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Performance analysis of MySQL's FULLTEXT indexes and LIKE queries for full text search

When searching for text in a MySQL table, you have two choices:

  • The LIKE operator
  • FULLTEXT indexes (which currently only work on MyISAM tables, but will one day work on InnoDB tables. The workaround right now is to extract your search text to a separate MyISAM table, so your main table can remain InnoDB.)

I always wondered how those two methods would scale as the number of records increases. So I made an experiment.

The experiment

Here is the setting:

  • I created table of records that store searchable text in a TEXT column called search_text
  • The sample records in that table were generated texts of 50 random English words.
  • I used different vocabulary sizes for the generated texts (we will see that this has major impact on FULLTEXT performance).
  • I queried the table with a query of three random words from the same vocabulary.
  • I used sample sizes from 1,000 to 100,000 records, meaning 50,000 to 5,000,000 indexed words.
  • For each batch I measured the runtime of 50 queries, discarded the top 5 and bottom 5 measurements and averaged over the rest.

I compared two approaches:

  1. A LIKE query (WHERE search_text LIKE "%word1%" AND search_text LIKE "%word2%" AND search_text LIKE "%word3%")
  2. A boolean FULLTEXT query (WHERE MATCH(search_text) AGAINST ("+word1 +word2 +word3" IN BOOLEAN MODE))

The results

Horizontal axis is number of records, vertical axis is average time to process a query:


I took away several lessons from this:

LIKE is a lot more efficient than I thought
For a medium-sized data set of up to 10,000 records (500,000 words) or so, LIKE only takes a fraction of a second. This means when optimizing a typical Rails action, you should probably look further than the database. A view can easily take many times longer to render. So measure before blaming the database.
FULLTEXT performs better when your text has low redundancy
FULLTEXT performance differs by a factor of 78 between a vocabulary of 1,000 words and 100,000 words. I guess that larger vocabularies result in a very wide but shallow inverted index that can quickly determine if a query has matches or not. An educated person has a passive vocabulary of 15,000 to 20,000 words, so FULLTEXT should work well for natural language texts.
Both LIKE and FULLTEXT scale linearly
While the FULLTEXT approach was many times faster than the LIKE approach in my tests, both approaches seem to scale linearly with the number of records. For a typical web projects where you need to index well under 5 million words, FULLTEXT will be fast enough to serve your searches until the project reaches end-of-life. But if you expect your data to grow indefinitely, FULLTEXT can only postpone the scaling pain and you will eventually need to deal with it, probably using a non-Mysql solution.

By refactoring problematic code and creating automated tests, makandra can vastly improve the maintainability of your Rails application.

Author of this card:

Henning Koch
chart.png, compared.xls
About this deck:
We are makandra and do test-driven, agile Ruby on Rails software development.
License for source code
Posted by Henning Koch to makandropedia