mongodb-query-exporter

Prometheus MongoDB query exporter

MongoDB aggregation query exporter for Prometheus.

Features

• Support for gauge metrics
• Pull and Push (Push is only supported for MongoDB >= 3.6)
• Supports multiple MongoDB servers
• Metric caching support

Note that this is not designed to be a replacement for the MongoDB exporter to instrument MongoDB internals. This application exports custom MongoDB metrics in the prometheus format based on the queries (aggregations) you want.

Installation

Get Prometheus MongoDB aggregation query exporter, either as a binaray from the latest release or packaged as a Docker image.

Helm Chart

For kubernetes users there is an official helm chart for the MongoDB query exporter. Please read the installation instructions here.

Docker

You can run the exporter using docker (This will start it using the example config provided in the example folder):

docker run -e MDBEXPORTER_CONFIG=/config/configv3.yaml -v $(pwd)/example:/config ghcr.io/raffis/mongodb-query-exporter:latest

Usage

$ mongodb-query-exporter

Use the -help flag to get help information.

If you use MongoDB Authorization, best practices is to create a dedicated readonly user with access to all databases/collections required:

1. Create a user with 'read' on your database, like the following (replace username/password/db!):

db.getSiblingDB("admin").createUser({
    user: "mongodb_query_exporter",
    pwd: "secret",
    roles: [
        { role: "read", db: "mydb" }
    ]
})

1. Set environment variable MONGODB_URI before starting the exporter:

export MDBEXPORTER_MONGODB_URI=mongodb://mongodb_query_exporter:secret@localhost:27017

Note: The URI is substituted using env variables ${MY_ENV}, given that you may also pass credentials from other env variables. See the example bellow.

If you use x.509 Certificates to Authenticate Clients, pass in username and authMechanism via connection options to the MongoDB uri. Eg:

mongodb://CN=myName,OU=myOrgUnit,O=myOrg,L=myLocality,ST=myState,C=myCountry@localhost:27017/?authMechanism=MONGODB-X509

Credentials from env variables

You can pass in credentials from env variables.

Given the following URI the exporter will look for the ENV variables called MY_USERNAME and MY_PASSWORD and automatically use them at the referenced position within the URI.

export MY_USERNAME=mongodb_query_exporter
export MY_PASSWORD=secret
export MDBEXPORTER_MONGODB_URI=mongodb://${MY_USERNAME}:${MY_PASSWORD}@localhost:27017

Access metrics

The metrics are by default exposed at /metrics.

curl localhost:9412/metrics

Configuration

The exporter is looking for a configuration in ~/.mongodb_query_exporter/config.yaml and /etc/mongodb_query_exporter/config.yaml or if set the path from the env MDBEXPORTER_CONFIG.

You may also use env variables to configure the exporter:

Note if you have multiple MongoDB servers you can inject an env variable for each instead using MDBEXPORTER_MONGODB_URI:

1. MDBEXPORTER_SERVER_0_MONGODB_URI=mongodb://srv1:27017
2. MDBEXPORTER_SERVER_1_MONGODB_URI=mongodb://srv2:27017
3. ...

Configure your metrics

Since the v1.0.0 release you should use the config version v3.0 to profit from the latest features. See the configuration version matrix bellow.

Example:

