April 24, 2026 - Use it when you want to track distributions — request latency, response sizes — and need to calculate quantiles (p50, p95, p99) in your queries. from prometheus_client import Histogram h = Histogram('request_latency_seconds', 'Description of histogram') h.observe(4.7) # Observe 4.7 (seconds ...

hist = getattr(registry, '_openstacksdk_histogram', None) if not hist: hist = prometheus_client.Histogram( 'openstack_http_response_time', 'Time taken for an http response to an OpenStack service', labelnames=[ 'method', 'endpoint', 'service_type', 'status_code' ], registry=registry, ) registry._openstacksdk_histogram = hist return hist

Use it when you want to track ... (p50, p95, p99) in your queries. from prometheus_client import Histogram h = Histogram('request_latency_seconds', 'Description of histogram') h.observe(4.7) # Observe 4.7 (seconds in ...

Example for a Histogram: · from prometheus_client import Histogram · · h = Histogram('request_size_bytes', 'Request size (bytes)') h.observe(512) # Observe 512 (bytes) · Example for a Histogram using time: ...

In contrast to the usual counter-like histograms, gauge histograms are rarely directly exposed by instrumented programs and are thus not (yet) usable in instrumentation libraries, but they are represented in newer versions of the protobuf exposition format and in OpenMetrics . They are also created regularly by PromQL expressions. For example, the outcome of applying the rate function to a counter histogram is a gauge histogram, in the same way as the outcome of applying the rate function to a counter is a gauge.

May 28, 2025 - ... from prometheus_client import ... specified buckets request_duration_histogram = Histogram( 'http_request_duration_seconds', 'HTTP request duration in seconds', buckets=(0.1, 0.2, 0.3, 0.4, 0.5) ) # Simulate 20 request durations ...

November 15, 2023 - from prometheus_client import Histogram h = Histogram('request_latency_seconds', 'Description of histogram') h.observe(4.7, {'trace_id': 'abc123'}) Exemplars are only rendered in the OpenMetrics exposition format. If using the HTTP server or apps in this library, content negotiation can be ...

For a native histogram (including an NHCB), you extract the sum and count of observations with the functions histogram_sum and histogram_count, respectively. For example, to calculate the average request duration over the last 5m from a native histogram called http_request_duration_seconds, use the following PromQL expression:

January 26, 2026 - Python example: from prometheus_client import Histogram, start_http_server import time import random # Define histogram with custom buckets request_latency = Histogram( 'http_request_duration_seconds', 'HTTP request latency in seconds', ['method', ...

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Each time the histogram is scraped by Prometheus, the values are not reset. This means that the counts in each bucket are cumulative over the lifetime of the metric (at least in the memory of each process) and that it’s really the change in each bucket’s values the tells us the distribution of observations since the last scrape. Let’s put all of these ideas into practice. The examples below can all be found here along with a Docker Compose file for running a sample application and Prometheus.

May 28, 2025 - ... from prometheus_client import ... specified buckets request_duration_histogram = Histogram( 'http_request_duration_seconds', 'HTTP request duration in seconds', buckets=(0.1, 0.2, 0.3, 0.4, 0.5) ) # Simulate 20 request durations ...

from prometheus_client import Summary s = Summary('request_latency_seconds', 'Description of summary') s.observe(4.7) # Observe 4.7 (seconds in this case) ... The Python client doesn't store or expose quantile information at this time.

December 2, 2019 - For example, you could measure request duration for a specific HTTP request call using histograms. Rather than storing every duration for every request, Prometheus will make an approximation by storing the frequency of requests that fall into ...

May 17, 2022 - I am looking for a python code for histogram. To calculate response time and http request.

from prometheus_client import Summary s = Summary('request_latency_seconds', 'Description of summary') s.observe(4.7) # Observe 4.7 (seconds in this case) ... The Python client doesn't store or expose quantile information at this time.

A histogram SHOULD have the same default buckets as other client libraries. Buckets MUST NOT be changeable once the metric is created. ... Some way to time code for users in seconds. In Python this is the time() decorator/context manager. In Java this is startTimer/observeDuration.

To experiment with Histogram, create ... from prometheus_client import Histogram LATENCY = Histogram('server_latency_seconds', 'Time to serve a web page', buckets=[ 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20]) class ServerHandler(http.server.BaseHTTPRequestHandler): ...

Prometheus instrumentation library for Python applications - prometheus/client_python