Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use workload management, tune your queries, and use Amazon Redshift's interleaved sorting features.

1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Tony Gibbs
Data Warehousing Solutions Architect
April 19, 2017
Deep Dive on Amazon Redshift
Query Lifecycle and Storage Subsystem

2. Deep Dive Overview
• Amazon Redshift history and development
• Cluster architecture
• Concepts and terminology
• Storage deep dive
• Query lifecycle
• New & upcoming features
• Open Q&A

3. Amazon Redshift History & Development

4. Columnar
MPP
OLAP
AWS Identity
and Access
Management
(IAM)
Amazon
VPC
Amazon SWF
Amazon S3 AWS KMS Amazon
Route 53
Amazon
CloudWatch
Amazon
EC2
PostgreSQL
Amazon Redshift

5. February 2013
April 2017
> 100 Significant Patches
> 150 Significant Features

6. Amazon Redshift Cluster Architecture

7. Amazon Redshift Cluster Architecture
Massively parallel, shared nothing
Leader node
• SQL endpoint
• Stores metadata
• Coordinates parallel SQL processing
Compute nodes
• Local, columnar storage
• Executes queries in parallel
• Load, backup, restore
10 GigE
(HPC)
Ingestion
Backup
Restore
SQL Clients/BI Tools
128GB RAM
16TB disk
16 cores
S3 / EMR / DynamoDB / SSH
JDBC/ODBC
128GB RAM
16TB disk
16 coresCompute
Node
128GB RAM
16TB disk
16 coresCompute
Node
128GB RAM
16TB disk
16 coresCompute
Node
Leader
Node

8. Compute & Leader Node Components

9. 128GB RAM
16TB disk
16 cores
128GB RAM
16TB disk
16 cores
Compute Node
128GB RAM
16TB disk
16 cores
Compute Node
128GB RAM
16TB disk
16 cores
Compute Node
Leader Node

10. • Parser & rewriter
• Planner & optimizer
• Code generator
• Input: optimized plan
• Output: >=1 C++
functions
• Compiler
• Task scheduler
• WLM
• Admission
• Scheduling
• PostgreSQL catalog tables
128GB RAM
16TB disk
16 cores
128GB RAM
16TB disk
16 cores
Compute Node
128GB RAM
16TB disk
16 cores
Compute Node
128GB RAM
16TB disk
16 cores
Compute Node
Leader Node

11. • Query execution processes
• Backup & restore processes
• Replication processes
• Local Storage
• Disks
• Slices
• Tables
• Columns
• Blocks
128GB RAM
16TB disk
16 cores
128GB RAM
16TB disk
16 cores
128GB RAM
16TB disk
16 cores
128GB RAM
16TB disk
16 cores
Leader Node
Compute NodeCompute Node Compute Node

12. Concepts & Terminology

13. Designed for I/O Reduction
Columnar storage
Data compression
Zone maps
aid loc dt
CREATE TABLE deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
);
aid loc dt
1 SFO 2016-09-01
2 JFK 2016-09-14
3 SFO 2017-04-01
4 JFK 2017-05-14
• Accessing dt with row storage:
o Need to read everything
o Unnecessary I/O

14. Designed for I/O Reduction
aid loc dt
Designed for I/O Reduction
Columnar storage
Data compression
Zone maps
CREATE TABLE deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
);
aid loc dt
1 SFO 2016-09-01
2 JFK 2016-09-14
3 SFO 2017-04-01
4 JFK 2017-05-14
• Accessing dt with columnar storage
o Only scan blocks for relevant column

15. Designed for I/O Reduction
Columnar storage
Data compression
Zone maps
aid loc dt
CREATE TABLE deep_dive (
aid INT ENCODE LZO
,loc CHAR(3) ENCODE BYTEDICT
,dt DATE ENCODE RUNLENGTH
);
aid loc dt
1 SFO 2016-09-01
2 JFK 2016-09-14
3 SFO 2017-04-01
4 JFK 2017-05-14
• Columns grow and shrink independently
• Reduces storage requirements
• Reduces I/O

16. Designed for I/O Reduction
Columnar storage
Data compression
Zone maps
aid loc dt
1 SFO 2016-09-01
2 JFK 2016-09-14
3 SFO 2017-04-01
4 JFK 2017-05-14
aid loc dt
CREATE TABLE deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
);
• In-memory block metadata
• Contains per-block MIN and MAX value
• Effectively prunes blocks which cannot
contain data for a given query
• Eliminates unnecessary I/O

