Month: January 2019

The popular monitoring software Prometheus has recently introduced (from 2.6) new knobs which regulate how many queries can be executed concurrently.

There are even the same knobs for different interfaces. Here they are with their default values:

• –storage.remote.read-concurrent-limit=10
• –query.max-concurrency=20

The latter is an upper-bound value to the former option. However, the question is:

How do you choose sensible values for them?

Well, I think I have the answer.

The number should be picked such that it does not exceed the number of threads of execution on your (virtual) machine. Ideally, it should be a bit lower because if your machine will encounter huge queries, it is (probably) going to also use the CPU for other operations such as sending the packets over a network.

I recently noticed empirically that executing a “huge” (let’s not delve into the definition here) query makes my Prometheus Docker container start using just a bit more than 100% of the CPU. This gave me an idea for this blog post.

Go has a concept called goroutines which are light-weight threads that are run later on “real” threads. Also, a single goroutine is an indivisible unit of work that can be scheduled only on one thread of execution at any time.  So, the question becomes: is more than one goroutine spawned during the parsing of a query?

Let’s delve into the code. We will go bottom-up: we are going to work our way upwards. Sorry if I will miss some kind of detail – you can find all of the exact information in the source code.

Prometheus has a type v1.API which handles the HTTP calls. The api.queryRange function gets spawned in a fresh, new goroutine which handles the request and returns the result. The API type itself has a member called QueryEngine which handles the lifetime of queries from the beginning till the end, and it is connected to a querier which queries the underlying TSDB.

In that function, a new range query using that querying engine is created with NewRangeQuery and then the Exec method is called on it which actually does the query. A context is passed to it which is used to limit the amount of time that it can take to perform the query.

For better or worse, the Prometheus code has a lot of types. Thus, to avoid blowing this post out of proportion and just copying, and pasting the source code, I will sum things up.

It trickles down to selecting blocks according to the specified time range. Then the blocks which are in that range are iterated over and a NewBlockQuerier is created, and then they are joined into a bigger querier which is returned for evaluating the expression that was passed. For the write-ahead-log, a segments querier is created which handles the queries that touch the WAL.

With remote read, it is a bit different. That mechanism employs what is called a fanout type which implements the queryable interface. In essence, it sends those queries to other Prometheus instances and merges them. Here, it may be that more goroutines are spawned but they are not performing much of active work – only sending a query and waiting for a result – thus we will not count them. The same principle of passing a context everywhere is used which limits the amount of time it can take.

The comment in prometheus/tsdb/db.go:801says:

// Querier returns a new querier over the data partition for the given time range.

// A goroutine must not handle more than one open Querier.

It is almost a mantra in the quality assurance world to always add a test case to your unit, integration, or any other tests whenever you find a new error in your software product which checks for exactly that case. Over time I have noticed that the same principle should be applied to monitoring.

Consider adding new alerting rules whenever you run into anomalous behavior and afterward you see the metrics which have indicated it. Treat it as if they are tests but they are “real-time” tests which provide not just more confidence in your software but also more transparency. After all, all tests do not show that there are no bugs in your program but that at least those defined use cases work:

After a while (and I am talking about only a few months of evolution) you will have a collection of tests which do, in fact, prove the absence of many bugs. Not all of course, but any relevant ones will be covered. — DonWells

It would be nice if we could add all of those alerting rules at the beginning but unfortunately, that is not possible because the connections between different metrics increase exponentially just like the different number of states that your program might have. However, not all is lost because we can at least add “simple” alerting rules which clearly show that something wrong is going on e.g. the amount of responses with HTTP code 500 has increased over the last 5 minutes.

It seems to me that in the future we might get that kind of alerting rules together with the software. One caveat is that the syntax of the alerting rules is different for almost each monitoring system. Hopefully, something like OpenMetrics project will be able to change the status quo. After all, metrics are just floating point values with timestamps and labels (a hash map of string pairs) attached to them. It should not be hard to invent a new, platform-agnostic syntax for these things.

Looks like that is already happening bit by bit. Developers and users are starting to use things like jsonnet to automate the generation of alerting rules, dashboards. For example, there is an initiative called “mix-ins” which are essentially small archived packages with jsonnet scripts and their supporting libraries so that you could install a “mix-in” for your software. However, it is still in beta but the future is looking bright.