How to search millions of record in SQL table faster?
Short of that, ensuring that you have an index on the column being matched with the pattern will help performance, but by the sounds of it, you’ve tried this and it didn’t help a great deal. Stop using LIKE statement. You could use fulltext search, but it will require MyISAM table and isn’t all that good solution.
Which is the most slow query in MySQL?
This user_match_ratings table contains over 220 million rows (9 gig data or almost 20 gig in indexes). Queries against this table routinely show up in slow.log (threshold > 2 seconds) and is the most frequently logged slow query in the system:
How to optimize queries for 25 + million rows?
I am only interested in 4 columns for my criteria and the result should output the count only, for all queries. columns needed: TABLE, FIELD, AFTER, DATE, and there is an index on each of DATE and TABLE. After creating a temp table with only the fields I need, it went down to a 1:40 minutes, which is still very bad.
Why is the speed of an index slowed down?
Indexes are slowed down whenever they have to go lookup (“bookmark lookup”) data that the index itself doesn’t contain.
How to fetch a record from 10 million records?
If your table contains 10 million records for an hour, then 5 min part contain about 1/12 or 800k records. If you have 1000 unique keys f.e. then you have about 1/1000 or 100k records with the same unique key. Therefore index should be defined with UniqueKey first then Timestamp: CREATE TABLE . . . . .
How long does it take to add index to 80 million records?
Table with 80 million records and adding an index takes more than 18 hours (or forever)! Now what? A short recap of what happened. I am working with 71 million records (not much compared to billions of records processed by others).
How to query 10 million records in MySQL?
For query you have to order index fields in range from producing the smallest result set to the broader condition. If your table contains 10 million records for an hour, then 5 min part contain about 1/12 or 800k records. If you have 1000 unique keys f.e. then you have about 1/1000 or 100k records with the same unique key.