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Community Blog Data Sampling in PostgreSQL

Data Sampling in PostgreSQL

This article gives a quick walkthrough on how to perform data sampling in PostgreSQL using a comprehensive example.

Background

The online database is large. Therefore, we usually build a test database using data sampling. In addition to evenly extracting data, data sampling also encrypts data by hiding or encrypting some sensitive fields.

PostgreSQL 9.5 and later versions support the TABLESAMPLE syntax. For more information, see the following webpages:

For a version earlier than 9.5, customize functions to implement data sampling.

Example

Use functions for data sampling in PostgreSQL versions earlier than 9.5. PostgreSQL v.9.5 and later versions provide the SQL syntax for data sampling.

1) Specify the schema, table name, number of records to be sampled, and dispersion of sampling, and return the sampling records that meet the requirements. The dispersion ranges from 0 to 100, indicating the data range for sampling. The code is as follows:

create or replace function samp_rows(nsp name, rel name, cnt int8, dist float8 default 100.0) returns setof record as $$
declare
  pages int8;
  tups float8;
begin
  if dist<0 or dist>100 then 
    raise notice 'dist must between 0 and 100';
    return;
  end if;
  select relpages,reltuples into pages,tups from pg_class where oid=(quote_ident(nsp)||'.'||quote_ident(rel))::regclass;
  if not found then
    return;
  end if;
  if cnt >= tups then   -- 输入的采样记录数大于实际的记录数, 直接返回全表  
    -- ctid可以反映采样的随机度, 仅用于观察, 实际使用中可以不返回ctid  
    return query execute 'select ctid, * from '||quote_ident(nsp)||'.'||quote_ident(rel)||' t limit '||cnt;
  else
    -- 如果采样的记录数小于实际的记录数,则根据随机数的概率来返回采样结果  cnt/tups 即全离散时的采样概率   
    -- 离散度0-100,越大表示采样的结果越离散,可以理解为随机采样时覆盖的数据面,0表示连续采样,100则表示在所有数据中随机采样。  
    if dist = 100.0 then 
      return query execute 'select ctid, * from '||quote_ident(nsp)||'.'||quote_ident(rel)||' where random() <= '||cnt/tups||' limit '||cnt; 
    elsif (dist/100.0) <= (cnt/tups) then 
      return query execute 'select ctid, * from '||quote_ident(nsp)||'.'||quote_ident(rel)||' limit '||cnt; 
    else 
      return query execute 'select ctid, * from '||quote_ident(nsp)||'.'||quote_ident(rel)||' where random() <= '||(cnt/tups) * (100.0/dist)||' limit '||cnt; 
    end if; 
  end if; 
end;
$$ language plpgsql strict;  

The statistics relpages and reltuples are used to evaluate probability. Therefore, the table must contain the statistics, which are automatically collected by PG without manual intervention as long as autovacuum is enabled.

Consider the test below.

postgres=# create table test(id int, info text, crt_time timestamp);
CREATE TABLE
Time: 2.522 ms
postgres=# insert into test select generate_series(1,10000000), md5(random()::text), now();
INSERT 0 10000000
Time: 46274.872 ms

Randomly sample 10 records from the whole table.

postgres=# select * from samp_rows('public', 'test', 10) as t(ctid tid, c1 int, c2 text, c3 timestamp);
    ctid    |   c1    |                c2                |             c3             
------------+---------+----------------------------------+----------------------------
 (88846,70) | 9506592 | 83f2ed9c48f5c850a80b09219f8ef81d | 2016-09-29 23:59:20.619389
 (2223,92)  |  237953 | 2bd148a78ba8681150494f7beada073f | 2016-09-29 23:59:20.619389
 (4675,95)  |  500320 | 9588232acc62ce109ac91a28744ff75a | 2016-09-29 23:59:20.619389
 (11396,86) | 1219458 | 38cb4f0b0f8e3c4d2c867c52aa9e9276 | 2016-09-29 23:59:20.619389
 (12434,77) | 1330515 | d02e3b81543895f142f706b45589916c | 2016-09-29 23:59:20.619389
 (20625,14) | 2206889 | 989f65dba839e3ad0d404b33b3dcbc54 | 2016-09-29 23:59:20.619389
 (46954,59) | 5024137 | 71b84624b57271d224b368d141267f40 | 2016-09-29 23:59:20.619389
 (50156,4)  | 5366696 | fa96056fa5e8a914a999921d21f89c1d | 2016-09-29 23:59:20.619389
 (50533,42) | 5407073 | 988dd648caabeb2bbdb9700ab4c39e85 | 2016-09-29 23:59:20.619389
 (52526,59) | 5620341 | a02eb2e3499b985e719f61eb4f2d44f3 | 2016-09-29 23:59:20.619389
(10 rows)

