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SNMP and TimescaleDB Integration

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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 <a href="/integrations/snmp-influxdb/">SNMP and InfluxDB</a>.

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Infrastructure Monitoring with Telegraf

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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.

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Input and output integration overview

<p>The SNMP plugin allows you to collect a variety of metrics from SNMP (Simple Network Management Protocol) agents. It provides flexibility in how data is retrieved, whether collecting single metrics or entire tables.</p>

<p>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.</p>

Integration details

SNMP

<p>This plugin uses polling to gather metrics from SNMP agents, supporting retrieval of individual OIDs and complete SNMP tables. It can be configured to handle multiple SNMP versions, authentication, and other features.</p>

TimescaleDB

<p>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.</p> <p>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.</p>

Configuration

SNMP

<pre><code class="language-toml"> [[inputs.snmp]] agents = ["udp://127.0.0.1:161"] [[inputs.snmp.field]] oid = "RFC1213-MIB::sysUpTime.0" name = "sysUptime" conversion = "float(2)" [[inputs.snmp.field]] oid = "RFC1213-MIB::sysName.0" name = "sysName" is_tag = true [[inputs.snmp.table]] oid = "IF-MIB::ifTable" name = "interface" inherit_tags = ["sysName"] [[inputs.snmp.table.field]] oid = "IF-MIB::ifDescr" name = "ifDescr" is_tag = true </code></pre>

TimescaleDB

<pre><code class="language-toml"># 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 &gt; 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 &amp; set the log level for the Postgres driver. # log_level = "warn" # trace, debug, info, warn, error, none </code></pre>

Input and output integration examples

SNMP

<ol> <li><strong>Basic SNMP Configuration</strong>: Collect metrics from a local SNMP agent using typical SNMP community string settings. This setup is ideal for local monitoring of device performance.</li> <li><strong>Advanced SNMPv3 Setup</strong>: Securely collect metrics using SNMPv3 with authentication and encryption to enhance security. This configuration is recommended for production environments.</li> <li><strong>Collect Interface Metrics</strong>: Configure the plugin to collect interface metrics from the device’s SNMP table. Utilize fields to capture specific data points for traffic analysis.</li> <li><strong>Join Two SNMP Tables</strong>: By using translation fields, join data from two SNMP tables for a comprehensive view of correlated performance metrics.</li> </ol>

TimescaleDB

<ol> <li> <p><strong>Real-Time IoT Data Ingestion</strong>: 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.</p> </li> <li> <p><strong>Cloud Application Performance Monitoring</strong>: 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.</p> </li> <li> <p><strong>Historical Data Analysis and Reporting</strong>: 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.</p> </li> <li> <p><strong>Adaptive Alerting and Anomaly Detection</strong>: 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.</p> </li> </ol>

Feedback

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Useful Links

Documentation

Telegraf Quickstart Training

Infrastructure Monitoring with Telegraf

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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