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StatsD and PostgreSQL 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/statsd-influxdb/">StatsD 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 StatsD input plugin captures metrics from a StatsD server by running a listener service in the background, allowing for comprehensive performance monitoring and metric aggregation.</p>

<p>The Telegraf PostgreSQL plugin allows you to efficiently write metrics to a PostgreSQL database while automatically managing the database schema.</p>

Integration details

StatsD

<p>The StatsD input plugin is designed to gather metrics from a StatsD server by running a backgrounded StatsD listener service while Telegraf is active. This plugin leverages the format of the StatsD messages as established by the original Etsy implementation, which allows for various types of metrics including gauges, counters, sets, timings, histograms, and distributions. The capabilities of the StatsD plugin extend to parsing tags and extending the standard protocol with features that accommodate InfluxDB’s tagging system. It can handle messages sent via different protocols (UDP or TCP), manage multiple metric metrics effectively, and offers advanced configurations for optimal metric handling such as percentiles calculation and data transformation templates. This flexibility empowers users to track application performance comprehensively, making it an essential tool for robust monitoring setups.</p>

PostgreSQL

<p>The PostgreSQL plugin enables users to write metrics to a PostgreSQL database or a compatible database, providing robust support for schema management by automatically updating missing columns. The plugin is designed to facilitate integration with monitoring solutions, allowing users to efficiently store and manage time series data. It offers configurable options for connection settings, concurrency, and error handling, and supports advanced features such as JSONB storage for tags and fields, foreign key tagging, templated schema modifications, and support for unsigned integer data types through the pguint extension.</p>

Configuration

StatsD

<pre><code class="language-toml">[[inputs.statsd]] ## Protocol, must be "tcp", "udp4", "udp6" or "udp" (default=udp) protocol = "udp" ## MaxTCPConnection - applicable when protocol is set to tcp (default=250) max_tcp_connections = 250 ## Enable TCP keep alive probes (default=false) tcp_keep_alive = false ## Specifies the keep-alive period for an active network connection. ## Only applies to TCP sockets and will be ignored if tcp_keep_alive is false. ## Defaults to the OS configuration. # tcp_keep_alive_period = "2h" ## Address and port to host UDP listener on service_address = ":8125" ## The following configuration options control when telegraf clears it's cache ## of previous values. If set to false, then telegraf will only clear it's ## cache when the daemon is restarted. ## Reset gauges every interval (default=true) delete_gauges = true ## Reset counters every interval (default=true) delete_counters = true ## Reset sets every interval (default=true) delete_sets = true ## Reset timings &amp; histograms every interval (default=true) delete_timings = true ## Enable aggregation temporality adds temporality=delta or temporality=commulative tag, and ## start_time field, which adds the start time of the metric accumulation. ## You should use this when using OpenTelemetry output. # enable_aggregation_temporality = false ## Percentiles to calculate for timing &amp; histogram stats. percentiles = [50.0, 90.0, 99.0, 99.9, 99.95, 100.0] ## separator to use between elements of a statsd metric metric_separator = "_" ## Parses tags in the datadog statsd format ## http://docs.datadoghq.com/guides/dogstatsd/ ## deprecated in 1.10; use datadog_extensions option instead parse_data_dog_tags = false ## Parses extensions to statsd in the datadog statsd format ## currently supports metrics and datadog tags. ## http://docs.datadoghq.com/guides/dogstatsd/ datadog_extensions = false ## Parses distributions metric as specified in the datadog statsd format ## https://docs.datadoghq.com/developers/metrics/types/?tab=distribution#definition datadog_distributions = false ## Keep or drop the container id as tag. Included as optional field ## in DogStatsD protocol v1.2 if source is running in Kubernetes ## https://docs.datadoghq.com/developers/dogstatsd/datagram_shell/?tab=metrics#dogstatsd-protocol-v12 datadog_keep_container_tag = false ## Statsd data translation templates, more info can be read here: ## https://github.com/influxdata/telegraf/blob/master/docs/TEMPLATE_PATTERN.md # templates = [ # "cpu.* measurement*" # ] ## Number of UDP messages allowed to queue up, once filled, ## the statsd server will start dropping packets allowed_pending_messages = 10000 ## Number of worker threads used to parse the incoming messages. # number_workers_threads = 5 ## Number of timing/histogram values to track per-measurement in the ## calculation of percentiles. Raising this limit increases the accuracy ## of percentiles but also increases the memory usage and cpu time. percentile_limit = 1000 ## Maximum socket buffer size in bytes, once the buffer fills up, metrics ## will start dropping. Defaults to the OS default. # read_buffer_size = 65535 ## Max duration (TTL) for each metric to stay cached/reported without being updated. # max_ttl = "10h" ## Sanitize name method ## By default, telegraf will pass names directly as they are received. ## However, upstream statsd now does sanitization of names which can be ## enabled by using the "upstream" method option. This option will a) replace ## white space with '_', replace '/' with '-', and remove characters not ## matching 'a-zA-Z_\-0-9\.;='. #sanitize_name_method = "" ## Replace dots (.) with underscore (_) and dashes (-) with ## double underscore (__) in metric names. # convert_names = false ## Convert all numeric counters to float ## Enabling this would ensure that both counters and guages are both emitted ## as floats. # float_counters = false </code></pre>