Time: 997.933 ms

Next, randomly sample 10 records from 20% of the table.

postgres=# select * from samp_rows('public', 'test', 10, 20.0) as t(ctid tid, c1 int, c2 text, c3 timestamp);
    ctid    |   c1    |                c2                |             c3             
------------+---------+----------------------------------+----------------------------
 (53248,74) | 5697610 | c833355249d72dd7c7679d1e857106ec | 2016-09-29 23:59:20.619389
 (56404,63) | 6035291 | 0b43555f5e06684a546c61e04bf31ead | 2016-09-29 23:59:20.619389
 (56643,61) | 6060862 | 205d8cead5fd828f4b7898c9533f057c | 2016-09-29 23:59:20.619389
 (57482,15) | 6150589 | 88a6ea385f972745611fcc9a7ac63620 | 2016-09-29 23:59:20.619389
 (63422,49) | 6786203 | f7615ebbecafa6e7f2590980e97dc21e | 2016-09-29 23:59:20.619389
 (65723,98) | 7032459 | 1c7fc819d2990470608f24cfcf06f841 | 2016-09-29 23:59:20.619389
 (73309,84) | 7844147 | 840dac30c69394c8faf1ce280cc26657 | 2016-09-29 23:59:20.619389
 (73606,48) | 7875890 | 0115573ad12642d1a39a1e28a0c138a8 | 2016-09-29 23:59:20.619389
 (75288,94) | 8055910 | f4cb320309a5abc29d5413c631643304 | 2016-09-29 23:59:20.619389
 (76649,65) | 8201508 | 422364b88440b9ed38e4327080998f8b | 2016-09-29 23:59:20.619389
(10 rows)

Time: 383.163 ms

Also, randomly sample 10 records from 1% of the table.

postgres=# select * from samp_rows('public', 'test', 10, 1.0) as t(ctid tid, c1 int, c2 text, c3 timestamp);
    ctid    |   c1    |                c2                |             c3             
------------+---------+----------------------------------+----------------------------
 (76660,85) | 8202705 | ecbbaeb19c9e223b18de807e3a891704 | 2016-09-29 23:59:20.619389
 (76684,16) | 8205204 | a1a7c251d0751d0d40005a82af330357 | 2016-09-29 23:59:20.619389
 (76703,42) | 8207263 | be9701285b99d2c76efe3362c27b7b4c | 2016-09-29 23:59:20.619389
 (77124,49) | 8252317 | b9ddff0fb85b02e6b1eebfce7938a791 | 2016-09-29 23:59:20.619389
 (77301,49) | 8271256 | 2b32b83b118fb8560d41d8841b7707ec | 2016-09-29 23:59:20.619389
 (77321,44) | 8273391 | 844555ec12e0ff82581abd37302b851c | 2016-09-29 23:59:20.619389
 (77390,99) | 8280829 | 4d84ac5f7b957e66f1d9a5c05411b064 | 2016-09-29 23:59:20.619389
 (77464,97) | 8288745 | 66080fa16f99f27aa5392615371b8c10 | 2016-09-29 23:59:20.619389
 (77534,88) | 8296226 | cfed15f4f05b1af7a4d92ddc759202b0 | 2016-09-29 23:59:20.619389
 (77671,95) | 8310892 | 0070304cbe99722705a1b0c471c98132 | 2016-09-29 23:59:20.619389
(10 rows)

Time: 17.056 ms

Both the dispersion and performance meet the requirements.

