A common question around Prometheus client libraries is how much RAM they'll use on a busy process. There tends to be disbelief when we say it's the same as an inactive server. Let's look deeper.

The simplest way to test this is a small benchmark:

from prometheus_client import Counter import resource print("Before creating counters: ", resource.getrusage(0).ru_maxrss) counters = [] for i in range(1000): counters.append(Counter("counter{0}".format(i), "help")) print("After creating counters: ", resource.getrusage(0).ru_maxrss) for i in range(10): for c in counters: c.inc() print("After 10 increments each: ", resource.getrusage(0).ru_maxrss) for i in range(1000): for c in counters: c.inc() print("After 1000 increments each: ", resource.getrusage(0).ru_maxrss)

When run this produces for me:

('Before creating counters: ', 12792) ('After creating counters: ', 13844) ('After 10 increments each: ', 13844) ('After 1000 increments each: ', 13844)

So the claim that a busy server is going to use the same amount of RAM as a quiet server is shown to be true.

Why is this? Surely there's buffering going on of all the increments?

The answer is no. The counter is just a value that is updated in memory upon an increment. If you were to look at the core of what a client library does, ignoring all the concurrency handling it is simply the constant memory function:

def inc(self, amount) self.value += amount

Gauges are similarly simple, and Histograms are essentially just a convenient wrapper around a set of Counters; so both Gauges and Histograms are also constant memory. The quantiles in a Summary vary by implementation, it should be bounded in client libraries but if you're worried use a quantile-less Summary (which is two Counters) or a Histogram instead.

If you're wondering how Prometheus can work off just this single value rather than a stream of buffered events, check out How does a Prometheus Counter work?

(With the Java client the above claim is for practical purposes correct, but not the full truth. For performance it uses a Striped64, which grows its internal data structures when it encounters contention. However this growth is bounded based on the number of CPUs in the machine, and is thus constant memory.)

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