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Metric Drop & Aggregation Recommender

Try for free

Find metrics and labels that no Grafana dashboard, alert, recording rule or query uses. Get ranked drop & aggregation recommendations with savings, plus OTel Collector, Prometheus relabel and recording-rule configs.

Pricing

from $500.00 / 1,000 metrics analyzeds

Rating

0.0

(0)

Developer

Justin Hagerty

Actor stats

Bookmarked

Total users

Monthly active users

11 days ago

Last modified

Categories

Developer tools

Automation

You can access the Metric Drop & Aggregation Recommender programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

$echo '{
<  "metricInventory": "{\\n  \\"metrics\\": [\\n    {\\n      \\"name\\": \\"http_server_requests_total\\",\\n      \\"type\\": \\"counter\\",\\n      \\"series\\": 12000,\\n      \\"scrapeInterval\\": \\"30s\\",\\n      \\"labels\\": {\\n        \\"service\\": 8,\\n        \\"route\\": 40,\\n        \\"method\\": 5,\\n        \\"status_code\\": 6,\\n        \\"pod\\": 60,\\n        \\"env\\": [\\n          \\"prod\\",\\n          \\"staging\\"\\n        ]\\n      }\\n    },\\n    {\\n      \\"name\\": \\"http_server_request_duration_seconds\\",\\n      \\"type\\": \\"histogram\\",\\n      \\"buckets\\": 14,\\n      \\"series\\": 32000,\\n      \\"labels\\": {\\n        \\"service\\": 8,\\n        \\"route\\": 40,\\n        \\"method\\": 5,\\n        \\"pod\\": 60,\\n        \\"env\\": [\\n          \\"prod\\",\\n          \\"staging\\"\\n        ]\\n      }\\n    },\\n    {\\n      \\"name\\": \\"grpc_server_handled_total\\",\\n      \\"type\\": \\"counter\\",\\n      \\"series\\": 6000,\\n      \\"labels\\": {\\n        \\"grpc_service\\": 10,\\n        \\"grpc_method\\": 50,\\n        \\"grpc_code\\": 17,\\n        \\"pod\\": 60\\n      }\\n    },\\n    {\\n      \\"name\\": \\"go_memstats_alloc_bytes\\",\\n      \\"type\\": \\"gauge\\",\\n      \\"series\\": 60,\\n      \\"labels\\": {\\n        \\"service\\": 8,\\n        \\"pod\\": 60\\n      }\\n    },\\n    {\\n      \\"name\\": \\"go_memstats_heap_inuse_bytes\\",\\n      \\"type\\": \\"gauge\\",\\n      \\"series\\": 60,\\n      \\"labels\\": {\\n        \\"service\\": 8,\\n        \\"pod\\": 60\\n      }\\n    },\\n    {\\n      \\"name\\": \\"go_memstats_sys_bytes\\",\\n      \\"type\\": \\"gauge\\",\\n      \\"series\\": 60,\\n      \\"labels\\": {\\n        \\"service\\": 8,\\n        \\"pod\\": 60\\n      }\\n    },\\n    {\\n      \\"name\\": \\"kafka_consumer_lag\\",\\n      \\"type\\": \\"gauge\\",\\n      \\"series\\": 3000,\\n      \\"labels\\": {\\n        \\"topic\\": 30,\\n        \\"partition\\": 50,\\n        \\"consumer_group\\": 4,\\n        \\"pod\\": 20\\n      }\\n    }\\n  ]\\n}",
<  "dashboards": [
<    {
<      "title": "Service Overview",
<      "time": {
<        "from": "now-6h",
<        "to": "now"
<      },
<      "templating": {
<        "list": [
<          {
<            "name": "service",
<            "type": "query",
<            "query": "label_values(http_server_requests_total, service)"
<          }
<        ]
<      },
<      "panels": [
<        {
<          "id": 1,
<          "title": "Request rate",
<          "targets": [
<            {
<              "refId": "A",
<              "expr": "sum by (service) (rate(http_server_requests_total{env=\\"prod\\", service=~\\"$service\\"}[$__rate_interval]))"
<            }
<          ]
<        },
<        {
<          "id": 2,
<          "title": "p99 latency",
<          "targets": [
<            {
<              "refId": "A",
<              "expr": "histogram_quantile(0.99, sum by (le, service) (rate(http_server_request_duration_seconds_bucket{env=\\"prod\\"}[$__rate_interval])))"
<            }
<          ]
<        },
<        {
<          "id": 3,
<          "type": "row",
<          "title": "Kafka",
<          "collapsed": true,
<          "panels": [
<            {
<              "id": 4,
<              "title": "Lag by topic",
<              "targets": [
<                {
<                  "refId": "A",
<                  "expr": "max by (topic) (kafka_consumer_lag)"
<                }
<              ]
<            }
<          ]
<        }
<      ]
<    }
<  ],
<  "ruleFiles": [
<    "groups:\\n  - name: service-alerts\\n    rules:\\n      - alert: HighErrorRate\\n        expr: |\\n          sum by (service) (rate(http_server_requests_total{env=\\"prod\\", status_code=~\\"5..\\"}[5m]))\\n            / sum by (service) (rate(http_server_requests_total{env=\\"prod\\"}[5m])) > 0.05\\n        for: 10m\\n        annotations:\\n          summary: \\"High error rate on {{ $labels.service }}\\"\\n"
<  ]
<}' |
<apify call michiganpublictech/metric-usage-optimizer --silent --output-dataset

Prometheus & OTel Metric Drop and Aggregation Recommender API through CLI

The Apify CLI is the official tool that allows you to use Metric Drop & Aggregation Recommender locally, providing convenience functions and automatic retries on errors.

Install Apify CLI

Using installation script (macOS/Linux):

$curl -fsSL https://apify.com/install-cli.sh | bash

Using installation script (Windows):

$irm https://apify.com/install-cli.ps1 | iex

Using Homebrew:

$brew install apify-cli

Using npm:

$npm install -g apify-cli

Other API clients include:

Metric Drop & Aggregation Recommender API in Python

Metric Drop & Aggregation Recommender API in JavaScript

Metric Drop & Aggregation Recommender OpenAPI definition

Metric Drop & Aggregation Recommender API

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