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How are indexed lookups done in MySQL database?
So indexed lookups are done in two parts: First MySQL goes to an index and pulls from it the primary key values that it needs to find, then it does a second lookup on the primary key index to find where those values are. The net of this is that for very large tables (1-200 Million plus rows) indexing against tables is more restrictive.
How many rows can mysql query at a time?
In this way we’d process the data 10-100,000 rows at a time (Join against id’s 1-100,000 then 100,001-200,000, etc). This was significantly faster than joining against the entire table. Using indexes on very large tables that aren’t based on the primary key is also much more difficult.
Is it remotely plausible for MySQL reasonably perform queries on billions of?
Assuming I index everything properly (which is a topic for another question) and am not trying to shuffle hundreds of MiB across the network, is it remotely plausible for MySQL to handle this? The scan data will be coming from files in the XML-based mzML format.
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 big of a table do you need to index?
The net of this is that for very large tables (1-200 Million plus rows) indexing against tables is more restrictive. You need fewer, simpler indexes. And doing even simple select statements that are not directly on an index may never come back. Where clauses must hit indexes or forget about it. But all that being said, things did actually work.
How big is the largest MySQL database ever?
If you do absolutely need in-SQL access to individual datapoints ensure you reduce the size of each row to the bare minimum number of fields and the smallest datatype possible. The largest MySQL I’ve ever personally managed was ~100 million rows.