OpenTelemetry and MongoDB Integration

Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.

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This is not the recommended configuration for real-time query at scale. For query and compression optimization, high-speed ingest, and high availability, you may want to consider OpenTelemetry and InfluxDB.

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Time series database
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Table of Contents

Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

See Ways to Get Started

Input and output integration overview

The OpenTelemetry Input Plugin enables the collection of observed data for analysis and monitoring.

The MongoDB Plugin allows you to send metrics to a MongoDB instance.

Integration details

OpenTelemetry

This plugin receives traces, metrics, and logs from OpenTelemetry clients and agents via gRPC. It supports configuration options for service address, connection timeout, message size, and attributes to be included as tags.

MongoDB

This plugin sends metrics to MongoDB, automatically creating time series collections where they don’t already exist. Time series collections require MongoDB 5.0+.

Configuration

OpenTelemetry

[[inputs.opentelemetry]]
  ## Override the default (0.0.0.0:4317) destination OpenTelemetry gRPC service
  ## address:port
  # service_address = "0.0.0.0:4317"

  ## Override the default (5s) new connection timeout
  # timeout = "5s"

  ## gRPC Maximum Message Size
  # max_msg_size = "4MB"

  ## Override the default span attributes to be used as line protocol tags.
  ## These are always included as tags:
  ## - trace ID
  ## - span ID
  ## Common attributes can be found here:
  ## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
  # span_dimensions = ["service.name", "span.name"]

  ## Override the default log record attributes to be used as line protocol tags.
  ## These are always included as tags, if available:
  ## - trace ID
  ## - span ID
  ## Common attributes can be found here:
  ## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
  ## When using InfluxDB for both logs and traces, be certain that log_record_dimensions
  ## matches the span_dimensions value.
  # log_record_dimensions = ["service.name"]

  ## Override the default profile attributes to be used as line protocol tags.
  ## These are always included as tags, if available:
  ## - profile_id
  ## - address
  ## - sample
  ## - sample_name
  ## - sample_unit
  ## - sample_type
  ## - sample_type_unit
  ## Common attributes can be found here:
  ## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
  # profile_dimensions = []

  ## Override the default (prometheus-v1) metrics schema.
  ## Supports: "prometheus-v1", "prometheus-v2"
  ## For more information about the alternatives, read the Prometheus input
  ## plugin notes.
  # metrics_schema = "prometheus-v1"

  ## Optional TLS Config.
  ## For advanced options: https://github.com/influxdata/telegraf/blob/v1.18.3/docs/TLS.md
  ##
  ## Set one or more allowed client CA certificate file names to
  ## enable mutually authenticated TLS connections.
  # tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]
  ## Add service certificate and key.
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"

MongoDB

[[outputs.mongodb]]
              # connection string examples for mongodb
              dsn = "mongodb://localhost:27017"
              # dsn = "mongodb://mongod1:27017,mongod2:27017,mongod3:27017/admin&replicaSet=myReplSet&w=1"

              # overrides serverSelectionTimeoutMS in dsn if set
              # timeout = "30s"

              # default authentication, optional
              # authentication = "NONE"

              # for SCRAM-SHA-256 authentication
              # authentication = "SCRAM"
              # username = "root"
              # password = "***"

              # for x509 certificate authentication
              # authentication = "X509"
              # tls_ca = "ca.pem"
              # tls_key = "client.pem"
              # # tls_key_pwd = "changeme" # required for encrypted tls_key
              # insecure_skip_verify = false

              # database to store measurements and time series collections
              # database = "telegraf"

              # granularity can be seconds, minutes, or hours.
              # configuring this value will be based on your input collection frequency.
              # see https://docs.mongodb.com/manual/core/timeseries-collections/#create-a-time-series-collection
              # granularity = "seconds"

              # optionally set a TTL to automatically expire documents from the measurement collections.
              # ttl = "360h"

Input and output integration examples

OpenTelemetry

  1. Basic Setup: Use this plugin to gather metrics from your OpenTelemetry-enabled applications running in a microservices architecture.
  2. Comprehensive Monitoring: Combine logs and traces to provide full visibility of your application performance and detect issues quickly.
  3. Data Enrichment: Enhance your metrics by including additional span dimensions and attributes, which can provide valuable context for analysis.

MongoDB

  1. Log Management: Integrate this plugin to send application logs directly to MongoDB for structured storage and flexible querying. You can analyze logs as time series data, aggregating logs by hour, day, or month.

  2. Metric Capture: Use the plugin to capture system metrics (CPU, memory usage) in real-time and store them in MongoDB. The time-series collections will allow for efficient queries over time ranges.

  3. Monitoring Solutions: Combine this output plugin with inputs from various sources, such as disk usage metrics, network statistics, or application performance data. It allows for consolidated monitoring dashboards with historical trends saved in MongoDB.

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.

Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

See Ways to Get Started

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