Redshift optimization involves identifying patterns that slow query performance using tools like the Query Inspection Dashboard. Common issues include large nested loop joins, excessive network activity, scanning, inappropriate join cardinality, and disk-based steps. The document outlines strategies for diagnosing and applying corrective actions to each issue, such as rewriting queries, adding indexes, and updating statistics.

1. Redshift
Optimization
Fabio Beltramini
Customer Success Analyst
Looker

2. Fabio Beltramini

4. Looker Blocks

5. “I don’t need that! I have the AWS
Console”

6. “Ok, maybe I do need
that”

7. Half the Battle:
What to Optimize

8. Common Pitfalls
DO
DON’T

9. Identifying Patterns with the Block

10. Identifying Patterns with the Block

11. The Tricky Half:
Diagnosis &
Corrective
Action

12. Query Inspection
Dashboard
Query Inspection Dashboard

13. 1) Large Nested Loop
Joins
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Example join conditions:
ON a.date > b.date
ON a.text LIKE b.text
ON a.x=b.x OR a.y=b.y
The Tricky Half: Diagnosis & Corrective Action

14. Large Nested
Loop Joins
Corrective Action
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15. 2) Excessive network
activity
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The Tricky Half: Diagnosis & Corrective Action

16. Excessive
Network Activity
Corrective Action
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17. 3) Excessive scanning
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The Tricky Half: Diagnosis & Corrective Action

18. Excessive
Scanning
Corrective Action
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19. 4) Inappropriate
Join Cardinality
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The Tricky Half: Diagnosis & Corrective Action
Example cardinality issues:
FROM a
JOIN b ON b.fk = a.pk
JOIN c ON c.fk = a.pk
FROM house
JOIN rooms ON rooms.house_id = house.id
JOIN residents ON residents.house_id = house.id

20. Inappropriate
Join Cardinality
Corrective Action
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21. 5) Disk-based steps
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The Tricky Half: Diagnosis & Corrective Action

22. Disk-based
Steps
Corrective Action
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23. Recap
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