speeding up wildcard text lookups - postgresql

I have a simple table in Postgres with a bit over 8 million rows. The column of interest holds short text strings, typically one or more words total length less than 100 characters. It is set as 'character varying (100)'. The column is indexed. A simple look up like below takes > 3000 ms.
SELECT a, b, c FROM t WHERE a LIKE '?%'
Yes, for now, the need is to simply find the rows where "a" starts with the entered text. I want to bring the speed of look up down to under 100 ms (the appearance of instantaneous). Suggestions? Seems to me that full text search won't help here as my column of text is too short, but I would be happy to try that if worthwhile.
Oh, btw I also loaded the exact same data in mongodb and indexed column "a". Loading the data in mongodb was amazingly quick (mongodb++). Both mongodb and Postgres are pretty much instantaneous when doing exact lookups. But, Postgres actually shines when doing trailing wildcard searches as above, consistently taking about 1/3 as long as mongodb. I would be happy to pursue mongodb if I could speed that up as this is only a readonly operation.
Update: First, a couple of EXPLAIN ANALYZE outputs
EXPLAIN ANALYZE SELECT a, b, c FROM t WHERE a LIKE 'abcd%'
"Seq Scan on t (cost=0.00..282075.55 rows=802 width=40)
(actual time=1220.132..1220.132 rows=0 loops=1)"
" Filter: ((a)::text ~~ 'abcd%'::text)"
"Total runtime: 1220.153 ms"
I actually want to compare Lower(a) with the search term which is always at least 4 characters long, so
EXPLAIN ANALYZE SELECT a, b, c FROM t WHERE Lower(a) LIKE 'abcd%'
"Seq Scan on t (cost=0.00..302680.04 rows=40612 width=40)
(actual time=4.681..3321.387 rows=788 loops=1)"
" Filter: (lower((a)::text) ~~ 'abcd%'::text)"
"Total runtime: 3321.504 ms"
So I created an index
CREATE INDEX idx_t ON t USING btree (Lower(Substring(a, 1, 4) ));
"Seq Scan on t (cost=0.00..302680.04 rows=40612 width=40)
(actual time=3243.841..3243.841 rows=0 loops=1)"
" Filter: (lower((a)::text) = 'abcd%'::text)"
"Total runtime: 3243.860 ms"
Seems the only time an index is being used is when I am looking for an exact match
EXPLAIN ANALYZE SELECT a, b, c FROM t WHERE a = 'abcd'
"Index Scan using idx_t on geonames (cost=0.00..57.89 rows=13 width=40)
(actual time=40.831..40.923 rows=17 loops=1)"
" Index Cond: ((ascii_name)::text = 'Abcd'::text)"
"Total runtime: 40.940 ms"
Found a solution by implementing an index with varchar_pattern_ops, and am now looking for an even quicker lookups.

The PostgreSQL query planner is smart, but not an AI. To make it use an index on an expression use the exact same form of expression in the query.
With an index like this:
CREATE INDEX t_a_lower_idx ON t (lower(substring(a, 1, 4)));
Or simpler in PostgreSQL 9.1:
CREATE INDEX t_a_lower_idx ON t (lower(left(a, 4)));
Use this query:
SELECT * FROM t WHERE lower(left(a, 4)) = 'abcd';
Which is 100% functionally equivalent to:
SELECT * FROM t WHERE lower(a) LIKE 'abcd%'
Or:
SELECT * FROM t WHERE a ILIKE 'abcd%'
But not:
SELECT * FROM t WHERE a LIKE 'abcd%'
This is a functionally different query and you need a different index:
CREATE INDEX t_a_idx ON t (substring(a, 1, 4));
Or simpler with PostgreSQL 9.1:
CREATE INDEX t_a_idx ON t (left(a, 4));
And use this query:
SELECT * FROM t WHERE left(a, 4) = 'abcd';
Left anchored search terms of variable length
Case insensitive. Index:
Edit: Almost forgot: If you run your db with any other locale than the default 'C', you need to specify the operator class explicitly - text_pattern_ops in my example:
CREATE INDEX t_a_lower_idx
ON t (lower(left(a, <insert_max_length>)) text_pattern_ops);
Query:
SELECT * FROM t WHERE lower(left(a, <insert_max_length>)) ~~ 'abcdef%';
Can utilize the index and is almost as fast as the variant with a fixed length.
You may be interested in this post on dba.SE with more details about pattern matching, especially the last part about the operators ~>=~ and ~<~.

