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?
Given your specifications (plus additional info in the comments):
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 the above specifications, you don't need it. An estimate to replace the full count will do just fine, available at almost no cost:
SELECT (reltuples / relpages * (pg_relation_size(oid) / 8192))::bigint AS ct
FROM pg_class
WHERE oid = 'big'::regclass; -- your table name
Detailed explanation:
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 id
s to the big table. This should be very fast with the index in place.
Finally trim surplus id
s that have not been eaten by dupes and gaps. Every row has a completely equal chance to be picked.
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;
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.
For repeated use with the same table 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 (reltuples / relpages * (pg_relation_size(oid) / 8192))::bigint
FROM pg_class
WHERE 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);
We can make this generic to work for any table with a unique integer column (typically the PK): Pass the table as polymorphic type and (optionally) the name of the PK column and use EXECUTE
:
CREATE OR REPLACE FUNCTION f_random_sample(_tbl_type anyelement
, _id text = 'id'
, _limit int = 1000
, _gaps real = 1.03)
RETURNS SETOF anyelement
LANGUAGE plpgsql VOLATILE ROWS 1000 AS
$func$
DECLARE
-- safe syntax with schema & quotes where needed
_tbl text := pg_typeof(_tbl_type)::text;
_estimate int := (SELECT (reltuples / relpages
* (pg_relation_size(oid) / 8192))::bigint
FROM pg_class -- get current estimate from system
WHERE oid = _tbl::regclass);
BEGIN
RETURN QUERY EXECUTE format(
$$
WITH RECURSIVE random_pick AS (
SELECT *
FROM (
SELECT 1 + trunc(random() * $1)::int
FROM generate_series(1, $2) g
LIMIT $2 -- hint for query planner
) r(%2$I)
JOIN %1$s USING (%2$I) -- eliminate misses
UNION -- eliminate dupes
SELECT *
FROM (
SELECT 1 + trunc(random() * $1)::int
FROM random_pick -- just to make it recursive
LIMIT $3 -- hint for query planner
) r(%2$I)
JOIN %1$s USING (%2$I) -- eliminate misses
)
TABLE random_pick
LIMIT $3;
$$
, _tbl, _id
)
USING _estimate -- $1
, (_limit * _gaps)::int -- $2 ("surplus")
, _limit -- $3
;
END
$func$;
Call with defaults (important!):
SELECT * FROM f_random_sample(null::big); --!
Or more specifically:
SELECT * FROM f_random_sample(null::"my_TABLE", 'oDD ID', 666, 1.15);
About the same performance as the static version.
Related:
This is safe against SQL injection. See:
If your requirements allow identical sets for repeated calls (and we are talking about repeated calls) 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.
TABLESAMPLE SYSTEM (n)
Where n
is a percentage. The manual:
The
BERNOULLI
andSYSTEM
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 anyreal
-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 theBERNOULLI
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:
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.