Percona Monitoring and Management 1.5.3 Is Now Available

Percona Monitoring and Management

Percona Monitoring and ManagementPercona announces the release of Percona Monitoring and Management 1.5.3. This release contains fixes for bugs found after the release of Percona Monitoring and Management 1.5.2, as well as some important fixes and improvements not related to the previous release.


  • PMM-1874: The read timeout of the proxy server (/prometheus) has been increased from the default of 60 seconds to avoid nxginx gateway timeout error when loading data-rich dashboards.
  • PMM-1863: We improved our handling of temporary Grafana credentials

Bug fixes

  • PMM-1828: On CentOS 6.9, pmm-admin list incorrectly reported that no monitoring services were running.
  • PMM-1842: It was not possible to restart the mysql:queries monitoring service after PMM Client was upgraded from version 1.0.4.
  • PMM-1797: It was not possible to update the CloudWatch data source credentials.
  • PMM-1829: When the user clicked a link in the Query Abstract column, an outdated version of QAN would open.
  • PMM-1836PMM Server installed in a Docker container could not be started if the updating procedure had been temporarily interrupted.
  • PMM-1871: In some cases, RDS instances could not be discovered.
  • PMM-1845: Converted FLUSH SLOW LOGS to FLUSH NO_WRITE_TO_BINLOG SLOW LOGS so that GTID event isn’t created
  • PMM-1816: Fixed a rendering error in Firefox.

Percona Monitoring and Management 1.5.0 Is Now Available

Percona Monitoring and Management

Percona announces the release of Percona Monitoring and Management 1.5.0 on November 28, 2017.

This release focuses on the following features:

  • Enhanced support for MySQL on Amazon RDS and Amazon Aurora – Dedicated Amazon Aurora dashboard offers maximum visibility into key database characteristics, eliminating the need for additional monitoring nodes.  We renamed Amazon RDS OS Metrics to Amazon RDS / Aurora MySQL Metrics
  • Simpler configuration – Percona Monitoring and Management now offers easier configuration of key Amazon RDS and Amazon Aurora settings via a web interface
  • One-click data collection – One button retrieves vital information on server performance to assist with troubleshooting
  • Improved interface – A simple, consistent user interface makes it faster and more fluid to switch between Query Analytics and Metrics Monitor

Highlights from our new Amazon RDS / Aurora MySQL Metrics dashboard:

Shared elements for Amazon Aurora MySQL and RDS MySQL

Amazon Aurora MySQL unique elements

Amazon RDS for MySQL unique elements

We’ve integrated Query Analytics into Metrics Monitor, and it appears as a separate dashboard known as PMM Query Analytics.

With this release, Percona Monitoring and Management introduces a new deployment option via AWS Marketplace. This is in addition to our distribution method of Amazon Machine Images (AMI).

We have upgraded Grafana and Prometheus in this release. PMM now includes Grafana 4.6.1. One of the most prominent features that the upgraded Grafana offers is the support of annotations. You can mark a point or select a region in a graph and give it a meaningful description. For more information, see the release highlights.

Prometheus version 1.8.2, shipped with this release, offers a number of bug fixes. For more information, see the Prometheus change log.

New features

  • PMM-434PMM enables monitoring of Amazon RDS and Amazon Aurora metrics
  • PMM-1133Query Analytics is available from Grafana as a dashboard
  • PMM-1470: Integrated Cloudwatch metrics into Prometheus
  • PMM-699: Combined AWS RDS and Amazon Aurora metrics into one dashboard
  • PMM-722: Distributed the MariaDB dashboard graph elements among other existing dashboards and removed the MariaDB dashboard. Further, we renamed the MyISAM dashboard  to MyISAM/Aria Metrics
  • PMM-1258: The DISABLE_UPDATES option enables preventing manual updates when PMM Server is run from a Docker container.
  • PMM-1500: Added InnoDB Buffer Disk Reads to graph InnoDB Buffer Pool Requests to better understand missed InnoDB BP cache hits


