The document discusses various techniques for performance tuning at different levels in Amazon Redshift including table design, querying, infrastructure configuration, and data management. Table design recommendations include choosing an appropriate distribution style, distkey, and sortkey to optimize data distribution and joins. Query tuning tips include avoiding SELECT *, using EXPLAIN to view query plans, managing query queues, and replacing cross joins. Infrastructure recommendations cover node types, disk choices, and workload management queues. Data-level techniques include enabling compression, updating statistics, cleaning data before loading, and removing non-ASCII characters.

1. RedShift-Performance Tuning In Few Clicks
Sadagopan.K.V

2. Performance Tuning Levels
● Table Design
● Query
● INFRA level
(Clusters)
● Data migration

3. Performance Tuning - Redshift
Figure out the requirement

4. Table Design
● Felicitous distribution style
○ ALL
○ KEY
○ EVEN
Use DISTSTYLE ALL only when the table is not
big.
● Tag the correct DISTKEY
○ Helps in data distribution.
○ Plays a key role in self joins

5. Table Design-Contd
● SORTKEY
○ Use the option of multiple sort keys
○ Date,timestamp,count etc.
○ Correctly sorted tables will easy work for self
joins
● Primary Key & Foreign key
○ Used by query planner
○ Helps in faster self joins

6. Querying
● Allergic to SELECT *
○ Reduces Scan time & I/O
○ Easy query plan
● EXPLAIN command to view the query
plan

7. Querying-contd
● Query Queues
○ Avoid execution bottlenecks
○ High response
● Don’t use Cross-joins unless
absolutely necessary
○ reduces nested loops & faster the
execution

8. Querying-contd
● Use CASE expression
○ similar to if/then/else
○ Avoids multiple selects

9. INFRA Level
● Nodes , Processors & Slices
○ More Nodes , More Performance
○ Yeah its more costier !!
● Node Types
○ Dense Nodes
○ Compute Nodes

10. INFRA Level
● Choose the right Disk
○ SSD/HDD
○ Data & Clustering
○ Less Data High Performance- SSD
○ More Data - HDD
● WLM queue (Workload Management)
○ Clear concurrency & memory management
○ Allocate memory for user groups for query
execution

11. Data Level
● Enable Data compression
○ Reduces storage & I/O in turn increase
query performance
○ compression for table columns
● Update System Stats
○ updating system stats (Analyse for every
data update)
○ Ease query plan

12. Data Level
● Clean up the data before
loading.
○ EMR
○ Structured data
● Remove NON ASCII
○ Supports 4 byte
UTF-8

13. Questions

14. Thank you