17. SELECT COUNT(*) FROM deep_dive WHERE dt = '09-JUNE-2013'
MIN: 01-JUNE-2013
MAX: 20-JUNE-2013
MIN: 08-JUNE-2013
MAX: 30-JUNE-2013
MIN: 12-JUNE-2013
MAX: 20-JUNE-2013
MIN: 02-JUNE-2013
MAX: 25-JUNE-2013
Unsorted Table
MIN: 01-JUNE-2013
MAX: 06-JUNE-2013
MIN: 07-JUNE-2013
MAX: 12-JUNE-2013
MIN: 13-JUNE-2013
MAX: 18-JUNE-2013
MIN: 19-JUNE-2013
MAX: 24-JUNE-2013
Sorted By Date
Zone Maps

18. Terminology and Concepts: Data Sorting
• Goals:
• Physically order rows of table data based on certain column(s)
• Optimize effectiveness of zone maps
• Enable MERGE JOIN operations
• Impact:
• Enables rrscans to prune blocks by leveraging zone maps
• Overall reduction in block I/O
• Achieved with the table property SORTKEY defined over one or more columns
• Optimal SORTKEY is dependent on:
• Query patterns
• Data profile
• Business requirements

19. Terminology and Concepts: Slices
A slice can be thought of like a “virtual compute node”
• Unit of data partitioning
• Parallel query processing
Facts about slices:
• Each compute node has either 2, 16, or 32 slices
• Table rows are distributed to slices
• A slice processes only its own data

20. Data Distribution
• Distribution style is a table property which dictates how that table’s data is
distributed throughout the cluster:
• KEY: Value is hashed, same value goes to same location (slice)
• ALL: Full table data goes to first slice of every node
• EVEN: Round robin
• Goals:
• Distribute data evenly for parallel processing
• Minimize data movement during query processing
KEY
ALL
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
EVEN

21. Data Distribution: Example
CREATE TABLE deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE (EVEN|KEY|ALL);
CN1
Slice 0 Slice 1
CN2
Slice 2 Slice 3
Table: deep_dive
User
Columns
System
Columns
aid loc dt ins del row

22. Data Distribution: EVEN Example
CREATE TABLE deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE EVEN;
CN1
Slice 0 Slice 1
CN2
Slice 2 Slice 3
INSERT INTO deep_dive VALUES
(1, 'SFO', '2016-09-01'),
(2, 'JFK', '2016-09-14'),
(3, 'SFO', '2017-04-01'),
(4, 'JFK', '2017-05-14');
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 0 Rows: 0 Rows: 0 Rows: 0
(3 User Columns + 3 System Columns) x (4 slices) = 24 Blocks (24 MB)
Rows: 1 Rows: 1 Rows: 1 Rows: 1

23. Data Distribution: KEY Example #1
CREATE TABLE deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE KEY DISTKEY (loc);
CN1
Slice 0 Slice 1
CN2
Slice 2 Slice 3
INSERT INTO deep_dive VALUES
(1, 'SFO', '2016-09-01'),
(2, 'JFK', '2016-09-14'),
(3, 'SFO', '2017-04-01'),
(4, 'JFK', '2017-05-14');
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 2 Rows: 0 Rows: 0
(3 User Columns + 3 System Columns) x (2 slices) = 12 Blocks (12 MB)
Rows: 0Rows: 1
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 2Rows: 0Rows: 1

24. Data Distribution: KEY Example #2
CREATE TABLE deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE KEY DISTKEY (aid);
CN1
Slice 0 Slice 1
CN2
Slice 2 Slice 3
INSERT INTO deep_dive VALUES
(1, 'SFO', '2016-09-01'),
(2, 'JFK', '2016-09-14'),
(3, 'SFO', '2017-04-01'),
(4, 'JFK', '2017-05-14');
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 0 Rows: 0 Rows: 0 Rows: 0
(3 User Columns + 3 System Columns) x (4 slices) = 24 Blocks (24 MB)
Rows: 1 Rows: 1 Rows: 1 Rows: 1