PostgreSQL

<pre><code class="language-toml"># Publishes metrics to a postgresql 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 age of a connection 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 backoff 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 ## 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

StatsD

<ol> <li> <p><strong>Real-time Application Performance Monitoring</strong>: Utilize the StatsD input plugin to monitor application performance metrics in real-time. By configuring your application to send various metrics to a StatsD server, teams can leverage this plugin to analyze performance bottlenecks, track user activity, and ensure resource optimization dynamically. The combination of historical and real-time metrics allows for proactive troubleshooting and enhances the responsiveness of issue resolution processes.</p> </li> <li> <p><strong>Tracking User Engagement Metrics in Web Applications</strong>: Use the StatsD plugin to gather user engagement statistics, such as page views, click events, and interaction times. By sending these metrics to the StatsD server, businesses can derive valuable insights into user behavior, enabling them to make data-driven decisions to improve user experience and interface design based on quantitative feedback. This can significantly enhance the effectiveness of marketing strategies and product development efforts.</p> </li> <li> <p><strong>Infrastructure Health Monitoring</strong>: Deploy the StatsD plugin to monitor the health of your server infrastructure by tracking metrics such as resource utilization, server response times, and network performance. With this setup, DevOps teams can gain detailed visibility into system performance, effectively anticipating issues before they escalate. This enables a proactive approach to infrastructure management, minimizing downtimes and ensuring optimal service delivery.</p> </li> <li> <p><strong>Creating Comprehensive Service Dashboards</strong>: Integrate StatsD with visualization tools to create comprehensive dashboards that reflect the status and health of services across an architecture. For instance, combining data from multiple services logged through StatsD can transform raw metrics into actionable insights, showcasing system performance trends over time. This capability empowers stakeholders to maintain oversight and drive decisions based on visualized data sets, enhancing overall operational transparency.</p> </li> </ol>

PostgreSQL

<ol> <li> <p><strong>Real-Time Analytics with Complex Queries</strong>: Leverage the PostgreSQL plugin to store metrics from various sources in a PostgreSQL database, enabling real-time analytics using complex queries. This setup can help data scientists and analysts uncover patterns and trends, as they manipulate relational data across multiple tables while utilizing PostgreSQL’s robust query optimization features. Specifically, users can create sophisticated reports with JOIN operations across different metric tables, revealing insights that would typically remain hidden in embedded systems.</p> </li> <li> <p><strong>Integrating with TimescaleDB for Time-Series Data</strong>: Utilize the PostgreSQL plugin within a TimescaleDB instance to efficiently handle and analyze time-series data. By implementing hypertables, users can achieve greater performance and partitioning of topics over the time dimension. This integration allows users to run analytical queries over large amounts of time-series data while retaining the full power of PostgreSQL’s SQL queries, ensuring reliability and efficiency in metrics analysis.</p> </li> <li> <p><strong>Data Versioning and Historical Analysis</strong>: Implement a strategy using the PostgreSQL plugin to maintain different versions of metrics over time. Users can set up an immutable data table structure where older versions of tables are retained, enabling easy historical analysis. This approach not only provides insights into data evolution but also aids compliance with data retention policies, ensuring that the historical integrity of the datasets remains intact.</p> </li> <li> <p><strong>Dynamic Schema Management for Evolving Metrics</strong>: Use the plugin’s templating capabilities to create a dynamically changing schema that responds to metric variations. This use case allows organizations to adapt their data structure as metrics evolve, adding necessary fields and ensuring adherence to data integrity policies. By leveraging templated SQL commands, users can extend their database without manual intervention, facilitating agile data management practices.</p> </li> </ol>

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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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