All Integrations / StatsD and Elasticsearch Integration

StatsD and Elasticsearch Integration

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

Get This Integration for Free
info

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>.

5B+

Telegraf downloads

#1

Time series database
Source: DB Engines

1B+

Downloads of InfluxDB

2,800+

Contributors

Table of Contents

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

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 Elasticsearch Plugin seamlessly sends metrics to an Elasticsearch server. The plugin handles template creation and dynamic index management, and supports various Elasticsearch-specific features to ensure data is formatted correctly for storage and retrieval.</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>

Elasticsearch

<p>This plugin writes metrics to Elasticsearch, a distributed, RESTful search and analytics engine capable of storing large amounts of data in near real-time. It is designed to handle Elasticsearch versions 5.x through 7.x and utilizes its dynamic template features to manage data type mapping properly. The plugin supports advanced features such as template management, dynamic index naming, and integration with OpenSearch. It also allows configurations for authentication and health monitoring of the Elasticsearch nodes.</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>

Elasticsearch

<pre><code class="language-toml"> [[outputs.elasticsearch]] ## The full HTTP endpoint URL for your Elasticsearch instance ## Multiple urls can be specified as part of the same cluster, ## this means that only ONE of the urls will be written to each interval urls = [ "http://node1.es.example.com:9200" ] # required. ## Elasticsearch client timeout, defaults to "5s" if not set. timeout = "5s" ## Set to true to ask Elasticsearch a list of all cluster nodes, ## thus it is not necessary to list all nodes in the urls config option enable_sniffer = false ## Set to true to enable gzip compression enable_gzip = false ## Set the interval to check if the Elasticsearch nodes are available ## Setting to "0s" will disable the health check (not recommended in production) health_check_interval = "10s" ## Set the timeout for periodic health checks. # health_check_timeout = "1s" ## HTTP basic authentication details. ## HTTP basic authentication details # username = "telegraf" # password = "mypassword" ## HTTP bearer token authentication details # auth_bearer_token = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9" ## Index Config ## The target index for metrics (Elasticsearch will create if it not exists). ## You can use the date specifiers below to create indexes per time frame. ## The metric timestamp will be used to decide the destination index name # %Y - year (2016) # %y - last two digits of year (00..99) # %m - month (01..12) # %d - day of month (e.g., 01) # %H - hour (00..23) # %V - week of the year (ISO week) (01..53) ## Additionally, you can specify a tag name using the notation {{tag_name}} ## which will be used as part of the index name. If the tag does not exist, ## the default tag value will be used. # index_name = "telegraf-{{host}}-%Y.%m.%d" # default_tag_value = "none" index_name = "telegraf-%Y.%m.%d" # required. ## Optional Index Config ## Set to true if Telegraf should use the "create" OpType while indexing # use_optype_create = false ## Optional TLS Config # tls_ca = "/etc/telegraf/ca.pem" # tls_cert = "/etc/telegraf/cert.pem" # tls_key = "/etc/telegraf/key.pem" ## Use TLS but skip chain &amp; host verification # insecure_skip_verify = false ## Template Config ## Set to true if you want telegraf to manage its index template. ## If enabled it will create a recommended index template for telegraf indexes manage_template = true ## The template name used for telegraf indexes template_name = "telegraf" ## Set to true if you want telegraf to overwrite an existing template overwrite_template = false ## If set to true a unique ID hash will be sent as sha256(concat(timestamp,measurement,series-hash)) string ## it will enable data resend and update metric points avoiding duplicated metrics with different id's force_document_id = false ## Specifies the handling of NaN and Inf values. ## This option can have the following values: ## none -- do not modify field-values (default); will produce an error if NaNs or infs are encountered ## drop -- drop fields containing NaNs or infs ## replace -- replace with the value in "float_replacement_value" (default: 0.0) ## NaNs and inf will be replaced with the given number, -inf with the negative of that number # float_handling = "none" # float_replacement_value = 0.0 ## Pipeline Config ## To use a ingest pipeline, set this to the name of the pipeline you want to use. # use_pipeline = "my_pipeline" ## Additionally, you can specify a tag name using the notation {{tag_name}} ## which will be used as part of the pipeline name. If the tag does not exist, ## the default pipeline will be used as the pipeline. If no default pipeline is set, ## no pipeline is used for the metric. # use_pipeline = "{{es_pipeline}}" # default_pipeline = "my_pipeline" # # Custom HTTP headers # To pass custom HTTP headers please define it in a given below section # [outputs.elasticsearch.headers] # "X-Custom-Header" = "custom-value" ## Template Index Settings ## Overrides the template settings.index section with any provided options. ## Defaults provided here in the config # template_index_settings = { # refresh_interval = "10s", # mapping.total_fields.limit = 5000, # auto_expand_replicas = "0-1", # codec = "best_compression" # } </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>

Elasticsearch

<ol> <li> <p><strong>Time-based Indexing</strong>: Use this plugin to store metrics in Elasticsearch to index each metric based on the time collected. For example, CPU metrics can be stored in a daily index named<code>telegraf-2023.01.01</code>, allowing easy time-based queries and retention policies.</p> </li> <li> <p><strong>Dynamic Templates Management</strong>: Utilize the template management feature to automatically create a custom template tailored to your metrics. This allows you to define how different fields are indexed and analyzed without manually configuring Elasticsearch, ensuring an optimal data structure for querying.</p> </li> <li> <p><strong>OpenSearch Compatibility</strong>: If you are using AWS OpenSearch, you can configure this plugin to work seamlessly by activating compatibility mode, ensuring your existing Elasticsearch clients remain functional and compatible with newer cluster setups.</p> </li> </ol>

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.

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

Related Integrations

HTTP and InfluxDB Integration

The HTTP plugin collects metrics from one or more HTTP(S) endpoints. It supports various authentication methods and configuration options for data formats.

View Integration

Kafka and InfluxDB Integration

This plugin reads messages from Kafka and allows the creation of metrics based on those messages. It supports various configurations including different Kafka settings and message processing options.

View Integration

Kinesis and InfluxDB Integration

The Kinesis plugin allows for reading metrics from AWS Kinesis streams. It supports multiple input data formats and offers checkpointing features with DynamoDB for reliable message processing.

View Integration

Start building now

Download Telegraf today or learn more about InfluxDB.

telegraf Get started with InfluxDB
Product & Solutions
Developers
Company

548 Market St, PMB 77953
San Francisco, California 94104

© 2025 InfluxData Inc. All Rights Reserved.

Legal Security Cookie Policy Comparison