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HTTP and Azure Application Insights 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/http-influxdb/">HTTP and InfluxDB</a>.

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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.

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Input and output integration overview

<p>The HTTP plugin allows for the collection of metrics from specified HTTP endpoints, handling various data formats and authentication methods.</p>

<p>This plugin writes Telegraf metrics to Azure Application Insights, enabling powerful monitoring and diagnostics.</p>

Integration details

HTTP

<p>The HTTP plugin collects metrics from one or more HTTP(S) endpoints, which should have metrics formatted in one of the supported input data formats. It also supports secrets from secret-stores for various authentication options and includes globally supported configuration settings.</p>

Azure Application Insights

<p>The Azure Application Insights plugin integrates Telegraf with Azure’s Application Insights service, facilitating the seamless transmission of metrics from various sources to a centralized monitoring platform. This plugin empowers users to harness the capabilities of Azure Application Insights, a powerful application performance management tool, allowing developers and IT operations teams to gain valuable insights into the performance, availability, and usage of their applications. By employing this plugin, users can monitor application telemetry and operational data efficiently, contributing to better diagnostics and improved application performance.</p> <p>Key features of this plugin include the ability to specify an instrumentation key for the Application Insights resource, configure the endpoint URL for tracking, and enable additional diagnostic logging for a more comprehensive analysis. Furthermore, the plugin provides context tagging capabilities, allowing the addition of specific Application Insights context tags to enhance the contextual information associated with metrics being sent. These features collectively make the Azure Application Insights Output Plugin a vital tool for organizations looking to optimize their monitoring capabilities within Azure.</p>

Configuration

HTTP

<pre><code class="language-toml">[[inputs.http]] ## One or more URLs from which to read formatted metrics. urls = [ "http://localhost/metrics", "http+unix:///run/user/420/podman/podman.sock:/d/v4.0.0/libpod/pods/json" ] ## HTTP method # method = "GET" ## Optional HTTP headers # headers = {"X-Special-Header" = "Special-Value"} ## HTTP entity-body to send with POST/PUT requests. # body = "" ## HTTP Content-Encoding for write request body, can be set to "gzip" to ## compress body or "identity" to apply no encoding. # content_encoding = "identity" ## Optional Bearer token settings to use for the API calls. ## Use either the token itself or the token file if you need a token. # token = "eyJhbGc...Qssw5c" # token_file = "/path/to/file" ## Optional HTTP Basic Auth Credentials # username = "username" # password = "pa$$word" ## OAuth2 Client Credentials. The options 'client_id', 'client_secret', and 'token_url' are required to use OAuth2. # client_id = "clientid" # client_secret = "secret" # token_url = "https://indentityprovider/oauth2/v1/token" # scopes = ["urn:opc:idm:__myscopes__"] ## HTTP Proxy support # use_system_proxy = false # http_proxy_url = "" ## Optional TLS Config ## Set to true/false to enforce TLS being enabled/disabled. If not set, ## enable TLS only if any of the other options are specified. # tls_enable = ## Trusted root certificates for server # tls_ca = "/path/to/cafile" ## Used for TLS client certificate authentication # tls_cert = "/path/to/certfile" ## Used for TLS client certificate authentication # tls_key = "/path/to/keyfile" ## Password for the key file if it is encrypted # tls_key_pwd = "" ## Send the specified TLS server name via SNI # tls_server_name = "kubernetes.example.com" ## Minimal TLS version to accept by the client # tls_min_version = "TLS12" ## List of ciphers to accept, by default all secure ciphers will be accepted ## See https://pkg.go.dev/crypto/tls#pkg-constants for supported values. ## Use "all", "secure" and "insecure" to add all support ciphers, secure ## suites or insecure suites respectively. # tls_cipher_suites = ["secure"] ## Renegotiation method, "never", "once" or "freely" # tls_renegotiation_method = "never" ## Use TLS but skip chain &amp; host verification # insecure_skip_verify = false ## Optional Cookie authentication # cookie_auth_url = "https://localhost/authMe" # cookie_auth_method = "POST" # cookie_auth_username = "username" # cookie_auth_password = "pa$$word" # cookie_auth_headers = { Content-Type = "application/json", X-MY-HEADER = "hello" } # cookie_auth_body = '{"username": "user", "password": "pa$$word", "authenticate": "me"}' ## cookie_auth_renewal not set or set to "0" will auth once and never renew the cookie # cookie_auth_renewal = "5m" ## Amount of time allowed to complete the HTTP request # timeout = "5s" ## List of success status codes # success_status_codes = [200] ## Data format to consume. ## Each data format has its own unique set of configuration options, read ## more about them here: ## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md # data_format = "influx" </code></pre>

Azure Application Insights

<pre><code class="language-toml">[[outputs.application_insights]] ## Instrumentation key of the Application Insights resource. instrumentation_key = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxx" ## Regions that require endpoint modification https://docs.microsoft.com/en-us/azure/azure-monitor/app/custom-endpoints # endpoint_url = "https://dc.services.visualstudio.com/v2/track" ## Timeout for closing (default: 5s). # timeout = "5s" ## Enable additional diagnostic logging. # enable_diagnostic_logging = false ## NOTE: Due to the way TOML is parsed, tables must be at the END of the ## plugin definition, otherwise additional config options are read as part of ## the table ## Context Tag Sources add Application Insights context tags to a tag value. ## ## For list of allowed context tag keys see: ## https://github.com/microsoft/ApplicationInsights-Go/blob/master/appinsights/contracts/contexttagkeys.go # [outputs.application_insights.context_tag_sources] # "ai.cloud.role" = "kubernetes_container_name" # "ai.cloud.roleInstance" = "kubernetes_pod_name" </code></pre>

Input and output integration examples

HTTP

<ol> <li><strong>Collecting Metrics from Localhost:</strong> The plugin can fetch metrics from an HTTP endpoint like <code>http://localhost/metrics</code>, allowing for easy local monitoring.</li> <li><strong>Using Unix Domain Sockets:</strong> You can specify metrics collection from services over Unix domain sockets by using the http+unix scheme, for example, <code>http+unix:///path/to/service.sock:/api/endpoint</code>.</li> </ol>

Azure Application Insights

<ol> <li> <p><strong>Application Performance Monitoring</strong>: Utilize the Azure Application Insights plugin to continuously monitor the performance of your web applications or microservices. By sending Telegraf metrics directly to Application Insights, teams can visualize real-time application performance data, enabling proactive tuning and optimization of application resources. This setup not only enhances the reliability of applications but also ensures user satisfaction through consistent performance monitoring.</p> </li> <li> <p><strong>Integrated Logging and Telemetry</strong>: Combine this plugin with centralized logging solutions to provide a comprehensive observability stack. By sending telecom data to Azure Application Insights, teams can correlate performance metrics with log data and gain deeper insights into application behavior, allowing for more efficient troubleshooting and root cause analysis.</p> </li> <li> <p><strong>Contextual Monitoring of Cloud Resources</strong>: Use the context tagging feature to enrich your application metrics with specific contextual information related to your cloud environment. This enhanced context can be invaluable for understanding the performance of cloud-native applications, enabling better scaling decisions and resource management based on real usage patterns.</p> </li> <li> <p><strong>Real-time Alerts Setup</strong>: Configure Application Insights to trigger alerts based on specific metrics received via this plugin. This allows teams to be notified of performance degradation or anomalies in real-time, enabling immediate action to mitigate issues and maintain high availability of applications.</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.

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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.

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