IPMI Sensor and TimescaleDB Integration
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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 IPMI Sensor Plugin facilitates the collection of server health metrics directly from hardware via the IPMI protocol, querying sensor data from either local or remote systems.
This output plugin delivers a reliable and efficient mechanism for routing Telegraf collected metrics directly into TimescaleDB. By leveraging PostgreSQL’s robust ecosystem combined with TimescaleDB’s time series optimizations, it supports high-performance data ingestion and advanced querying capabilities.
Integration details
IPMI Sensor
The IPMI Sensor plugin is designed to gather bare metal metrics via the command line utility ipmitool
, which interfaces with the Intelligent Platform Management Interface (IPMI). This protocol provides management and monitoring capabilities for hardware components in server systems, allowing for the retrieval of critical system health metrics such as temperature, fan speeds, and power supply status from both local and remote servers. When configured without specified servers, the plugin defaults to querying the local machine’s sensor statistics using the ipmitool sdr
command. In scenarios covering remote hosts, authentication is supported through username and password using the command format ipmitool -I lan -H SERVER -U USERID -P PASSW0RD sdr
. This flexibility allows users to monitor systems effectively across various environments. The plugin also supports multiple sensor types, including chassis power status and DCMI power readings, catering to administrators needing real-time insight into server operations.
TimescaleDB
TimescaleDB is an open source time series database built as an extension to PostgreSQL, designed to handle large scale, time-oriented data efficiently. Launched in 2017, TimescaleDB emerged in response to the growing need for a robust, scalable solution that could manage vast volumes of data with high insert rates and complex queries. By leveraging PostgreSQL’s familiar SQL interface and enhancing it with specialized time series capabilities, TimescaleDB quickly gained popularity among developers looking to integrate time series functionality into existing relational databases. Its hybrid approach allows users to benefit from PostgreSQL’s flexibility, reliability, and ecosystem while providing optimized performance for time series data.
The database is particularly effective in environments that demand fast ingestion of data points combined with sophisticated analytical queries over historical periods. TimescaleDB has a number of innovative features like hypertables which transparently partition data into manageable chunks and built-in continuous aggregation. These allow for significantly improved query speed and resource efficiency.
Configuration
IPMI Sensor
[[inputs.ipmi_sensor]]
## Specify the path to the ipmitool executable
# path = "/usr/bin/ipmitool"
## Use sudo
## Setting 'use_sudo' to true will make use of sudo to run ipmitool.
## Sudo must be configured to allow the telegraf user to run ipmitool
## without a password.
# use_sudo = false
## Servers
## Specify one or more servers via a url. If no servers are specified, local
## machine sensor stats will be queried. Uses the format:
## [username[:password]@][protocol[(address)]]
## e.g. root:passwd@lan(127.0.0.1)
# servers = ["USERID:PASSW0RD@lan(192.168.1.1)"]
## Session privilege level
## Choose from: CALLBACK, USER, OPERATOR, ADMINISTRATOR
# privilege = "ADMINISTRATOR"
## Timeout
## Timeout for the ipmitool command to complete.
# timeout = "20s"
## Metric schema version
## See the plugin readme for more information on schema versioning.
# metric_version = 1
## Sensors to collect
## Choose from:
## * sdr: default, collects sensor data records
## * chassis_power_status: collects the power status of the chassis
## * dcmi_power_reading: collects the power readings from the Data Center Management Interface
# sensors = ["sdr"]
## Hex key
## Optionally provide the hex key for the IMPI connection.
# hex_key = ""
## Cache
## If ipmitool should use a cache
## Using a cache can speed up collection times depending on your device.
# use_cache = false
## Path to the ipmitools cache file (defaults to OS temp dir)
## The provided path must exist and must be writable
# cache_path = ""
TimescaleDB
# Publishes metrics to a TimescaleDB database
[[outputs.postgresql]]
