HAProxy and Azure Data Explorer Integration

Input and output integration overview

This plugin gathers and reports statistics from HAProxy, a popular open-source load balancer and proxy server, to help in monitoring and optimizing its performance.

The Azure Data Explorer plugin allows integration of metrics collection with Azure Data Explorer, enabling users to analyze and query their telemetry data efficiently. With this plugin, users can configure ingestion settings to suit their needs and leverage Azure’s powerful analytical capabilities.

Integration details

HAProxy

The HAProxy plugin for Telegraf enables users to gather statistics directly from a HAProxy server via its stats socket or HTTP statistics page. HAProxy is a widely employed software load balancer and proxy server that provides high availability and performance for TCP and HTTP applications. By integrating with HAProxy, this plugin allows users to monitor and analyze various performance metrics such as active server counts, request rates, response codes, and session statuses in real-time, facilitating better decision-making and proactive management of network resources. Key features include support for both HTTP and socket-based metrics collection, compatibility with basic authentication for secure access, and configurable options for metric field naming, allowing for customization tailored to user preferences.

Azure Data Explorer

The Azure Data Explorer plugin allows users to write metrics, logs, and time series data collected from various Telegraf input plugins into Azure Data Explorer, Azure Synapse, and Real-Time Analytics in Fabric. This integration serves as a bridge, allowing applications and services to monitor their performance metrics or logs efficiently. Azure Data Explorer is optimized for analytics over large volumes of diverse data types, making it an excellent choice for real-time analytics and monitoring solutions in cloud environments. The plugin empowers users to configure metrics ingestion based on their requirements, define table schemas dynamically, and set various ingestion methods while retaining flexibility regarding roles and permissions needed for database operations. This supports scalable and secure monitoring setups for modern applications that utilize cloud services.

Configuration

HAProxy

[[inputs.haproxy]]
  ## List of stats endpoints. Metrics can be collected from both http and socket
  ## endpoints. Examples of valid endpoints:
  ##   - http://myhaproxy.com:1936/haproxy?stats
  ##   - https://myhaproxy.com:8000/stats
  ##   - socket:/run/haproxy/admin.sock
  ##   - /run/haproxy/*.sock
  ##   - tcp://127.0.0.1:1936
  ##
  ## Server addresses not starting with 'http://', 'https://', 'tcp://' will be
  ## treated as possible sockets. When specifying local socket, glob patterns are
  ## supported.
  servers = ["http://myhaproxy.com:1936/haproxy?stats"]

  ## By default, some of the fields are renamed from what haproxy calls them.
  ## Setting this option to true results in the plugin keeping the original
  ## field names.
  # keep_field_names = 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 & host verification
  # insecure_skip_verify = false

Azure Data Explorer

[[outputs.azure_data_explorer]]
  ## The URI property of the Azure Data Explorer resource on Azure
  ## ex: endpoint_url = https://myadxresource.australiasoutheast.kusto.windows.net
  endpoint_url = ""

  ## The Azure Data Explorer database that the metrics will be ingested into.
  ## The plugin will NOT generate this database automatically, it's expected that this database already exists before ingestion.
  ## ex: "exampledatabase"
  database = ""

  ## Timeout for Azure Data Explorer operations
  # timeout = "20s"

  ## Type of metrics grouping used when pushing to Azure Data Explorer.
  ## Default is "TablePerMetric" for one table per different metric.
  ## For more information, please check the plugin README.
  # metrics_grouping_type = "TablePerMetric"

  ## Name of the single table to store all the metrics (Only needed if metrics_grouping_type is "SingleTable").
  # table_name = ""

  ## Creates tables and relevant mapping if set to true(default).
  ## Skips table and mapping creation if set to false, this is useful for running Telegraf with the lowest possible permissions i.e. table ingestor role.
  # create_tables = true

  ##  Ingestion method to use.
  ##  Available options are
  ##    - managed  --  streaming ingestion with fallback to batched ingestion or the "queued" method below
  ##    - queued   --  queue up metrics data and process sequentially
  # ingestion_type = "queued"

Input and output integration examples

HAProxy

1. Dynamic Load Adjustment: Utilize the HAProxy plugin to monitor traffic patterns in real time, enabling automated adjustments to load balancing algorithms. By continuously gathering metrics on server loads and request rates, system administrators can dynamically allocate resources, ensuring that no single server becomes a bottleneck, thus enhancing overall application performance and availability.

2. Historical Performance Analytics: Integrate this plugin with a time series database to collect HAProxy metrics over time, allowing you to analyze historical performance and traffic trends. This can facilitate predictive analysis and planning for capacity, giving businesses insights into peak traffic times and helping to identify potential future resource needs.

3. Alerting on Anomalies: Implement alerting workflows that trigger when unusual patterns are detected in HAProxy metrics, such as sudden spikes in error rates or drops in request handling capacity. By leveraging this plugin, operations teams can receive timely notifications, allowing for swift intervention and minimizing the impact of potential downtime on end-users.

Azure Data Explorer

1. Real-Time Monitoring Dashboard: By integrating metrics from various services into Azure Data Explorer using this plugin, organizations can build comprehensive dashboards that reflect real-time performance metrics. This allows teams to respond proactively to performance issues and optimize system health without delay.

2. Centralized Log Management: Utilize Azure Data Explorer to consolidate logs from multiple applications and services. By utilizing the plugin, organizations can streamline their log analysis processes, making it easier to search, filter, and derive insights from historical data accumulated over time.

3. Data-Driven Alerting Systems: Enhance monitoring capabilities by configuring alerts based on metrics sent via this plugin. Organizations can set thresholds and automate incident responses, significantly reducing downtime and improving the reliability of critical operations.

4. Machine Learning Model Training: By leveraging the data sent to Azure Data Explorer, organizations can perform large-scale analytics and prepare the data for feeding into machine learning models. This plugin enables the structuring of data that can subsequently be used for predictive analytics, leading to enhanced decision-making capabilities.

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.