It is clearly documented that a regular expression search does not use any indexes for a variety of implementation. The only possible way for using indexes with regular expressions is limited to a prefix search like a*.

Related

High RDS CPU Utilization By Select Query

I am using a query for finding my results. My table has almost 2000000 records.
Each time when query is executing it's taking high CPU.
Can anybody help me to get out of this?
I am using below query as:
select * from demo.document_details
where udid ilike '06AAACT2727Q1Z0:HR1000249801:%'
and active='Y'
and ewb_no=''
Query Plan is :
Seq Scan on document_details (cost=0.00..469135.94 rows=86 width=1132) (actual time=1711.248..2116.794 rows=1 loops=1)
Filter: (((udid)::text ~~ '06AAACT2727Q1Z0:HR1000249801:%'::text) AND ((active)::text = 'Y'::text) AND ((ewb_no)::text = ''::text))
Rows Removed by Filter: 2047478
Planning time: 0.348 ms
Execution time: 2116.870 ms
You need an index. If your collation is not 'C', then then index should specify "text_pattern_ops", so that it can support the prefix matching.
create index on demo.document_details (udid text_pattern_ops);
You might also include "active" and "ewb_no" in the index, depending on how selective those columns are, and whether most of your queries which search on "udid" also include those. If you do include them, you should include them in the index before "udid", because using the LIKE for a prefix is basically a range criterion, and once you have a range criterion on an indexed column, it greatly impairs the utility of columns occuring after it in the index definition. (Imagine the difference between looking in phone book for "Joh%, James" and "Johnson, J%").

Why doesn't postresql use all columns in a multi-column index?

