SQL Index to Optimize WHERE Query

Question

I have a Postgres table with several columns, one column is the datetime that the column was last updated. My query is to get all the updated rows between a start and end time. It is my understanding for this query to use WHERE in this query instead of BETWEEN. The basic query is as follows:

SELECT * FROM contact_tbl contact
WHERE contact."UpdateTime" >= '20150610' and contact."UpdateTime" < '20150618'

I am new at creating SQL queries, I believe this query is doing a full table scan. I would like to optimize it if possible. I have placed a Normal index on the UpdateTime column, which takes a long time to create, but with this index the query is faster. One thing I am not sure about is if have to keep recalculating this index if the table gets bigger/columns get changed. Also, I am considering a CLUSTERED index on the UpdateTime row, but I wanted to ask if there was a canonical way of optimizing this/if I was on the right track first

Answer 1

Placing an index on UpdateTime is correct. It will allow the index to be used instead of full table scans.

2 WHERE conditions like the above vs. using the BETWEEN keyword are the exact same: http://dev.mysql.com/doc/refman/5.7/en/comparison-operators.html#operator_between

BETWEEN is just "syntactical sugar" for those that like that syntax better.

Indexes allow for faster reads, but slow down writes (because like you mention, the new data has to be inserted into the index as well). The entire index does not need to be recalculated. Indexes are smart data structures, so the extra data can be added without a lot of extra work, but it does take some.

You're probably doing many more reads than writes, so using an index is a good idea.

If you're doing lots of writes and few reads, then you'd want to think a bit more about it. It would then come down to business requirements. Although overall the throughput may be slowed, read latency may not be a requirement but write latency may be, in which case you wouldn't want the index.

For instance, think of this lottery example: Everytime someone buys a ticket, you have to record their name and the ticket number. However the only time you ever have to read that data, is after the 1 and only drawing to see who had that ticket number. In this database, you wouldn't want to index the ticket number since they'll be so many writes and very few reads.