UPDATE vs INSERT performance

Question

Am I correct to assume that an UPDATE query takes more resources than an INSERT query?

Answer 1

I am not a database guru but here my two cents:

Personally I don't think you have much to do in this regard, even if INSERT would be faster (all to be proven), can you convert an update in an insert?! Frankly I don't think you can do it all the times.

During an INSERT you don't usually have to use WHERE to identify which row to update but depending on your indices on that table the operation can have some cost.

During an update if you do not change any column included in any indices you could have quick execution, if the where clause is easy and fast enough.

Nothing is written on stones and really I would imagine it depends on whole database setup, indices and so on.

Anyway, found this one as a reference:

Top 84 MySQL Performance Tips

Answer 2

If you plan to perform a large processing (such as rating or billing for a cellular company), this question has a huge impact on system performance.

Performing large scale updates vs making many new tables and index has proven to reduce my company billing process form 26 hours to 1 hour!

I have tried it on 2 million records for 100,000 customer.
I first created the billing table and then every customer summary calls, I updated the billing table with the duration, price, discount.. a total of 10 fields.

In the second option I created 4 phases.
Each phase reads the previous table(s), creates index (after the table insert completed) and using: "insert into from select .." I have created the next table for the next phase.

Summary
Although the second alternative requires much more disk space (all views and temporary tables deleted at the end) there are 3 main advantages to this option:

1. It was 4 time faster than option 1.
2. In case there was a problem in the middle of the process I could start the process from the point it failed, as all the tables for the beginning of the phase were ready and the process could restart from this point. If the process fails implementing the first option, you will need to start the all the process all over again.
3. This made the development and QA work much faster as they could work parallel.