## Specify connection address via the standard libpq connection string:
## host=... user=... password=... sslmode=... dbname=...
## Or a URL:
## postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full]
## See https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING
##
## All connection parameters are optional. Environment vars are also supported.
## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE
## All supported vars can be found here:
## https://www.postgresql.org/docs/current/libpq-envars.html
##
## Non-standard parameters:
## pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts.
## pool_min_conns (default: 0) - Minimum size of connection pool.
## pool_max_conn_lifetime (default: 0s) - Maximum connection age before closing.
## pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing.
## pool_health_check_period (default: 0s) - Duration between health checks on idle connections.
# connection = ""
## Postgres schema to use.
# schema = "public"
## Store tags as foreign keys in the metrics table. Default is false.
# tags_as_foreign_keys = false
## Suffix to append to table name (measurement name) for the foreign tag table.
# tag_table_suffix = "_tag"
## Deny inserting metrics if the foreign tag can't be inserted.
# foreign_tag_constraint = false
## Store all tags as a JSONB object in a single 'tags' column.
# tags_as_jsonb = false
## Store all fields as a JSONB object in a single 'fields' column.
# fields_as_jsonb = false
## Name of the timestamp column
## NOTE: Some tools (e.g. Grafana) require the default name so be careful!
# timestamp_column_name = "time"
## Type of the timestamp column
## Currently, "timestamp without time zone" and "timestamp with time zone"
## are supported
# timestamp_column_type = "timestamp without time zone"
## Templated statements to execute when creating a new table.
# create_templates = [
# '''CREATE TABLE {{ .table }} ({{ .columns }})''',
# ]
## Templated statements to execute when adding columns to a table.
## Set to an empty list to disable. Points containing tags for which there is
## no column will be skipped. Points containing fields for which there is no
## column will have the field omitted.
# add_column_templates = [
# '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
# ]
## Templated statements to execute when creating a new tag table.
# tag_table_create_templates = [
# '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''',
# ]
## Templated statements to execute when adding columns to a tag table.
## Set to an empty list to disable. Points containing tags for which there is
## no column will be skipped.
# tag_table_add_column_templates = [
# '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
# ]
## The postgres data type to use for storing unsigned 64-bit integer values
## (Postgres does not have a native unsigned 64-bit integer type).
## The value can be one of:
## numeric - Uses the PostgreSQL "numeric" data type.
## uint8 - Requires pguint extension (https://github.com/petere/pguint)
# uint64_type = "numeric"
## When using pool_max_conns > 1, and a temporary error occurs, the query is
## retried with an incremental backoff. This controls the maximum duration.
# retry_max_backoff = "15s"
## Approximate number of tag IDs to store in in-memory cache (when using
## tags_as_foreign_keys). This is an optimization to skip inserting known
## tag IDs. Each entry consumes approximately 34 bytes of memory.
# tag_cache_size = 100000
## Cut column names at the given length to not exceed PostgreSQL's
## 'identifier length' limit (default: no limit)
## (see https://www.postgresql.org/docs/current/limits.html)
## Be careful to not create duplicate column names!
# column_name_length_limit = 0
## Enable & set the log level for the Postgres driver.
# log_level = "warn" # trace, debug, info, warn, error, none
Input and output integration examples
IPMI Sensor
-
Centralized Monitoring Dashboard: Utilize the IPMI Sensor plugin to gather metrics from multiple servers and compile them into a centralized monitoring dashboard. This enables real-time visibility into server health across data centers. Administrators can track metrics like temperature and power usage, helping them make data-driven decisions about resource allocation, potential failures, and maintenance schedules.
-
Automated Power Alerts: Incorporate the plugin into an alerting system that monitors chassis power status and triggers alerts when anomalies are detected. For instance, if the power status indicates a failure or if watt values exceed expected thresholds, automated notifications can be sent to operations teams, ensuring prompt attention to hardware issues.
-
Energy Consumption Analysis: Leverage the DCMI power readings collected via the plugin to analyze energy consumption patterns of hardware over time. By integrating these readings with analytics platforms, organizations can identify opportunities to reduce power usage, optimize efficiency, and potentially decrease operational costs in large server farms or cloud infrastructures.
-
Health Check Automation: Schedule regular health checks by using the IPMI Sensor Plugin to collect data from a fleet of servers. This data can be logged and compared against historical performance metrics to identify trends, outliers, or signs of impending hardware failure, allowing IT teams to take proactive measures and reduce downtime.
TimescaleDB
-
Real-Time IoT Data Ingestion: Use the plugin to collect and store sensor data from thousands of IoT devices in real time. This setup facilitates immediate analysis, helping organizations monitor operational efficiency and respond quickly to changing conditions.
-
Cloud Application Performance Monitoring: Leverage the plugin to feed detailed performance metrics from distributed cloud applications into TimescaleDB. This integration supports real-time dashboards and alerts, enabling teams to swiftly identify and mitigate performance bottlenecks.
-
Historical Data Analysis and Reporting: Implement a system where long-term metrics are stored in TimescaleDB for comprehensive historical analysis. This approach allows businesses to perform trend analysis, generate detailed reports, and make data-driven decisions based on archived time-series data.
-
Adaptive Alerting and Anomaly Detection: Integrate the plugin with automated anomaly detection workflows. By continuously streaming metrics to TimescaleDB, machine learning models can analyze data patterns and trigger alerts when anomalies occur, enhancing system reliability and proactive maintenance.
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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