Amazon Dynamo DB for Developers (김일호) - AWS DB Day

DynamoDB for developers
김일호, Solutions Architect
Time : 15:10 – 16:30

Agenda
§Tip 1. DynamoDB Index(LSI, GSI)
§Tip 2. DynamoDB Scaling
§Tip 3. DynamoDB Data Modeling
§Scenario based Best Practice
§DynamoDB Streams
§Nexon use-case

Tip 1. DynamoDB Index(LSI, GSI)
Tip 2. DynamoDB Scaling
Tip 3. DynamoDB Data Modeling
Scenario based Best Practice
DynamoDB Streams
Nexon use-case

Local secondary index (LSI)
§Alternate range key attribute
§Index is local to a hash key (or partition)
A1
(hash)
A3
(range)
A2
(table key)
A1
(hash)
A2
(range)
A3 A4 A5
LSIs A1
(hash)
A4
(range)
A2
(table key)
A3
(projected)
Table
KEYS_ONLY
INCLUDE A3
A1
(hash)
A5
(range)
A2
(table key)
A3
(projected)
A4
(projected)
ALL
10 GB max per hash key,
i.e. LSIs limit the # of ran
ge keys!

Global secondary index (GSI)
§Alternate hash (+range) key
§Index is across all table hash keys (partitions)
A1
(hash)
A2 A3 A4 A5
GSIs A5
(hash)
A4
(range)
A1
(table key)
A3
(projected)
Table
INCLUDE A3
A4
(hash)
A5
(range)
A1
(table key)
A2
(projected)
A3
(projected) ALL
A2
(hash)
A1
(table key) KEYS_ONLY
RCUs/WCUs provisioned
separately for GSIs
Online indexing

How do GSI updates work?
Table
Primary ta
ble
Primary ta
ble
Primary ta
ble
Primary ta
ble
Global Seco
ndary Index
Client
2. Asynchronous
update (in progress)
If GSIs don’t have enough write capacity, table writes will be throttled!

LSI or GSI?
§LSI can be modeled as a GSI
§If data size in an item collection > 10 GB, use GSI
§If eventual consistency is okay for your scenario, use
GSI!

Scaling
§Throughput
§ Provision any amount of throughput to a table
§Size
§ Add any number of items to a table
§ Max item size is 400 KB
§ LSIs limit the number of range keys due to 10 GB limit
§Scaling is achieved through partitioning

Throughput
§Provisioned at the table level
§ Write capacity units (WCUs) are measured in 1 KB per second
§ Read capacity units (RCUs) are measured in 4 KB per second
§ RCUs measure strictly consistent reads
§ Eventually consistent reads cost 1/2 of consistent reads
§Read and write throughput limits are independent
WCURCU

Partitioning math
Number of Partitions
By Capacity (Total RCU / 3000) + (Total WCU / 1000)
By Size Total Size / 10 GB
Total Partitions CEILING(MAX (Capacity, Size))

Partitioning example
Table size = 8 GB, RCUs = 5000, WCUs = 500
RCUs per partition = 5000/3 = 1666.67
WCUs per partition = 500/3 = 166.67
Data/partition = 10/3 = 3.33 GB
RCUs and WCUs are uniformly sprea
d across partitions
Numberof Partitions
By Capacity (5000 / 3000) + (500 / 1000) = 2.17
By Size 8 / 10 = 0.8
Total Partitions CEILING(MAX (2.17, 0.8)) = 3

Allocation of partitions
§A partition split occurs when
§ Increased provisioned throughput settings
§ Increased storage requirements
http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GuidelinesForTables.html

Example: hot keys
Partition
Time
Heat

Getting the most out of DynamoDB throughput
“To get the most out of
DynamoDB throughput, create
tables where the hash key
element has a large number of
distinct values, and values are
requested fairly uniformly, as
randomly as possible.”
—DynamoDB Developer Guide
§Space: access is evenly
spread over the key-
space
§Time: requests arrive
evenly spaced in time

What causes throttling?
§ If sustained throughput goes beyond provisioned throughput per partition
§ Non-uniform workloads
§ Hot keys/hot partitions
§ Very large bursts
§ Mixing hot data with cold data
§ Use a table per time period
§ From the example before:
§ Table created with 5000 RCUs, 500 WCUs
§ RCUs per partition = 1666.67
§ WCUs per partition = 166.67
§ If sustained throughput > (1666 RCUs or 166 WCUs) per key or partition, DynamoDB may
throttle requests
§ Solution: Increase provisioned throughput

