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Introduce functions for interacting with Prometheus API (#257)
* Introduce functions for executing PromQL instant and range queries towards the Monitor Promehteus endpoints. * Add get_series endpoint * Add get_labels endpoint * Add get_label_values function. * Add get_metadata function. * In all functions expect both token and host to be passed as arguments. * poetry lock * Apply linting comments
1 parent 67ca483 commit 6b7be9f

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+1174
-12
lines changed

‎.github/workflows/ci-pull-request.yml

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@@ -15,11 +15,12 @@ jobs:
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matrix:
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python_version:
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# https://python-release-cycle.glitch.me/
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- "3.7"
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- "3.8"
2019
- "3.9"
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- "3.10"
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- "3.11"
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- "3.12"
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- "3.13"
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4

‎examples/get_data_promql_advanced.py

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#!/usr/bin/env python
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#
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# This script shows the basics of getting data out of Sysdig Monitor by executing a PromQL query
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# that returns the top 5 Kubernetes workloads consuming the highest percentage of their allocated CPU
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# by comparing actual usage to defined CPU limits. The query is executed over a 5-minute time window.
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#
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import sys
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import time
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from datetime import datetime
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from sdcclient import SdcClient
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def print_prometheus_results_as_table(results):
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if not results:
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print("No data found for the query.")
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return
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# Store time series data
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all_timestamps = set()
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label_keys = []
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time_series_by_label = {}
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for series in results:
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metric = series.get("metric", {})
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label = ','.join(f'{k}={v}' for k, v in sorted(metric.items()))
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label_keys.append(label)
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time_series_by_label[label] = {}
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for timestamp, value in series.get("values", []):
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ts = int(float(timestamp))
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all_timestamps.add(ts)
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time_series_by_label[label][ts] = value
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# Prepare header
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label_keys = sorted(set(label_keys))
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all_timestamps = sorted(all_timestamps)
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print(f"{'Timestamp':<25} | " + " | ".join(f"{label}" for label in label_keys))
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print("-" * (26 + len(label_keys) * 25))
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# Print each row, filling in missing values with "N/A"
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for ts in all_timestamps:
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dt = datetime.fromtimestamp(ts).isoformat()
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row_values = []
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for label in label_keys:
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value = time_series_by_label.get(label, {}).get(ts, "N/A")
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row_values.append(value)
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print(f"{dt:<25} | " + " | ".join(f"{val:>20}" for val in row_values))
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#
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# Parse arguments
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#
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if len(sys.argv) != 3:
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print(('usage: %s <sysdig-token> <hostname>' % sys.argv[0]))
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print('You can find your token at https://app.sysdigcloud.com/#/settings/user')
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sys.exit(1)
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sdc_token = sys.argv[1]
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hostname = sys.argv[2]
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sdclient = SdcClient(sdc_token, hostname)
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#
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# A PromQL query to execute. The query retrieves the top 5 workloads in a specific Kubernetes
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# cluster that are using the highest percentage of their allocated CPU resources. It calculates
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# this by comparing the actual CPU usage of each workload to the CPU limits set for them and
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# then ranks the results to show the top 5.
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#
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query = '''
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topk (5,
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sum by (kube_cluster_name, kube_namespace_name, kube_workload_name) (
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rate(
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sysdig_container_cpu_cores_used{
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kube_cluster_name="dev-cluster"
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}[10m]
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)
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)
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/
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sum by (kube_cluster_name, kube_namespace_name, kube_workload_name) (
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kube_pod_container_resource_limits{
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kube_cluster_name="dev-cluster",
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resource="cpu"
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}
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)
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)
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'''
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#
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# Time window:
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# - end is the current time
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# - start is the current time minus 5 minutes
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#
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end = int(time.time())
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start = end - 5 * 60 # 5 minutes ago
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#
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# Step:
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# - resolution step, how far should timestamp of each resulting sample be apart
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#
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step = 60
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#
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# Load data
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#
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ok, response_json = sdclient.get_data_promql(query, start, end, step)
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#
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# Show the result
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#
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if ok:
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#
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# Read the response. The JSON looks like this:
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#
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# {
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# "data": {
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# "result": [
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# {
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# "metric": {},
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# "values": [
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# [
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# 1744210080,
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# "0.58"
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# ],
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# [
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# 1744210140,
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# "0.58"
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# ],
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# [
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# 1744210200,
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# "0.58"
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# ],
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# [
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# 1744210260,
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# "0.5799999999999998"
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# ],
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# [
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# 1744210320,
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# "0.5799999999999998"
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# ],
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# [
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# 1744210380,
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# "0.5799999999999998"
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# ]
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# ]
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# }
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# ],
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# "resultType": "matrix"
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# },
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# "status": "success"
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# }
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#
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#
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# Print summary (what, when)
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#
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results = response_json.get("data", {}).get("result", [])
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print_prometheus_results_as_table(results)
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else:
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print(response_json)
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sys.exit(1)

