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Prometheus as an Engine for MySQL Monitoring

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Prometheus as an Engine for MySQL Monitoring

Here's a quick tutorial for using Prometheus as an Engine for MySQL monitoring, with a look at data models, computation models, and storage.

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When I first discovered Graphite years ago, I was very impressed with its monitoring capabilities.  Compared to many RRD-based tools that were popular at the time (like Cacti), Graphite separated the captured data and graphs, allowing you to do all kinds of math and transformations while visualizing data. For example, I could plot the relationship between system queries and disk IO, and capture how the system was getting more IO bound over time. It also had reasonably high performance, allowing me to capture high-resolution data for medium-sized systems.

Just last year I discovered Prometheus, and it also impressed me. I think it has the potential to take Graphite’s flexibility to the next level. Though I am in no way a Prometheus expert, I  want to share my understanding and thoughts on it so far.

Data Model

The data model was perhaps what attracted me to Prometheus the most. While it’s not obvious at first, when you do figure it out it has fantastic flexibility.

In the data model used by Whisper and Carbon in Graphite, you will use something like this to store MySQL data:

myapp.store.mysql.db01.status.questions = 5000

You can set up any hierarchy you like, but it has to have a hierarchy.

What Prometheus does instead is allow you to use a set of key-value pairs. The same data shown above could be presented like this:

questions_total{app=”myapp”,subsystem=”store”,engine=”mysql”,host=”db01”, source=”status”} = 5000

(You most likely wouldn’t use this exact structure, but it’s good for illustration.)

The difference between these approaches it that Prometheus provides you multiple dimensions on which you can filter and aggregate, plus you can add those dimensions later as you need them (without needing to redesign your tree hierarchy).

These labels are very dynamic, and I can change them in a second. For example, a MySQL server reporting as a “Master” might start reporting as a “Slave” in the very next second, and its data will be aggregated differently.

This is especially important in the modern, often cloud-based and virtualized world. For example, using Prometheus it is very easy to tag servers by their region or availability zones. I can also do things like computing MySQL space usage by both the database and storage engine. The possibilities are endless.

Data Capture

Unlike Graphite – where the main model is push and the hosts themselves choose what kind of information they want to push to the monitoring system and at which intervals – with Prometheus you set up “Exporters” that have the ability to export the data. It is up to the Prometheus server configuration to choose what data to sample and how frequently.

The clear benefit of Prometheus’ approach is that you can have as many servers as you like pulling the data, so it is very easy to create a development version of your system and play around with it – without affecting production. It also provides a simple pathway to high availability.

(Both the push and pull approaches have their benefits and drawbacks. Brian Brazil wrote an excellent article advertising the pull model of monitoring.)

Prometheus does create a few challenges for me. Unless I want to set up Service Discovery, it is a hassle to monitor any development/test VMs I might spin up (that would otherwise not be open to external access at all). While this isn’t the main use case for Prometheus, it is helpful for me to test the dashboard’s behavior with different operating systems, workloads, etc.

A more significant issue I discovered is dealing with some data that can’t be captured to multiple locations because the data capture causes the data to change.

Here is a specific example: if I look at the events_statements_summary_by_digest table in PERFORMANCE_SCHEMA, there is a MAX_TIMER_WAIT field that shows me what the maximum query execution time is for the query pattern. If I want to get the maximum query execution time for every minute, for example, I would need to “truncate” the table to reset the statistics and let the maximum value be computed again. If I don’t perform that operation, the data becomes meaningless. If I make the exporter reset the statistics during the poll, however, I can’t pull it from two Prometheus servers.

This is one instance where Prometheus’ performance schema design could be better. I could set up a Cron job or Event to clear out the statistics regularly and get a  proper maximum value for every five minutes, but that isn’t an overly convenient solution.

Another issue I discovered is that Prometheus doesn’t have any protection from bad (long) samples, or a very good method of detecting of them. Let’s imagine that I have a MySQL server and I’m sampling status data every second. For some reason the call to SHOW GLOBAL STATUS took five seconds to execute. The truth is we don’t really know where in those five seconds the SHOW GLOBAL STATUS output corresponds – it might be at very start, it might be at the very end. As such, you don’t really know how to process the counters. Whatever you do, you’re likely to be wrong. My preference in this case it to simply discard such samples, because even missing one percent of the samples is unlikely to change the whole picture. Constantly questioning whether you really had a couple of seconds where the QPS spiked to ten times the normal rate, or that it’s an invalid sample, is not something I on which I want to waste a lot of time!

My preferred approach is to configure the SHOW GLOBAL STATUS capture so that if it takes more than ten percent of the capture interval, it will be discarded. For example, with a one-second capture I would allow 100ms for the capture. If the system is not keeping up with this scale, I would be better to not fool myself and reduce the capture resolution to around five seconds.

The only protection Prometheus allows is to configure the scrape_timeout, but unfortunately, it is only limited to one-second resolution at this point. This is too coarse for any high-resolution capture.

Finally, it is also inconvenient to specify different resolutions for different data. In MySQL, there is often a lot of data that I want to capture, but the resolution needed for each capture is different. For example, SHOW GLOBAL STATUS with one-second resolution is the must. At the same time, capturing the table size information from INFORMATION_SCHEMA with a one-second resolution would put too much load on MySQL, especially if there are a lot of tables. That level of resolution, in this case, isn’t really needed.

An attractive thing about Prometheus is that the Prometheus development team uses it a lot for MySQL monitoring, so the MySQL Exporter is really good. Most MySQL monitoring plugins I find resort to reporting just a few basics statistics, which is not nearly enough for advanced diagnostics. The Prometheus MySQL exporter gets tons of stuff and has been adding more in every version.