I am using the extension
CREATE EXTENSION btree_gin;
I have an index that looks like this...
create index boundaries2 on rets USING GIN(source, isonlastsync, status, (geoinfo::jsonb->'boundaries'), ctcvalidto, searchablePrice, ctcSortOrder);
before I started messing with it, the index looked like this, with the same results that I'm about to share, so it seems minor variations in the index definition don't make a difference:
create index boundaries on rets USING GIN((geoinfo::jsonb->'boundaries'), source, status, isonlastsync, ctcvalidto, searchablePrice, ctcSortOrder);
I give pgsql 11 this query:
explain analyze select id from rets where ((geoinfo::jsonb->'boundaries' ?| array['High School: Torrey Pines']) AND source='SDMLS'
AND searchablePrice>=800000 AND searchablePrice<=1200000 AND YrBlt>=2000 AND EstSF>=2300
AND Beds>=3 AND FB>=2 AND ctcSortOrder>'2019-07-05 16:02:54 UTC' AND Status IN ('ACTIVE','BACK ON MARKET')
AND ctcvalidto='9999-12-31 23:59:59 UTC' AND isonlastsync='true') order by LstDate desc, ctcSortOrder desc LIMIT 3000;
with result...
Limit (cost=120.06..120.06 rows=1 width=23) (actual time=472.849..472.850 rows=1 loops=1)
-> Sort (cost=120.06..120.06 rows=1 width=23) (actual time=472.847..472.848 rows=1 loops=1)
Sort Key: lstdate DESC, ctcsortorder DESC
Sort Method: quicksort Memory: 25kB
-> Bitmap Heap Scan on rets (cost=116.00..120.05 rows=1 width=23) (actual time=472.748..472.841 rows=1 loops=1)
Recheck Cond: ((source = 'SDMLS'::text) AND (((geoinfo)::jsonb -> 'boundaries'::text) ?| '{"High School: Torrey Pines"}'::text[]) AND (ctcvalidto = '9999-12-31 23:59:59+00'::timestamp with time zone) AND (searchableprice >= 800000) AND (searchableprice <= 1200000) AND (ctcsortorder > '2019-07-05 16:02:54+00'::timestamp with time zone))
Rows Removed by Index Recheck: 93
Filter: (isonlastsync AND (yrblt >= 2000) AND (estsf >= 2300) AND (beds >= 3) AND (fb >= 2) AND (status = ANY ('{ACTIVE,"BACK ON MARKET"}'::text[])))
Rows Removed by Filter: 10
Heap Blocks: exact=102
-> Bitmap Index Scan on boundaries2 (cost=0.00..116.00 rows=1 width=0) (actual time=471.762..471.762 rows=104 loops=1)
Index Cond: ((source = 'SDMLS'::text) AND (((geoinfo)::jsonb -> 'boundaries'::text) ?| '{"High School: Torrey Pines"}'::text[]) AND (ctcvalidto = '9999-12-31 23:59:59+00'::timestamp with time zone) AND (searchableprice >= 800000) AND (searchableprice <= 1200000) AND (ctcsortorder > '2019-07-05 16:02:54+00'::timestamp with time zone))
Planning Time: 0.333 ms
Execution Time: 474.311 ms
(14 rows)
The Question
Why are the columns status and isonlastsync not used by the Bitmap Index Scan on boundaries2?
It can do so if it predicts that filtering out those columns will be faster. This is usually the case if cardinality on columns is very low and you will fetch large enough portion of all rows; this is true for boolean like isonlastsync and usually true for status columns with just a few distinct values.
Rows Removed by Filter: 10 this is very little to filter out, either because your table does not hold large number of rows or most of them fit into condition you specified for those two columns. You might try generating more data in that table or selecting rows with rare status.
I suggest doing partial indexes (with WHERE condition), at least for boolean value and remove those two columns to make this index a bit more lightweight.
I cannot tell you why, but I can help you optimize the query.
You should not use a multi-column GIN index, but a GIN index on only the jsonb expression and a b-tree index on the other columns.
The order of columns matters: put the oned used in an equality condition first, with the most selective in the beginning. Next put the column with the must selective inequality or IN conditions. For the following columns, the order doesn't matter, as they will only act as filters in the index scan.
Make sure that the indexes are cached in RAM.
I'd expect you to be faster that way.
I think you're asking yourself the wrong question. As Lukasz answered already, PostgreSQL may find inefficient to check all columns in the index. The problem here is that your index is too big on disk.
Probably by trying to make this SQL faster you added as many columns as possible to the index, but it is backfiring.
The trick is to realize how much data PostgreSQL has to read to find your records. If your index contains too much data, it will have to read a lot. Also, be aware that low cardinality columns don't play well with BTree and common index types; generally you want to avoid indexing them.
To have an index as small as possible and it's quick to do lookups you have to find the column with more cardinality, or better, the column that will return less rows for your query. My guess is "ctcSortOrder". This will be the first column of your index.
Now look, after filtering by the 1st column, which column has now the most cardinality or will filter out most rows. I have no idea on your data, but "source" looks like a good candidate.
Try to avoid jsonb searches unless they are the primary source of the cardinality, and keep the index as a Btree. BTree is several times faster.
And like Lukasz suggested, look on partial indexes. For example add "WHERE Status IN ('ACTIVE','BACK ON MARKET') AND isonlastsync='true'" as these two may be common for all your searches.
Bottom line is, having a simpler, smaller index is faster than having all columns indexed. And the order of the columns does matter a lot. Stick with BTree unless there is a good reason (lots of cardinality in non-btree compatible types).
If your table is huge (>10M rows) consider table partitioning, for example by ctcSortOrder. But I don't think this is your case.

How PostgreSQL execute query?

