Find optimizations for the userstats and ipv6servers databases on meronense

optimize the userstats DB queries

Here are a few potential optimization strategies that you could consider for the merge() function:

Use a temporary index on the merged_part table: Since you are joining the imported and merged_part tables multiple times, having an index on the merged_part table can potentially speed up the queries. You can create a temporary index on the merged_part table using the CREATE INDEX statement before the FOR loop, and then drop the index after the loop using the DROP INDEX statement.

Use a LATERAL join instead of a correlated subquery: In the SELECT statement inside the FOR loop, you are using a correlated subquery to find the val of the preceding entry in the merged_part table. Instead of using a correlated subquery, you can use a LATERAL join to achieve the same result. A LATERAL join can be more efficient than a correlated subquery because it can allow the optimizer to use an index scan instead of a sequential scan.

Use a BETWEEN condition instead of AND conditions in the ON clause of the JOIN statement: In the JOIN statement inside the FOR loop, you are using two AND conditions to find adjacent intervals in the merged_part table. Instead of using two AND conditions, you can use a BETWEEN condition, which may be more efficient.

Use a CASE expression instead of multiple IF statements: In the FOR loop, you are using multiple IF statements to handle different cases. Instead of using multiple IF statements, you can use a single CASE expression, which may be more efficient because it can avoid unnecessary branching.

Use a BULK COLLECT and FORALL loop to insert multiple rows at once: Instead of inserting each row one at a time using a FOR loop, you can use a BULK COLLECT and FORALL loop to insert multiple rows at once. This can be more efficient because it can reduce the overhead of repeatedly calling the INSERT statement.

Use the ON CONFLICT clause in the INSERT statement to avoid unnecessary updates: In the FOR loop, you are using an UPDATE statement followed by an INSERT statement to handle conflicts. Instead of using an UPDATE statement, you can use the ON CONFLICT clause in the INSERT statement to handle conflicts. This can be more efficient because it avoids the need to perform an unnecessary UPDATE operation.

Here are a few optimization startegies for the aggregate() function:

Use a temporary index on the update table: Since you are performing multiple GROUP BY and JOIN operations on the update table, having an index on the update table can potentially speed up the queries. You can create a temporary index on the update table using the CREATE INDEX statement before the INSERT and UPDATE statements, and then drop the index after the statements using the DROP INDEX statement.

Use a LATERAL join instead of a correlated subquery: In the SELECT statement inside the INSERT statement that inserts into the aggregated table, you are using a correlated subquery to find the val of the preceding entry in the update_no_dimensions table. Instead of using a correlated subquery, you can use a LATERAL join to achieve the same result. A LATERAL join can be more efficient than a correlated subquery because it can allow the optimizer to use an index scan instead of a sequential scan.

Use a CASE expression instead of multiple IF statements: In the UPDATE statements, you are using multiple IF statements to handle different cases. Instead of using multiple IF statements, you can use a single CASE expression, which may be more efficient because it can avoid unnecessary branching.

Use a BULK COLLECT and FORALL loop to insert or update multiple rows at once: Instead of inserting or updating each row one at a time using a FOR loop, you can use a BULK COLLECT and FORALL loop to insert or update multiple rows at once. This can be more efficient because it can reduce the overhead of repeatedly calling the INSERT or UPDATE statement.

Use the ON CONFLICT clause in the INSERT statement to avoid unnecessary updates: In the INSERT statement that inserts into the aggregated table, you are using an UPDATE statement followed by an INSERT statement to handle conflicts. Instead of using an UPDATE statement, you can use the ON CONFLICT clause in the INSERT statement to handle conflicts. This can be more efficient because it avoids the need to perform an unnecessary UPDATE operation.

Here are a few suggestions for optimizing the combine() function:

Index the imported table on the stats_start column to improve the performance of the DELETE statement that uses this column in the WHERE clause.

Consider using a JOIN instead of multiple subqueries in the INSERT statement. This may improve the performance of the query by reducing the number of scans and reads.

Use the LEAST and GREATEST functions only when necessary, as they can be computationally expensive. For example, in the INSERT statement, you could consider using the CASE statement to compute the low and high values instead of using LEAST and GREATEST.

Consider using the EXPLAIN ANALYZE command to understand the performance characteristics of the combine function and identify any additional optimization opportunities.

