Arista LANZ and Apache Druid Integration

Input and output integration overview

The Arista LANZ plugin is designed for reading latency and congestion metrics from Arista LANZ, helping users monitor their network performance effectively.

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

Arista LANZ

This plugin provides a consumer for use with Arista Networks’ Latency Analyzer (LANZ). Metrics are read from a stream of data via TCP through port 50001 on the switches management IP. The data is in Protobuffers format, allowing for efficient transportation and parsing of data. LANZ is utilized to monitor network latency and congestion in real-time, which is vital for maintaining optimal performance in networking environments. The underlying technology, Arista’s latency analysis, provides insights into various network operations and infrastructure behaviors, making it a crucial tool for network engineering and management.

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

Arista LANZ

[[inputs.lanz]]
  ## URL to Arista LANZ endpoint
  servers = [
    "tcp://switch1.int.example.com:50001",
    "tcp://switch2.int.example.com:50001",
  ]

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

Arista LANZ

1. Real-Time Latency Monitoring: This plugin can be used to set up a monitoring dashboard that tracks real-time latency metrics across multiple interfaces. By gathering and visualizing this data, network admins can swiftly identify and rectify latency issues before they impact service quality. The challenge lies in efficiently handling the influx of metrics from various sources without overwhelming the infrastructure or incurring excessive processing delays.

2. Congestion Analysis for Traffic Engineering: Users can leverage the LANZ plugin to analyze congestion records, enabling the optimization of network traffic flows. By applying historical pattern recognition to the metrics collected, IT teams can make informed decisions on traffic management strategies, thus improving overall network efficiency. This requires implementing robust data storage and analysis capabilities to derive actionable insights from the raw metrics.

3. Integration with Alerting Systems: Integrate the metrics from this plugin with alerting systems to automatically notify network engineers of any significant changes in latency or congestion. By setting thresholds based on historical data trends, this use case enhances proactive incident management, allowing teams to address potential issues proactively. The technical challenge here is establishing the right balance in threshold settings to minimize false positives while ensuring genuine issues are flagged promptly.

4. Network Optimization Reports: Utilize the metrics gathered through the LANZ plugin to generate periodic reports that detail network performance, latency trends, and congestion events. These reports can help stakeholders understand network health over time and guide infrastructure investments. The challenge involves structuring and formatting the output data to make it comprehensible and actionable for various audiences.

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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