Amazon DynamoDB Design Patterns & Best Practices

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Amazon DynamoDB
Design Patterns
& Best Practices

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Highlights
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Internet-scale Database Requirements
Unlimited throughput
• Social applications
• Online gaming
Elasticity and flexibility
• Application could go viral at any time
• Must handle sudden traffic without code changes
Predictable performance
• Low latency
• No latency increase or throughput decrease with increase in data
set size or throughput
No administration
2

What is Amazon DynamoDB?
Fully managed NoSQL database service
Accessible via simple web service APIs
3
Id Title Year
1 Terminator 1984
2 Titanic 1997
Movies Table
Client

What can I do with DynamoDB?
Offload operating and scaling a highly available
distributed database cluster to AWS
• Serve any level of request traffic
• Store and retrieve any amount of data
• Pay a low price for what you use
Fast time to market
4

DEMO
DynamoDB Speed Test! (www.DynaSpeed.net)

Region
Availability Zone Availability ZoneAvailability Zone
Cluster
controller
100 c1.mediums /
200 virtual CPUs
DynamoDB
Demo Architecture…

Demo Architecture…
DynamoDB
Master
node/cluster
controller
Worker nodes

DATA MODEL, DATA TYPES & API
9

Tables, Items, Attributes
Table is a collection of Items
Item is a collection of Attributes (name-value pairs)
Primary key is required
10
HashKey Attribute1 Attribute2 Attribute3
item1 userid=bob email=bob@gmail.com joindate=20121221 Sex=M
item2 userid=ken email=ken@yahoo.com joindate=20130210
UserProfiles

Data Types
Scalar data types
• String, Number, Binary
Multi-valued types
• String Set, Number Set, Binary Set
11

Indexing
Data Indexed by primary key
Type of primary keys
• Hash
• Hash + Range

Local Secondary Index
Alternate range key
Index local to the hash
key
All indexe data local to
the partition
user=bob file=file1.txt date=2013/01/10 size=200 url=s3://bucket/bob/file1.txt
user=bob file=folder1/file1 date=2013/12/21 size=100 url=s3://bucket/bob/folder1/file1
user=bob file=folder1/file2 data=2013/01/10 size=100 url=s3://bucket/bob/folder1/file1
user=ken file=folder1/file1 date=2013/02/10 size=300 shared=Y url=s3://bucket/ken/folder2/file1
user=ken file=file2.jpg date=2013/02/10 size=300 shared=Y url=s3://bucket/ken/file2.jpg
user=bob date=2013/01/10 file=folder1/file2
user=bob date=2013/01/10 file=file1.txt
user=ken date=2013/02/10 file=folder1/file1
user=ken date=2013/02/10 file=file2.jpg
DateIndex
File MetadataTable

Partitioning
Data is auto-partitioned by hash key
Auto-partitioning driven by:
• Table size
• Provisioned throughput DynamoDB table
Client
Partition 1
Partition N

Provisioned Throughput Model
Throughput declared/updated via API or console
• CreateTable (foo, reads/sec = 100, writes/sec = 100)
• UpdateTable (foo, reads/sec = 10000, writes/sec = 10000)
DynamoDB handles the rest
• Capacity is reserved and available when needed
• Throughput increases trigger repartitioning and reallocation
High performance at any scale

API
CreateTable
UpdateTable
DeleteTable
DescribeTable
ListTables
PutItem
GetItem
UpdateItem
DeleteItem
BatchGetItem
BatchWriteItem
Query
Scan
manage tables
query specific
items OR scan
the full table
read and
write items
bulk get or
update

Read Patterns
GetItem (table, key) -> Item
Query (table, hash_key, [range_key_condition]) -> Items
BatchGetItem (table1:key1, …tableN:keyN) -> items
Scan (table) -> Items
17

Write Patterns
PutItem (table, key, [attributes])
UpdateItem (table, key, [attributes])
DeleteItem (table, key)
BatchWriteItem
(table1:key1[:attributes]…tableN:keyN[:attributes])
18

DYNAMODB CHARACTERISTICS
19

High Availability and Durability
Multi-datacenter (AZ) replication and failover
• If one machine or datacenter fails, another serves your requests
• High availability
• Protects against data loss

What DynamoDB Manages For You
Hardware provisioning
Cross-availability zone replication
Monitoring and handling of hardware failures
• Replicas automatically regenerated whenever necessary
Hardware and Software updates
ADMIN

DynamoDB Scale out
Data is automatically partitioned
Partitions are fully independent
No limits as long as workload is well spread

