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Displaying posts with tag: metrics (reset)
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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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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Determining I/O Throughput for a System

At Kscope this year, I attended a half day in-depth session entitled Data Warehousing Performance Best Practices, given by Maria Colgan of Oracle. In that session, there was a section on how to determine I/O throughput for a system, because in data warehousing I/O per second (iops) is less important than I/O throughput (how much actual data goes through, not just how many reads/writes).

The section contained an Oracle-specific in-database tool, and a standalone tool that can be used on many operating systems, regardless of whether or not a database exists:

If Oracle is installed, run DBMS_RESOURCE_MANAGER.CALIBRATE_IO:

SET SERVEROUTPUT ON
DECLARE
lat INTEGER;
iops INTEGER;
mbps INTEGER;
BEGIN
-- DBMS_RESOURCE_MANAGER.CALIBRATE_IO(<DISKS>, <MAX_LATENCY>,iops,mbps,lat);
DBMS_RESOURCE_MANAGER.CALIBRATE_IO (2, 10, iops, mbps, lat); …
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Showing entries 1 to 7