Can anyone explain why PostgreSQL works so:
If I execute this query
SELECT
*
FROM project_archive_doc as PAD, project_archive_doc as PAD2
WHERE
PAD.id = PAD2.id
it will be simple JOIN and EXPLAIN will looks like this:
Hash Join (cost=6.85..13.91 rows=171 width=150)
Hash Cond: (pad.id = pad2.id)
-> Seq Scan on project_archive_doc pad (cost=0.00..4.71 rows=171 width=75)
-> Hash (cost=4.71..4.71 rows=171 width=75)
-> Seq Scan on project_archive_doc pad2 (cost=0.00..4.71 rows=171 width=75)
But if I will execute this query:
SELECT *
FROM project_archive_doc as PAD
WHERE
PAD.id = (
SELECT PAD2.id
FROM project_archive_doc as PAD2
WHERE
PAD2.project_id = PAD.project_id
ORDER BY PAD2.created_at
LIMIT 1)
there will be no joins and EXPLAIN looks like:
Seq Scan on project_archive_doc pad (cost=0.00..886.22 rows=1 width=75)"
Filter: (id = (SubPlan 1))
SubPlan 1
-> Limit (cost=5.15..5.15 rows=1 width=8)
-> Sort (cost=5.15..5.15 rows=1 width=8)
Sort Key: pad2.created_at
-> Seq Scan on project_archive_doc pad2 (cost=0.00..5.14 rows=1 width=8)
Filter: (project_id = pad.project_id)
Why it is so and is there any documentation or articles about this?
Without table definitions and data it's hard to be specific for this case. In general, PostgreSQL is like most SQL databases in that it doesn't treat SQL as a step-by-step program for how to execute a query. It's more like a description of what you want the results to be and a hint about how you want the database to produce those results.
PostgreSQL is free to actually execute the query however it can most efficiently do so, so long as it produces the results you want.
Often it has several choices about how to produce a particular result. It will choose between them based on cost estimates.
It can also "understand" that several different ways of writing a particular query are equivalent, and transform one into another where it's more efficient. For example, it can transform an IN (SELECT ...) into a join, because it can prove they're equivalent.
However, sometimes apparently small changes to a query fundamentally change its meaning, and limit what optimisations/transformations PostgreSQL can make. Adding a LIMIT or OFFSET inside a subquery prevents PostgreSQL from flattening it, i.e. combining it with the outer query by tranforming it into a join. It also prevents PostgreSQL from moving WHERE clause entries between the subquery and outer query, because that'd change the meaning of the query. Without a LIMIT or OFFSET clause, it can do both these things because they don't change the query's meaning.
There's some info on the planner here.