  • PMM-1577: Updated Prometheus to version 1.8.2
  • PMM-1603: Updated Grafana to version 4.6.1
  • PMM-1669: The representation of numeric values in the Context Switches graph in the System Overview dashboard was changed to improve readability.
  • PMM-1575: Templating rules were improved for the MyRocks and TokuDB dashboards so that only those instances with these storage engines are displayed

Bug fixes

  • PMM-1082: The CPU Usage graph on the Trends dashboard showed incorrect spikes
  • PMM-1549: The authentication of the mongodb:queries monitoring service did not work properly when the name of the database to authenticate was not provided.
  • PMM-1673: Fixed display issue with Microsoft Internet Explorer 11

MySQL Dashboard Improvements in Percona Monitoring and Management 1.4.0

In this blog post, I’ll walk through some of the improvements to the Percona Monitoring and Management (PMM) MySQL dashboard in release 1.4.0.

As the part of Percona Monitoring and Management development, we’re constantly looking for better ways to visualize information and help you to spot and resolve problems faster. We’ve made some updates to the MySQL dashboard in the 1.4.0 release. You can see those improvements in action in our Percona Monitoring and Management Demo Site: check out the MySQL Overview and MySQL InnoDB Metrics dashboards.

MySQL Client Thread Activity

Percona Monitoring and Management 1

One of the best ways to characterize a MySQL workload is to look at the number of MySQL server-client connections (Threads Connected). You should compare this number to how many of those threads are actually doing something on the server side (Threads Running), rather than just sitting idle waiting for a client to send the next request.

MySQL can handle thousands of connected threads quite well. However, many threads (hundred) running concurrently often increases query latency. Increased internal contention can make the situation much worse.

The problem with those metrics is that they are extremely volatile – one second you might have a lot of threads connected and running, and then none. This is especially true when some stalls on the MySQL level (or higher) causes pile-ups.

To provide better insight, we now show Peak Threads Connected and Peak Threads Running to help easily spot such potential pile-ups, as well as Avg Threads Running. These stats allow you look at a high number of threads connected and running to see if it there are just minor spikes (which tend to happen in many systems on a regular basis), or something more prolonged that warrants deeper investigation.

To simplify it even further: Threads Running spiking for a few seconds is OK, but spikes persisting for 5-10 seconds or more are often signs of problems that are impacting users (or problems about to happen).

InnoDB Logging Performance

Percona Monitoring and Management 2

Since I wrote a blog post about Choosing MySQL InnoDB Log File Size, I thought it would be great to check out how long the log file space would last (instead of just looking at how much log space is written per hour). Knowing how long the innodb_log_buffer_size lasts is also helpful for tuning this variable, in general.

This graph shows you how much data is written to the InnoDB Log Files, which helps to understand your disk bandwidth consumption. It also tells you how long it will take to go through your combined Redo Log Space and InnoDB Log Buffer Size (at this rate).

As I wrote in the blog post, there are a lot of considerations for choosing the InnoDB log file size, but having enough log space to accommodate all the changes for an hour is a good rule of thumb. As we can see, this system is close to full at around 50 minutes.

When it comes to innodb_log_buffer_sizeeven if InnoDB is not configured to flush the log at every transaction commit, it is going to be flushed every second by default. This means 10-15 seconds is usually good enough to accommodate the spikes. This system has it set at about 40 seconds (which is more than enough).

InnoDB Read-Ahead

Percona Monitoring and Management 3

This graph helps you understand how InnoDB Read-Ahead is working out, and is a pretty advanced graph.

In general, Innodb Read-Ahead is not very well understood. I think in most cases it is hard to tell if it is helping or hurting the current workload in its current configuration.

The for Read-Ahead in any system (not just InnoDB) is to pre-fetch data before it is really needed (in order to reduce latency and improve performance). The risk, however, is pre-fetching data that isn’t needed. This is wasteful.

InnoDB has two Read-Ahead options: Linear Read-Ahead (designed to speed up workloads that have physically sequential data access) and Random Read-Ahead (designed to help workloads that tend to access the data in the same vicinity but not in a linear order).