Consistently Low Latencies
Typically single digit millisecond average Put and Get
latencies
Custom SSD based storage platform
• Performance independent of table size
• No need for working set to fit in memory

Authentication & Wire format
Session based authentication
• Client establishes session via AWS Security Token Service
(STS) and retrieves token
• Client signs with session token valid for a few hours
• Streamlines authentication to minimize latency
Request and Response parameters encoded in JSON
• Widely adopted industry standard
• Relatively compact and efficient to parse
24

Consistency
Strictly or eventually consistent reads
• Specified at API level for maximum flexibility
• Throughput, not latency tradeoff
Strictly consistent writes
• Atomic increment/decrement and get
• Conditional write a.k.a. optimistic concurrency control
GetItem & Query APIs support eventually consistent and
consistent reads
Scan & BatchGetItem only support eventually consistent
reads
25

Transactions
Supports Item level transaction
• UpdateItem, PutItem and DeleteItem operate at the Item level
and their changes are ACID
• UpdateItem supports atomic ADD and Get
No multi-item or cross table transactions
• While BatchWriteItem operates on multiple items and across
tables, but it only supports transactions at an item level
26

MODELING RELATIONSHIPS IN
DYNAMODB

Modeling 1:1 relationships
Use a table with a hash key
Examples:
• Users
• Hash key = UserId
• Games
• Hash key = GameId
Users Table
Hash key Attributes
UserId = bob Email = bob@gmail.com, JoinDate = 2011-11-15
UserId = fred Email = fred@yahoo.com, JoinDate = 2011-12-
01, Sex = M
Games Table
Hash key Attributes
GameId = Game1 LaunchData = 2011-10-15, Version = 2,
GameId = Game2 LaunchDate = 2010-05-12, Version = 3,
GameId = Game3 LaunchDate = 2012-01-20, version = 1

Modeling 1:N relationships
Use a table with hash and range key
Example:
• One (1) User can play many (N) Games
• User_Games table
– Hash key = UserId
– Range key = GameId
User Games table
Hash Key Range key Attributes
UserId = bob GameId = Game1 HighScore = 10500,
ScoreDate = 2011-10-20
UserId = fred GameId = Game2 HIghScore = 12000,
UserId = bob GameId = Game3 HighScore = 20000,

Modeling N:M relationships
Use two hash and range tables
Example:
• One User can play many Games
• Hash key = UserId
• Range key = GameId
• One Game can have many Users
• Hash key = GameId
• Range key = UserId
User_Games
Hash Key Range key
UserId = bob GameId = Game1
UserId = fred GameId = Game2
UserId = bob GameId = Game3
Game_Users
Hash Key Range key
GameId = Game1 UserId = bob
GameId = Game2 UserId = fred
GameId = Game3 UserId = bob

Modeling Multi-tenancy
Use tenant id as the hash key
• Example: UserId in the User Profiles and User
Scores tables:

MODELING EXAMPLE

Example1:
Multi-tenant application for storing file metadata
Access Patterns
1. Get bob’s profile
2. List files owned by ‘bob’
3. List bob’s files created between T1 and T2
4. List bob’s shared files
5. List bob’s files by descending order of file size

Entities and Relationships
Entities:
• Users
• Files
Relationship
• One User has many Files (1:N)

Users (hash)
user=bob email=bob@gmail.com joindate=‘2012/12/21’
user=ken email=ken@yahoo.com joindate=‘2013/02/10’
Files (hash-range)
user=ken file=folder1/file1 date=2013/02/10 size=300 shared=Y url=s3://bucket/ken/folder2/file1
user=ken file=file2.jpg date=2013/02/10 size=300 shared=Y url=s3://bucket/ken/file2.jpg
DynamoDB Data Model
Hash key (Tenant ID)
Range key

Primary Index Get & Query
Get bob’s profile
• GetItem (table = Users, user = ‘bob’)
List files owned by ‘bob’
• Query (table = Files, user = “bob”)
user=bob email=bob@gmail.com joindate=‘2012/12/21’

Local Secondary Index Query
List bob’s files & folders created between T1 and T2
• Query (table = Users, user = bob, IndexName = DateIndex, date
BETWEEN 2013/01/10 and 2013/01/20)
user=bob date=2013/01/10 file=file1.txt
user=ken date=2013/02/10 file=folder1/file1
user=ken date=2013/02/10 file=file2.jpg
DateIndex

Local Secondary (sparse) Index Query
List bob’s shared files & folders
• Query (Table = Users, user = bob, IndexName = SharedIndex,
shared = Y)
• No matches found
user=ken shared=Y file=folder1/file1
user=ken shared=Y file=file2.jpg
SharedIndex