I also very much like that the Prometheus Exporters are designed using HTTP protocol. This means it is very easy to debug or see what kind of data they capture. They present it simply using a web-browser:

HTTP browser

Computational Model

I think the basic operations in Prometheus are pretty intuitive, but if you look at some of the advanced behaviors you’re going to find some inconveniences and some things that are likely to surprise you.

One inconvenience is that Prometheus is mainly designed for working with high-resolution data. If there are more than five-minute holes (by default) in the time series, they could disappear from the graphs. As I mentioned, for MySQL there is quite a lot of information that it makes sense to capture at a resolution lower than five minutes.

Prometheus functions are looking in the “past,” and designed in a way that the value of the function at any time (T) when it could be computed is not going to change. It all looks clean and logical, but it causes issues with holes in the data capture.  

As an example, let’s imagine following five seconds where the total number of questions from the start successfully scrapped some seconds but not others (due to a network issue, overload, etc.):

1  –  10
2  –  20
3  –  X
4  –  X
5  –  200

 When we capture data of “counter” type the most important value it has is not the actual counter value at the given time but the rate of change of the counter at different time intervals. If in this case, for example, the query rate was ten QPS for intervals one through two seconds, this can be clearly computed. But what was the query rate in the three through four second interval? We don’t really have exact data, but that is fine: we know there have been 180 queries during the two through five second interval, giving us 60 QPS (which we can use for the three through four seconds interval).

This is NOT, however, how Prometheus will compute it if you use a high irate() function (which is suppose to give you highest resolution possible). When you evaluate irate() at T=4, it doesn’t have access to the T=5 value, even if it is in the database. Instead, it will look back and find the matching previous interval (one through two) and use the corresponding value of ten QPS.

I find this pretty puzzling and inconvenient.

There is also the rate() function, which can be used to get the average rate for the period.  Unfortunately, it can’t estimate the rate for a smaller period based on the available data for a longer period. So for example, if I ask rate() function to compute a query rate at T=4, looking one second back, it will return no data. This isn’t a big deal when you’re working with data manually, but if you’re building zoomable dashboards it means you can zoom in to the point where the data will disappear (rather than stopping at the best possible value available).


Prometheus has its own high-performance storage system which is based in part on LevelDB. It is highly optimized for time series and can achieve a very high level of compression. Be ready, though: all your label combinations will create a different time series on the low level, and will require a lot of files. This isn’t really a problem with SSD drives and modern file systems, but it something to look out for.

The capture engine and storage systems are rather efficient. Even though Prometheus does not have built-in clustering for “scaling out,” you can reportedly get more than 300K metrics per second captured on a single node. You can also use multiple Prometheus servers as needed.

The problem I found with Prometheus’ storage is that it is very self-contained: you can only use it from Prometheus or access it from the HTTP API. There are no tools at this point to export it for advanced analysis with R, or to dump the whole database into something like JSON format so it can be loaded into a different database engine. Some of these features might already be on the roadmap.

Purging and Aggregation

Retention configuration in Prometheus is pretty spartan. You can set storage.local.retention to the length you want to store the data, but that’s it. You can’t configure it to purge different data at different times. You can run multiple Prometheus instances to achieve this, but it’s quite a hassle.  It’s also not possible to instruct Prometheus to automatically build summaries in order to execute low-resolution queries faster.

For example, if I have MySQL’s query rate captured every second, but I want to view the data over a long time period (e.g., how it changed over last three months to estimate growth trends), data aggregated at hour intervals would be enough for that purpose.

There is support for recording rules to help achieve some of this, but it is not explicit or convenient in my opinion.

Looking at the Prometheus roadmap, some of these issues might not be fixed in Prometheus but achieved through integrating other systems such as InfluxDB (where experimental support already exists).


A lot of these limitations make sense if you look at the purpose for which Prometheus was created: getting high-resolution data and being able to provide as much troubleshooting information as possible to its Alerting engine. It is not really designed for storing extensive history. Too bad! I would very much like to get both of those properties in the single system!


As you install Prometheus, it has a built-in Expression Browser, which is great for debugging and interactive analyses. It also allows you to see what data you actually have in the database. It will disappoint you, however, if you’re looking for beautiful graphs!

HTTP installer

This shows I have the information about MySQL query rate from two servers, as well as the available and configured labels.

If I want to pick one server and look at the average rate of queries per five minutes, I can do this:

HTTP graphs

There are some tools available in the graph to chose the time range and resolution.

You should aware that visualizing data with the rate() function often shows you things that do not exist. In this case, it looks like the number of queries was gradually creeping up. In reality, I just started the benchmark so the number of queries jumped almost immediately. This is what the real situation looks like (using irate()):

HTTP graphs 2

As I explained before, irate() does not handle missing data points very well, plus it behaves somewhat bizarrely when you “zoom out” – providing instant rate information at sparse intervals (e.g., the instant rate computed every one second over 60 seconds) rather than smoothing things to averages.

There is also the PromDash tool available for Prometheus, which gives you nicer looking dashboards and supports a lot of Prometheus’ features. Now that Grafana has official support for Prometheus, it is my preferred tool to build dashboards – especially since it supports multiple data sources besides Prometheus.


I’m very excited about Prometheus. It allows me to get a lot of information easily and use it for Performance analysis in benchmarking or troubleshooting. It would be great if it also had a simple integrated solution for long term storage and trending. I am also looking forward to better integration with Grafana and better documentation on how to create Prometheus-based dashboards – especially with some Prometheus-based examples!  

Note: All above was written about Prometheus 0.16.1. Prometheus is rapidly evolving and may  change with newer versions.

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Opinions expressed by DZone contributors are their own.


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