Matview 2

[lusterchris]

To improve the performance of your query in Aurora PostgreSQL 16, consider the following strategies:

1. Review and Optimize Indexes

• Indexes on Join Columns: Ensure that you have indexes on the columns used for joining, particularly portfolio_uid and as_of_date.
• Composite Indexes: Consider creating composite indexes that match the WHERE and JOIN clauses in your query. For example:

   CREATE INDEX idx_portfolio_uid_as_of_date ON am_portfolio_rollup_revisions(portfolio_uid, as_of_date);

• Partial Indexes: If applicable, create partial indexes on the archive_timestamp IS NULL condition to speed up queries that filter on this.

2. Analyze and Optimize Your Query

• Use EXPLAIN: Use the EXPLAIN command to understand the query plan and identify bottlenecks.
• Reduce Complexity: Ensure that the joins are necessary. If there are unnecessary columns or tables, remove them.
• Check for Filtering Early: Ensure you are filtering data as early as possible in the query. This may require restructuring joins or using subqueries.

3. Materialized Views

• If the data doesn't change frequently, consider using a materialized view to store precomputed results. This will speed up read operations significantly:

   CREATE MATERIALIZED VIEW mv_portfolio_summary AS
   SELECT 
       pb.portfolio_uid,
       cal.as_of_date,
       SUM(am_pru.value_usd) AS value_usd,
       SUM(am_pru.spendable_accruals_usd) AS spendable_accruals_usd,
       SUM(am_pru.accruals_usd) AS accruals_usd,
       SUM(am_pru.variation_margin_usd) AS variation_margin_usd
   FROM portfolio_breakdowns pb
   JOIN am_portfolio_rollups am_pru ON pb.am_portfolio_uid = am_pru.portfolio_uid
   JOIN rollup_dates cal ON am_pru.as_of_date = cal.as_of_date
   WHERE am_pru.as_of_date <@ pb.effective_during
   GROUP BY pb.portfolio_uid, cal.as_of_date;

4. Query Rewriting

• Make sure the syntax is correct, especially the SUM() and join conditions. Here's a revised query:

   SELECT 
       pb.portfolio_uid,
       cal.as_of_date,
       SUM(am_pru.value_usd) AS value_usd,
       SUM(am_pru.spendable_accruals_usd) AS spendable_accruals_usd,
       SUM(am_pru.accruals_usd) AS accruals_usd,
       SUM(am_pru.variation_margin_usd) AS variation_margin_usd
   FROM portfolio_breakdowns pb
   JOIN am_portfolio_rollups am_pru 
       ON pb.am_portfolio_uid = am_pru.portfolio_uid 
       AND am_pru.as_of_date <@ pb.effective_during
   JOIN rollup_dates cal 
       ON am_pru.as_of_date = cal.as_of_date
   GROUP BY pb.portfolio_uid, cal.as_of_date
   ORDER BY pb.portfolio_uid, cal.as_of_date;

5. Partitioning Large Tables

• If am_portfolio_rollup_revisions is very large, consider partitioning it by date or portfolio UID. This can greatly enhance performance for certain types of queries.

6. Use Connection Pooling

• If the application that queries the database can utilize connection pooling, it can help manage the number of concurrent connections efficiently.

7. Configuration Tuning

• Ensure that your Aurora PostgreSQL instance is properly tuned for your workload, adjusting parameters such as work_mem, maintenance_work_mem, and others as necessary.

8. Regular Maintenance

• Regularly analyze and vacuum the tables to maintain the statistics and performance.

Example of EXPLAIN Usage

To identify where the bottlenecks are, you can run:

EXPLAIN ANALYZE 
SELECT 
    pb.portfolio_uid,
    cal.as_of_date,
    SUM(am_pru.value_usd) AS value_usd,
    SUM(am_pru.spendable_accruals_usd) AS spendable_accruals_usd,
    SUM(am_pru.accruals_usd) AS accruals_usd,
    SUM(am_pru.variation_margin_usd) AS variation_margin_usd
FROM portfolio_breakdowns pb
JOIN am_portfolio_rollups am_pru 
    ON pb.am_portfolio_uid = am_pru.portfolio_uid 
    AND am_pru.as_of_date <@ pb.effective_during
JOIN rollup_dates cal 
    ON am_pru.as_of_date = cal.as_of_date
GROUP BY pb.portfolio_uid, cal.as_of_date
ORDER BY pb.portfolio_uid, cal.as_of_date;

By implementing these strategies, you should see an improvement in the performance of your query

The new query I provided has a few corrections and optimizations compared to the original one. Here’s a breakdown of the key differences and how they contribute to improved performance:

1. Syntax Corrections

• SUM Function Syntax: In the original query, there was a typo with sum(am_pru.value_usd> AS value_usd. The > should be removed to correctly aggregate values.
• Proper Join Conditions: The join conditions were clarified, making sure they were accurately expressed.

2. Clarity and Readability

• Formatted Joins: The join conditions are clearly laid out, enhancing readability and making it easier for future maintainers to understand the query structure.
• Consistent Formatting: The revised query maintains consistent formatting, making it easier to identify each component.

3. Explicit Grouping and Ordering

• The grouping and ordering are explicitly clear and correctly implemented. Ensuring that the query correctly groups and orders by the necessary columns can lead to better execution plans, especially with indexes.

4. Aggregation Logic

• The aggregation now uses the correct syntax and structure, which ensures that the database engine can optimize the summation process effectively. This can improve execution time since it avoids errors that could lead to inefficient execution paths.

5. Optimization Suggestions in Context

• While the structural changes are important, I also suggested strategies such as:
  • Indexing: Encouraging the creation of appropriate indexes for the join columns and filters can significantly enhance the query's performance.
  • Materialized Views: If the data is static or changes infrequently, using a materialized view can dramatically speed up query execution by precomputing results.

Summary

The main differences are focused on correcting syntax errors, ensuring proper join conditions, and enhancing readability. These changes help the query run more efficiently by:

• Allowing the query planner to better understand how to execute the query.
• Ensuring that the aggregation is performed correctly and effectively.
• Making it easier to maintain and optimize the query further if necessary.

By addressing these aspects, the likelihood of generating a more efficient execution plan is increased, leading to better performance.

[lusterchris]

When configuring max_parallel_workers in PostgreSQL, it's important to understand that there isn't a one-size-fits-all formula for determining the optimal value. Instead, the decision involves a nuanced assessment of several interacting factors:

System Resources: The available CPU cores and RAM play a significant role, but the relationship is not strictly linear. While more cores can allow for more parallel workers, system overhead and resource contention must also be considered.

Workload Characteristics: The types of queries executed against the database greatly influence parallelism. Workloads with varying degrees of complexity and concurrency may respond differently to changes in parallel worker settings. Thus, what works for one set of queries might not be optimal for another.

Memory Usage: Each parallel worker consumes memory, which can limit the number of effective workers if the total memory available is a constraint. Finding a balance is key; too many workers can lead to excessive memory usage and degrade performance.

Dynamic Behavior: PostgreSQL's planner dynamically decides whether to use parallel workers based on cost estimates. Settings like parallel_setup_cost and parallel_tuple_cost influence these decisions, meaning that simply increasing max_parallel_workers doesn’t guarantee better performance.

Testing and Monitoring: Ultimately, the best way to determine the right value is through empirical testing and monitoring. This involves observing the performance under different loads and making adjustments based on real-world behavior rather than adhering to a theoretical formula.

In conclusion, while guidelines and heuristics can provide a starting point, the optimal configuration for max_parallel_workers requires careful consideration of our specific environment, workload, and continuous performance analysis.