Due to potential overhead, only Linear Read-Ahead is enabled by default. You need to enable Random Read-Ahead separately if you want to determine its impact on your workload

Back to the graph in question: we show a number of pages pre-fetched by Linear and Random Read-Aheads to confirm if these are even in use with your workload. We show Number of Pages Fetched but Never Accessed (evicted without access) – shown as both the number of pages and as a percent of pages. If Fetched but Never Accessed is more than 30% or so, Read-Ahead might be producing more waste instead of helping your workload. It might need tuning.

We also show the portion of IO requests that InnoDB Read-Ahead served, which can help you understand the portion of resources spent on InnoDB Read-Ahead

Due to the timing of how InnoDB increments counters, the percentages of IO used for Read-Ahead and pages evicted without access shows up better on larger scale graphs.


I hope you find these graphs helpful. We’ll continue making Percona Monitoring and Management more helpful for troubleshooting database systems and getting better performance!


Percona Monitoring and Management 1.4.0 Is Now Available

Percona Monitoring and ManagementPercona announces the release of Percona Monitoring and Management 1.4.0.

This release introduces the support of external Prometheus exporters so that you can create dashboards in the Metrics monitor even for the monitoring services other than those provided with PMM client packages. To attach an existing external Prometheus exporter, run pmm-admin add external:metrics NAME_OF_EXPORTER URL:PORT.

The list of attached monitoring services is now available not only in the tabular format but also as a JSON file to enable automatic verification of your configuration. To view the list of monitoring services in the JSON format run pmm-admin list --json.

In this release, Prometheus and Grafana have been upgraded. Prometheus version 1.7.2, shipped with this release, offers a number of bug fixes that will contribute to its smooth operation inside PMM. For more information, see the Prometheus change log.

Version 4.5.2 of Grafana, included in this release of PMM, offers a number of new tools that will facilitate data analysis in PMM:

  • New query editor for Prometheus expressions features syntax highlighting and autocompletion for metrics, functions and range vectors.
    Percona Monitoring and Management
  • Query inspector provides detailed information about the query. The primary goal of graph inspector is to enable analyzing a graph which does not display data as expected.
    Percona Monitoring and Management

The complete list of new features in Graphana 4.5.0 is available from What’s New in Grafana v4.5.

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

New features

  • PMM-1520: Prometheus upgraded to version 1.7.2.
  • PMM-1521: Grafana upgraded to version 4.5.2.
  • PMM-1091: The pmm-admin list produces a JSON document as output if the --json option is supplied.
  • PMM-507: External exporters are supported with pmm-admin.
  • PMM-1622: docker images of PMM Server are available for downloading as tar packages.


  • PMM-1553: Consul upgraded to the 0.8 release.

Bug fixes

  • PMM-1172: In some cases, the TABLES section of a query in QAN could contain no data and display the List of tables is empty error. The Query and Explain sections had the relevant values.
  • PMM-1519: A Prometheus instance could be forced to shut down if it contained too many targets (more than 50). When started the next time, Prometheus initiated a time-consuming crash recovery routine which took long on large installations.

Percona Monitoring and Management 1.3.2 Is Now Available

Percona Monitoring Management

Percona Monitoring ManagementPercona announces the release of Percona Monitoring and Management 1.3.2. This release only contains bug fixes related to usability.

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

Bug fixes

  • PMM-1529: When the user selected “Today”, “This week”, “This month” or “This year” range in Metrics Monitor and clicked the Query Analytics button, the QAN page opened reporting no data for the selected range even if the data were available.
    Percona Monitoring and Management
  • PMM-1528: In some cases, the page not found error could appear instead of the QAN page after upgrading by using the Upgrade button.
  • PMM-1498 : In some cases, it was not possible to shut down the virtual machine containing the PMM Server imported as an OVA image.

Other bug fixes in this release: PMM-913, PMM-1215, PMM-1481PMM-1483, PMM-1507



Webinar Wednesday, October 11, 2017: Percona Monitoring and Management (PMM) Demonstration

Percona Monitoring and Management

Percona Monitoring and Management (PMM)Join Percona’s Product Manager Michael Coburn as he presents a Percona Monitoring and Management (PMM) Demonstration on Wednesday, October 11, 2017, at 10:00 am PDT / 1:00 pm EDT (UTC-7).

