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Displaying posts with tag: Performance (reset)
Best Practice for Creating Indexes on your MySQL Tables

By having appropriate indexes on your MySQL tables, you can greatly enhance the performance of SELECT queries. But, did you know that adding indexes to your tables in itself is an expensive operation, and may take a long time to complete depending on the size of your tables? During this time, you are also likely to experience a degraded performance of queries as your system resources are busy in index-creation work as well. In this blog post, we discuss an approach to optimize the MySQL index creation process in such a way that your regular workload is not impacted.

MySQL Rolling Index Creation

We call this approach a ‘Rolling Index Creation’ - if you have a MySQL master-slave replica set, you can create the index one node at a time in a rolling fashion. You should create the index only on the slave nodes so the master’s …

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How to Improve MySQL AWS Performance 2X Over Amazon RDS at The Same Cost

AWS is the #1 cloud provider for open-source database hosting, and the go-to cloud for MySQL deployments. As organizations continue to migrate to the cloud, it’s important to get in front of performance issues, such as high latency, low throughput, and replication lag with higher distances between your users and cloud infrastructure. While many AWS users default to their managed database solution, Amazon RDS, there are alternatives available that can improve your MySQL performance on AWS through advanced customization options and unlimited EC2 instance type support. ScaleGrid offers a compelling alternative to hosting MySQL on AWS that offers better performance, more control, and no cloud vendor lock-in and the same price as Amazon RDS. In this post, we compare the performance of MySQL Amazon RDS …

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MySQL EXPLAIN ANALYZE

MySQL 8.0.18 was just released, and it contains a brand new feature to analyze and understand how queries are executed: EXPLAIN ANALYZE.

What is it?

EXPLAIN ANALYZE is a profiling tool for your queries that will show you where MySQL spends time on your query and why.…

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MySQL 8.0 Clone Plugin and its internal process.

MySQL 8 has recently released clone plugin which makes DBA’s task of rebuilding the DB servers more easy.

  • Cloning is a process of creating an exact copy of the original. In technical terms cloning alias to (Backup + Recovery), MySQL database cloning requires a sequence of actions to be performed manually or in a scripted fashion with and without the tools involved.
  • Cloning is the first step when you want to configure the replication slave or Joining a new server to the InnoDB cluster. There was no native support for auto provisioning earlier. Percona XtraDB Cluster (MySQL + Galera Cluster) does cloning using xtrabackup tool by default when a new node joins the cluster.
  • Now MySQL simplified this task, In this post, We will see how to clone the database using clone plugin and its internals.
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Save Space on MySQL data with Column Compression

                                   Recently, One of our client reached our Remote DBA team with a requirement to reduce the size of the table as it is having many text columns with huge number of records. At preliminary check , I have validated the table size and its row format, as it was  in compressed  format already. 

Later I checked on other possibilities to compress the text columns further, At that time, then I came across per-column compression feature in Percona MySQL server (From 5.7.17-11) which features individual column compression and we were using …

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Make It Faster: Improving MySQL Write Performance for Tungsten Cluster Slaves

Overview The Skinny

In this blog post we explore various options for performance tuning MySQL server for better slave replication performance.

A Tungsten Cluster relies upon the Tungsten Replicator to move events from the master node to the slaves. Once the event has been transferred to the slave as THL on disk, the slave applier will then attempt to write it to the database. The Replicator can only apply events as fast as MySQL allows. If the MySQL server is somehow slow or blocking, then the Replicator will be as well.

A properly-tuned database server in addition to infrastructure and SysAdmin best practices will go quite a long way towards high-performance slave apply.

The Question Recently, a customer asked us:

During one of our load tests, we had a peak of 60k writes/min, averaging …

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One, two, three, MySQLers… OOW 2019, Percona Live, ProxySQL Tech Day!

As people are getting back from vacations and application traffic is going back and hitting database backend hard, it is a good time to think about performance and prepare for the winter holiday spike. Whenever you are on-premise or in the cloud, questions are the same:

  • Are we satisfied with the current MySQL performance?
  • How much traffic are we currently serving?
  • Is there enough capacity to stay stable as traffic increases?
  • What would be the best way to stay cost-effective as we grow?

Unfortunately, the answers are not so obvious and require a thorough performance analysis. However, there is a chance that your problem has already been solved by someone else. I do not mean StackOverflow (which is a beautiful place, by the way), but some higher level questions which people usually discuss at conferences, and we have several of them ongoing or just a couple of weeks away.

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What’s Faster? COUNT(*) or COUNT(1)?

One of the biggest and undead myths in SQL is that COUNT(*) is faster than COUNT(1). Or was it that COUNT(1) is faster than COUNT(*)? Impossible to remember, because there’s really no reason at all why one should be faster than the other. But is the myth justified?

Let’s measure!

How does COUNT(…) work?

But first, let’s look into some theory. The two ways to count things are not exactly the same thing. Why?

  • COUNT(*) counts all the tuples in a group
  • COUNT(<expr>) counts all the tuples in a group for which <expr> evaluates to something that IS NOT NULL

This distinction can be quite useful. Most of the time, we’ll simply COUNT(*) for convenience, but there are (at least) two cases where we don’t want that, for example:

When outer joining

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What are the MySQL Metrics That Really Make a Difference?

Author: Robert Agar

MySQL is one of the most popular relational database platforms in the world. As such, it is used as the backend of many mission-critical applications across all sectors of business and industry. If you are a DBA or database developer there is a high probability that you are working with MySQL now or will be in the near future.

One of the primary responsibilities of a DBA is to optimize the performance of their databases. There are many ways to accomplish this feat, and all of them have an important point in common. You need knowledge concerning the operation of your systems before you can expect to make intelligent modifications to them. All of the methods used to tune and optimize your databases are identified by studying metrics regarding their current performance and using this data to plan appropriate action.

The right tools are required to gather the information needed to …

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MySQL 5.6/Maria 10.1 : How we got from 30k qps to 101k qps…..

Late one evening, I was staring at one of our large MySQL installations and noticed the database was hovering around 7-10 run queue length (48 cores, ~500 gigs memory, fusionIO cards). I had been scratching my head on how to get more throughput from the database. This blog records the changes I made to tune performance in order to achieve a 300% better throughput in MySQL. I tested my theories on MySQL 5.6/Maria 10.1. While with 5.7 DBAs would turn to performance_schema for the supporting metrics, I hope that you find the process interesting nevertheless.

View from an Oracle RDBMS DBA…

For context, I came to MySQL from a background as an Oracle RDBMS DBA, and this informs my expectations. For this exercise, unlike with Oracle RDBMS, I had no access to view wait events so that I could see where my database was struggling. At least, no access in MySQL 5.6/Maria 10.1 without taking a performance hit by using …

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