QUERY

[lusterchris]

QUERY 1

To optimize your PostgreSQL query and improve its execution time, here are several strategies you can try:

1. Fix Syntax Issues

• Before proceeding, it seems your query contains some syntax errors (e.g., missing commas, misplaced parentheses). Fix these to ensure proper execution.

2. Add Missing Indexes

• Indexes on portfolio_uid, as_of_date, listing_uid, and country columns can greatly improve performance. If these indexes don’t exist, create them:

   CREATE INDEX idx_position_portfolio_asof_listing ON onyx_api.position_rollups(portfolio_uid, as_of_date, listing_uid);
   CREATE INDEX idx_listing_groups_asof_country ON onyx_api.listing_groups(as_of_date, country);
   CREATE INDEX idx_portfolio_rollups_portfolio_asof ON onyx_api.portfolio_rollups(portfolio_uid, as_of_date);
   CREATE INDEX idx_calendar_is_business_date ON codex.calendar(is_business_date, calendar_date);

3. Reduce Data with Filtered Joins

• Apply filters earlier in the query, especially for large datasets. Filters like country ILIKE 'Russian Federation' should be applied as early as possible in the query.

4. Check JOIN Conditions

• Review your JOIN conditions. Ensure you are using appropriate index scans and matching on indexed columns to avoid unnecessary nested loops.
• Ensure pos.listing_uid = lg.listing_uid is indexed to make the JOIN efficient.

5. Optimize the Date Range

• If you are frequently querying specific date ranges (e.g., BETWEEN '2004-01-30' AND '2024-09-01'), consider adding partial indexes:

   CREATE INDEX idx_pos_date_filtered ON onyx_api.position_rollups(as_of_date) 
   WHERE as_of_date BETWEEN '2004-01-30' AND '2024-09-01';

6. Use Parallel Queries

• Ensure your RDS PostgreSQL instance is configured to allow parallel query execution. You can check and adjust this by setting the following parameters:

   SET max_parallel_workers_per_gather = 4;
   SET parallel_setup_cost = 1000;
   SET parallel_tuple_cost = 0.1;

7. Window Function Optimization

• Since your query uses WINDOW functions, ensure that there are indexes on columns involved in PARTITION BY and ORDER BY.
• Optimize by limiting the number of rows processed by the WINDOW function.

8. Avoid Full Sorts

• Your execution plan shows a Full-sort in the query, which is costly. Ensure that sorting happens on indexed columns and try to limit the result set size early by using LIMIT if possible.

9. Use ANALYZE and Vacuum

• Ensure your tables are vacuumed and analyzed to keep the statistics up-to-date for the query planner.

   VACUUM ANALYZE onyx_api.position_rollups;

10. Optimize Aggregate Functions

• Aggregation functions like SUM and MAX can be expensive over large datasets. Ensure these functions are computed after filtering rows to reduce unnecessary calculations.

Conclusion

• Run EXPLAIN ANALYZE after implementing these changes to check the performance improvement.
• By optimizing indexes, reviewing join conditions, filtering earlier, and utilizing parallel query execution, your query performance should improve significantly.

[lusterchris]

GOOD ONE.

Summary of Query Performance Optimization:

After analyzing the performance of the query, which initially took 83 seconds, I implemented a set of targeted optimizations, resulting in a significant reduction in query execution time to just 4 seconds. One of the key enhancements was the creation of a partial index on the onyx_api.position_rollups table:

sql Copy code CREATE INDEX idx_pos_date_filtered ON onyx_api.position_rollups(as_of_date) WHERE as_of_date BETWEEN '2004-01-30' AND '2024-09-01'; Reasoning Behind the Optimization:

Partial Index for Efficient Data Access: This partial index focuses specifically on the date range ('2004-01-30' to '2024-09-01'), which aligns directly with the query’s frequent date filtering. By limiting the scope of the index to this date range, we reduce the index size significantly, which accelerates index scans. PostgreSQL can directly access the relevant rows without the overhead of scanning the full dataset or a broader index.

Reduction in I/O Operations: As a result of this partial index, the database engine needs to perform fewer I/O operations. Instead of scanning the entire table or a full index, it can target the smaller, more focused index, which resides in memory much more easily. This drastically reduces query execution time, especially for larger datasets.

Improved Query Planner Efficiency: The PostgreSQL query planner, which decides how to execute the query, now prefers this partial index due to its smaller size and relevance to the query’s conditions. This leads to faster execution by reducing the need for unnecessary scans and processing. The planner’s decision-making has improved as it can better optimize for the subset of data that is actively queried, which is the date range in question.

Index Maintenance Overhead Reduction: Another benefit of the partial index is that it only covers the rows within the specified date range, leading to less frequent index maintenance (insert/update operations) for rows outside this range. This reduces the load on the system and ensures that the query remains performant even under high transaction volumes.

Future Scalability: This index design not only improves current performance but also positions the database for future scalability. Should future queries focus on different date ranges, additional partial indexes can be created, ensuring continued performance optimization without unnecessarily burdening the system.

Outcome:

This series of optimizations, particularly the introduction of the partial index, reduced the query execution time from 83 seconds to 4 seconds—an improvement of over 95%. This demonstrates a strategic approach to database tuning by aligning index design with actual query patterns and data access behavior.

Conclusion:

The successful application of these optimizations showcases an in-depth understanding of PostgreSQL's indexing mechanisms and the importance of targeted improvements to minimize resource consumption while maximizing performance. This performance gain will also contribute to reducing overall system load, improving user experience, and ensuring that our infrastructure scales effectively with increasing data volumes.