How can you optimize database performance if you can’t see what’s happening? Percona Monitoring and Management (PMM) is a free, open source database troubleshooting and performance optimization platform for MySQL and MongoDB. PMM uses Metrics Monitor (Grafana + Prometheus) for visualization of data points. It also has Query Analytics (QAN), to help identify and quantify non-performant queries and provide thorough time-based analysis to ensure that your data works as efficiently as possible.

Michael Coburn will provide a brief demo of PMM. He will also cover newly released features in PMM such as QAN for MongoDB, new MyRocks dashboards and tooltips for metrics monitoring.

By the end of the webinar you will have a better understanding of how to:

  • Observe database performance from a system and service metrics perspective
  • See database performance from the queries executing in MySQL and MongoDB
  • Leverage the metrics and queries from PMM to make informed decisions about crucial database resources: scaling your database tier, database resource utilization and management, and having your team focus on the most critical database events

Register for the webinar here.

Michael CoburnMichael Coburn, Principal Technical Account Manager

Michael joined Percona as a Consultant in 2012. He progressed through various roles including Managing Consultant, Principal Architect, Technical Account Manager, and Technical Support Engineer. He is now leading Product Management for Percona Monitoring and Management.



Graph Descriptions for Metrics Monitor in Percona Monitoring and Management 1.3.0

PMM 1.3.0

The Metrics Monitor of Percona Monitoring and Management 1.3.0 (PMM) provides graph descriptions to display more information about the monitored data without cluttering the interface.

Percona Monitoring and Management 1.3.0 is a free and open-source platform for managing and monitoring MySQL®, MariaDB® and MongoDB® performance. You can run PMM in your own environment for maximum security and reliability. It provides thorough time-based analysis for MySQL, MariaDB® and MongoDB servers to ensure that your data works as efficiently as possible.

Each dashboard graph in PMM contains a lot of information. Sometimes, it is not easy to understand what the plotted line represents. The metric labels and the plotted data are limited and have to account for the space they can use in dashboards. It is simply not possible to provide additional information which might be helpful when interpreting the monitored metrics.

The new version of the PMM dashboards introduces on-demand descriptions with more details about the metrics in the given graph and about the data.

Percona Monitoring and Management 1.3.0

These on-demand descriptions are available when you hover the mouse pointer over the icon at the top left corner of each graph. The descriptions do not use the valuable space of your dashboard. The graph descriptions appear in small boxes. If more information exists about the monitored metrics, the description contains a link to the associated documentation.

Percona Monitoring and Management 1.3.0

In release 1.3.0 of PMM, Metrics Monitor only starts to use this convenient tool. In subsequent releases, graph descriptions are going to become a standard attribute of each Metrics Monitor dashboard.


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, MyRocks has the following advantages compared to other storage engines (if your workload uses fast storage, such as SSD):

  • Requires less storage space
  • Provides more storage endurance
  • Ensures better IO capacity

MyRocks Database Operations

This graph will help you visualize MyRocks database operations of Next and Seek attributes:

MyRocks Cache Activity

We also have a graph to help you visualize the count of Hits and Misses on the MyRocks cache:

MyRocks Cache Data Bytes Read/Write

Finally, another important MyRocks graph will help you understand the volume of data read and written to the MyRocks cache:

Please note that the MyRocks storage engine is not suitable (yet) for production workloads, but if you are testing this technology take a moment to install PMM in order to take advantage of our new MySQL MyRocks Metrics dashboard!

In PMM, you can view the metrics provided by the information schema as well as various data reported by the RocksDB engine’s status used by your MySQL database instance.


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 dataset was first mentioned at “I have every publicly available Reddit comment for research,” and currently you can find it at However, there is no guarantee that will provide this dataset in the future. I think it would be valuable for Amazon or another cloud provider made this dataset available for researchers, just as Amazon provides

The dataset contains 2.86 billion records to the end of 2016 and is 709GB in size, uncompressed. This dataset is valuable for a variety of research scenarios, from simple stats to natural language processing and machine learning.

