Fast Data at Scale - AWS Summit Tel Aviv 2017

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Team Leader, Solutions Architects
21/6/2017
Fast Data at Scale
Liron Dor

Objectives
• Redis fundamentals
• Amazon ElastiCache – Redis
• Use cases
• Amazon DynamoDB Accelerator (DAX)
• Amazon DynamoDB – What’s new

Redis – The In-Memory Leader
Powerful
~200 commands + Lua scripting
In-memory
Utility data structures
strings, lists, hashes, sets, sorted
sets, bitmaps & HyperLogLogs
Simple
Atomic operations
supports transactions
Ridiculously fast!
<1ms latency for most commands
Highly Available
replication
Persistence
Open Source

Redis Data Types - String
• Can contain a max value of 512 MB.
• Great for storing Counters, HTML, Images, JSON objects, etc.
• Support: Append, Bitmap operations, increment / decrement
numbers
• Example:
• Key: page:index.html
• Value: <HTML><HEAD> ….
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
• Example:
• Key: unique_users_id:21-6-17
• Value: {432344,323534,564533}
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
• Example:
• Key: game:mygame:HighScore
• Value: {(mike , 50) , (dan, 75) , (emma, 79)}
value: mike
score: 50 score: 75
value: dan value: emma
score: 79
value: lina
score: 123
value: luke
score: 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
• Example:
• Key: tasks
• Values: [100] à [205] à [95]

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 delete individual
fields by Key
• Example:
• Key: customers:123
• Value: Name ==> Jack
Family ==> Longhorn
Time ==> XXX
Field 2 value 2
Field 3 value 3
Field 4 value 4

Additional capabilities
Run Lua scripts Geospatial Queries Publish / Subscribe

In-Memory Key-Value NoSQL Store
Redis and Memcached
Low latency / High throughput
Fully managed; Zero admin
Hardened by Amazon
Horizontal Scale of up to 3.5 TiB per cluster
Amazon
ElastiCache

Redis Multi-AZ with Automatic Failover
Open-Source Compatible
Easy to Deploy, Use and Monitor
No Cross-AZ Data Transfer Costs
Managed Service
Resizable
Deploy with CloudFormation
Backup / Restore

Hardened by Amazon
Optimized Swap Memory
•Mitigate the risk of increased
swap usage during syncs and
snapshots.
Dynamic write throttling
•Improved output buffer
management when the node’s
memory is close to being
exhausted.
Smoother failovers
•Clusters recover faster as replicas
avoid flushing their data to do a
full re-sync with the primary.
Amazon
ElastiCache

Redis Cluster – Automatic Client-Side Sharding
S5
S1
S2
S4 S3
Client
• 16384 hash slots per Cluster
• Slots distributed across the Cluster Shards
• Use Redis client with Cluster support
Shard S1 = slots 0 – 3276
Shard S2 = slots 3277 – 6553
Shard S3 = slots 6554 – 9829
Shard S4 = slots 9830 – 13106
Shard 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 – 10909
slots 5455 – 10909slots 0 - 5454 slots 0 - 5454
slots 10910 – 16363
slots 10910 – 16363
Redis Cluster – Multi AZ
A 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 – 10909
slots 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 – 10909
slots 5455 – 10909
Shard
ReplicaReplica Primary
slots 0 - 5454 slots 0 - 5454
slots 10910 – 16363
slots 10910 – 16363

Availability Zone A
slots 0 - 5454
Redis Cluster
slots 10910 – 16363
slots 10910 – 16363
slots 10910 – 16363
Shard
Replica PrimaryReplica
slots 5455 – 10909 slots 0 - 5454
slots 5455 – 10909
slots 0 - 5454 slots 5455 – 10909

Best Practices
• 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 (version 2.8.22 and later)
• Swap usage should be zero or very low and scale if not
• Read-replicas in a different AZ
• Use 2 read replicas per primary for production workload
• Write to the primary, read from the read-replicas

Caching
Clients
Amazon
ElastiCache
Amazon
DynamoDB
Cache
Reads/Writes
DB
Reads/Writes
Application
Load
Balancing
Amazon
EC2
Amazon
RDS
ü Better Performance - Microseconds Speed
ü Cost Effective
ü Higher Throughput - ~ 20M / RPS
DB
Reads/Writes
AWS
Lambda
Aurora Only

ALB
For situations where you need an external session store
• Especially needed when using ASGs
• ElasticCache is optimal for high-volume reads
Session Store
EC2 with
Auto Scaling Group
Amazon
ElastiCache

