ActiveMQ and Parquet Integration

Input and output integration overview

The ActiveMQ Input Plugin collects metrics from the ActiveMQ message broker through its Console API, providing insights into the performance and status of message queues, topics, and subscribers.

This plugin writes metrics to parquet files, utilizing a schema based on the metrics grouped by name. It supports file rotation and buffered writing for optimal performance.

Integration details

ActiveMQ

The ActiveMQ Input Plugin interfaces with the ActiveMQ Console API to gather metrics related to queues, topics, and subscribers. ActiveMQ, a widely-used open-source message broker, supports various messaging protocols and provides a robust Web Console for management and monitoring. This plugin allows users to track essential metrics including queue sizes, consumer counts, and message counts across different ActiveMQ entities, thereby enhancing observability within messaging systems. Users can configure various parameters such as the WebConsole URL and basic authentication credentials to tailor the plugin to their environment. The metrics collected can be used for monitoring the health and performance of messaging applications, facilitating proactive management and troubleshooting.

Parquet

The Parquet output plugin for Telegraf writes metrics to parquet files, which are columnar storage formats optimized for analytics. By default, this plugin groups metrics by their name, writing them to a single file. If a metric’s schema does not align with existing schemas, those metrics are dropped. The plugin generates an Apache Arrow schema based on all grouped metrics, ensuring that the schema reflects the union of all fields and tags. It operates in a buffered manner, meaning it temporarily holds metrics in memory before writing them to disk for efficiency. Parquet files require proper closure to ensure readability, and this is crucial when using the plugin, as improper closure can lead to unreadable files. Additionally, the plugin supports file rotation after specific time intervals, preventing overwrites of existing files and schema conflicts when a file with the same name already exists.

Configuration

ActiveMQ

[[inputs.activemq]]
  ## ActiveMQ WebConsole URL
  url = "http://127.0.0.1:8161"

  ## Required ActiveMQ Endpoint
  ##   deprecated in 1.11; use the url option
  # server = "192.168.50.10"
  # port = 8161

  ## Credentials for basic HTTP authentication
  # username = "admin"
  # password = "admin"

  ## Required ActiveMQ webadmin root path
  # webadmin = "admin"

  ## Maximum time to receive response.
  # response_timeout = "5s"

  ## 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

Parquet

[[outputs.parquet]]
  ## Directory to write parquet files in. If a file already exists the output
  ## will attempt to continue using the existing file.
  # directory = "."
  
  ## Files are rotated after the time interval specified. When set to 0 no time
  ## based rotation is performed.
  # rotation_interval = "0h"
  
  ## Timestamp field name
  ## Field name to use to store the timestamp. If set to an empty string, then
  ## the timestamp is omitted.
  # timestamp_field_name = "timestamp"

Input and output integration examples

ActiveMQ

1. Proactive Queue Monitoring: Use the ActiveMQ plugin to monitor queue sizes in real-time for a high-volume trading application. This implementation allows teams to receive alerts when queue sizes exceed a certain threshold, enabling rapid response to potential downtime caused by backlogs, thereby ensuring continuous availability of trading operations.

2. Performance Baselines and Anomaly Detection: Integrate this plugin with machine learning frameworks to establish performance baselines for message throughput. By analyzing historical data collected through this plugin, teams can flag anomalies in processing rates, leading to quicker identification of issues impacting service reliability and performance.

3. Cross-Messaging System Analytics: Combine metrics from ActiveMQ with those from other messaging systems in a centralized dashboard. Users can visualize and compare performance data, such as enqueue and dequeue rates, providing valuable insights into the overall messaging architecture and assisting in optimizing the message flow between different brokers.

4. Subscriber Performance Insights: Leverage the subscriber metrics collected by this plugin to analyze behavior patterns and optimize configuration for consumer applications. Understanding metrics such as dispatched queue size and counter values can guide adjustments to improve processing efficiency and resource allocation.

Parquet

1. Data Lake Ingestion: Utilize the Parquet plugin to store metrics from various sources into a data lake. By writing metrics in parquet format, you establish a standardized and efficient way to manage time-series data, enabling faster querying capabilities and seamless integration with analytics tools like Apache Spark or AWS Athena. This setup can significantly improve data retrieval times and analysis workflows.

2. Long-term Storage of Metrics: Implement the Parquet plugin in a monitoring setup where metrics are collected over time from multiple applications. This allows for long-term storage of performance data in a compact format, making it cost-effective to store vast amounts of historical data while preserving the ability for quick retrieval and analysis later on. By archiving metrics in parquet files, organizations can maintain compliance and create detailed reports from historical performance trends.

3. Analytics and Reporting: After writing metrics to parquet files, leverage tools like Apache Arrow or PyArrow to perform complex analytical queries directly on the files without needing to load all the data into memory. This can enhance reporting capabilities, allowing teams to generate insights and visualization from large datasets efficiently, thereby improving decision-making processes based on accurate, up-to-date performance metrics.

4. Integrating with Data Warehouses: Use the Parquet plugin as part of a data integration pipeline that feeds into a modern data warehouse. By converting metrics to parquet format, the data can be easily ingested by systems like Snowflake or Google BigQuery, enabling powerful analytics and business intelligence capabilities that drive actionable insights from the collected metrics.

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.