3/30/2023 0 Comments Postgres ilike index![]() Using GIN indexes for pattern matchingįortunately the pg_trgm extensions offers a second operator class to get the job done. It takes ages to create, it is large, it is a lot slower than a sequential scan. In short: A Gist index might not be the right thing to use here. (actual time=13750.850.99070.510 rows=1 loops=1)Īctually the query is still going to show horrible execution times. Index Scan using idx_gist on t_hash (cost=8.04 rows=5000 width=37) Test=# explain analyze SELECT * FROM t_hash WHERE md5 LIKE '%e2345679a%' The PostgreSQL optimizer has decided to go for a “Bitmap Index Scan”. Why is that the case? Let us take a look what the execution plan has to say:īitmap Heap Scan on t_hash (cost=42.90 rows=5000 width=37) We have really “optimized” the query? Instead of 4.7 seconds PostgreSQL needs almost 2 minutes to do the job. Keep in mind the table is just 3.5 GB – the index is 2.5 times larges.īut, indexes will always make things faster, right? Well, actually no … Public | idx_gist | index | hs | t_hash | 8782 MB | Schema | Name | Type | Owner | Table | Size | Description What is also noteworthy is that the index is really large: Even with 4 GB of maintenance_work_mem the process will take 40 minutes. What is important to mention is that even higher maintenance_work_mem settings will NOT speed up the process. What you can already see is that the index needs quite some time to build. Test=# CREATE INDEX idx_gist ON t_hash USING gist (md5 gist_trgm_ops) Here is how this type of index can be deployed: What many people do to speed up fuzzy searching in PostgreSQL is to use Gist indexes. Gist: Deploying a trigram indexes with Gist To index LIKE the pg_trgm module supports two PostgreSQL index types: Gist and GIN. All these tokens will show up in the index as you will see later on. What you can observe is that a trigram is like a sliding 3 character window. The natural question arising now is: What is a trigram? Let us take a look and see: The extension is part of the PostgreSQL contrib package and should therefore be present on the vast majority of systems:Īs you can see enabling the extension is easy. The pg_trgm extension implements “trigrams” which is a way to help with fuzzy search. So what can we do to solve this problem? pg_trgm: Advanced indexingįortunately PostgreSQL offers a module which can do a lot of trickery in the area of pattern matching. Reading 3.2 GB to fetch just a single is now to efficient at all. Schema | Name | Type | Owner | Size | Description The reason for bad performance is that the table is actually quite large, and the database has to read it from the beginning to the end to process the request: But: It also means that we are readily sacrificing two CPU cores to answer this query returning just a single row. ![]() That is basically a good thing because the execution time is cut in half. > Parallel Seq Scan on t_hash (cost=83.88 rows=2083 width=37)īecause of the size of the table the PostgreSQL query optimizer will go for a parallel query. Test=# explain SELECT * FROM t_hash WHERE md5 LIKE '%e2345679a%' To see what is going on under the hood I decided to include the execution plan of the SQL statement: The user experience is already going to suffer and there is a good chance that a long running query like that will already increase the load on your server quite substantially. In 90+% of all applications out there this is already way too long. On my iMac, the query takes 4.7 seconds to complete. ![]() Test=# SELECT * FROM t_hash WHERE md5 LIKE '%e2345679a%' Mind that the percent symbol is not just at the end but also at the beginning of the pattern: Let us turn our attention to LIKE: The Following query selects a substring which exists in the data only once. Running simple LIKE queries in PostgreSQL The following listing shows what the data looks like in general: What we got here are 50 million ids and their hashes. A simple md5 hash is more than sufficient to prove my point here. To avoid searching the web for sample data I decided to generate some data. These index types are not equally efficient, so it makes sense to dig into the subject matter and figure out what is best when.īefore we get started I have created some sample data. Both index type can handle LIKE as well as ILIKE. In this blog post you will learn mostly about Gist and GIN indexing. What can PostgreSQL do to speed up those operations and what can be done in general to first understand the problem and secondly to achieve better PostgreSQL database performance. People use those things all over the place in their application and therefore it makes sense to approach the topic from a performance point of view. LIKE and ILIKE are two fundamental SQL features.
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