PostgreSQL index not used for query on IP ranges

I'm using PostgreSQL 9.2 and have a table of IP ranges. Here's the SQL:
CREATE TABLE ips (
id serial NOT NULL,
begin_ip_num bigint,
end_ip_num bigint,
country_name character varying(255),
CONSTRAINT ips_pkey PRIMARY KEY (id )
)
I've added plain B-tree indices on both begin_ip_num and end_ip_num:
CREATE INDEX index_ips_on_begin_ip_num ON ips (begin_ip_num);
CREATE INDEX index_ips_on_end_ip_num ON ips (end_ip_num );
The query being used is:
SELECT ips.* FROM ips
WHERE 3065106743 BETWEEN begin_ip_num AND end_ip_num;
The problem is that my BETWEEN query is only using the index on begin_ip_num. After using the index, it filters the result using end_ip_num. Here's the EXPLAIN ANALYZE result:
Index Scan using index_ips_on_begin_ip_num on ips (cost=0.00..2173.83 rows=27136 width=76) (actual time=16.349..16.350 rows=1 loops=1)
Index Cond: (3065106743::bigint >= begin_ip_num)
Filter: (3065106743::bigint <= end_ip_num)
Rows Removed by Filter: 47596
Total runtime: 16.425 ms
I've already tried various combinations of indices including adding a composite index on both begin_ip_num and end_ip_num.
Try a multicolumn index, but with reversed order on the second column:
CREATE INDEX index_ips_begin_end_ip_num ON ips (begin_ip_num, end_ip_num DESC);
Ordering is mostly irrelevant for a single-column index, since it can be scanned backwards almost as fast. But it is important for multicolumn indexes.
With the index I propose, Postgres can scan the first column and find the address, where the rest of the index fulfills the first condition. Then it can, for each value of the first column, return all rows that fulfill the second condition, until the first one fails. Then jump to the next value of the first column, etc.
This is still not very efficient and Postgres may be faster just scanning the first index column and filtering for the second. Very much depends on your data distribution.
Either way, CLUSTER using the multicolumn index from above can help performance:
CLUSTER ips USING index_ips_begin_end_ip_num
This way, candidates fulfilling your first condition are packed onto the same or adjacent data pages. Can help performance a lot with if you have lots of rows per value of the first column. Else it is hardly effective.
(There are also non-blocking external tools for the purpose: pg_repack or pg_squeeze.)
Also, is autovacuum running and configured properly or have you run ANALYZE on the table? You need current statistics for Postgres to pick appropriate query plans.
What would really help here is a GiST index for a int8range column, available since PostgreSQL 9.2. See:
Optimizing queries on a range of timestamps (two columns)
If your IP ranges can be covered with one of the built-in network types inet or cidr, consider to replace your two bigint columns. Or, better yet, look to the additional module ip4r by Andrew Gierth (not in the standard distribution. The indexing strategy changes accordingly.
Barring that, you can check out this related answer on dba.SE with using a sophisticated regime with partial indexes. Advanced stuff, but it delivers great performance:
Can spatial index help a “range - order by - limit” query
I had exactly this same problem on a nearly identical dataset from maxmind.com's free geiop table. I solved it using Erwin's tip about range types and GiST indexes. The GiST index was key. Without it I was querying at best about 3 rows per second. With it I queried nearly 500000 rows in under 10 seconds! Since Erwin didn't post detailed instructions on how to do this, I thought I'd add them, here...
First of all, you must add a new column having the range type, note that int8range is required for bigint types. Next set its values appropriately, note that the '[]' parameter indicates to make the range inclusive at lower and upper bounds (rtfm). Finally add the index, note that the GiST index is where all the performance advantage comes from.
alter table ips add column iprange int8range;
update ips set iprange=int8range(begin_ip_num, end_ip_num, '[]');
create index index_ips_on_iprange on ips using gist (iprange);
Having laid the groundwork, you can now use the '<#' contained-by operator to search specific addresses against the table. See http://www.postgresql.org/docs/9.2/static/functions-range.html
SELECT "ips".* FROM "ips" WHERE (3065106743::bigint <# iprange);
I'm a bit late to this party, but this is what works really well for me.
Consider installing ip4r extension. It basically allows you to define a column that can hold IP ranges. The name of the extension implies it is just for IPv4, but currently it is also support IPv6.
After you populate table with ranges within that column all you need, is to create GIST index:
CREATE INDEX ip_zip_ip4_range ON ip_zip USING gist (ip4_range);
I have almost 10 million ranges in my database, but queries take fraction of a milisecond:
region=> select count(*) from ip_zip ;
count
---------
9566133
region=> explain analyze select * from ip_zip where '8.8.8.8'::ip4 <<= ip4_range;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on ip_zip (cost=234.55..25681.29 rows=9566 width=22) (actual time=0.085..0.086 rows=1 loops=1)
Recheck Cond: ('8.8.8.8'::ip4r <<= ip4_range)
Heap Blocks: exact=1
-> Bitmap Index Scan on ip_zip_ip4_range (cost=0.00..232.16 rows=9566 width=0) (actual time=0.055..0.055 rows=1 loops=1)
Index Cond: ('8.8.8.8'::ip4r <<= ip4_range)
Planning time: 0.106 ms
Execution time: 0.118 ms
(7 rows)
region=> explain analyze select * from ip_zip where '254.50.22.54'::ip4 <<= ip4_range;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on ip_zip (cost=234.55..25681.29 rows=9566 width=22) (actual time=0.059..0.059 rows=1 loops=1)
Recheck Cond: ('254.50.22.54'::ip4r <<= ip4_range)
Heap Blocks: exact=1
-> Bitmap Index Scan on ip_zip_ip4_range (cost=0.00..232.16 rows=9566 width=0) (actual time=0.048..0.048 rows=1 loops=1)
Index Cond: ('254.50.22.54'::ip4r <<= ip4_range)
Planning time: 0.102 ms
Execution time: 0.145 ms
(7 rows)
I believe your query looks like WHERE [constant] BETWEEN begin_ip_num AND end_ipnum or
As far as I know Postgres doesn't have "AND-EQUAL " access plan, so you need to add a composite index on 2 columns as suggested by Erwin Brandstetter.

