Note: This article contains links to Chinese blog articles.
Precision marketing is a hot topic in the advertising industry. One of my previous articles describes how to use arrays and GIN indexes on PostgreSQL to tag people in real-time. (Source to Chinese article: Marketing to trillions of people in milliseconds - database design for real-time recommendations systems with trillions of user tags)
The above methods require certain adjustments to the program (of course if the user already has a PostgreSQL technology stack, modifications will be minimal). See below for an example.
Let’s assume a user uses multiple columns to indicate different attributes, and each attribute matches the value range of certain TAGs.
create table user_tags(uid int8 primary key, lab1 int, lab2 text, lab3 timestamp, lab4 text, lab5 interval, lab6 json);
The user's original tag and statistical inquiry may look like this:
select * from user_tags where lab1 ? xxx and lab2 ? xxx or lab4 ? xxx;
select xx, count(*) from user_tags where lab1 ? xxx and lab2 ? xxx or lab4 ? xxx group by xxx; As the value range may be continuous, to use the method in Progress is driven by the lazy - database design for real-time recommendation systems with trillions of user tags, we need to create a library of tags, cascade the data, and convert the queries.
Continuous queries like between and, need to be converted into hash queries. While this makes programming more complicated, it could also provide the most optimal performance.
Does PostgreSQL provide a better method?
The answer is yes. Solutions are created for the lazy, and the lazy thereby drive progress!
You can refer to the following articles to learn more about PostgreSQL solutions.
1.Use bitmapand, bitmapor, and any index to tag people.
2.Use the varbitx extension to tag people.
Let’s see how it works in scenarios with continuous queries, equality queries, and queries with multiple combinations of conditions.
Create a TAG table
postgres=# create table tbl_label(uid int primary key, c1 int, c2 text, c3 numeric, c4 timestamp, c5 interval, c6 int);
CREATE TABLE
Time: 31.145 ms Insert a batch of data
postgres=# insert into tbl_label select id,
random()*10000, md5(random()::text),
10000*random(), clock_timestamp(),
(random()*1000::int||' hour')::interval,
random()*99999
from generate_series(1,10000000) t(id);
INSERT 0 10000000 Data model
postgres=# select * from tbl_label limit 10;
uid | c1 | c2 | c3 | c4 | c5 | c6
-----+------+----------------------------------+------------------+----------------------------+------------------+-------
1 | 1692 | 505662aa4a6b33e1775cea660063ba58 | 9761.26249413937 | 2017-06-12 18:38:57.515097 | 322:06:55.266882 | 67699
2 | 8611 | a60d564b7f4d58029dfd5e16f0457305 | 1003.07232700288 | 2017-06-12 18:38:57.515282 | 780:59:39.081975 | 89283
3 | 290 | f226358e08372d4b79c8ecdd27172244 | 8240.20517989993 | 2017-06-12 18:38:57.515296 | 261:29:59.658099 | 87975
4 | 7829 | 32bc5d97731ddaf2c1630218e43d1e85 | 9061.87939457595 | 2017-06-12 18:38:57.515303 | 760:47:18.267513 | 76409
5 | 7735 | 3813b4bcdaadc21a55da143f6aceeac9 | 6651.74870751798 | 2017-06-12 18:38:57.515309 | 512:45:50.116217 | 11252
6 | 9945 | ff72917169cdea9225e429e438f22586 | 2114.50539063662 | 2017-06-12 18:38:57.515316 | 63:30:34.15014 | 33288
7 | 9144 | 7cf4067f22c5edbb1fc4e08ecee7242c | 5662.74457611144 | 2017-06-12 18:38:57.515322 | 890:30:28.360096 | 55484
8 | 2433 | 8ac9732bec2b1c175483c16e82467653 | 9184.17258188128 | 2017-06-12 18:38:57.515328 | 343:34:40.88581 | 53265
9 | 8113 | 2dd724e82dc7c2a15dfda45f6a41cd53 | 5094.92502082139 | 2017-06-12 18:38:57.515334 | 635:16:39.096908 | 63410
10 | 3893 | b8abdb00228f09efb04c1e2a8a022c22 | 6397.02362008393 | 2017-06-12 18:38:57.51534 | 295:26:24.752753 | 17894
(10 rows) Analyze table statistics
postgres=# analyze tbl_label ;
ANALYZE Check the hash degree for each column
Description of n_distinct
-1 indicates that each row in the column is unique.
>=1 indicates the number of unique values in the column
<1 indicates the number/total number of unique values in the column
Description of correlation
It indicates the linear correlation between this column and the data stack storage, where 1 indicates perfect positive correlation. As the value gets closer to 0, the data distribution is more discrete. <0 indicates an inverse correlation.