1:1 relationships or key-values
§Use a table or GSI with a hash key
§Use GetItem or BatchGetItem API
§Example: Given an SSN or license number, get
attributes
Users Table
Hash key Attributes
SSN = 123-45-6789 Email = johndoe@nowhere.com, License = TDL25478134
SSN = 987-65-4321 Email = maryfowler@somewhere.com, License = TDL78309234
Users-Email-GSI
Hash key Attributes
License = TDL78309234 Email = maryfowler@somewhere.com, SSN = 987-65-4321
License = TDL25478134 Email = johndoe@nowhere.com, SSN = 123-45-6789

1:N relationships or parent-children
§Use a table or GSI with hash and range key
§Use Query API
Example:
§ Given a device, find all readings between epoch X, Y
Device-measurements
Hash Key Range key Attributes
DeviceId = 1 epoch = 5513A97C Temperature = 30, pressure = 90
DeviceId = 1 epoch = 5513A9DB Temperature = 30, pressure = 90

N:M relationships
§Use a table and GSI with hash and range key elements
switched
§Use Query API
Example: Given a user, find all games. Or given a game,
find all users.
User-Games-Table
Hash Key Range key
UserId = bob GameId = Game1
UserId = fred GameId = Game2
UserId = bob GameId = Game3
Game-Users-GSI
Hash Key Range key
GameId = Game1 UserId = bob
GameId = Game2 UserId = fred
GameId = Game3 UserId = bob

Documents (JSON)
§ New data types (M, L, BOOL,
NULL) introduced to support
JSON
§ Document SDKs
§ Simple programming model
§ Conversion to/from JSON
§ Java, JavaScript, Ruby, .NET
§ Cannot index (S,N) elements
of a JSON object stored in M
§ Only top-level table attributes
can be used in LSIs and
GSIs without
Streams/Lambda
JavaScript DynamoDB
string S
number N
boolean BOOL
null NULL
array L
object M

Rich expressions
§Projection expression to get just some of the attributes
§ Query/Get/Scan: ProductReviews.FiveStar[0]

Rich expressions
§Projection expression to get just some of the attributes
§ Query/Get/Scan: ProductReviews.FiveStar[0]
ProductReviews: {
FiveStar: [
"Excellent! Can't recommend it highly enough! Buy it!",
"Do yourself a favor and buy this." ],
OneStar: [
"Terrible product! Do not buy this." ] }
]
}

Rich expressions
§Filter expression
§ Query/Scan: #VIEWS > :num
§Update expression
§ UpdateItem: set Replies = Replies + :num

Rich expressions
§ Conditional expression
§ Put/Update/DeleteItem
§ attribute_not_exists (#pr.FiveStar)
§ attribute_exists(Pictures.RearView)
1. First looks for an item whose primary key matches that of the
item to be written.
2. Only if the search returns nothing is there no partition key
present in the result.
3. Otherwise, the attribute_not_exists function above fails and
the write will be prevented.

Game logging
Storing time series data

Time series tables
Events_table_2015_April
Event_id
(Hash key)
Timestamp
(range key)
Attribute1 …. Attribute N
Events_table_2015_March
Event_id
(Hash key)
Timestamp
(range key)
Events_table_2015_Feburary
Event_id
(Hash key)
Timestamp
(range key)
Events_table_2015_January
Event_id
(Hash key)
Timestamp
(range key)
RCUs = 1000
WCUs = 100
RCUs = 10000
WCUs = 10000
RCUs = 100
WCUs = 1
RCUs = 10
WCUs = 1
Current table
Older tables
Hot dataCold data
Don’t mix hot and cold data; archive cold data to Amazon S3

Important when:
Use a table per time period
§Pre-create daily, weekly, monthly tables
§Provision required throughput for current table
§Writes go to the current table
§Turn off (or reduce) throughput for older tables
Dealing with time series data

Item shop catalog
Popular items (read)

Partition 1
2000 RCUs
Partition K
2000 RCUs
Partition M
2000 RCUs
Partition 50
2000 RCU
Scaling bottlenecks
Product A Product B
Gamers
ItemShopCatalog Table
SELECT Id, Description, ...
FROM ItemShopCatalog

Requests Per Second
Item Primary Key
Request Distribution Per Hash Key
DynamoDB Requests

Partition 1 Partition 2
ItemShopCatalog Table
User
DynamoDB
User
Cache
popular items
SELECT Id, Description, ...
FROM ProductCatalog

Requests Per Second
Item Primary Key
Request Distribution Per Hash Key
DynamoDB Requests Cache Hits

Multiplayer online gaming
Query filters vs.
composite key indexes

GameId Date Host Opponent Status
d9bl3 2014-10-02 David Alice DONE
72f49 2014-09-30 Alice Bob PENDING
o2pnb 2014-10-08 Bob Carol IN_PROGRESS
b932s 2014-10-03 Carol Bob PENDING
ef9ca 2014-10-03 David Bob IN_PROGRESS
Games Table
Multiplayer online game data
Hash key

Query for incoming game requests
§DynamoDB indexes provide hash and range
§What about queries for two equalities and a range?
SELECT * FROM Game
WHERE Opponent='Bob‘
AND Status=‘PENDING'
ORDER BY Date DESC
(hash)
(range)
(?)