Now let’s see what simple info we can collect from this dataset using ClickHouse and, a GUI tool for ClickHouse. In this first round, we’ll figure some basic stats, like number of comments per month, number of authors per month and number of subreddits. I also added how many comments in average are left for a post.

Queries to achieve this:

SELECT toYYYYMM(created_date) dt,count(*) comments FROM commententry1 GROUP BY dt ORDER BY dt
SELECT toYYYYMM(created_date) dt,count(DISTINCT author) authors FROM commententry1 GROUP BY dt ORDER BY dt
SELECT toYYYYMM(created_date) dt,count(DISTINCT subreddit) subreddits FROM commententry1 GROUP BY dt ORDER BY dt
SELECT toYYYYMM(created_date) dt,count(*)/count(distinct link_id) comments_per_post FROM commententry1 GROUP BY dt ORDER BY dt

And the graphical result:
It impressive to see the constant growth in comments (to 70mln per month by the end of 2016) and authors (to 3.5mln for the same time period). There is something interesting happening with subreddits, which jump up and down. It’s interesting to see that the average count of comments per post stays stable, with a slight decline to 13 comments/post by the end of 2016.

Now let’s check most popular subreddits:

SELECT subreddit,count(*) cnt FROM commententry1 GROUP BY subreddit ORDER BY cnt DESC limit 100

and using a treemap (available in

We can measure subreddits that get the biggest increase in comments in 2016 compared to 2015:

SELECT subreddit,cntnew-cntold diff FROM (SELECT subreddit,count(*) cntnew FROM commententry1 WHERE toYear(created_date)=2016 GROUP BY subreddit) ALL INNER JOIN (SELECT subreddit,count(*) cntold FROM commententry1 WHERE toYear(created_date)=2015 GROUP BY subreddit) USING (subreddit) ORDER BY diff DESC LIMIT 50


Obviously, Reddit was affected by the United States Presidential Election 2016, but not just that. The gaming community saw an increase in Overwatch, PokemonGO and Dark Souls 3.

Now we can try to run our own DB-Ranking, but only based on Reddit comments. This is how I can do this for MySQL, PostgreSQL and MongoDB:

SELECT toStartOfQuarter(created_date) Quarter,
sum(if(positionCaseInsensitive(body,'mysql')>0,1,0)) mysql,
sum(if(positionCaseInsensitive(body,'postgres')>0,1,0)) postgres,
sum(if(positionCaseInsensitive(body,'mongodb')>0,1,0)) mongodb
FROM commententry1
GROUP BY Quarter ORDER BY Quarter;

I would say the result is aligned with, where MySQL is the most popular among the three, followed by PostgreSQL and then MongoDB. There is an interesting spike for PostgreSQL in the second quarter in 2015, caused by a bot in “leagueoflegend” tournaments. The bot was actively announcing that it is powered by PostgreSQL in the comments, like this:

To highlight more ClickHouse features: along with standard SQL functions, it provides a variety of statistical functions (for example, Quantile calculations). We can try to see the distribution of the number of comments left by authors:

        count(*) AS cnt
    FROM commententry1
    WHERE author != '[deleted]'
    GROUP BY author

The result is:

quantileExact(0.1)(cnt) - 1
quantileExact(0.2)(cnt) - 1
quantileExact(0.3)(cnt) - 1
quantileExact(0.4)(cnt) - 2
quantileExact(0.5)(cnt) - 4
quantileExact(0.6)(cnt) - 7
quantileExact(0.7)(cnt) - 16
quantileExact(0.8)(cnt) - 42
quantileExact(0.9)(cnt) - 160
quantileExact(0.99)(cnt) - 2271

Which means that 30% of authors left only one comment, and 50% of authors left four comments or less.

