elasticache/memcached/
Contact us
aws.amazon.com/contact-us/
Get started

1) Install php, apache php redis client
(e.g., yum install php apache php-pecl-redis)
2) Configure “php.ini”
session.save_handler = rediscluster
session.save_path = "
seed[]=cluster-discovery-endpoint:6379&
timeout=2&
read_timeout=2&
failover=error&
persistent=1"
3) Restart httpd
4) Begin using session data
For situations where you need an
external session store:
• Especially needed when using ASGs
• Cache is optimal for high-volume reads
PHP Example
Session Store Details
Auto scaling Instances

Example Caching Application
# App code
save_user(17, {"name": “John Doe"})
user = get_user(17)
# Write Through
def save_user(user_id, values):
record = db.query("update users ... where id = ?", user_id, values)
cache.set(user_id, record, 300) # TTL
return 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
Write through
1. Updated DB
2. SET in cache
Lazy load
1. GET from cache.
2. If MISS get from DB
3. Then SET in cache

Building real-time applications with Amazon ElastiCache - ADB204 - Anaheim AWS Summit

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Building real-time applications with Amazon ElastiCache - ADB204 - Anaheim AWS Summit