25. Data Distribution: ALL Example
CREATE TABLE loft_deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE ALL;
CN1
Slice 0 Slice 1
CN2
Slice 2 Slice 3
INSERT INTO deep_dive VALUES
(1, 'SFO', '2016-09-01'),
(2, 'JFK', '2016-09-14'),
(3, 'SFO', '2017-04-01'),
(4, 'JFK', '2017-05-14');
Rows: 0 Rows: 0
(3 User Columns + 3 System Columns) x (2 slice) = 12 Blocks (12 MB)
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 0Rows: 1Rows: 2Rows: 4Rows: 3
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 0Rows: 1Rows: 2Rows: 4Rows: 3

26. Terminology and Concepts: Data Distribution
KEY
• The key creates an even distribution of data
• Joins are performed between large fact/dimension tables
• Optimizing merge joins and group by
ALL
• Small and medium size dimension tables (< 2-3M)
EVEN
• When key cannot produce an even distribution

27. Storage Deep Dive

28. Storage Deep Dive: Disks
• Amazon Redshift utilizes locally attached storage
devices
• Compute nodes have 2.5-3x the advertised storage capacity
• 1, 3, 8, or 24 disks depending on node type
• Each disk is split into two partitions
• Local data storage, accessed by local CN
• Mirrored data, accessed by remote CN
• Partitions are raw devices
• Local storage devices are ephemeral in nature
• Tolerant to multiple disk failures on a single node

29. Storage Deep Dive: Blocks
Column data is persisted to 1 MB immutable blocks
Each block contains in-memory metadata:
• Zone Maps (MIN/MAX value)
• Location of previous/next block
• Blocks are individually compressed with 1 of 11 encodings
A full block contains between 16 and 8.4 million values

30. Storage Deep Dive: Columns
• Column: Logical structure accessible via SQL
• Physical structure is a doubly linked list of blocks
• These blockchains exist on each slice for each column
• All sorted & unsorted blockchains compose a column
• Column properties include:
• Distribution Key
• Sort Key
• Compression Encoding
• Columns shrink and grow independently, 1 block at a time
• Three system columns per table-per slice for MVCC

31. Block Properties: Design Considerations
• Small writes:
• Batch processing system, optimized for processing massive data
• For 1MB size + immutable blocks, we clone blocks on write to avoid
fragmentation
• Small write (~1-10 rows) has similar cost to a larger write (~100 K
rows)
• UPDATE and DELETE:
• Immutable blocks means that we only logically delete rows on
UPDATE or DELETE
• Must VACUUM or DEEP COPY to remove ghost rows from table

32. Column Properties: Design Considerations
• Compression:
• COPY automatically analyzes and compresses data when loading into empty
tables
• ANALYZE COMPRESSION checks existing tables and proposes optimal
compression algorithms for each column
• Changing column encoding requires a table rebuild
• DISTKEY and SORTKEY influence performance (orders of magnitude)
• Distribution keys:
• A poor DISTKEY can introduce data skew and an unbalanced workload
• A query completes only as fast as the slowest slice completes
• Sort keys:
• A sort key is only effective as the data profile allows it to be
• Selectivity needs to be considered

33. Query Lifecycle

34. Terminology and Concepts: Slices
A slice can be thought of like a “virtual compute node”
• Unit of data partitioning
• Parallel query processing
Facts about slices:
• Each compute node has either 2, 16, or 32 slices
• Table rows are distributed to slices
• A slice processes only its own data

35. 128GB RAM
16TB disk
16 cores
128GB RAM
16TB disk
16 cores
Compute Node
128GB RAM
16TB disk
16 cores
Compute Node
128GB RAM
16TB disk
16 cores
Compute Node
Leader Node
• Parser & Rewriter
• Planner & Optimizer
• Code Generator
• Input: Optimized plan
• Output: >=1 C++
functions
• Compiler
• Task Scheduler
• WLM
• Admission
• Scheduling
• PostgreSQL Catalog
Tables
• Amazon Redshift System
Tables (STV)

36. Query Execution Terminology
Step:
• An individual operation needed during query execution
• Steps are combined to allow compute nodes to perform a join
• Examples: scan, sort, hash, aggr
Segment:
• A combination of several steps that can be done by a single process
• Smallest compilation unit executable by a slice
• Segments within a stream run in parallel
Stream:
• A collection of combined segments
• Output to the next stream or SQL client