Uid is auto-increment, c4 increases by time, so the correlation is 1, a perfect positive correlation.
postgres=# select tablename,attname,n_distinct,correlation from pg_stats where tablename='tbl_label';
tablename | attname | n_distinct | correlation
-----------+---------+------------+-------------
tbl_label | uid | -1 | 1
tbl_label | c1 | 10018 | 0.00431651
tbl_label | c2 | -0.957505 | -0.00796595
tbl_label | c3 | -1 | 0.00308372
tbl_label | c4 | -1 | 1
tbl_label | c5 | -1 | 0.000382809
tbl_label | c6 | 100688 | 0.00156045
(7 rows) Based on the above statistical information, we should create btree indexes for a unique column, and create gin indexes (inverted indexes) for a loose column to achieve the best effect.
In order to enable common types to support gin, we need to create the btree_gin extension.
postgres=# create extension btree_gin;
CREATE EXTENSION
Create compound gin indexes for c1 and c6.
postgres=# set maintenance_work_mem ='32GB';
SET
Time: 0.168 ms
postgres=# create index idx_tbl_label_1 on tbl_label using gin(c1,c6);
CREATE INDEX
Time: 1504.542 ms (00:01.505) Query testing shows that queries on any combination of c1 and c6 are very efficient .
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl_label where c1 between 1 and 100;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on public.tbl_label (cost=125.76..8759.97 rows=10074 width=80) (actual time=40.856..50.480 rows=9922 loops=1)
Output: uid, c1, c2, c3, c4, c5, c6
Recheck Cond: ((tbl_label.c1 >= 1) AND (tbl_label.c1 <= 100))
Heap Blocks: exact=7222
Buffers: shared hit=7982
-> Bitmap Index Scan on idx_tbl_label_1 (cost=0.00..123.24 rows=10074 width=0) (actual time=39.773..39.773 rows=9922 loops=1)
Index Cond: ((tbl_label.c1 >= 1) AND (tbl_label.c1 <= 100))
Buffers: shared hit=760
Planning time: 0.105 ms
Execution time: 51.043 ms
(10 rows)
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl_label where c1 between 1 and 100 or c6=100;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on public.tbl_label (cost=134.36..8799.70 rows=10085 width=80) (actual time=41.133..50.187 rows=9932 loops=1)
Output: uid, c1, c2, c3, c4, c5, c6
Recheck Cond: (((tbl_label.c1 >= 1) AND (tbl_label.c1 <= 100)) OR (tbl_label.c6 = 100))
Heap Blocks: exact=7228
Buffers: shared hit=7992
-> BitmapOr (cost=134.36..134.36 rows=10085 width=0) (actual time=40.045..40.045 rows=0 loops=1)
Buffers: shared hit=764
-> Bitmap Index Scan on idx_tbl_label_1 (cost=0.00..123.24 rows=10074 width=0) (actual time=40.031..40.031 rows=9922 loops=1)
Index Cond: ((tbl_label.c1 >= 1) AND (tbl_label.c1 <= 100))
Buffers: shared hit=760
-> Bitmap Index Scan on idx_tbl_label_1 (cost=0.00..6.08 rows=11 width=0) (actual time=0.012..0.012 rows=10 loops=1)
Index Cond: (tbl_label.c6 = 100)
Buffers: shared hit=4
Planning time: 0.125 ms
Execution time: 50.758 ms
(15 rows)
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl_label where c1 between 1 and 100 and c6=100;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on public.tbl_label (cost=22.50..24.02 rows=1 width=80) (actual time=36.193..36.193 rows=0 loops=1)
Output: uid, c1, c2, c3, c4, c5, c6
Recheck Cond: ((tbl_label.c1 >= 1) AND (tbl_label.c1 <= 100) AND (tbl_label.c6 = 100))
Buffers: shared hit=763
-> Bitmap Index Scan on idx_tbl_label_1 (cost=0.00..22.50 rows=1 width=0) (actual time=36.190..36.190 rows=0 loops=1)
Index Cond: ((tbl_label.c1 >= 1) AND (tbl_label.c1 <= 100) AND (tbl_label.c6 = 100))
Buffers: shared hit=763
Planning time: 0.115 ms
Execution time: 36.226 ms
(9 rows) So let’s create btree indexes for the other columns. The n_distinct values of the other columns indicate that the values in these columns are basically unique, so we are going to create btree indexes, which allows us to precisely locate the value we need.
Create brin indexes for columns in good linear correlation. Principles and selection of indexes will be detailed later.
postgres=# create index idx_tbl_label2 on tbl_label using btree(c2);
CREATE INDEX
Time: 1388.756 ms (00:01.389)
postgres=# create index idx_tbl_label3 on tbl_label using btree(c3);
CREATE INDEX
Time: 1028.865 ms (00:01.029) Queries on combinations of multiple columns are very efficient.