Postgresql index on xpath expression gives no speed up

We are trying to create OEBS-analog functionality in Postgresql. Let's say we have a form constructor and need to store form results in database (e.g. email bodies). In Oracle you could use a table with 150~ columns (and some mapping stored elsewhere) to store each field in separate column. But in contrast to Oracle we would like to store all the form in postgresql xml field.
The example of the tree is
<row xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<object_id>2</object_id>
<pack_form_id>23</pack_form_id>
<prod_form_id>34</prod_form_id>
</row>
We would like to search through this field.
Test table contains 400k rows and the following select executes in 90 seconds:
select *
from params
where (xpath('//prod_form_id/text()'::text, xmlvalue))[1]::text::int=34
So I created this index:
create index prod_form_idx
ON params using btree(
((xpath('//prod_form_id/text()'::text, xmlvalue))[1]::text::int)
);
And it made no difference. Still 90 seconds execution. EXPLAIN plan show this:
Bitmap Heap Scan on params (cost=40.29..6366.44 rows=2063 width=292)
Recheck Cond: ((((xpath('//prod_form_id/text()'::text, xmlvalue, '{}'::text[]))[1])::text)::integer = 34)
-> Bitmap Index Scan on prod_form_idx (cost=0.00..39.78 rows=2063 width=0)
Index Cond: ((((xpath('//prod_form_id/text()'::text, xmlvalue, '{}'::text[]))[1])::text)::integer = 34)
I am not the great plan interpreter so I suppose this means that index is being used. The question is: where's all the speed? And what can i do in order to optimize this kind of queries?
Well, at least the index is used. You get a bitmap index scan instead of a normal index scan though, which means the xpath() function will be called lots of times.
Let's do a little check :
CREATE TABLE foo ( id serial primary key, x xml, h hstore );
insert into foo (x,h) select XMLPARSE( CONTENT '<row xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<object_id>2</object_id>
<pack_form_id>' || n || '</pack_form_id>
<prod_form_id>34</prod_form_id>
</row>' ),
('object_id=>2,prod_form_id=>34,pack_form_id=>'||n)::hstore
FROM generate_series( 1,100000 ) n;
test=> EXPLAIN ANALYZE SELECT count(*) FROM foo;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------
Aggregate (cost=4821.00..4821.01 rows=1 width=0) (actual time=24.694..24.694 rows=1 loops=1)
-> Seq Scan on foo (cost=0.00..4571.00 rows=100000 width=0) (actual time=0.006..13.996 rows=100000 loops=1)
Total runtime: 24.730 ms
test=> explain analyze select * from foo where (h->'pack_form_id')='123';
QUERY PLAN
----------------------------------------------------------------------------------------------------
Seq Scan on foo (cost=0.00..5571.00 rows=500 width=68) (actual time=0.075..48.763 rows=1 loops=1)
Filter: ((h -> 'pack_form_id'::text) = '123'::text)
Total runtime: 36.808 ms
test=> explain analyze select * from foo where ((xpath('//pack_form_id/text()'::text, x))[1]::text) = '123';
QUERY PLAN
------------------------------------------------------------------------------------------------------
Seq Scan on foo (cost=0.00..5071.00 rows=500 width=68) (actual time=4.271..3368.838 rows=1 loops=1)
Filter: (((xpath('//pack_form_id/text()'::text, x, '{}'::text[]))[1])::text = '123'::text)
Total runtime: 3368.865 ms
As we can see,
scanning the whole table with count(*) takes 25 ms
extracting one key/value from a hstore adds a small extra cost, about 0.12 µs/row
extracting one key/value from a xml using xpath adds a huge cost, about 33 µs/row
Conclusions :
xml is slow (but everyone knows that)
if you want to put a flexible key/value store in a column, use hstore
Also since your xml data is pretty big it will be toasted (compressed and stored out of the main table). This makes the rows in the main table much smaller, hence more rows per page, which reduces the efficiency of bitmap scans since all rows on a page have to be rechecked.
You can fix this though. For some reason the xpath() function (which is very slow, since it handles xml) has the same cost (1 unit) as say, the integer operator "+"...
update pg_proc set procost=1000 where proname='xpath';
You may need to tweak the cost value. When given the right info, the planner knows xpath is slow and will avoid a bitmap index scan, using an index scan instead, which doesn't need rechecking the condition for all rows on a page.
Note that this does not solve the row estimates problem. Since you can't ANALYZE the inside of the xml (or hstore) you get default estimates for the number of rows (here, 500). So, the planner may be completely wrong and choose a catastrophic plan if some joins are involved. The only solution to this is to use proper columns.