Amazon Redshift

Database Developer Guide (API Version 2012-12-01)

Welcome
- Are You a First-Time Amazon Redshift User?
- Are You a Database Developer?
- Prerequisites
Amazon Redshift System Overview
- Data Warehouse System Architecture
- Performance
- Columnar Storage
- Internal Architecture and System Operation
- Workload Management
- Using Amazon Redshift with Other Services
Getting Started Using Databases
- Step 1: Create a Database
- Step 2: Create a Database User
  - Delete a Database User
- Step 3: Create a Database Table
  - Insert Data Rows into a Table
  - Select Data from a Table
- Step 4: Load Sample Data
- Step 5: Query the System Tables
  - Determine the Process ID of a Running Query
- Step 6: Cancel a Query
- Step 7: Clean Up Your Resources
Proof of Concept Playbook
Amazon Redshift Best Practices
- Best Practices for Designing Tables
- Best Practices for Loading Data
- Best Practices for Designing Queries
- Working with Advisor
  - Access Advisor
  - Advisor Recommendations
Tutorial: Tuning Table Design
- Step 1: Create a Test Data Set
- Step 2: Establish a Baseline
- Step 3: Select Sort Keys
- Step 4: Select Distribution Styles
- Step 5: Review Compression Encodings
- Step 6: Recreate the Test Data Set
- Step 7: Retest System Performance After Tuning
- Step 8: Evaluate the Results
- Step 9: Clean Up Your Resources
- Summary
Tutorial: Loading Data from Amazon S3
- Step 1: Launch a Cluster
- Step 2: Download the Data Files
- Step 3: Upload the Files to an Amazon S3 Bucket
- Step 4: Create the Sample Tables
- Step 5: Run the COPY Commands
- Step 6: Vacuum and Analyze the Database
- Step 7: Clean Up Your Resources
- Summary
Tutorial: Configuring WLM Queues to Improve Query Processing
- Section 1: Understanding the Default Queue Processing Behavior
- Section 2: Modifying the WLM Query Queue Configuration
- Section 3: Routing Queries to Queues Based on User Groups and Query Groups
- Section 4: Using wlm_query_slot_count to Temporarily Override Concurrency Level in a Queue
- Section 5: Cleaning Up Your Resources
Tutorial: Querying Nested Data with Amazon Redshift Spectrum
- Step 1: Create an External Table That Contains Nested Data
- Step 2: Query Your Nested Data in Amazon S3 with SQL Extensions
- Nested Data Use Cases
- Nested Data Limitations
Managing Database Security
- Amazon Redshift Security Overview
- Default Database User Privileges
- Superusers
- Users
- Groups
- Schemas
- Example for Controlling User and Group Access
Designing Tables
- Choosing a Column Compression Type
- Choosing a Data Distribution Style
- Choosing Sort Keys
  - Comparing Sort Styles
- Defining Constraints
- Analyzing Table Design
Using Amazon Redshift Spectrum to Query External Data
- Getting Started With Amazon Redshift Spectrum
- IAM Policies for Amazon Redshift Spectrum
- Creating Data Files for Queries in Amazon Redshift Spectrum
- Creating External Schemas
- Creating External Tables
- Improving Amazon Redshift Spectrum Query Performance
- Monitoring Metrics
- Troubleshooting Queries
Loading Data
- Using COPY to Load Data
- Updating with DML
- Updating and Inserting
- Performing a Deep Copy
- Analyzing Tables
  - ANALYZE Command History
- Vacuuming Tables
  - Managing Vacuum Times
  - Vacuum Column Limit Exceeded Error
- Managing Concurrent Write Operations
Unloading Data
- Unloading Data to Amazon S3
- Unloading Encrypted Data Files
- Unloading Data in Delimited or Fixed-Width Format
- Reloading Unloaded Data
Creating User-Defined Functions
- UDF Security and Privileges
- Creating a Scalar SQL UDF
- Creating a Scalar Python UDF
- Naming UDFs
- Logging Errors and Warnings
Tuning Query Performance
- Query Processing
- Analyzing and Improving Queries
- Troubleshooting Queries
Implementing Workload Management
- Defining Query Queues
- WLM Query Queue Hopping
- Short Query Acceleration
- Modifying the WLM Configuration
- WLM Queue Assignment Rules
- Assigning Queries to Queues
- Dynamic and Static Properties
  - WLM Dynamic Memory Allocation
  - Dynamic WLM Example
- Query Monitoring Rules
- WLM System Tables and Views
SQL Reference
- Amazon Redshift SQL
  - SQL Functions Supported on the Leader Node
  - Amazon Redshift and PostgreSQL
- Using SQL
- SQL Commands
- SQL Functions Reference
- Reserved Words
System Tables Reference
- System Tables and Views
- Types of System Tables and Views
- Visibility of Data in System Tables and Views
- STL Tables for Logging
- STV Tables for Snapshot Data
- System Views
- System Catalog Tables
Configuration Reference
- Modifying the Server Configuration
- analyze_threshold_percent
- datestyle
- describe_field_name_in_uppercase
- enable_result_cache_for_session
- extra_float_digits
- max_cursor_result_set_size
- query_group
- search_path
- statement_timeout
- timezone
- wlm_query_slot_count
Sample Database
- CATEGORY Table
- DATE Table
- EVENT Table
- VENUE Table
- USERS Table
- LISTING Table
- SALES Table
Time Zone Names and Abbreviations
- Time Zone Names
- Time Zone Abbreviations
Document History

Welcome

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This is the Amazon Redshift Database Developer Guide.

Amazon Redshift is an enterprise-level, petabyte scale, fully managed data warehousing service.

This guide focuses on using Amazon Redshift to create and manage a data warehouse. If you work with databases as a designer, software developer, or administrator, it gives you the information you need to design, build, query, and maintain your data warehouse.

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