I have a MySQL database that contains over 100 tables. This database is
used by a CMS and every 6 hours, a PHP script uses all these tables with a
lot of logic, calculations and SQL joins and generates a large dataset with
over 1 million rows and inserts it into a table that we call "deals". This
is basically a table with 50 columns and a lot of optimized indexes and
each row has the same stuff, something like this:
dealid, price, productname, productcolor, productdimensions, onstock,
This "deals" table is then used by many other front- and backend
applications for all kinds of purposes. Examples are: a page on which a
customer can filter and then see results. A page on which a customer can
find information about a deal. An XML feed that is used by affiliates that
contains all products of brand X (+/- 150,000 rows from the 1,000,000 rows
table). An iOS app that is used to show you what kind of accessoires there
are available for your iPhone. All these applications use the same
1,000,000 row MySQL table to easily find and display data.
These applications are experiencing speed issues as the "deals" table
grows. MySQL seems to have a lot of trouble handling these kind of numbers.
I would not expect that to be the case but perhaps having only one MySQL
server with 32GB RAM and 8 cores is a problem. It's not an option to
upgrade to a couple of servers with 128GB RAM and also I'm not really
convinced that this would actually solve the problem.
I've made a little PHP script that, besides insert the 1,000,000 rows into
MySQL, also indexes them in elasticsearch. Then, I tested both the MySQL
table and the ES index in a simple sandbox filtering application. The speed
of the ES based application was absolutely amazing compared to the MySQL
based application. So it looks like speed-wise, ES wins.
So.. is this the right usage of an ES instance? Or am I incorrect thinking
ES would be the best solution for my MySQL speed issue?
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