We have recently seen some convergence of different database technologies. Many customers are evaluating heterogeneous migrations as their database needs have evolved or changed. Evaluating the best database to use for a job isn't as clear as it was ten years ago. We'll discuss the ideal use cases for relational and nonrelational data services, including Amazon ElastiCache for Redis, Amazon DynamoDB, Amazon Aurora, Amazon Neptune, and Amazon Redshift. This session digs into how to evaluate a new workload for the best managed database option. Please join us for a speaker meet-and-greet following this session at the Speaker Lounge (ARIA East, Level 1, Willow Lounge). The meet-and-greet starts 15 minutes after the session and runs for half an hour.

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Matching the Database to the
Workload
Rick Houlihan
Principal Technologist, NoSQL
AWS
D A T 3 0 1

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Agenda for This Session
• Database workload classifications
• Traditional approaches to scaling RDBMS
• How NoSQL databases compare
• The flavors of NoSQL on AWS
• What database to use when

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Why did you choose this database?
“Because we heard X is the best new thing.”
“Because we have a site license for X.”
“Because X is what we know how to use.”

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Why did you choose this database?
“Because this database is purpose built to support
what my application is designed to do.”

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Types of Database Workloads
• Online Transaction Processing (OLTP)
Most common type of app
• Online Analytics Processing (OLAP)
BI and ad-hoc data projections
• Decision Support Systems (DSS)
Long running query aggregations and projections
Operations
Analytics

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Sizing the Workload
Unbounded problems are harder to solve
“I need a root cause analysis engine to correlate transaction level events to trading patterns
across global markets.”
Problems with limited scope are easier to solve
“I need a system to manage inventory in my store.”

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Sizing the Database

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Scaling an RDBMS

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Sharded Relational DBs?
A B C D
?

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NoSQL Databases
• Denormalize and shard to
provide horizontal scale
• Near unbounded throughput
and storage
Collection 1
1 TB
Shard A
500
GB
Shard B
500
GB

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00 55 A954 FFAA00 FF
Partition Keys in NoSQL
Partition Key uniquely identifies an item
Partition Key is used for building an unordered hash index
Allows table to be partitioned for scale
Id = 1
Name = Jim
Hash (1) = 7B
Id = 2
Name = Andy
Dept = Eng
Hash (2) = 48
Id = 3
Name = Kim
Dept = Ops
Hash (3) = CD
Key Space

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The Iron Triangle of Data - All About CAP
C
A
PConsistency:
all clients always have
the same view of data
Partition tolerance:
the system works well despite
physical network partitions
Availability:
all clients can always
read and write
CA
MSSQL
Oracle
DB2
MySQL
Aster Data
Greenplum
Postgres
CP
Big Table
Hypertable
HBase
MongoDB
Terastore
Couchbase
Scalaris
DynamoDB
BerkeleyDB
Memcached
Redis
Pick Two
AP
Voldemort
Tokyo Cabinet
KAI
DynamoDB
Cassandra
SimpleDB
CouchDB
Riak
Data Models:
Relational
Wide Column
Document
Key/Value

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Technology Adoption and the Hype Curve

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DW | Big Data Processing | Ad hoc
AWS Databases and Analytics
Broadest and deepest portfolio purpose-built for builders
Business Intelligence & Machine Learning
Data Movement
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams
Amazon QuickSight
Relational Databases
RDS
Aurora
Data lake (Batch/ETL)
S3/Glacier
(Storage)
Glue
(ETL & Data Catalog)
Machine Learning
Macie
(Data Protection)
NoSQL Databases
Analytics (OLAP/DSS)
DynamoDB
(Wide Column/Document)
ElastiCache
(Indexed Key Value)
Amazon
Redshift
EMR Athena
Kinesis Data
Analytics
Elasticsearch
Service
Real-time
Opertional (OLTP)
Neptune
(Graph)
QLDB
(Ledger)
Timestream
(TSDB)

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Amazon RDS
Managed relational database service with a choice of six popular database engines
Easy to administer Highly flexible Available &
durable
Fast
No need for infrastructure
provisioning, installing and
maintaining database
software.
Scale database compute
and storage with a few
mouse clicks and zero
downtime.
Multi-AZ:
Automatically
replicates data.
Automated backup,
snapshots, failover.
Choose between dual SSD-
backed storage for high-
performance OLTP.

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Amazon DynamoDB
Fast and flexible NoSQL database service for any scale
Key-value NoSQL database that supports both document and wide column structures
Fast, consistent
performance
Highly scalable Fully managed
Business-critical
reliability
Consistent single-digit
millisecond latencies at any
scale. DAX speeds up times
to microseconds.
Auto-scaling tables serving
millions of requests per
second, storing hundreds of
terabytes of data.
Automatic provisioning
and infrastructure
management.
Data replicated across
multiple AZs and accessed
with regionally available
APIs.