In general, ClickHouse was a pleasure to use when running analytical queries. However, I should note the missing support of WINDOW functions is a huge limitation. Even MySQL 8.0, which recently was released as RC, provides support for WINDOW functions. I hope ClickHouse will implement this as well.


sysbench Histograms: A Helpful Feature Often Overlooked

Sysbench Histograms

Sysbench HistogramsIn 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 performance is uniform, if you have stalls or otherwise high variance
  • percentile=99 computes 99 percentile response time, rather than 95 percentile (the default); I like looking at 99 percentile stats as it is a better measure of performance
  • histogram=on produces a histogram at the end of the run (as shown below)

The first thing to note about this histogram is that it is exponential. This means the width of the buckets changes with higher values. It starts with 0.001 ms (one microsecond) and gradually grows. This design is used so that sysbench can deal with workloads with requests that take small fractions of milliseconds, as well as accommodate requests that take many seconds (or minutes).

Next, we learn some us very interesting things about typical request response time distribution for databases. You might think that this distribution would be close to some to some “academic” distributions, such as normal distribution. In reality, we often observe is something of a “camelback” distribution (not a real term) – and our “camel” can have more than two humps (especially for simple requests such as the single primary key lookup shown here).

Why do request response times tend to have this distribution? It is because requests can take multiple paths inside the database. For example, certain requests might get responses from the MySQL Query Cache (which will result in the first hump). A second hump might come from resolving lookups using the InnoDB Adaptive Hash Index. A third hump might come from finding all the data in memory (rather than the Adaptive Hash Index). Finally, another hump might coalesce around the time (or times) it takes to execute on requests that require disk IO.    

You also will likely see some long-tail data that highlights the fact that MySQL and Linux are not hard, real-time systems. As an example, this very simple run with a single thread (and thus no contention) has an outlier at around 18ms. Most of the requests are served within 0.2ms or less.

As you add contention, row-level locking, group commit and other issues, you are likely to see even more complicated diagrams – which can often show you something unexpected:

