Zipkin and Graylog Integration

Input and output integration overview

The Zipkin Input Plugin allows for the collection of tracing information and timing data from microservices. This capability is essential for diagnosing latency troubles within complex service-oriented environments.

The Graylog plugin allows you to send Telegraf metrics to a Graylog server, utilizing the GELF format for structured logging.

Integration details

Zipkin

This plugin implements the Zipkin HTTP server to gather trace and timing data necessary for troubleshooting latency issues in microservice architectures. Zipkin is a distributed tracing system that helps gather timing data across various microservices, allowing teams to visualize the flow of requests and identify bottlenecks in performance. The plugin offers support for input traces in JSON or thrift formats based on the specified Content-Type. Additionally, it utilizes span metadata to track the timing of requests, enhancing the observability of applications that adhere to the OpenTracing standard. As an experimental feature, its configuration and schema may evolve over time to better align with user requirements and advancements in distributed tracing methodologies.

Graylog

The Graylog plugin is designed for sending metrics to a Graylog instance using the GELF (Graylog Extended Log Format) format. GELF helps standardize the logging data, making it easier for systems to send and analyze logs. The plugin adheres to the GELF specification, which lays out requirements for specific fields within the payload. Notably, the timestamp must be in UNIX format, and if present, the plugin sends the timestamp as-is to Graylog without alterations. If omitted, it automatically generates a timestamp. Additionally, any extra fields not explicitly defined by the spec will be prefixed with an underscore, helping to keep the data organized and compliant with GELF’s requirements. This capability is particularly valuable for users monitoring applications and infrastructure in real-time, as it allows for seamless integration and improved visibility across multiple systems.

Configuration

Zipkin

[[inputs.zipkin]]
  ## URL path for span data
  # path = "/api/v1/spans"

  ## Port on which Telegraf listens
  # port = 9411

  ## Maximum duration before timing out read of the request
  # read_timeout = "10s"
  ## Maximum duration before timing out write of the response
  # write_timeout = "10s"

Graylog

[[outputs.graylog]]
  ## Endpoints for your graylog instances.
  servers = ["udp://127.0.0.1:12201"]

  ## Connection timeout.
  # timeout = "5s"

  ## The field to use as the GELF short_message, if unset the static string
  ## "telegraf" will be used.
  ##   example: short_message_field = "message"
  # short_message_field = ""

  ## According to GELF payload specification, additional fields names must be prefixed
  ## with an underscore. Previous versions did not prefix custom field 'name' with underscore.
  ## Set to true for backward compatibility.
  # name_field_no_prefix = false

  ## Connection retry options
  ## Attempt to connect to the endpoints if the initial connection fails.
  ## If 'false', Telegraf will give up after 3 connection attempt and will
  ## exit with an error. If set to 'true', the plugin will retry to connect
  ## to the unconnected endpoints infinitely.
  # connection_retry = false
  ## Time to wait between connection retry attempts.
  # connection_retry_wait_time = "15s"

  ## 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

Input and output integration examples

Zipkin

1. Latency Monitoring in Microservices: Use the Zipkin Input Plugin to capture and analyze tracing data from a microservices architecture. By visualizing the request flow and pinpointing latency sources, development teams can optimize service interactions, improve response times, and ensure a smoother user experience across services.

2. Performance Optimization in Essential Services: Integrate the plugin within critical services to monitor not only the response times but also track specific annotations that could highlight performance issues. The ability to gather span data can help prioritize areas needing performance enhancements, leading to targeted improvements.

3. Dynamic Service Dependency Mapping: With the collected trace data, automatically map service dependencies and visualize them in dashboards. This helps teams understand how different services interact and the impact of failures or slowdowns, ultimately leading to better architectural decisions and faster resolutions of issues.

4. Anomaly Detection in Service Latency: Combine Zipkin data with machine learning models to detect unusual patterns in service latencies and request processing times. By automatically identifying anomalies, operations teams can respond proactively to emerging issues before they escalate into critical failures.

Graylog

1. Enhanced Log Management for Cloud Applications: Use the Graylog Telegraf plugin to aggregate logs from cloud-deployed applications across multiple servers. By integrating this plugin, teams can centralize logging data, making it easier to troubleshoot issues, monitor application performance, and maintain compliance with logging standards.

2. Real-Time Security Monitoring: Leverage the Graylog plugin to collect and send security-related metrics and logs to a Graylog server for real-time analysis. This allows security teams to quickly identify anomalies, track potential breaches, and respond to incidents promptly by correlating logs from various sources within the infrastructure.

3. Dynamic Alerting and Notification System: Implement the Graylog plugin to enhance alerting mechanisms in your infrastructure. By sending metrics to Graylog, teams can set up dynamic alerts based on log patterns or unexpected behavior, enabling proactive monitoring and rapid incident response strategies.

4. Cross-Platform Log Consolidation: Use the Graylog plugin to facilitate cross-platform log consolidation across diverse environments such as on-premises, hybrid, and cloud. By standardizing logging in the GELF format, organizations can ensure consistent monitoring and troubleshooting practices, regardless of where their services are hosted.

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.