Apache Zookeeper and Apache Druid Integration

Input and output integration overview

The Zookeeper Telegraf plugin collects and reports metrics from Zookeeper servers, facilitating monitoring and performance analysis. It utilizes the ‘mntr’ command output to gather essential statistics critical for maintaining Zookeeper’s operational health.

This plugin allows Telegraf to send JSON-formatted metrics to Apache Druid over HTTP, enabling real-time ingestion for analytical queries on high-volume time-series data.

Integration details

Apache Zookeeper

The Zookeeper plugin for Telegraf is designed to collect vital statistics from Zookeeper servers by executing the ‘mntr’ command. This plugin serves as a monitoring tool that captures important metrics related to Zookeeper’s performance, including connection details, latency, and various operational statistics, facilitating the assessment of the health and efficiency of Zookeeper deployments. In contrast to the Prometheus input plugin, which is recommended when the Prometheus metrics provider is enabled, the Zookeeper plugin accesses raw output from the ‘mntr’ command, rendering it tailored for configurations that do not adopt Prometheus for metrics reporting. This unique approach allows administrators to gather Java Properties formatted metrics directly from Zookeeper, ensuring comprehensive visibility into Zookeeper’s operational state and enabling timely responses to performance anomalies. It specifically excels in environments where Zookeeper operates as a centralized service for maintaining configuration information and names for distributed systems, thus providing immeasurable insights essential for troubleshooting and capacity planning.

Apache Druid

This configuration uses Telegraf’s HTTP output plugin with json data format to send metrics directly to Apache Druid, a real-time analytics database designed for fast, ad hoc queries on high-ingest time-series data. Druid supports ingestion via HTTP POST to various components like the Tranquility service or native ingestion endpoints. The JSON format is ideal for structuring Telegraf metrics into event-style records for Druid’s columnar and time-partitioned storage engine. Druid excels at powering interactive dashboards and exploratory queries across massive datasets, making it an excellent choice for real-time observability and monitoring analytics when integrated with Telegraf.

Configuration

Apache Zookeeper

[[inputs.zookeeper]]
  ## An array of address to gather stats about. Specify an ip or hostname
  ## with port. ie localhost:2181, 10.0.0.1:2181, etc.

  ## If no servers are specified, then localhost is used as the host.
  ## If no port is specified, 2181 is used
  servers = [":2181"]

  ## Timeout for metric collections from all servers. Minimum timeout is "1s".
  # timeout = "5s"

  ## Float Parsing - the initial implementation forced any value unable to be
  ## parsed as an int to be a string. Setting this to "float" will attempt to
  ## parse float values as floats and not strings. This would break existing
  ## metrics and may cause issues if a value switches between a float and int.
  # parse_floats = "string"

  ## Optional TLS Config
  # enable_tls = false
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## If false, skip chain & host verification
  # insecure_skip_verify = true

Apache Druid

[[outputs.http]]
  ## Druid ingestion endpoint (e.g., Tranquility, HTTP Ingest, or Kafka REST Proxy)
  url = "http://druid-ingest.example.com/v1/post"

  ## Use POST method to send events
  method = "POST"

  ## Data format for Druid ingestion (expects JSON format)
  data_format = "json"

  ## Optional headers (may vary depending on Druid setup)
  # [outputs.http.headers]
  #   Content-Type = "application/json"
  #   Authorization = "Bearer YOUR_API_TOKEN"

  ## Optional timeout and TLS settings
  timeout = "10s"
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"
  # insecure_skip_verify = false

Input and output integration examples

Apache Zookeeper

1. Cluster Health Monitoring: Integrate the Zookeeper plugin to monitor the health and performance of a distributed application relying on Zookeeper for configuration management and service discovery. By tracking metrics such as session count, latency, and data size, DevOps teams can identify potential issues before they escalate, ensuring high availability and reliability across applications.

2. Performance Benchmarks: Utilize the plugin to benchmark Zookeeper performance in varying workload scenarios. This not only helps in understanding how Zookeeper behaves under load but also assists in tuning configurations to optimize throughput and reduce latency during peak operations.

3. Alerting for Anomalies: Combine this plugin with alerting tools to create a proactive monitoring system that notifies engineers if specific Zookeeper metrics exceed threshold limits, such as open file descriptor counts or high latency values. This enables teams to respond promptly to issues that could impact service reliability.

4. Historical Data Analysis: Store the metrics collected by the Zookeeper plugin in a time-series database to analyze historical performance trends. This allows teams to evaluate the impact of changes over time, assess the effectiveness of scaling actions, and plan for future capacity needs.

Apache Druid

1. Real-Time Application Monitoring Dashboard: Use Telegraf to collect metrics from application servers and send them to Druid for immediate analysis and visualization in dashboards. Druid’s low-latency querying allows users to interactively explore system behavior in near real-time.

2. Security Event Aggregation: Aggregate and forward security-related metrics such as failed logins, port scans, or process anomalies to Druid. Analysts can build dashboards to monitor threat patterns and investigate incidents with millisecond-level granularity.

3. IoT Device Analytics: Collect telemetry from edge devices via Telegraf and send it to Druid for fast, scalable processing. Druid’s time-partitioned storage and roll-up capabilities are ideal for handling billions of small JSON events from sensors or gateways.

4. Web Traffic Behavior Exploration: Use Telegraf to capture web server metrics (e.g., requests per second, latency, error rates) and forward them to Druid. This enables teams to drill down into user behavior by region, device, or request type with subsecond query performance.

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