version: 3.0
bind: 0.0.0.0:9412
log:
  encoding: json
  level: info
  development: false
  disableCaller: false
global:
  queryTimeout: 3s
  maxConnection: 3
  defaultCache: 0
servers:
- name: main
  uri: mongodb://localhost:27017
aggregations:
- database: mydb
  collection: objects
  servers: [main] #Can also be empty, if empty the metric will be used for every server defined
  metrics:
  - name: myapp_example_simplevalue_total
    type: gauge #Can also be empty, the default is gauge
    help: 'Simple gauge metric'
    value: total
    overrideEmpty: true # if an empty result set is returned..
    emptyValue: 0       # create a metric with value 0
    labels: []
    constLabels: []
  cache: 0
  mode: pull
  pipeline: |
    [
      {"$count":"total"}
    ]
- database: mydb
  collection: queue
  metrics:
  - name: myapp_example_processes_total
    type: gauge
    help: 'The total number of processes in a job queue'
    value: total
    labels: [type,status]
    constLabels: []
  mode: pull
  pipeline: |
    [
      {"$group": {
        "_id":{"status":"$status","name":"$class"},
        "total":{"$sum":1}
      }},
      {"$project":{
        "_id":0,
        "type":"$_id.name",
        "total":"$total",
        "status": {
          "$switch": {
              "branches": [
                 { "case": { "$eq": ["$_id.status", 0] }, "then": "waiting" },
                 { "case": { "$eq": ["$_id.status", 1] }, "then": "postponed" },
                 { "case": { "$eq": ["$_id.status", 2] }, "then": "processing" },
                 { "case": { "$eq": ["$_id.status", 3] }, "then": "done" },
                 { "case": { "$eq": ["$_id.status", 4] }, "then": "failed" },
                 { "case": { "$eq": ["$_id.status", 5] }, "then": "canceled" },
                 { "case": { "$eq": ["$_id.status", 6] }, "then": "timeout" }
              ],
              "default": "unknown"
          }}
      }}
    ]
- database: mydb
  collection: events
  metrics:
  - name: myapp_events_total
    type: gauge
    help: 'The total number of events (created 1h ago or newer)'
    value: count
    labels: [type]
    constLabels: []
  mode: pull
  # Note $$NOW is only supported in MongoDB >= 4.2
  pipeline: |
    [
      { "$sort": { "created": -1 }},
      {"$limit": 100000},
      {"$match":{
        "$expr": {
          "$gte": [
            "$created",
            {
              "$subtract": ["$$NOW", 3600000]
            }
          ]
        }
      }},
      {"$group": {
        "_id":{"type":"$type"},
        "count":{"$sum":1}
      }},
      {"$project":{
        "_id":0,
        "type":"$_id.type",
        "count":"$count"
      }}
    ]

See more examples in the /example folder.

Supported config versions

Cache & Push

Prometheus is designed to scrape metrics. During each scrape the mongodb-query-exporter will evaluate all configured metrics. If you have expensive queries there is an option to cache the aggregation result by setting a cache ttl. However it is more effective to avoid cache and design good aggregation pipelines. In some cases a different scrape interval might also be a solution. For individual aggregations and/or MongoDB servers older than 3.6 it might still be a good option though.

A better approach is using push instead a static cache, see bellow.

Example:

aggregations:
- metrics:
  - name: myapp_example_simplevalue_total
    help: 'Simple gauge metric which is cached for 5min'
    value: total
  servers: [main]
  mode: pull
  cache: 5m
  database: mydb
  collection: objects
  pipeline: |
    [
      {"$count":"total"}
    ]

To reduce load on the MongoDB server (and also scrape time) there is a push mode. Push automatically caches the metric at scrape time preferred (If no cache ttl is set). However the cache for a metric with mode push will be invalidated automatically if anything changes within the configured MongoDB collection. Meaning the aggregation will only be executed if there have been changes during scrape intervals.

Note: This requires at least MongoDB 3.6.

Example:

aggregations:
- metrics:
  - name: myapp_example_simplevalue_total
    help: 'Simple gauge metric'
    value: total
  servers: [main]
  # With the mode push the pipeline is only executed if a change occured on the collection called objects
  mode: push
  database: mydb
  collection: objects
  pipeline: |
    [
      {"$count":"total"}
    ]

Debug

The mongodb-query-exporters also publishes a counter metric called mongodb_query_exporter_query_total which counts query results for each configured aggregation. Furthermore you might increase the log level to get more insight.

Used by

• The balloon helm chart implements the mongodb-query-exporter to expose general stats from the MongoDB like the number of total nodes or files stored internally or externally. See the config-map here.

Please submit a PR if your project should be listed here!