Secondary Index
Opponent Date GameId Status Host
Alice 2014-10-02 d9bl3 DONE David
Carol 2014-10-08 o2pnb IN_PROGRESS Bob
Bob 2014-09-30 72f49 PENDING Alice
Bob 2014-10-03 b932s PENDING Carol
Bob 2014-10-03 ef9ca IN_PROGRESS David
Approach 1: Query filter
BobHash key Range key

Secondary Index
Approach 1: Query filter
Bob
Opponent Date GameId Status Host
Alice 2014-10-02 d9bl3 DONE David
Carol 2014-10-08 o2pnb IN_PROGRESS Bob
Bob 2014-09-30 72f49 PENDING Alice
Bob 2014-10-03 b932s PENDING Carol
Bob 2014-10-03 ef9ca IN_PROGRESS David
SELECT * FROM Game
WHERE Opponent='Bob'
ORDER BY Date DESC
FILTER ON Status='PENDING'
(filtered out)

Important when:
Use query filter
§Send back less data “on the wire”
§Simplify application code
§Simple SQL-like expressions
§ AND, OR, NOT, ()
Your index isn’t entirely selective

Approach 2: composite key
StatusDate
DONE_2014-10-02
IN_PROGRESS_2014-10-08
IN_PROGRESS_2014-10-03
PENDING_2014-09-30
PENDING_2014-10-03
Status
DONE
IN_PROGRESS
IN_PROGRESS
PENDING
PENDING
Date
2014-10-02
2014-10-08
2014-10-03
2014-10-03
2014-09-30
+ =

Secondary Index
Opponent StatusDate GameId Host
Alice DONE_2014-10-02 d9bl3 David
Carol IN_PROGRESS_2014-10-08 o2pnb Bob
Bob IN_PROGRESS_2014-10-03 ef9ca David
Bob PENDING_2014-09-30 72f49 Alice
Bob PENDING_2014-10-03 b932s Carol
Hash key Range key

Opponent StatusDate GameId Host
Alice DONE_2014-10-02 d9bl3 David
Carol IN_PROGRESS_2014-10-08 o2pnb Bob
Bob IN_PROGRESS_2014-10-03 ef9ca David
Bob PENDING_2014-09-30 72f49 Alice
Bob PENDING_2014-10-03 b932s Carol
Secondary Index
Bob
SELECT * FROM Game
WHERE Opponent='Bob'
AND StatusDate BEGINS_WITH 'PENDING'

Needle in a sorted haystack
Bob

Sparse indexes
Id
(Hash)
User Game Score Date Award
1 Bob G1 1300 2012-12-23
2 Bob G1 1450 2012-12-23
3 Jay G1 1600 2012-12-24
4 Mary G1 2000 2012-10-24 Champ
5 Ryan G2 123 2012-03-10
6 Jones G2 345 2012-03-20
Game-scores-table
Award
(Hash)
Id User Score
Champ 4 Mary 2000
Award-GSI
Scan sparse hash GSIs

Important when:
Replace filter with indexes
§Concatenate attributes to form useful
§secondary index keys
§Take advantage of sparse indexes
You want to optimize a query as much
as possible
Status + Date

Big data analytics
with DynamoDB

Transactional Data Processing
§DynamoDB is well-suited for transactional processing:
• High concurrency
• Strong consistency
• Atomic updates of single items
• Conditional updates for de-dupe and optimistic concurrency
• Supports both key/value and JSON document schema
• Capable of handling large table sizes with low latency data
access

Case 1: Store and Index Metadata for Objects
Stored in Amazon S3

Case 1: Use Case
§We have a large number of digital audio files stored in
Amazon S3 and we want to make them searchable:
§Use DynamoDB as the primary data store for the
metadata.
§Index and query the metadata using Elasticsearch.

Case 1: Steps to Implement
1. Create a Lambda function that reads the metadata
from the ID3 tag and inserts it into a DynamoDB
table.
2. Enable S3 notifications on the S3 bucket storing the
audio files.
3. Enable streams on the DynamoDB table.
4. Create a second Lambda function that takes the
metadata in DynamoDB and indexes it using
Elasticsearch.
5. Enable the stream as the event source for the
Lambda function.

Case 1: Key Takeaways
§DynamoDB + Elasticsearch = Durable, scalable, highly-
available database with rich query capabilities.
§Use Lambda functions to respond to events in both
DynamoDB streams and Amazon S3 without having to
manage any underlying compute infrastructure.

