How fast is mysql database

The physical size of the database matters as well: think of backups, for instance. Depending on your engine, your physical db files on grow, but don't shrink, for instance with innodb. So deleting a lot of rows, doesn't help to shrink your physical files. The database size does matter. If you have more than one table with more than a million records, then performance starts indeed to degrade. The number of records does of course affect the performance: MySQL can be slow with large tables.

If you hit one million records you will get performance problems if the indices are not set right for example no indices for fields in "WHERE statements" or "ON conditions" in joins. If you hit 10 million records, you will start to get performance problems even if you have all your indices right. Hardware upgrades - adding more memory and more processor power, especially memory - often help to reduce the most severe problems by increasing the performance again, at least to a certain degree.

That's true. Another thing that usually works is to just reduce the quantity of data that's repeatedly worked with. Query performance is fine.

What has become a nightmare is backups, restores, adding slaves, or anything else that deals with the whole dataset, or even DDL on large tables. Getting a clean import of a dump file has become problematic. In order to make the process stable enough to automate, various choices needed to be made to prioritize stability over performance. If we ever had to recover from a disaster using a SQL backup, we'd be down for days.

Horizontally scaling SQL is also pretty painful, and in most cases leads to using it in ways you probably did not intend when you chose to put your data in SQL in the first place. Shards, read slaves, multi-master, et al, they are all really shitty solutions that add complexity to everything you ever do with the DB, and not one of them solves the problem; only mitigates it in some ways.

I would strongly suggest looking at moving some of your data out of MySQL or really any SQL when you start approaching a dataset of a size where these types of things become an issue. Update: a few years later, and our dataset has grown to about GiB. Everything I said before holds. It still performs just fine, but the problems of running full dataset operations have become worse. It's kind of pointless to talk about "database performance", "query performance" is a better term here.

And the answer is: it depends on the query, data that it operates on, indexes, hardware, etc. Also watch out for complex joins. Transaction complexity can be a big factor in addition to transaction volume. I once was called upon to look at a mysql that had "stopped working".

And sure enough, the table that had stopped accepting transactions was exactly 2GB on disk. But with regards to the performance curve I'm told that it was working like a champ right up until it didn't work at all! This experience always serves for me as a nice reminder that there're always dimensions above and below the one you naturally suspect. For example, for a system with GPS monitoring of cars is not relevant query data from the positions of the car in previous months.

Therefore the data can be passed to other historical tables for possible consultation and reduce the execution times of the day to day queries. If you have proper indexes, use proper engines don't use MyISAM where multiple DMLs are expected , use partitioning, allocate correct memory depending on the use and of course have good server configuration, MySQL can handle data even in terabytes! For example, i worked with a table of drugs which has a column generic name where it has more than 15 characters for each drug in that table.

I put a query to compare the generic name of drugs between two tables. The query takes more minutes to run. The Same,if you compare the drugs using the drug index,using an id column as said above , it takes only few seconds. Database size DOES matter in terms of bytes and table's rows number. These are regular indexes, but their sort order is reversed. This is supported by MySQL 8 which you should use anyway and is useful to process new data first.

Doing so allows us to use the same index if we plan to put many rows in the where clause. If we do put a separate index for each row, MySQL execution plan will find all the results from each where clause and then find only the ones that match all indexes.

Finding all the results and then merging them means a massive waste of resources! Using the same index on multiple rows, MySQL execution plan will find only the results from the first row, and then it will filter just those results using the second row where clause.

And so on for more rows. The drawback of an index over more than one row is the index size, sometimes an index can get quite big, as can be seen in the picture below taken using phpmyadmin.

For the queries, we want to optimize we need to check the execution plan also called Query Plan. The reason is that what we think is the right way may not be the right way for MySQL. Furthermore, our indexes may not be usable for the query we are running.

There are fewer rows than what the explanation showed. This means that you need to make sure that the columns checked with the where clause are indexed or are small so that a scan will be fast. In this example, we are doing a calculation on the column with the primary key; look what MySQL will do:. It performed a full table scan, even though we have a primary key!

The way to solve it is to add a precalculated field, index it, and use that index field in the where clause. When doing a join between one or more tables all type of joins: inner join, left join, right join, etc. Otherwise, MySQL will need to do a full table scan to do a proper join.

How can you know if this is the case? You should be an expert on using it by now. MySQL out of the box configuration is modest and designed to support operations for web sites like WordPress. I helped a friend the same one I wrote about , and the first thing I checked even before the table structure is the memory allocated to MySQL.

It was on default, which was 2GB on his machine. Changing it to 48GB increased performance drastically. The number of instances the buffer pool is divided into, this is to allow for better concurrency with threads.

On Windows 32bit, the default is the buffer pool size divided by MB. Allowing null values absence of any value in a column in your database is a really bad idea unless the field can logically have a null value. The presence of null value can adversely affect your database results.

For instance, if you want to get the sum of all orders in a database, the anticipated result might behave badly if a particular order record has a null amount.

The biggest downside to having many columns is extra IO and storage overhead. Having wide tables can be extremely expensive and causes storage overhead. It is ideal no to go above a hundred unless your business logic specifically necessitates this. As opposed to creating one wide table, splitting it apart into logical structures can be beneficial.

Suppose you are creating an employee table. In certain instances, you realize that an employee can have multiple addresses. This can help reduce the network usage while fetching large dataset.

Reduce the join statements in queries. An SQL statement with a poorly designed pattern that involves a lot of joins may not work well. A rule of thumb is to have utmost a dozen joins for each query. A database index is a data structure that improves the speed of operations in a table. Indexes can be created using one or more columns, providing the basis for both rapid random lookups and efficient ordering of access to records. Thus, we should start indexing by adding these columns to the index.

Try to apply force-index if mysql is not using proper index. If you have an index that can optimize one side of the query and a different index to optimize the other side, a union clause can make the query run faster. The query above can run far much slower compared to the below query which uses a union operator merge the results of 2 separate fast queries that take advantage of the indexes.

As we know, caching is used to improve performance. It will faster the site or application. The MySQL query cache is a global one shared among the sessions.