Amazon
Kinesis
Analytics
AWS
Lambda
Amazon
Kinesis
Streams
Amazon
Kinesis
Streams
Data
Sources
Amazon
ElastiCache
De-duplicate,
Aggregate, Sort,
Enrich, etc.
cleansed
stream
Streaming Data Enrichment

Streaming Data Analytics
Data
Sources
1
Amazon
Kinesis
Streams
Amazon
EMR
(Spark Streaming)
Amazon
S3
Amazon
EC2
Amazon Redshift
Spark Redis Connector
Data Lake
Amazon
ElastiCache

ElastiCache Take Away
• ElastiCache is not only caching
• Powerful and easy to use
• High throughput / low latency – good fit for Big Data

Amazon DynamoDB
Fast and flexible NoSQL database
service for any scale

DynamoDB consistent performance at scale
“We cannot afford to waste a 3 or 4 milliseconds...”
“We have 20ms to make a decision… ”
Consistent Single-Digit Millisecond Latency
Requests (millions)
Latency (milliseconds)

DynamoDB
Cache
Your Applications
Traditional side cache
Your Application
DynamoDB
Your Applications
Caching made simple
DynamoDB Accelerator
Your Application
DynamoDB
DynamoDB
Traditional Cache Cache Made Easy
DynamoDB Accelerator (DAX)

DynamoDB
Your Applications
• Fully managed, highly available: handles all software management,
fault tolerant, replication across multi-AZs within a region
• DynamoDB API compatible: seamlessly caches DynamoDB API
calls, no application re-writes required
• Write-through: DAX handles caching for writes
• Flexible: Configure DAX for one table or many
• Scalable: scales-out to any workload with up to 10 nodes
• Manageability: fully integrated AWS service: Amazon CloudWatch,
Tagging for DynamoDB, AWS Console
• Security: Amazon VPC, AWS IAM, AWS CloudTrail, AWS
Organizations
Features
Your Application
DynamoDB
DynamoDB Accelerator (DAX)

What did we just do?
AWS VPC
EC2
App
DDB
SDK
DynamoDB
DAX
SDK
DAX

High Availability
AMAZON VPC
EC2
App
DAX
SDK
DynamoDB
AZ1
AZ2
AZ3

DynamoDB Accelerator (DAX): Write-through
DynamoDB
Your Applications
t=1 PutItem
t=2 Write
t=3 Populate cache
t=4 Return result
Your Application
DynamoDB

DynamoDB Accelerator (DAX): Reads (Cache Miss)
t=1 GetItem
t=2 Cache miss
t=3 GetItem
t=4 Populate cache
t=5 Return item
DynamoDB
Your Applications
Your Application
DynamoDB

DynamoDB Accelerator (DAX): Reads (Cache Hit)
DynamoDB
Your Applications
t=1 GetItem
t=2 Cache hit
t=3 Return item
Your Application
DynamoDB

DAX has two caches
Query Cache {query, scan}
Item Cache {GetItem, PutItem}
key, value
query text, result set

Cache Eviction
Time-to-live (TTL) Least Recently Used
(LRU)
Write-through
eviction

• Read APIs: GetItem, BatchGetItem, Query, Scan
• Modify APIs: PutItem, UpdateItem, DeleteItem, BatchWriteItem
• Control plane APIs: Not supported (CreateTable, DeleteTable, etc.)
DAX is API compatible with DynamoDB

Q: How easy is it to add in-
memory caching with DAX?
A: Comment out the this code
and add this code

Scaling DAX
. . .
Scale Up
(15.25 GiB to 244 GiB)
Scale-out
Scale Out
(10 nodes)
Scale-up
. . .

Use Case #1: Unpredictable spikes

Use Case #2: Speed
Response times in microseconds

Use Case #3: Hot Keys
Partition 1
Partition 2
Partition 3
Partition 4
Partition 5
Partition 6
Partition 7
Partition 8
Partition 9
Partition 10
Partition 11
Partition 12
Partition 13
Partition 14
Partition 15
00:00 00:01 00:02 00:03 00:04
• Example DynamoDB table with 15 partitions
• Hot partitions marked in Red
• Cold Partitions marked in Blue

Amazon DynamoDB – What’s New
• Auto Scaling for DynamoDB
• TTL
• Table tagging
• VPC Endpoint for DynamoDB
(Preview)

Key Benefits
• Fast performance: Microseconds response
times at millions of reads/sec from single DAX
cluster
• Ease of use: DynamoDB API compatible -
requires minimal code change for existing
applications, simplifying developer experience
• Lower costs: Reduce provisioned read
capacity for DynamoDB tables for tables with
hot data
DynamoDB Accelerator (DAX) Take Away
DynamoDB
Your Applications
Your Application
DynamoDB

Fast Data at Scale - AWS Summit Tel Aviv 2017

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