Case 2 – Execute Queries Against Multiple Data Sources Using
DynamoDB and Hive

Case 2: Use Case
We want to enrich our audio file metadata stored in
DynamoDB with additional data from the Million Song
dataset:
§Million song data set is stored in text files.
§ID3 tag metadata is stored in DynamoDB.
§Use Amazon EMR with Hive to join the two datasets
together in a query.

1. Spin up an Amazon EMR cluster
with Hive.
2. Create an external Hive table
using the
DynamoDBStorageHandler.
3. Create an external Hive table
using the Amazon S3 location of
the text files containing the
Million Song project metadata.
4. Create and run a Hive query that
joins the two external tables
together and writes the joined
results out to Amazon S3.
5. Load the results from Amazon
S3 into DynamoDB.

§Use Amazon EMR to quickly provision a Hadoop
cluster with Hive and to tear it down when done.
§Use of Hive with DynamoDB allows items in
DynamoDB tables to be queried/joined with data from a
variety of sources.

Case 3 – Store and Analyze Sensor Data with
DynamoDB and Amazon Redshift
Dashboard

Case 3: Use Case
A large number of sensors are taking readings at regular intervals.
You need to aggregate the data from each reading into a data
warehouse for analysis:
• Use Amazon Kinesis to ingest the raw sensor data.
• Store the sensor readings in DynamoDB for fast access and real-
time dashboards.
• Store raw sensor readings in Amazon S3 for durability and
backup.
• Load the data from Amazon S3 into Amazon Redshift using AWS
Lambda.

1. Create two Lambda functions to
read data from the Amazon
Kinesis stream.
2. Enable the Amazon Kinesis
stream as an event source for
each Lambda function.
3. Write data into DynamoDB in
one of the Lambda functions.
4. Write data into Amazon S3 in the
other Lambda function.
5. Use the aws-lambda-redshift-
loader to load the data in
Amazon S3 into Amazon
Redshift in batches.

§ Amazon Kinesis + Lambda + DynamoDB = Scalable, durable,
highly available solution for sensor data ingestion with very low
operational overhead.
§ DynamoDB is well-suited for near-realtime queries of recent
sensor data readings.
§ Amazon Redshift is well-suited for deeper analysis of sensor data
readings spanning longer time horizons and very large numbers of
records.
§ Using Lambda to load data into Amazon Redshift provides a way
to perform ETL in frequent intervals.

§Stream of updates to a
table
§Asynchronous
§Exactly once
§Strictly ordered
§ Per item
§Highly durable
§ Scale with table
§24-hour lifetime
§Sub-second latency
DynamoDB Streams

View type Destination
Old image—before update Name = John, Destination = Mars
New image—after update Name = John, Destination = Pluto
Old and new images Name = John, Destination = Mars
Name = John, Destination = Pluto
Keys only Name = John
View types
UpdateItem (Name = John, Destination = Pluto)

Stream
Table
Partition 1
Partition 2
Partition 3
Partition 4
Partition 5
Table
Shard 1
Shard 2
Shard 3
Shard 4
KCL
Worker
KCL
Worker
KCL
Worker
KCL
Worker
Amazon Kinesis Client
Library application
DynamoDB
client application
Updates
DynamoDB Streams and
Amazon Kinesis Client Library

DynamoDB Streams
Open Source Cross-Region
Replication Library
Asia Pacific (Sydney) EU (Ireland) Replica
US East (N. Virginia)
Cross-region replication

DynamoDB Streams and AWS Lambda

Triggers
Lambda function
Notify change
Derivative tables
Amazon CloudSearch
Amazon ElasticSearch
Amazon ElastiCache

Analytics with DynamoDB Streams
§Collect and de-dupe data in DynamoDB
§Aggregate data in-memory and flush periodically
§Performing real-time aggregation and analytics
EMR
Redshift
DynamoDB

HIT chose DynamoDB
§HIT Main Game DB
• 33 Tables
• CloudWatch Alarms
• Flexible Dashboards

Architecture
§The key is All gaming users directly access DynamoDB
170,000
Concurrent users
A main game DB
(33 Tables, NRT)
Game, PvP, Raid and etc ….
Unreal Engine + AWS .NET SDK

Main DynamoDB tables
Mail
Table
Jewel
Table
Friend
Table
Account
Table
Character
Table
Skills
Table
Mission
Table
Item
Table

Your idea J
Your application with DynamoDB
Table
Table Table
Table
Table
Table
Table
Table

Amazon Dynamo DB for Developers (김일호) - AWS DB Day

More Related Content

Similar to Amazon Dynamo DB for Developers (김일호) - AWS DB Day

More from Amazon Web Services Korea

Recently uploaded

Amazon Dynamo DB for Developers (김일호) - AWS DB Day