[Solved] PostgreSQL 12 Vs. Pandas Sub Query Optimization

How to optimize this SQL query?

In case you have your own slow SQL query, you can optimize it automatically here.

For the query above, the following recommendations will be helpful as part of the SQL tuning process.
You'll find 3 sections below:

  1. Description of the steps you can take to speed up the query.
  2. The optimal indexes for this query, which you can copy and create in your database.
  3. An automatically re-written query you can copy and execute in your database.
The optimization process and recommendations:
  1. Create Optimal Indexes (modified query below): The recommended indexes are an integral part of this optimization effort and should be created before testing the execution duration of the optimized query.
  2. Replace In Subquery With Correlated Exists (modified query below): In many cases, an EXISTS subquery with a correlated condition will perform better than a non correlated IN subquery.
Optimal indexes for this query:
CREATE INDEX t_holdings_idx_trade_date_ticker_company_na ON "t_ark_holdings" ("trade_date" desc,"ticker","company_name");
CREATE INDEX t_holdings_idx_ticker_trade_date ON "t_ark_holdings" ("ticker","trade_date");
The optimized query:
SELECT
        DISTINCT a.trade_date,
        a.ticker,
        a.company_name 
    FROM
        t_ark_holdings a 
    WHERE
        NOT EXISTS (
            SELECT
                1 
            FROM
                t_ark_holdings AS b1 
            WHERE
                (
                    b1.trade_date < a.trade_date
                ) 
                AND (
                    a.ticker = b1.ticker
                )
        ) 
    ORDER BY
        a.trade_date DESC,
        a.ticker,
        a.company_name

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* original question posted on StackOverflow here.