Best way to select random rows PostgreSQL

I want a random selection of rows in PostgreSQL, I tried this:

select * from table where random() < 0.01;

But some other recommend this:

select * from table order by random() limit 1000;

I have a very large table with 500 Million rows, I want it to be fast.

Which approach is better? What are the differences? What is the best way to select random rows?

Answer

Given your specifications (plus additional info in the comments),

• You have a numeric ID column (integer numbers) with only few (or moderately few) gaps.
• Obviously no or few write operations.
• Your ID column has to be indexed! A primary key serves nicely.

The query below does not need a sequential scan of the big table, only an index scan.

First, get estimates for the main query:

SELECT count(*) AS ct              -- optional
     , min(id)  AS min_id
     , max(id)  AS max_id
     , max(id) - min(id) AS id_span
FROM   big;

The only possibly expensive part is the count(*) (for huge tables). Given above specifications, you don’t need it. An estimate will do just fine, available at almost no cost (detailed explanation here):

SELECT reltuples AS ct FROM pg_class
WHERE oid = 'schema_name.big'::regclass;

As long as ct isn’t much smaller than id_span, the query will outperform other approaches.

WITH params AS (
   SELECT 1       AS min_id           -- minimum id <= current min id
        , 5100000 AS id_span          -- rounded up. (max_id - min_id + buffer)
    )
SELECT *
FROM  (
   SELECT p.min_id + trunc(random() * p.id_span)::integer AS id
   FROM   params p
         ,generate_series(1, 1100) g  -- 1000 + buffer
   GROUP  BY 1                        -- trim duplicates
) r
JOIN   big USING (id)
LIMIT  1000;                          -- trim surplus

• Generate random numbers in the id space. You have “few gaps”, so add 10 % (enough to easily cover the blanks) to the number of rows to retrieve.

• Each id can be picked multiple times by chance (though very unlikely with a big id space), so group the generated numbers (or use DISTINCT).

• Join the ids to the big table. This should be very fast with the index in place.

• Finally trim surplus ids that have not been eaten by dupes and gaps. Every row has a completely equal chance to be picked.

Short version

You can simplify this query. The CTE in the query above is just for educational purposes:

SELECT *
FROM  (
   SELECT DISTINCT 1 + trunc(random() * 5100000)::integer AS id
   FROM   generate_series(1, 1100) g
   ) r
JOIN   big USING (id)
LIMIT  1000;

Refine with rCTE

Especially if you are not so sure about gaps and estimates.

WITH RECURSIVE random_pick AS (
   SELECT *
   FROM  (
      SELECT 1 + trunc(random() * 5100000)::int AS id
      FROM   generate_series(1, 1030)  -- 1000 + few percent - adapt to your needs
      LIMIT  1030                      -- hint for query planner
      ) r
   JOIN   big b USING (id)             -- eliminate miss

   UNION                               -- eliminate dupe
   SELECT b.*
   FROM  (
      SELECT 1 + trunc(random() * 5100000)::int AS id
      FROM   random_pick r             -- plus 3 percent - adapt to your needs
      LIMIT  999                       -- less than 1000, hint for query planner
      ) r
   JOIN   big b USING (id)             -- eliminate miss
   )
TABLE  random_pick
LIMIT  1000;  -- actual limit

We can work with a smaller surplus in the base query. If there are too many gaps so we don’t find enough rows in the first iteration, the rCTE continues to iterate with the recursive term. We still need relatively few gaps in the ID space or the recursion may run dry before the limit is reached – or we have to start with a large enough buffer which defies the purpose of optimizing performance.

Duplicates are eliminated by the UNION in the rCTE.

The outer LIMIT makes the CTE stop as soon as we have enough rows.

This query is carefully drafted to use the available index, generate actually random rows and not stop until we fulfill the limit (unless the recursion runs dry). There are a number of pitfalls here if you are going to rewrite it.

Wrap into function

For repeated use with varying parameters:

CREATE OR REPLACE FUNCTION f_random_sample(_limit int = 1000, _gaps real = 1.03)
  RETURNS SETOF big
  LANGUAGE plpgsql VOLATILE ROWS 1000 AS
$func$
DECLARE
   _surplus  int := _limit * _gaps;
   _estimate int := (           -- get current estimate from system
      SELECT c.reltuples * _gaps
      FROM   pg_class c
      WHERE  c.oid = 'big'::regclass);
BEGIN
   RETURN QUERY
   WITH RECURSIVE random_pick AS (
      SELECT *
      FROM  (
         SELECT 1 + trunc(random() * _estimate)::int
         FROM   generate_series(1, _surplus) g
         LIMIT  _surplus           -- hint for query planner
         ) r (id)
      JOIN   big USING (id)        -- eliminate misses

      UNION                        -- eliminate dupes
      SELECT *
      FROM  (
         SELECT 1 + trunc(random() * _estimate)::int
         FROM   random_pick        -- just to make it recursive
         LIMIT  _limit             -- hint for query planner
         ) r (id)
      JOIN   big USING (id)        -- eliminate misses
   )
   TABLE  random_pick
   LIMIT  _limit;
END
$func$;

Call:

SELECT * FROM f_random_sample();
SELECT * FROM f_random_sample(500, 1.05);

You could even make this generic to work for any table: Take the name of the PK column and the table as polymorphic type and use EXECUTE … But that’s beyond the scope of this question. See:

• Refactor a PL/pgSQL function to return the output of various SELECT queries

Possible alternative

IF your requirements allow identical sets for repeated calls (and we are talking about repeated calls) I would consider a materialized view. Execute above query once and write the result to a table. Users get a quasi random selection at lightening speed. Refresh your random pick at intervals or events of your choosing.

Postgres 9.5 introduces TABLESAMPLE SYSTEM (n)

Where n is a percentage. The manual:

The BERNOULLI and SYSTEM sampling methods each accept a single argument which is the fraction of the table to sample, expressed as a percentage between 0 and 100. This argument can be any real-valued expression.

Bold emphasis mine. It’s very fast, but the result is not exactly random. The manual again:

The SYSTEM method is significantly faster than the BERNOULLI method when small sampling percentages are specified, but it may return a less-random sample of the table as a result of clustering effects.

The number of rows returned can vary wildly. For our example, to get roughly 1000 rows:

SELECT * FROM big TABLESAMPLE SYSTEM ((1000 * 100) / 5100000.0);

Related:

• Fast way to discover the row count of a table in PostgreSQL

Or install the additional module tsm_system_rows to get the number of requested rows exactly (if there are enough) and allow for the more convenient syntax:

SELECT * FROM big TABLESAMPLE SYSTEM_ROWS(1000);

See Evan’s answer for details.

But that’s still not exactly random.