Ceph and M3DB Integration

Input and output integration overview

The Ceph plugin for Telegraf helps in gathering performance metrics from both MON and OSD nodes in a Ceph storage cluster for effective monitoring and management.

This plugin allows Telegraf to stream metrics to M3DB using the Prometheus Remote Write protocol, enabling scalable ingestion through the M3 Coordinator.

Integration details

Ceph

The Ceph Storage Telegraf plugin is designed to collect performance metrics from Monitor (MON) and Object Storage Daemon (OSD) nodes within a Ceph storage cluster. Ceph, a highly scalable storage system, integrates its metrics collection through this plugin, facilitating easy monitoring of its components. With the introduction of this plugin in the 13.x Mimic release, users can effectively gather detailed insights into the performance and health of their Ceph infrastructure. It functions by scanning configured socket directories for specific Ceph service socket files, executing commands via the Ceph administrative interface, and parsing the returned JSON data for metrics. The metrics are organized based on top-level keys, allowing for efficient monitoring and analysis of cluster performance. This plugin provides valuable capabilities for managing and maintaining the performance of a Ceph cluster by allowing administrators to understand system behavior and identify potential issues proactively.

M3DB

This configuration uses Telegraf’s HTTP output plugin with prometheusremotewrite format to send metrics directly to M3DB through the M3 Coordinator. M3DB is a distributed time series database designed for scalable, high-throughput metric storage. It supports ingestion of Prometheus remote write data via its Coordinator component, which manages translation and routing into the M3DB cluster. This approach enables organizations to collect metrics from systems that aren’t natively instrumented for Prometheus (e.g., Windows, SNMP, legacy systems) and ingest them efficiently into M3’s long-term, high-performance storage engine. The setup is ideal for high-scale observability stacks with Prometheus compatibility requirements.

Configuration

Ceph

[[inputs.ceph]]
  ## This is the recommended interval to poll. Too frequent and you
  ## will lose data points due to timeouts during rebalancing and recovery
  interval = '1m'

  ## All configuration values are optional, defaults are shown below

  ## location of ceph binary
  ceph_binary = "/usr/bin/ceph"

  ## directory in which to look for socket files
  socket_dir = "/var/run/ceph"

  ## prefix of MON and OSD socket files, used to determine socket type
  mon_prefix = "ceph-mon"
  osd_prefix = "ceph-osd"
  mds_prefix = "ceph-mds"
  rgw_prefix = "ceph-client"

  ## suffix used to identify socket files
  socket_suffix = "asok"

  ## Ceph user to authenticate as, ceph will search for the corresponding
  ## keyring e.g. client.admin.keyring in /etc/ceph, or the explicit path
  ## defined in the client section of ceph.conf for example:
  ##
  ##     [client.telegraf]
  ##         keyring = /etc/ceph/client.telegraf.keyring
  ##
  ## Consult the ceph documentation for more detail on keyring generation.
  ceph_user = "client.admin"

  ## Ceph configuration to use to locate the cluster
  ceph_config = "/etc/ceph/ceph.conf"

  ## Whether to gather statistics via the admin socket
  gather_admin_socket_stats = true

  ## Whether to gather statistics via ceph commands, requires ceph_user
  ## and ceph_config to be specified
  gather_cluster_stats = false

M3DB

# Configuration for sending metrics to M3
[outputs.http]
  ## URL is the address to send metrics to
  url = "https://M3_HOST:M3_PORT/api/v1/prom/remote/write"

  ## HTTP Basic Auth credentials
  username = "admin"
  password = "password"

  ## Data format to output.
  data_format = "prometheusremotewrite"

  ## Outgoing HTTP headers
  [outputs.http.headers]
    Content-Type = "application/x-protobuf"
    Content-Encoding = "snappy"
    X-Prometheus-Remote-Write-Version = "0.1.0"

Input and output integration examples

Ceph

1. Dynamic Monitoring Dashboard: Utilize the Ceph plugin to create a real-time monitoring dashboard that visually represents the performance metrics of your Ceph cluster. By integrating these metrics into a centralized dashboard, system administrators can gain immediate insights into the health of the storage infrastructure, which aids in quickly identifying and addressing potential issues before they escalate.

2. Automated Alerting System: Implement the Ceph plugin in conjunction with an alerting solution to automatically notify administrators of performance degradation or operational issues within the Ceph cluster. By defining thresholds for key metrics, organizations can ensure prompt response actions, thereby improving overall system reliability and performance.

3. Performance Benchmarking: Use the metrics collected by this plugin to conduct performance benchmarking tests across different configurations or hardware setups of your Ceph storage cluster. This process can assist organizations in identifying optimal configurations that enhance performance and resource utilization, promoting a more efficient storage environment.

4. Capacity Planning and Forecasting: Integrate the metrics gathered from the Ceph storage plugin into broader data analytics and reporting tools to facilitate capacity planning. By analyzing historical metrics, organizations can forecast future utilization trends, enabling informed decisions about scaling storage resources effectively.

M3DB

1. Large-Scale Cloud Infrastructure Monitoring: Deploy Telegraf agents across thousands of virtual machines and containers to collect metrics and stream them into M3DB through the M3 Coordinator. This provides reliable, long-term visibility with minimal storage overhead and high availability.

2. Legacy System Metrics Ingestion: Use Telegraf to gather metrics from older systems that lack native Prometheus exporters (e.g., Windows servers, SNMP devices) and forward them to M3DB via remote write. This bridges modern observability workflows with legacy infrastructure.

3. Centralized App Telemetry Aggregation: Collect application-specific telemetry using Telegraf’s plugin ecosystem (e.g., exec, http, jolokia) and push it into M3DB for centralized storage and query via PromQL. This enables unified analytics across diverse data sources.

4. Hybrid Cloud Observability: Install Telegraf agents on-prem and in the cloud to collect and remote-write metrics into a centralized M3DB cluster. This ensures consistent visibility across environments while avoiding the complexity of running Prometheus federation layers.

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