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DynamoDB Schema
Table
Items
Attributes
Partition
Key
Sort
Key
Mandatory
Key-value access pattern
Determines data distribution
Optional
Model 1:N relationships
Enables rich query capabilities
All items for key
==, <, >, >=, <=
“begins with”
“between”
“contains”
“in”
sorted results
counts
top/bottom N values

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SQL vs. NoSQL Design Pattern

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Amazon Neptune
Fully managed graph database
Fast Reliable Open
Query billions of
relationships with
millisecond latency
Six replicas of your
data across three AZs
with full backup and
restore
Build powerful
queries easily with
Gremlin and SPARQL
Supports Apache
TinkerPop & W3C
RDF graph models
Easy

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Graph Workloads

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Normalized Graph Design Pattern

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De-normalized Graph Design Pattern
Node Relationship Endpoint
Bill
person Bill
visited Eiffel Tower
Alice
person Alice
visited Eiffel Tower
friend Bob
Bob
person Bob
born 7/14/90
friend Alice
Interest Mona Lisa
Leonardo daVinci person Leaonardo daVinci
La Jaconde a Washington
video La Jaconde…
about Mona Lisa
Eiffel Tower
place Eiffel Tower
located Paris
7/14/90 date 7/14/90
Paris place City
The Louvre
place Museum
location Paris
Mona Lisa
painting Mona Lisa
creator Leonardo daVinci
location The Louvre
Nodes are the vertices of a
graph
Relationships are the edges
of a graph
Select nodes to get
edges for an entity
Index Relationship and
Endpoint for edge type
and target aggregations
Follow the edges to
traverse the graph
Bob wants to see the
Mona Lisa. While he is in
Paris he would like to see
other things his friends
have enjoyed.

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Graph Query Types
Node Query (Primary)
What entities are in the graph?
Edge Query (Index)
What relationships do graph entities have?
Hybrid Query (Traversal)
How are entities related through each other?
RDBMS, NoSQL,
GraphDB
GraphDB

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Amazon Redshift – Data Warehousing
Fast, powerful, and simple data warehousing at 1/10 the cost
Massively parallel, petabyte scale
Fast Inexpensive Scalable Secure
Columnar storage technology
to improve I/O efficiency and
parallelize queries. Data load
scales linearly.
As low as $1,000 per
terabyte per year, 1/10
the cost of traditional
data warehouse solutions.
Resize your cluster up and
down as your
performance and capacity
needs change.
Data encrypted at rest and
transit. Isolate clusters with
VPC. Manage your own keys
with AWS KMS.

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Amazon Athena – Interactive Analysis
Interactive query service to analyze data in Amazon S3 using standard SQL
No infrastructure to set up or manage and no data to load
Ability to run SQL queries on data archived in Amazon Glacier (Coming soon)
Serverless
Zero setup cost. Just
point to Amazon S3,
and start querying.
Pay per query
Pay only for queries run.
Save 30–90% on per-
query costs through
compression.
Open
ANSI SQL interface,
JDBC/ODBC drivers, multiple
formats, compression types,
and complex joins and data
types.
Easy
Serverless. Zero
infrastructure. Zero
administration.

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QLDB (Preview)
Fully managed ledger database
Track and verify history of all changes made to your application’s data
Immutable and
transparent
Cryptographically
verifiable
Easy to useHighly scalable
Append-only, immutable journal
tracks history of all changes
which cannot be deleted or
modified. Get full visibility into
entire data lineage
All changes are
cryptographically
chained and verifiable
Executes 2 – 3X as many
transactions than ledgers in
common blockchain
frameworks
Flexible document model,
query with familiar SQL-
like interface

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Amazon Timestream (sign up for the preview today)
Fast, scalable, and fully managed time series database
1,000x faster at 1/10 the
cost of relational databases
Trillions of daily
events
Analytics optimized
for time series data
Serverless
Collect fast moving time-
series data from multiple
sources at the rate of
millions of inserts per
second
Capable of processing
trillions of events daily; the
adaptive query processing
engine maintains steady,
predictable performance
Built-in analytics for
interpolation, smoothing,
and approximation to
identify trends, patterns,
and anomalies
No servers to manage;
time-consuming tasks such
as hardware provisioning,
software patching, setup, &
configuration done for you

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Categories of Database
Optimized for
Storage
Optimized for
Compute
Optimized for
Relationships
Normalized relational
or dimensional DW
Denormalized document,
wide column or key value
Denormalized entity
relationship
Ad hoc queries and
aggregations
Instantiated views and
computed aggregations
Ad hoc
entity/relationship
aggregations
Scale vertically Scale horizontally Hybrid
Great for OLAP and
DSS
Built for OLTP or DSS at
scale
Designed for graph
traversals
SQL NoSQL Graph

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Infinite Scale:
The database can gracefully increase
size and throughput without practical
limits
The Iron Triangle of Purpose (The PIE Theorem)
I
P
E Efficiency:
The database will deliver required
query latency for the workload at all
times
Pattern Flexibility:
The database supports random access
patterns and ad hoc queries
PI
Amazon RDS
Elasticsearch
Aurora Serverless
Neptune
IE
Pick Two
PE
Data Models:
Relational
Wide Column
Document
Graph
Columnar
Unstructured
Amazon DynamoDB
Amazon Redshift
Athena

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Hundreds of Thousands of Customers Use DynamoDB

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Hundreds of Thousands More Use Amazon RDS

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Purpose Built Database Solutions from AWS
Provisioning
Capacity planning
Monitoring
OS patching
Hardware upgrades
Database upgrades
Security patches
Scaling
Monitoring
Performance tuning
Replication across data centers
Re-replicate on server failureProvision new regions
Infrastructure Software
With Zero Unplanned Downtime

34. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

35. Time: 15 minutes after this session
Location: Speaker Lounge (ARIA East, Level 1, Willow Lounge)
Duration: 30 min.

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