Now, just remove ctid as shown below.

create or replace function samp_rows(nsp name, rel name, cnt int8, dist float8 default 100.0) returns setof record as $$
declare
  pages int8;
  tups float8;
begin
  if dist<0 or dist>100 then 
    raise notice 'dist must between 0 and 100';
    return;
  end if;
  select relpages,reltuples into pages,tups from pg_class where oid=(quote_ident(nsp)||'.'||quote_ident(rel))::regclass;
  if not found then
    return;
  end if;
  if cnt >= tups then 
    return query execute 'select * from '||quote_ident(nsp)||'.'||quote_ident(rel)||' t limit '||cnt;
  else
    -- 如果采样的记录数小于实际的记录数,则根据随机数的概率来返回采样结果  cnt/tups 即全离散时的采样概率   
    -- 离散度0-100,越大表示采样的结果越离散,可以理解为随机采样时覆盖的数据面,0表示连续采样,100则表示在所有数据中随机采样。  
    if dist = 100.0 then 
      return query execute 'select * from '||quote_ident(nsp)||'.'||quote_ident(rel)||' where random() <= '||cnt/tups||' limit '||cnt; 
    elsif (dist/100.0) <= (cnt/tups) then 
      return query execute 'select * from '||quote_ident(nsp)||'.'||quote_ident(rel)||' limit '||cnt; 
    else 
      return query execute 'select * from '||quote_ident(nsp)||'.'||quote_ident(rel)||' where random() <= '||(cnt/tups) * (100.0/dist)||' limit '||cnt; 
    end if; 
  end if; 
end;
$$ language plpgsql strict;  

The following snippet shows the result.

postgres=# select * from samp_rows('public', 'test', 10, 1.0) as t(c1 int, c2 text, c3 timestamp);
   c1    |                c2                |             c3             
---------+----------------------------------+----------------------------
 8510389 | aa866b8fecfa5dc73e25df0eb8eb6e5d | 2016-09-29 23:59:20.619389
 8511823 | 3f637e7b4c2dc3ca72dec979d25d8945 | 2016-09-29 23:59:20.619389
 8542741 | 921dc72741ddcc5100a9204a9e1f67f2 | 2016-09-29 23:59:20.619389
 8560000 | 150138e96c00557a2904293a461a6bb6 | 2016-09-29 23:59:20.619389
 8571110 | 510bcdc6b4a3108cc7adc901a30932ed | 2016-09-29 23:59:20.619389
 8583953 | 555afdc0ca25da10a1fe0c980d6505a5 | 2016-09-29 23:59:20.619389
 8590392 | 03a3faf95d397784fa2c2aeccce296cc | 2016-09-29 23:59:20.619389
 8601889 | ecab338c982818673b024f5299717c11 | 2016-09-29 23:59:20.619389
 8603505 | c176f2e29c5fa0538054f2651cf9c4e3 | 2016-09-29 23:59:20.619389
 8620874 | 75ca3ff98cc8040d6ca3f16b6402a5d0 | 2016-09-29 23:59:20.619389
(10 rows)

Time: 15.660 ms

postgres=# select * from samp_rows('public', 'test', 10, 20.0) as t(c1 int, c2 text, c3 timestamp);
   c1    |                c2                |             c3             
---------+----------------------------------+----------------------------
 9217485 | dbe16993b20d736572a926bf1df05aea | 2016-09-29 23:59:20.619389
 9327154 | cf6414a9a49f0fa41e0465fa2d015054 | 2016-09-29 23:59:20.619389
 9664326 | 84c1f8ad26228c55b20085f80b2b3292 | 2016-09-29 23:59:20.619389
 9667704 | 1da59e4572a09456408d65e7babbe8e4 | 2016-09-29 23:59:20.619389
  221144 | 990aa3f26c8db1b8a9a48471a515b65f | 2016-09-29 23:59:20.619389
  443124 | 974d539de407b7f4824510992cb71ef3 | 2016-09-29 23:59:20.619389
  500254 | f8a70b3b0b2920618b17d63043444fbb | 2016-09-29 23:59:20.619389
  952485 | bef93b6c48a2f74c7a7cde5af1153ccb | 2016-09-29 23:59:20.619389
 1494854 | 7ed5e5873bcc7e4e12a6a424d39ac755 | 2016-09-29 23:59:20.619389
 1557716 | 1c79f1f3d0026b47a7470d700cb0baad | 2016-09-29 23:59:20.619389
(10 rows)
0 0 0
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