Local Secondary Index Query (backwards)
List bob’s files & folders by descending order of size
• Query (Table = Users, user = bob, IndexName = SizeIndex,
ScanIndexForward = false)
SizeIndex
user=bob size=100 file=folder1/file2
user=bob size=100 file=folder1/file1
user=bob size=200 file=file1.txt
user=ken size=0 file=file2.jpg
user=ken size=300 file=folder1/file1

BEST PRACTICES

Storing Large Items – Pattern 1
Break large attributes
across multiple
DynamoDB items
Store Large attributes
in Amazon S3
MESSAGE-ID
(hash key)
1 FROM = ‘user1’
TO = ‘user2’
DATE = ‘12/12/2011’
SUBJECT = ‘DynamoDB Best practices’
BODY= ‘The first few Kbytes…..’
BODY_OVERFLOW = ‘S3bucket+key’
MESSAGE-ID
(hash key)
PART
(range key)
1 0 FROM = ‘user1’
TO = ‘user2’
DATE = ‘12/12/2011’
SUBJECT = ‘DynamoDB Best practices’
BODY = ‘The first few Kbytes…..’
1 1 BODY = ‘ the next 64k’
1 2 BODY = ‘ the next 64k’
1 3 EOM
41

Storing Large Items – Pattern 2
Use a overflow table for large attributes
Retrieve items via BatchGetItems
Mail Box Table
ID (hash key)
Timestamp (range key)
Attribute1
Attribute2
Attribute3
….
AttributeN
LargeAttribute
MailBox Table
ID (hash key)
Timestamp (range key)
Attribute1
Attribute2
Attribute3
….
AttributeN
LargeAttributeUUID
Overflow Table
LargeAttributeUUID LargeAttribute

Storing Time Series Data
You application wants to
keep one year historic data
You can pre-create one
table per week (or per day
or per month) and insert
records into the appropriate
table based on timestamp
43
Events_table_2012
Event_id
(Hash key)
Timestamp
(range key)
Attribute1 …. Attribute N
Events_table_2012_05_week1
Event_id
(Hash key)
Timestamp
(range key)
Attribute1 …. Attribute NEvents_table_2012_05_week2
Event_id
(Hash key)
Timestamp
(range key)
Attribute1 …. Attribute NEvents_table_2012_05_week3
Event_id
(Hash key)
Timestamp
(range key)
Attribute1 …. Attribute N

Searching across items (with different hash keys)
Create additional tables
which will server as indexes
• Example: First_name_index
& Last_name_index
Query: Get me all the Users
data for First_name = ‘Tim’
• Query First_name_index for
hash key = ‘Tim’
• This will return User_id =
(101, 201)
• BatchGet (Users, [101, 201])
44
User_Id
(hash
key)
First_name Last_name …
101 Tim White
201 Tim Black
301 Ted White
401 Keith Brown
501 Keith White
601 Keith Black
First_name
(hash key)
User_id
(range key)
Tim 101
Tim 201
Ted 301
Keith 401
Keith 501
Keith 601
Last_name
(hash key)
User_id
(range key)
White 101
Black 201
White 301
Brown 401
White 501
Black 601
Users
First_name_index Last_name_index

Avoiding Hot Keys
Use multiple keys (aliases) instead of a
single hot key
Generate aliases by prefixing or suffixing
a known range (N)
Use BatchGetItem API to retrieve ticket
counts for all the aliases (1_Avatar,
2_Avatar, 3_Avatar,…, N_Avatar) and
sum them in your client application
45
MOVIES
MNAME (hash key)
1_Avatar TicketCount =
4,000,000
2,000,000
4,000,000
….
N_Avatar TicketCount =
4,000,000

When to use
Key-value or simple queries
Very high read/write rate
Need Auto-sharding
Need on-line scaling across
multiple nodes
Consistently low latency
No size or throughput limits
No Tuning
High durability
When not to use
Need multi-item/row or cross table
transactions
Need complex queries, joins
Need real-time Analytics on historic
data
When to use and when not to use DynamoDB

Questions?

Backup slides

DynamoDB / Elastic MapReduce integration
Harness Hadoop parallel processing pipeline to
• Perform complex analytics
• Join DynamoDB tables with outside data sources like S3
• Export data from DynamoDB to S3
• Import data from S3 into DynamoDB
Easy to leverage DynamoDB’s scale
49

S3
EMR DynamoDB
50

Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/amazon-
dynamodb-patterns-practices

Amazon DynamoDB Design Patterns & Best Practices

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