Here's an example of how the INSERT statement could be rewritten using a JOIN and the CASE statement:

INSERT INTO combined_country_transport
SELECT country.date AS date, country.country AS country,
       transport.transport AS transport,
       SUM(CASE WHEN transport.val + country.val - total.val > 0 THEN transport.val + country.val - total.val ELSE 0 END) AS low,
       SUM(CASE WHEN transport.val < country.val THEN transport.val ELSE country.val END) AS high
FROM update2 country
JOIN update2 transport ON country.date = transport.date AND country.fingerprint = transport.fingerprint AND country.nickname = transport.nickname
JOIN update2 total ON total.date = transport.date AND total.fingerprint = transport.fingerprint AND total.nickname = transport.nickname
WHERE country.country <> ''
AND transport.transport <> ''
AND total.country = ''
AND total.transport = ''
AND country.val > 0
AND transport.val > 0
AND total.val > 0
GROUP BY country.date, country.country, transport.transport;

Here is how the combined VIEW could be optimized:

Consider using a materialized view instead of a regular view. This will allow the view to be pre-computed and stored, which can improve the performance of queries that use the view.

If you are frequently querying the view with a specific set of filters, you can create a function that accepts those filters as arguments and dynamically generates the view's query with those filters applied. This can improve the performance of querying the view with the same filters multiple times.

Consider creating indexes on the columns that are used in the JOIN and WHERE clauses of the view's query. This can improve the performance of queries that use the view by allowing the database to more quickly retrieve the relevant rows from the underlying tables.

If you are using PostgreSQL 12 or later, you can use the WITH (NO_MATERIALIZE) option when creating the view to specify that the view should not be materialized. This can be useful if the view's underlying tables are frequently updated, as it will allow the view to always reflect the latest data.

Further optimize the ipv6servers db queries

Here is how the aggregate() function could be optimized:

Index the tables you are joining and grouping by. This will make the queries faster by allowing the database to quickly locate the rows it needs to process.

Use a multi-column index on the status_entries and server_descriptors tables, with the columns status_id, flag_id, and version_id as the leading columns in the index. This will allow the database to efficiently locate the rows it needs to process for the aggregated_flags and aggregated_versions INSERT statements.

Use the EXISTS operator instead of IN in the WHERE clause of the DELETE statement. This will allow the database to stop searching for a matching row as soon as it finds one, rather than checking all rows in the table.

Use the ON CONFLICT clause of the INSERT statement to update existing rows rather than deleting and re-inserting them. This will reduce the number of writes to the database and potentially improve performance.

Consider using a temporary table to store intermediate results, rather than performing multiple joins and aggregations on the same tables. This can potentially improve the performance of the function by reducing the amount of data that needs to be processed.

Use the EXPLAIN ANALYZE command to understand how the database is executing the queries in the function, and identify any potential performance bottlenecks. This can help you understand why the function is slow and identify areas for optimization.

Here is how the MATERIALIZED VIEW grouped_by_status_ipv6 could be optimized:

 Use the WHERE clause to filter rows as early as possible in the query, to minimize the number of rows that need to be processed by the rest of the query. For example, you could move the WHERE statuses.status_id IN (SELECT included_statuses_ipv6.status_id FROM included_statuses_ipv6) condition to the FROM clause, so that it filters rows from the statuses table before they are joined with the aggregated_ipv6 table.

Consider using a JOIN rather than a subquery in the WHERE clause. This can often be more efficient, especially if the subquery returns a large number of rows.

Use the GROUP BY clause to group rows before applying the aggregation functions. This can be more efficient than grouping rows after they have been aggregated.

Use the EXPLAIN ANALYZE command to see how the query is being executed, and identify any bottlenecks or areas for optimization.

If the view is used frequently, consider creating an index on the statuses table to improve the performance of the JOIN with the aggregated_ipv6 table.

If the view is used infrequently and the data is not expected to change often, consider creating a materialized view. This will store the results of the view in a table, which can improve the performance of queries against the view.

If the data in the statuses and aggregated_ipv6 tables is very large, you might consider partitioning the tables based on the valid_after column, so that queries only need to access a subset of the data.

It's worth noting that these optimization strategies may not necessarily result in a significant improvement in performance, and you should profile the function to determine which strategies are most effective in your specific case.