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Displaying posts with tag: Statistics (reset)
Fun with Bugs #69 - On Some Public Bugs Fixed in MySQL 5.7.23

Several MySQL releases happened yesterday, but of them all I am mostly interested in MySQL 5.7.23, as MySQL 5.7 (either directly or indirectly, via forks and upstream fixes they merge) is probably the most widely used MySQL GA release at the moment.

In this post (in a typical manner for this "Fun with Bugs" series)  I'd like to describe several bugs reported by MySQL Community users and fixed in MySQL 5.7.23. As usual, I'll try to concentrate mostly on InnoDB, replication, partitioning and optimizer-related bugs (if any).

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On Some Problematic Oracle MySQL Server Features

In one of my previous posts I stated that in Oracle's MySQL server some old enough features remain half-backed, not well tested, not properly integrated with each other, and not documented properly. It's time to prove this statement.

I should highlight from the very beginning that most of the features I am going to list are not that much improved by other vendors. But they at least have an option of providing other, fully supported storage engines that may overcome the problems in these features, while Oracle's trend to get rid of most engines but InnoDB makes MySQL users more seriously affected by any problems related to InnoDB.

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Catching Slow and Frequent Queries with ProxySQL

In this blog post,  I’ll look at how to catch slow and frequent queries with ProxySQL.

More and more people are using ProxySQL because it is a great tool and it can help DBAs a lot. But many people do not realize that it is more powerful than it looks. It has many features and possibilities. I am going to show you one of my favorite “tricks” / use cases.

There are plenty of blog posts explaining how ProxySQL works. I am not going to that again. Instead, let’s jump straight to the point. There is a table in ProxySQL called “stats.stats_mysql_query_digest”. It is one of my favorite tables because it basically records all the queries that were running against ProxySQL. Without collecting any queries on the MySQL server, I can find …

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Understand Your Prometheus Exporters with Percona Monitoring and Management (PMM)

In this blog post, I will look at the new dashboards in Percona Monitoring and Management (PMM) for Prometheus exporters.

Percona Monitoring and Management (PMM) uses Prometheus exporters to capture metrics data from the system it monitors. Those Prometheus exporters are an important part of your monitoring infrastructure, and understanding their performance and other operational details is critical for well-implemented monitoring.    

To help you with this we’ve added a number of new dashboards to Percona Monitoring and Management.

The Prometheus Exporters Overview dashboard provides a high-level overview of your installed Prometheus exporter …

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Big Dataset: All Reddit Comments – Analyzing with ClickHouse

In this blog, I’ll use ClickHouse and Tabix to look at a new very large dataset for research.

It is hard to come across interesting datasets, especially a big one (and by big I mean one billion rows or more). Before, I’ve used on-time airline performance available from BUREAU OF TRANSPORTATION STATISTICS. Another recent example is NYC Taxi and Uber Trips data, with over one billion records.

However, today I wanted to mention an interesting dataset I found recently that has been available since 2015. This is Reddit’s comments and submissions dataset, made possible thanks to Reddit’s generous API. The …

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Updating InnoDB Table Statistics Manually

In this post, we will discuss how to fix cardinality for InnoDB tables manually.

As a support engineer, I often see situations when the cardinality of a table is not correct. When InnoDB calculates the cardinality of an index, it does not scan the full table by default. Instead it looks at random pages, as determined by options innodb_stats_sample_pages, innodb_stats_transient_sample_pages and innodb_stats_persistent_sample_pages, or …

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Multi-Threaded Slave Statistics

In this blog post, I’ll talk about multi-threaded slave statistics printed in MySQL error log file.

MySQL version 5.6 and later allows you to execute replicated events using parallel threads. This feature is called Multi-Threaded Slave (MTS), and to enable it you need to modify the

slave_parallel_workers

 variable to a value greater than 1.

Recently, a few customers asked about the meaning of some new statistics printed in their error log files when they enable MTS. These error messages look similar to the example stated below:

[Note] Multi-threaded slave statistics for channel '': seconds elapsed = 123; events assigned = 57345; worker queues filled over overrun level = 0; waited due a Worker queue full = 0; waited due the total size = 0; waited at clock conflicts = 0 waited …
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Some Notes on Index Statistics in InnoDB

In MySQL 5.6 we introduced a huge improvement in the way that index and table statistics are gathered by InnoDB and subsequently used by the Optimizer during query optimization: Persistent Statistics. Some aspects of the way that Persistent Statistics work could be improved further though, and we’d really like your input on that.

How much to sample?

The statistics are gathered by picking some pages semi-randomly, analyzing them, and deriving some conclusions about the entire table and/or index from those analyzed pages. The number of pages sampled can be specified on a per-table basis with the STATS_SAMPLE_PAGES clause. For example:

ALTER TABLE t STATS_SAMPLE_PAGES=500;


This …

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What the Mean Really Means

When analyzing response time, or latency, you need much more information than an average provides. The average, commonly the arithmetic mean, shows the index of central tendency. But, as I found in earlier posts, the tendency is often not central, but may be skewed by outliers, or split by multiple modes. How often these factors occur was determined quantitatively, using tests and a survey of hundreds of production servers and different types of latency: over 95% had six-sigma outliers, and at least 20% had multiple modes. While these numerical results are useful, nothing beats a visualization, such as a histogram, …

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Modes and Modality

It is a truth universally acknowledged that the average is the index of central tendency. But what if the tendency isn’t central?

I’ve worked many performance issues where the latency or response time was multimodal, and higher-latency modes turned out to be the cause of the problem. Their existence isn’t shown by the average – the arithmetic mean; it could only be seen by examining the distribution as a histogram, density plot, heat map, or frequency trail. Once you know that more than one mode is present, it’s often straightforward to determine what causes the slower mode, by seeing what parameters of …

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