37. Visualizing Streams, Segments, and Steps
Stream 0
Segment 0
Step 0 Step 1 Step 2
Segment 1
Step 0 Step 1 Step 2 Step 3 Step 4
Segment 2
Step 0 Step 1 Step 2 Step 3
Segment 3
Step 0 Step 1 Step 2 Step 3 Step 4 Step 5
Stream 1
Segment 4
Step 0 Step 1 Step 2 Step 3
Segment 5
Step 0 Step 1 Step 2
Segment 6
Step 0 Step 1 Step 2 Step 3 Step 4
Stream 2
Segment 7
Step 0 Step 1
Segment 8
Step 0 Step 1
Time

38. Query Lifecycle
client
JDBC ODBC
Leader Node
Parser
Query Planner
Code Generator
Final Computations
Generate code
for all segments
of one stream
Explain Plans
Compute Node
Receive Compiled
Code
Run the Compiled
Code
Return results to
Leader
Compute Node
Receive Compiled
Code
Run the Compiled Code
Return results to
Leader
Return results to client
Segments in a stream
are executed
concurrently. Each
step in a segment is
executed serially.

39. Query Execution Deep Dive: Leader Node
• Leader node receives a query and parses the SQL
• Parser produces a logical representation of original query
• This query tree is input into the query optimizer (volt)
• Volt rewrites the query to maximize its efficiency
• Sometimes a single query is rewritten as several dependent statements in the
background
• Rewritten query is sent to the planner which generates 1+ query plans for the
execution with the best estimated performance
• Query plan is sent to execution engine, where it’s translated into steps, segments,
and streams
• Translated plan is sent to the code generator, which generates a C++ function for
each segment
• Generated C++ is compiled with gcc to a .o file and distributed to the compute nodes.

40. Query Execution Deep Dive: Compute Nodes
• Slices execute query segments in parallel
• Executable segments are created for one stream at a
time in sequence
• When the compute nodes are done, they return the
query results to leader node for final processing
• Leader node merges data into a single result set and
addresses any needed sorting or aggregation
• Leader node then returns results to the client

41. Visualizing Streams, Segments, and Steps
Stream 0
Segment 0
Step 0 Step 1 Step 2
Segment 1
Step 0 Step 1 Step 2 Step 3 Step 4
Segment 2
Step 0 Step 1 Step 2 Step 3
Segment 3
Step 0 Step 1 Step 2 Step 3 Step 4 Step 5
Stream 1
Segment 4
Step 0 Step 1 Step 2 Step 3
Segment 5
Step 0 Step 1 Step 2
Segment 6
Step 0 Step 1 Step 2 Step 3 Step 4
Stream 2
Segment 7
Step 0 Step 1
Segment 8
Step 0 Step 1
Time

42. Query Execution
Stream 0
Segment 0
Step 0 Step 1 Step 2
Segment 1
Step 0 Step 1 Step 2 Step 3 Step 4
Segment 2
Step 0 Step 1 Step 2 Step 3
Segment 3
Step 0 Step 1 Step 2 Step 3 Step 4 Step 5
Stream 1
Segment 4
Step 0 Step 1 Step 2 Step 3
Segment 5
Step 0 Step 1 Step 2
Segment 6
Step 0 Step 1 Step 2 Step 3 Step 4
Stream 2
Segment 7
Step 0 Step 1
Segment 8
Step 0 Step 1
Time
Stream 0
Segment 0
Step 0 Step 1 Step 2
Segment 1
Step 0 Step 1 Step 2 Step 3 Step 4
Segment 2
Step 0 Step 1 Step 2 Step 3
Segment 3
Step 0 Step 1 Step 2 Step 3 Step 4 Step 5
Stream 1
Segment 4
Step 0 Step 1 Step 2 Step 3
Segment 5
Step 0 Step 1 Step 2
Segment 6
Step 0 Step 1 Step 2 Step 3 Step 4
Stream 2
Segment 7
Step 0 Step 1
Segment 8
Step 0 Step 1
Stream 0
Segment 0
Step 0 Step 1 Step 2
Segment 1
Step 0 Step 1 Step 2 Step 3 Step 4
Segment 2
Step 0 Step 1 Step 2 Step 3
Segment 3
Step 0 Step 1 Step 2 Step 3 Step 4 Step 5
Stream 1
Segment 4
Step 0 Step 1 Step 2 Step 3
Segment 5
Step 0 Step 1 Step 2
Segment 6
Step 0 Step 1 Step 2 Step 3 Step 4
Stream 2
Segment 7
Step 0 Step 1
Segment 8
Step 0 Step 1
Stream 0
Segment 0
Step 0 Step 1 Step 2
Segment 1
Step 0 Step 1 Step 2 Step 3 Step 4
Segment 2
Step 0 Step 1 Step 2 Step 3
Segment 3
Step 0 Step 1 Step 2 Step 3 Step 4 Step 5
Stream 1
Segment 4
Step 0 Step 1 Step 2 Step 3
Segment 5
Step 0 Step 1 Step 2
Segment 6
Step 0 Step 1 Step 2 Step 3 Step 4
Stream 2
Segment 7
Step 0 Step 1
Segment 8
Step 0 Step 1
Slices
0
1
2
3