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl_label where c1 between 1 and 100 and c6=100 and c2='abc';
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------
Index Scan using idx_tbl_label2 on public.tbl_label (cost=0.42..3.45 rows=1 width=80) (actual time=0.032..0.032 rows=0 loops=1)
Output: uid, c1, c2, c3, c4, c5, c6
Index Cond: (tbl_label.c2 = 'abc'::text)
Filter: ((tbl_label.c1 >= 1) AND (tbl_label.c1 <= 100) AND (tbl_label.c6 = 100))
Buffers: shared read=3
Planning time: 0.248 ms
Execution time: 0.056 ms
(7 rows) The performance of queries on arbitrary conditions can be significantly improved by filtering data with bitmapAnd and bitmapOr and by using multiple indexes Operational principles are as follows:
How do I select an index? This will be detailed later.
I used GIN multi-column compound indexes in the above example, but there is actually another way around the issue. We can convert multiple columns into an array, and create array indexes (PostgreSQL expression indexes)
1.How can multiple columns be converted into an array?
postgres=# create or replace function to_array(VARIADIC anyarray) returns anyarray as
$$
select $1;
$$
language sql strict immutable;
CREATE FUNCTION
Example
postgres=# select to_array('a'::text,'b','c');
to_array
----------
{a,b,c}
(1 row)
postgres=# select to_array(now(),clock_timestamp());
to_array
-------------------------------------------------------------------
{"2017-06-12 17:50:47.992274+08","2017-06-12 17:50:47.992489+08"}
(1 row)
postgres=# select to_array(1,2,3);
to_array
----------
{1,2,3}
(1 row) 2.Array expression indexes
Example
create index idx_tbl_label_combin on tbl_label using gin (to_array(c1,c6));
When we have a set of different types of columns, we can convert them into a single uniform type and then create expression indexes. To convert the columns, we may need to use the immutable function. If you don't have the immutable function, you can easily create one.
postgres=# create index idx_tbl_label_combin1 on tbl_label using gin (to_array('c1:'||c1,'c6:'||c6)); 3.How to hit array expression indexes?
When the query condition is consistent with the expression of the index, it is hit.
Example
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl_label where to_array(c1,c6) && array[1,2];
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on public.tbl_label (cost=840.56..86397.30 rows=99750 width=80) (actual time=0.777..4.030 rows=2254 loops=1)
Output: uid, c1, c2, c3, c4, c5, c6
Recheck Cond: (ARRAY[tbl_label.c1, tbl_label.c6] && '{1,2}'::integer[])
Heap Blocks: exact=2242
Buffers: shared hit=2251
-> Bitmap Index Scan on idx_tbl_label_combin (cost=0.00..815.62 rows=99750 width=0) (actual time=0.465..0.465 rows=2254 loops=1)
Index Cond: (ARRAY[tbl_label.c1, tbl_label.c6] && '{1,2}'::integer[])
Buffers: shared hit=9
Planning time: 0.361 ms
Execution time: 4.176 ms
(10 rows)
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl_label where to_array('c1:'||c1,'c6:'||c6) && array['c1:1'];
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on public.tbl_label (cost=422.00..54015.43 rows=50000 width=80) (actual time=0.331..1.888 rows=1021 loops=1)
Output: uid, c1, c2, c3, c4, c5, c6
Recheck Cond: (ARRAY[('c1:'::text || (tbl_label.c1)::text), ('c6:'::text || (tbl_label.c6)::text)] && '{c1:1}'::text[])
Heap Blocks: exact=1019
Buffers: shared hit=1024
-> Bitmap Index Scan on idx_tbl_label_combin1 (cost=0.00..409.50 rows=50000 width=0) (actual time=0.195..0.195 rows=1021 loops=1)
Index Cond: (ARRAY[('c1:'::text || (tbl_label.c1)::text), ('c6:'::text || (tbl_label.c6)::text)] && '{c1:1}'::text[])
Buffers: shared hit=5
Planning time: 0.173 ms
Execution time: 1.972 ms
(10 rows) 1.When to choose btree?
B-tree indexes are suitable for columns with better selection properties (value of n_distinct is very big, or =-1), or there is a high proportion of unique values.
2.When to choose gin?
Contrary to btree indexes, GIN indexes are more efficient if the columns have poor selection properties.
In addition, as GIN indexes are inverted indexes, they are very appropriate for queries on combinations of multi-value elements, e.g. array, full-text search, token, etc.
As interfaces for GIN indexes are open, you can customize your GIN indexes according to the individual characteristics of your data. It supports more data types, e.g.similarity between images, text similarity, etc.
