HAProxy and MongoDB 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 MongoDB Telegraf Plugin enables users to send metrics to a MongoDB database, automatically managing time series collections.

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.

MongoDB

This plugin sends metrics to MongoDB and seamlessly integrates with its time series functionality, allowing for automatic creation of collections as time series when they don’t already exist. It requires MongoDB version 5.0 or higher to utilize the time series collections feature, which is vital for efficiently storing and querying time-based data. This plugin enhances the monitoring capabilities by ensuring that all relevant metrics are stored and organized correctly within MongoDB, providing users the ability to leverage MongoDB’s powerful querying and aggregation features for time series analysis.

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

MongoDB

[[outputs.mongodb]]
              # connection string examples for mongodb
              dsn = "mongodb://localhost:27017"
              # dsn = "mongodb://mongod1:27017,mongod2:27017,mongod3:27017/admin&replicaSet=myReplSet&w=1"

              # overrides serverSelectionTimeoutMS in dsn if set
              # timeout = "30s"

              # default authentication, optional
              # authentication = "NONE"

              # for SCRAM-SHA-256 authentication
              # authentication = "SCRAM"
              # username = "root"
              # password = "***"

              # for x509 certificate authentication
              # authentication = "X509"
              # tls_ca = "ca.pem"
              # tls_key = "client.pem"
              # # tls_key_pwd = "changeme" # required for encrypted tls_key
              # insecure_skip_verify = false

              # database to store measurements and time series collections
              # database = "telegraf"

              # granularity can be seconds, minutes, or hours.
              # configuring this value will be based on your input collection frequency.
              # see https://docs.mongodb.com/manual/core/timeseries-collections/#create-a-time-series-collection
              # granularity = "seconds"

              # optionally set a TTL to automatically expire documents from the measurement collections.
              # ttl = "360h"

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.

MongoDB

1. Dynamic Logging to MongoDB for IoT Devices: Utilize this plugin to collect and store metrics from a fleet of IoT devices in real-time. By sending device logs directly to MongoDB, you can create a centralized database that allows for easy access and querying of health metrics and performance data, enabling proactive maintenance and troubleshooting based on historical trends.

2. Time Series Analysis of Web Traffic: Use the MongoDB Telegraf Plugin to gather and analyze web traffic metrics over time. This application can help you understand peak usage times, user interactions, and behavior patterns, which can guide marketing strategies and infrastructure scaling decisions for improved user experience.

3. Automated Monitoring and Alerting System: Integrate the MongoDB plugin into an automated monitoring system that tracks application performance metrics. With time series collections, you can set up alerts based on specific thresholds, allowing your team to respond to potential issues before they affect users. This proactive management can enhance service reliability and overall performance.

4. Data Retention and TTL Management in Metrics Storage: Leverage the TTL feature for documents within MongoDB collections to auto-expire outdated metrics. This is particularly useful for environments where only recent performance data is relevant, preventing your MongoDB database from becoming cluttered with old metrics and ensuring efficient data management.

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