43. Design considerations for Redshift slices
DS2.8XL Compute Node
Ingestion Throughput:
• Each slice’s query processors can load one file at a time:
• Streaming decompression
• Parse
• Distribute
• Write
Realizing only partial node usage as 6.25% of slices are active
0 2 4 6 8 10 12 141 3 5 7 9 11 13 15

44. Design considerations for Amazon Redshift slices
Use at least as many input files
as there are slices in the cluster
With 16 input files, all slices are
working so you maximize
throughput
COPY continues to scale linearly
as you add nodes 16 Input Files
DS2.8XL Compute Node
0 2 4 6 8 10 12 141 3 5 7 9 11 13 15

45. Data Preparation for Redshift COPY
Export Data from Source System
• CSV Recommend (Simple Delimiter ',' or '|')
• Be aware of UTF-8 varchar columns (UTF-8 take 4 bytes per char)
• Be aware of your NULL character (N)
• GZIP Compress Files
• Split Files (1MB – 1GB after gzip compression)
Useful COPY Options for PoC Data
• MAXERRORS
• ACCEPTINVCHARS
• NULL AS

46. Open source tools
https://github.com/awslabs/amazon-redshift-utils
https://github.com/awslabs/amazon-redshift-monitoring
https://github.com/awslabs/amazon-redshift-udfs
Admin scripts
Collection of utilities for running diagnostics on your cluster
Admin views
Collection of utilities for managing your cluster, generating schema DDL, etc.
ColumnEncodingUtility
Gives you the ability to apply optimal column encoding to an established schema with data
already loaded
Amazon Redshift Engineering’s Advanced Table Design Playbook
https://aws.amazon.com/blogs/big-data/amazon-redshift-engineerings-advanced-table-design-
playbook-preamble-prerequisites-and-prioritization/

47. New & Upcoming Features

48. Recently Released Features
New Data Type – TIMESTAMPTZ
Support for Timestamp with Time zone
Multi-byte Object Names
Support for Multi-byte (UTF-8) characters for tables, columns, and other database object names
User Connection Limits
You can now set a limit on the number of database connections a user is permitted to have open
concurrently
Automatic Data Compression for CTAS
All newly created tables will leverage default encoding
New Column Encoding - ZSTD
Approximate Percentile Functions

49. Recently Released Features
Performance Enhancements
• Vacuum (10x faster for deletes)
• Snapshot Restore (2x faster)
• Queries (Up to 5x faster)
Copy Can Extend Sorted Region on Single Sort Key
• No need to vacuum when loading in sorted order
Enhanced VPC Routing
• Restrict S3 Bucket Access
Schema Conversion Tool - One-Time Data Exports
• Oracle
• Teradata
Schema Conversion Tool
• Vertica
• SQL Server

50. Monitor and
control cluster
resources
consumed by a
query
Get notified, abort
and reprioritize
long-running / bad
queries
Pre-defined
templates for
common use cases
Coming Soon: Query Monitoring Rules

51. BI tools SQL clientsAnalytics tools
Client AWS
Redshift
ADFS
Corporate
Active Directory IAM
Amazon Redshift
ODBC/JDBC
User groups Individual user
Single Sign-On
Identity providers
New Redshift
ODBC/JDBC
drivers. Grab the
ticket (userid) and
get a SAML
assertion.
Coming Soon: IAM Authentication

52. Automatic and Incremental Background VACUUM
• Reclaims space and sorts when Redshift clusters are idle
• Vacuum is initiated when performance can be enhanced
• Improves ETL and query performance
Coming Soon: Lots More …

53. Amazon Redshift Spectrum
Run SQL queries directly against data in S3
High Concurrency: Multiple
clusters access same data
No ETL: Query data in-place
using open file formats
Full Redshift SQL Support
S3
SQL
Check out the coming session on Amazon Redshift Spectrum

54. Thank you!
Tony Gibbs