Gauge and Counter allocate alot of memory

提供された Python 再現コードを prometheus-client 0.17.0 と Python 3.9 で実行し、個々のメトリクスオブジェクトと prometheus_client.REGISTRY の両方を測定することから始めます。完了条件は、報告されたメモリ割り当てが想定内であるかを確認するか、具体的なメモリ回帰を特定し、リポジトリに適切なものがあれば、焦点を絞ったテストまたはドキュメントの対象を示すことです。

Hey, I've noticed that a prometheus Gauge or Counter object needs alot of memory. Is this a normal/expected behaviour?

import prometheus_client
import objsize
labels={}
labels['test']='test'

print("Memory Registry at Start: " +str(objsize.get_deep_size(prometheus_client.REGISTRY)))
print("Memory Labels as dict: "+ str(objsize.get_deep_size(labels)))
gauge = prometheus_client.Gauge("test", "", list(sorted(labels.keys())))
gauge.labels(**labels).set(10)
print("Memory Gauge object: "+ str(objsize.get_deep_size(gauge)))
print("Memory Registry after one Gauge: "+str(objsize.get_deep_size(prometheus_client.REGISTRY)))
gauge2 = prometheus_client.Gauge("test2", "")
gauge2.set(10)
print("Memory Gauge object: "+ str(objsize.get_deep_size(gauge2)))
print("Memory Registry after second Gauge: "+str(objsize.get_deep_size(prometheus_client.REGISTRY)))
counter = prometheus_client.Counter("test3", "", list(sorted(labels.keys())))
counter.labels(**labels).inc(10)
print("Memory counter object: "+str(objsize.get_deep_size(counter)))
print("Memory Registry after Counter: "+str(objsize.get_deep_size(prometheus_client.REGISTRY)))
labels={}
labels['test']='test2'
gauge.labels(**labels).set(10)
print("Memory Gauge second label: "+str(objsize.get_deep_size(gauge)))
print("Memory Registry end: "+str(objsize.get_deep_size(prometheus_client.REGISTRY)))

results in this memory usage:

Memory Registry at Start: 3988
Memory Labels as dict: 285
Memory Gauge object: 2418
Memory Registry after one Gauge: 6725
Memory Gauge object: 1051
Memory Registry after second Gauge: 7723
Memory counter object: 2108
Memory Registry after Counter: 10027
Memory Gauge second label: 3376
Memory Registry end: 10931

In this Blog Post https://www.robustperception.io/memory-usage-of-prometheus-client-libraries/ ( I know it's old) a counter needed way less memory than in my usecase. I'm using python 3.9, on windows(but i've got the same problem in a docker container) and prometheus-client 0.17.0