Latency histogram (values are in milliseconds)
      value  ------------- distribution ------------- count
      0.050 |                                         1
      0.051 |                                         2
      0.052 |                                         2
      0.053 |                                         54
      0.053 |                                         79
      0.054 |                                         164
      0.055 |                                         883
      0.056 |*                                        1963
      0.057 |*                                        2691
      0.059 |**                                       4047
      0.060 |****                                     9480
      0.061 |******                                   15234
      0.062 |********                                 20723
      0.063 |********                                 20708
      0.064 |**********                               26770
      0.065 |*************                            35928
      0.066 |*************                            34520
      0.068 |************                             32247
      0.069 |************                             31693
      0.070 |***************                          41682
      0.071 |**************                           37862
      0.073 |********                                 22691
      0.074 |******                                   15907
      0.075 |****                                     10509
      0.077 |***                                      7853
      0.078 |****                                     9880
      0.079 |****                                     10853
      0.081 |***                                      9243
      0.082 |***                                      9280
      0.084 |***                                      8947
      0.085 |***                                      7869
      0.087 |***                                      8129
      0.089 |***                                      9073
      0.090 |***                                      8364
      0.092 |***                                      6781
      0.093 |**                                       4672
      0.095 |*                                        3356
      0.097 |*                                        2512
      0.099 |*                                        2177
      0.100 |*                                        1784
      0.102 |*                                        1398
      0.104 |                                         1082
      0.106 |                                         810
      0.108 |                                         742
      0.110 |                                         511
      0.112 |                                         422
      0.114 |                                         330
      0.116 |                                         259
      0.118 |                                         203
      0.120 |                                         165
      0.122 |                                         126
      0.125 |                                         108
      0.127 |                                         87
      0.129 |                                         83
      0.132 |                                         55
      0.134 |                                         42
      0.136 |                                         45
      0.139 |                                         41
      0.141 |                                         149
      0.144 |                                         456
      0.147 |                                         848
      0.149 |*                                        2128
      0.152 |**                                       4586
      0.155 |***                                      7592
      0.158 |*****                                    13685
      0.160 |*********                                24958
      0.163 |*****************                        44558
      0.166 |*****************************            78332
      0.169 |*************************************    98616
      0.172 |**************************************** 107664
      0.176 |**************************************** 107154
      0.179 |****************************             75272
      0.182 |******************                       49645
      0.185 |****************                         42793
      0.189 |*****************                        44649
      0.192 |****************                         44329
      0.196 |******************                       48460
      0.199 |*****************                        44769
      0.203 |**********************                   58578
      0.206 |***********************                  61373
      0.210 |**********************                   58758
      0.214 |******************                       48012
      0.218 |*************                            34533
      0.222 |**************                           36517
      0.226 |*************                            34645
      0.230 |***********                              28694
      0.234 |*******                                  17560
      0.238 |*****                                    12920
      0.243 |****                                     10911
      0.247 |***                                      9208
      0.252 |****                                     10556
      0.256 |***                                      7561
      0.261 |**                                       5047
      0.266 |*                                        3757
      0.270 |*                                        3584
      0.275 |*                                        2951
      0.280 |*                                        2078
      0.285 |*                                        2161
      0.291 |*                                        1747
      0.296 |*                                        1954
      0.301 |*                                        2878
      0.307 |*                                        2810
      0.312 |*                                        1967
      0.318 |*                                        1619
      0.324 |*                                        1409
      0.330 |                                         1205
      0.336 |                                         1193
      0.342 |                                         1151
      0.348 |                                         989
      0.354 |                                         985
      0.361 |                                         799
      0.367 |                                         671
      0.374 |                                         566
      0.381 |                                         537
      0.388 |                                         351
      0.395 |                                         276
      0.402 |                                         214
      0.409 |                                         143
      0.417 |                                         80
      0.424 |                                         85
      0.432 |                                         54
      0.440 |                                         41
      0.448 |                                         29
      0.456 |                                         16
      0.464 |                                         15
      0.473 |                                         11
      0.481 |                                         4
      0.490 |                                         9
      0.499 |                                         4
      0.508 |                                         3
      0.517 |                                         4
      0.527 |                                         4
      0.536 |                                         2
      0.546 |                                         4
      0.556 |                                         4
      0.566 |                                         4
      0.587 |                                         1
      0.597 |                                         1
      0.608 |                                         5
      0.619 |                                         3
      0.630 |                                         2
      0.654 |                                         2
      0.665 |                                         5
      0.677 |                                         26
      0.690 |                                         298
      0.702 |                                         924
      0.715 |*                                        1493
      0.728 |                                         1027
      0.741 |                                         1112
      0.755 |                                         1127
      0.768 |                                         796
      0.782 |                                         574
      0.797 |                                         445
      0.811 |                                         415
      0.826 |                                         296
      0.841 |                                         245
      0.856 |                                         202
      0.872 |                                         210
      0.888 |                                         168
      0.904 |                                         217
      0.920 |                                         163
      0.937 |                                         157
      0.954 |                                         204
      0.971 |                                         155
      0.989 |                                         158
      1.007 |                                         137
      1.025 |                                         94
      1.044 |                                         79
      1.063 |                                         52
      1.082 |                                         36
      1.102 |                                         25
      1.122 |                                         25
      1.142 |                                         16
      1.163 |                                         8
      1.184 |                                         5
      1.205 |                                         7
      1.227 |                                         2
      1.250 |                                         4
      1.272 |                                         3
      1.295 |                                         3
      1.319 |                                         2
      1.343 |                                         2
      1.367 |                                         1
      1.417 |                                         2
      1.791 |                                         1
      1.996 |                                         2
      2.106 |                                         2
      2.184 |                                         1
      2.264 |                                         1
      2.347 |                                         2
      2.389 |                                         1
      2.433 |                                         1
      2.477 |                                         1
      2.568 |                                         2
      2.615 |                                         1
      2.710 |                                         1
      2.810 |                                         1
      2.861 |                                         1
      3.187 |                                         1
      3.488 |                                         1
      3.816 |                                         1
      4.028 |                                         1
      6.913 |                                         1
      7.565 |                                         1
      8.130 |                                         1
     17.954 |                                         1

I hope you give sysbench histograms a try, and see what you can discover!

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