‎examples/get_data_promql_instant_advanced.py

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#!/usr/bin/env python
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#
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# This script shows the basics of getting data out of Sysdig Monitor by executing a PromQL query
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# that returns the top 5 Kubernetes workloads consuming the highest percentage of their allocated CPU
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# by comparing actual usage to defined CPU limits. The query is executed at a timestamp 5 minutes ago.
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#
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import sys
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import time
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from datetime import datetime
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from sdcclient import SdcClient
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def print_prometheus_instant_result(result):
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if not result:
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print("No data found for the instant query.")
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return
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# Determine if any result has labels
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has_labels = any(entry.get("metric") for entry in result)
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if has_labels:
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print(f"{'Timestamp':<25} | {'Metric':<40} | {'Value':>10}")
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print("-" * 80)
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else:
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print(f"{'Timestamp':<25} | {'Value':>10}")
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print("-" * 40)
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for entry in result:
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timestamp, value = entry.get("value", [None, None])
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dt = datetime.fromtimestamp(float(timestamp)).isoformat() if timestamp else "N/A"
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metric = entry.get("metric", {})
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if has_labels:
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label_str = ', '.join(f'{k}="{v}"' for k, v in sorted(metric.items()))
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print(f"{dt:<25} | {label_str:<40} | {value:>10}")
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else:
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print(f"{dt:<25} | {value:>10}")
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#
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# Parse arguments
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#
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if len(sys.argv) != 3:
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print(('usage: %s <sysdig-token> <hostname>' % sys.argv[0]))
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print('You can find your token at https://app.sysdigcloud.com/#/settings/user')
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sys.exit(1)
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sdc_token = sys.argv[1]
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hostname = sys.argv[2]
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sdclient = SdcClient(sdc_token, hostname)
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#
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# A PromQL query to execute. The query retrieves the top 5 workloads in a specific Kubernetes
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# cluster that are using the highest percentage of their allocated CPU resources. It calculates
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# this by comparing the actual CPU usage of each workload to the CPU limits set for them and
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# then ranks the results to show the top 5.
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#
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query = '''
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topk(5,
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sum by (kube_cluster_name, kube_namespace_name, kube_workload_name) (
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rate(
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sysdig_container_cpu_cores_used{
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kube_cluster_name="dev-cluster"
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}[10m]
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)
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)
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/
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sum by (kube_cluster_name, kube_namespace_name, kube_workload_name) (
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kube_pod_container_resource_limits{
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kube_cluster_name="dev-cluster",
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resource="cpu"
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}
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)
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)
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'''
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#
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# Time:
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# - the parameter is optional; if not set, the current time is used
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#
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time = int(time.time()) - 5 * 60 # 5 minutes ago
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#
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# Load data
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#
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ok, response_json = sdclient.get_data_promql_instant(query, 1744273000)
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#
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# Show the result
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#
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if ok:
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#
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# Read the response. The JSON looks like this:
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#
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# {
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# "result": [
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# {
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# "metric": {},
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# "value": [
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# 1744272414,
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# "0.58"
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# ]
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# }
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# ],
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# "resultType": "vector"
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# }
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#
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#
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# Print summary (what, when)
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#
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results = response_json.get("data", {}).get("result", [])
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print_prometheus_instant_result(results)
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else:
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print(response_json)
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sys.exit(1)

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