This makes the query run faster since they are fetched from memory if they are executed more than once. However, if your application updates the table frequently, this will invalidate any cached query and result set. You can check if your MySQL server has query cache enabled by running the command below:. This will depend on your MySQL installation. Don't set a very large query cache size value because this will degrade the MySQL server due to cached overhead and locking.
Values in the range of tens of megabytes are recommended. This value controls the amount of individual query result that can be can be cached. In this guide, we have shown you how to optimize your MySQL server hosted on Alibaba cloud for speed and performance. We believe that the guide will allow you to craft better queries and have a well-structured database structure that will not only be simple to maintain but also offer more stability to your software applications or website.
See the original article here. Thanks for visiting DZone today,. Edit Profile. Sign Out View Profile. Over 2 million developers have joined DZone. Like 9. Join the DZone community and get the full member experience. Join For Free. Prerequisites A valid Alibaba cloud account. A server running your favorite operating system that can support MySQL e. Ubuntu, Centos, Debian. Think of data as being names in an address book.
You can either flip through all the pages, or you can pull on the right letter tab to quickly locate the name you need. Use indexes to avoid unnecessary passes through tables. For example, you can add an index on picture. Now if you run the query, the process no longer involves scanning the entire list of pictures. First, all the albums are scanned to find the ones that belong to the user. This reduces the number of rows scanned to , The query is also about times faster than the original.
Each table uses a key for an optimal performance, making the query times faster than the original. This doesn't mean that you should add indexes everywhere because each index makes it longer to write to the database. You gain on read but lose on write. So only add indexes that actually increase read performance. If you're interested in attending the conference use this discount code when you register , for our readers: PCOS. Very good article! Updating this field takes around ms, and i'd like it to be a lot quicker as i do it on pretty much every pageload on my site.
I can't work out why it's so slow: there's around 55, records which shouldn't be problematically large i'd have thought. I've ommitted all other columns apart from id for clarity's sake. EDIT - i'd previously said the query was taking ms. This still feels a bit too long though. Is this actually a reasonable write time after all?
EDIT - all my timings come from manually entering sql queries in the mysql shell client. I do use MySQL in my Ruby on Rails web app, but that app is not involved for the purposes of this question: i'm purely looking at the database level.
Well, you appear to be performing the update in the most efficient manner - i. Assuming the ms to update is purely the time taken by the db server as opposed to the round trip in the web page , I can only think of a few things that might help:. You have indexed the column being updated - that typically adds a little time to the update as the index has to be maintained.
I see that you need to use that column, so you can't get rid of the index; but if you could, you might well see better performance. Batching updates is sometimes a good way of avoiding the real-time performance hit, but still achieving what you want.
You could have the web-triggered insert go into a holding table with a timestamp field, then offline batch update the real data. DB optimisation may help, but only if the db is not in good shape already - so things like memory allocation, tablespace fragmentation, buffer pools etc. There is not much you can do about this. You already have an index on your column, and it just takes some time to find the row using the index and update it.
The index might be fragmented, which will slow down your lookup. You can rebuild the index using analyze. Write user events id, now equivalent to a log file. Process the log file from another process such as Create Event or entirely in another programming language such as Java, you name it.
Let's call that the worker process wp. Blocking means they wait. Rather, the activity is logged much quicker, such as an fwrite language specific to a log file.
The log file Open for Append concept can be deployed to a dedicated directory that either has all user activity in 1 file, or 1 file per user. If you need to count your rows, make it simple by selecting your rows from sysindexes. Below is the best way to add rows to your table.
If you need to check that some data exists, you will need to carry out an action. People often try this:. This helps you to avoid counting every item on the table. If this still seems too hard to understand, do not hesitate to hire an SQL expert to help you. To order table data, avoid using GUIDs as much as possible because they can easily cause very fast break off your table. It is not necessary to use triggers, as whatever you plan on doing to your data will go through the same transactions as the previous operations.
If you go ahead and use triggers, you could lock several tables until the trigger completes its cycle. Split the data into different transactions to lock up just a few resources, making the transactions go faster. If you handle several tables in one transaction, you might lock them all until your transaction is complete. Avoid blocking off transactions by breaking them into several routines, with each routine operating singularly at a time.
This will reduce the amount and number of blocks, and will free up tables for operations to continue taking place. Double dipping is running different queries on tables and later putting the queries on temp tables, then joining the large tables and temp tables together. This takes a huge toll on performance. Stored procedures have so many advantages that make your work easier, and writing queries faster. They slow down traffic because with stored procedures, calls become shorter.
If you use profiler, and other tools that allow you to identify statistics concerning performance, it gets easier to trace. When you use stored procedures, you can use your plans of execution repeatedly. This poor code is responsible for much of the bad performance you will encounter. If you are not able to completely avoid them, the best you can do is minimize them by writing stored procedures that are completely your own, and have ORM use yours instead of those it creates.
Do not ever assume you have to complete every task of updating and deleting in one single day. This is wrong, especially archiving data. Take your time and work on the operation for as long as you need, while making use of the small batches. Working too quickly to finish the work only slows down your queries, and this might bring down your systems. Cursors cause many problems, especially to speed.
Besides low speed, they can also cause blockages where one operation leads to the blockage of other operations. This can last for longer than expected and it affects your systems concurrency, slowing everything down. Speed up your SQL queries by avoiding cursors. As queries get older from too much use, their performance tends to worsen. These uses are from upgrades, structural changes, database changes and applications.
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