3.When to choose gist?
GIST is a generalized index interface belonging to PostgreSQL. It is appropriate for various data types, particularly heterogeneous types, e.g. geometry index, spatial index, range index, etc.
Refer to the following for the principles of GIST indexes:
4.When to choose hash?
Hash indexes are useful if you are only dealing with equality queries, and the length of the indexed columns is very long, more than 1/3 of the database block length. PostgreSQL 10 hash indexes will generate WAL. To ensure reliability, it also supports stream replication.
Hash indexes are not recommended for PostgreSQL versions earlier than version 10, as rebuilding is required after a crash and prior versions do not support stream replication.
5.When to choose brin?
BRIN indexes can be used when the data and stack storage are linearly correlated.
A BRIN index is a block range index. It stores the atomic information (max value, min value, average value, null ratio, COUNT, etc.) of each data block (or consecutive data blocks).
It is particularly suitable for range scans.
1.btree
Supports sorting, “>=”, “<=”, “=”, “in”, “>”, “<” and other queries.
2.HASH
Supports “=” queries.
3.GIN
Supports different query demands based on different data types.
For example, if the data is made up of arrays intersection and "Contains" queries are most appropriate.
4.GIST
Supports different query demands based on different data types.
For example, if the data is spatial, appropriate query types include distance, KNN, "Contains", intersection, Left, and Right.
5.BRIN
Supports range queries, and “=” queries
The above explains how to select an index type, but does not mention how to optimize your indexes. In reality, the efficiency of an index is heavily reliant on data distribution.
For example
Therefore, the efficiency of our index queries can be greatly improved if our data is redistributed according to the characteristics of index scans.
For example, bitmap index scan (read in the order of Block Ids) can be used in PostgreSQL to reduce discrete IO.
1.btree data distribution optimization
Better linear correlation makes scanning and returning multiple data records more efficient.
2.Optimizing hash data distribution
Better linear correlation makes scanning and returning multiple data records more efficient.
3.Optimizing gin data distribution
For normal data, better linear correlation makes scanning and returning multiple data records more efficient.
For multi-value data (e.g. array, full-text searches, TOKENs), queries are more efficient the more the elements are concentrated (e.g. in element concentration analysis, where the x axis is the row number and the y axis is the element value, the more the data is concentrated).
It is usually not easy to achieve element concentration, but we have several methods to aggregate data. 1. Sort and restructure data based on the frequency of the elements. When users search frequently occurring terms, fewer blocks need to be searched and the IO enlargement is reduced. 2. Sort based on the value (of the count of searched elements x the hit number), and aggregate data in a descending order from the element that ranks first.
(The above methods might be a bit difficult to understand. I will publish an article that specifically addresses data restructuring optimization of GIN later.)
4.Optimizing gist data distribution
For normal data, better linear correlation makes scanning and returning multiple data records more efficient.
For spatial data, queries are more efficient the more the elements are concentrated (e.g. when data is consecutively distributed by geohash).
5.Optimizing brin data distribution
Better linear correlation makes scanning and returning multiple data records more efficient.
6.Optimizing data distribution for multi-column compound indexes
For multi-column compound indexes, the efficiency is dependent on the index type, and the above requirements are applicable.
One more condition: the better the linear correlation between multiple columns, the higher the efficiency.
To learn how to calculate linear correlation between multiple columns, see:
Use PostgreSQL to calculate the linear correlation between fields of any type
Data distribution has another benefit in that it significantly increases the compression ratio for column storage.
Application of PostgreSQL GIN single column concentration index
PostgreSQL - GIN index principles
PostgreSQL 9.3 pg_trgm improve support multi-bytes char and gist,gin index for reg-exp search
Drowning in Big Data? Start Getting Real Value Before It’s Too Late
How to Use HBase Client to Access Alibaba Cloud NoSQL Table Store
2,605 posts | 747 followers
FollowAlibaba Clouder - December 11, 2017
digoal - June 26, 2019
ApsaraDB - October 20, 2020
digoal - December 11, 2019
digoal - May 17, 2021
digoal - December 23, 2020
2,605 posts | 747 followers
Follow
AnalyticDB for PostgreSQL
An online MPP warehousing service based on the Greenplum Database open source program
Learn More
PolarDB for PostgreSQL
Alibaba Cloud PolarDB for PostgreSQL is an in-house relational database service 100% compatible with PostgreSQL and highly compatible with the Oracle syntax.
Learn More
Database Overview
ApsaraDB: Faster, Stronger, More Secure
Learn More
ApsaraDB RDS for PostgreSQL
An on-demand database hosting service for PostgreSQL with automated monitoring, backup and disaster recovery capabilities
Learn MoreMore Posts by Alibaba Clouder