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Displaying posts with tag: metrics (reset)
MyRocks Metrics Now in PMM 1.3.0

One of the most exciting features shipped in the Percona Monitoring and Management 1.3.0 (PMM) release is support for MyRocks metrics via a new Metrics Monitor dashboard titled MySQL MyRocks Metrics. The support in PMM follows the recent Percona Server for MySQL release 5.7.19 from September 6, where Percona delivered an EXPERIMENTAL version of MyRocks for non-Production usage.

The MyRocks storage engine from Facebook is based on RocksDB, a persistent key-value store for fast storage environments. MyRocks is optimized for fast storage and combines outstanding space and write efficiency with acceptable read performance. As a result, …

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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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Percona Monitoring and Management 1.3.1 Is Now Available

Percona announces the release of Percona Monitoring and Management 1.3.1. This release only contains bug fixes related to usability.

For install and upgrade instructions, see Deploying Percona Monitoring and Management.

Bug fixes

  • PMM-1271: In QAN, when the user selected a database host with no queries, the query monitor could still show metrics.
  • PMM-1512: When clicking …
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sysbench Histograms: A Helpful Feature Often Overlooked

In this blog post, I will demonstrate how to run and use sysbench histograms.

One of the features of sysbench that I often I see overlooked (and rarely used) is its ability to produce detailed query response time histograms in addition to computing percentile numbers. Looking at histograms together with throughput or latency over time provides many additional insights into query performance.

Here is how you get detailed sysbench histograms and performance over time:

sysbench --rand-type=uniform --report-interval=1 --percentile=99 --time=300 --histogram --mysql-password=sbtest oltp_point_select --table_size=400000000 run

There are a few command line options to consider:

  • report-interval=1 – prints out the current performance measurements every second, which helps see if …
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Complete Megalist: 25 Helpful Tools For Back-End Developers

 

The website or mobile app is the storefront for participating in the modern digital era. It’s your portal for inviting users to come and survey your products and services. Much attention focuses on front-end development; this is where the HMTL5, CSS, and JavaScript are coded to develop the landing page that everyone sees when they visit your site.

 

But the real magic happens on the backend. This is the ecosystem that really powers your website. One writer has articulated this point very nicely as follows:

 

The technology and programming that “power” a site—what your end user doesn’t see but what makes the site run—is called the back end. Consisting of the server, the database, and the server-side applications, it’s the behind-the-scenes functionality—the brain of a site. …

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Percona Monitoring and Management 1.2.2 is Now Available

Percona announces the release of Percona Monitoring and Management 1.2.2 on August 23, 2017.

For install and upgrade instructions, see Deploying Percona Monitoring and Management.

This release contains bug fixes related to performance and introduces various improvements. It also contains an updated version of Grafana.

Changes in PMM Server

We introduced the following changes in PMM Server 1.2.2:

Bug fixes

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Percona Monitoring and Management 1.2.1 is Now Available

Percona announces the release of Percona Monitoring and Management 1.2.1 on August 16, 2017.

For install and upgrade instructions, see Deploying Percona Monitoring and Management.

This hotfix release improves memory consumption.

Changes in PMM Server

We’ve introduced the following changes in PMM Server 1.2.1:

Bug fixes

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Saturation Metrics in PMM 1.2.0

One of the new graphs added to Percona Monitoring and Management (PMM) is saturation metrics. This blog post explains how to use the information provided by these graphs.

You might have heard about Brendan Gregg’s USE Method  (Utilization-Saturation-Errors) as a way to analyze the performance of any system. Our goal in PMM is to support this method fully over time, and these graphs take us one step forward.

When it comes to utilization, there are many graphs available in PMM. There is the CPU Usage graph: …

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How much disk space should I allocate for Percona Monitoring and Management?

I heard a frequent question at last week’s Percona Live conference regarding Percona Monitoring and Management (PMM): How much disk space should I allocate for PMM Server?

First, let’s review the three components of Percona Monitoring and Management that consume non-negligible disk space:

  1. Prometheus data source for the time series metrics
  2. Query Analytics (QAN) which uses Percona Server XtraDB (Percona’s enhanced version of the InnoDB storage engine)
  3. Orchestrator, also backed by Percona Server XtraDB

Of these, you’ll find that Prometheus is generally your largest consumer of disk space. Prometheus hits a steady state of disk utilization once you reach the defined storage.local.retention period. If you deploy Percona Monitoring and Management …

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Prophet: Forecasting our Metrics (or Predicting the Future)

In this blog post, we’ll look at how Prophet can forecast metrics.

Facebook recently released a forecasting tool called Prophet. Prophet can forecast a particular metric in which we have an interest. It works by fitting time-series data to get a prediction of how that metric will look in the future.

For example, it could be used to:

  • Predict how much HTTP traffic we will get, and scale accordingly when needed
  • See if a particular feature of our application will have success or if its usage will decline
  • Get an approximate date when our database server’s resources will be exhausted
  • Forecast new customer’s sign up and resize the staff accordingly
  • See what next year’s Black Friday or Cyber Monday will look like, and if we have the resources to handle them
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