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Memory usage of Prometheus client libraries

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.)

 

Want to know more about client library internals? Contact us.

Brian BrazilMemory usage of Prometheus client libraries
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