Zipkin and Sumo Logic 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 Sumo Logic plugin is designed to facilitate the sending of metrics from Telegraf to Sumo Logic’s HTTP Source. By utilizing this plugin, users can analyze their metric data in the Sumo Logic platform, leveraging various output data formats.

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.

Sumo Logic

This plugin facilitates the transmission of metrics to Sumo Logic’s HTTP Source, employing specified data formats for HTTP messages. Telegraf, which must be version 1.16.0 or higher, can send metrics encoded in several formats, including graphite, carbon2, and prometheus. These formats correspond to different content types recognized by Sumo Logic, ensuring that the metrics are correctly interpreted for analysis. Integration with Sumo Logic allows users to leverage a comprehensive analytics platform, enabling rich visualizations and insights from their metric data. The plugin provides configuration options such as setting URLs for the HTTP Metrics Source, choosing the data format, and specifying additional parameters like timeout and request size, which enhance flexibility and control in data monitoring workflows.

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"

Sumo Logic

[[outputs.sumologic]]
  ## Unique URL generated for your HTTP Metrics Source.
  ## This is the address to send metrics to.
  # url = "https://events.sumologic.net/receiver/v1/http/"

  ## Data format to be used for sending metrics.
  ## This will set the "Content-Type" header accordingly.
  ## Currently supported formats:
  ## * graphite - for Content-Type of application/vnd.sumologic.graphite
  ## * carbon2 - for Content-Type of application/vnd.sumologic.carbon2
  ## * prometheus - for Content-Type of application/vnd.sumologic.prometheus
  ##
  ## More information can be found at:
  ## https://help.sumologic.com/03Send-Data/Sources/02Sources-for-Hosted-Collectors/HTTP-Source/Upload-Metrics-to-an-HTTP-Source#content-type-headers-for-metrics
  ##
  ## NOTE:
  ## When unset, telegraf will by default use the influx serializer which is currently unsupported
  ## in HTTP Source.
  data_format = "carbon2"

  ## Timeout used for HTTP request
  # timeout = "5s"

  ## Max HTTP request body size in bytes before compression (if applied).
  ## By default 1MB is recommended.
  ## NOTE:
  ## Bear in mind that in some serializer a metric even though serialized to multiple
  ## lines cannot be split any further so setting this very low might not work
  ## as expected.
  # max_request_body_size = 1000000

  ## Additional, Sumo specific options.
  ## Full list can be found here:
  ## https://help.sumologic.com/03Send-Data/Sources/02Sources-for-Hosted-Collectors/HTTP-Source/Upload-Metrics-to-an-HTTP-Source#supported-http-headers

  ## Desired source name.
  ## Useful if you want to override the source name configured for the source.
  # source_name = ""

  ## Desired host name.
  ## Useful if you want to override the source host configured for the source.
  # source_host = ""

  ## Desired source category.
  ## Useful if you want to override the source category configured for the source.
  # source_category = ""

  ## Comma-separated key=value list of dimensions to apply to every metric.
  ## Custom dimensions will allow you to query your metrics at a more granular level.
  # dimensions = ""
</code></pre>

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.

Sumo Logic

1. Real-Time System Monitoring Dashboard: Utilize the Sumo Logic plugin to continuously feed performance metrics from your servers into a Sumo Logic dashboard. This setup allows tech teams to visualize system health and load in real-time, enabling quicker identification of any performance bottlenecks or system failures through detailed graphs and metrics.

2. Automated Alerting System: Configure the plugin to send metrics that trigger alerts in Sumo Logic for specific thresholds such as CPU usage or memory consumption. By setting up automated alerts, teams can proactively address issues before they escalate into critical failures, significantly improving response times and overall system reliability.

3. Cross-System Metrics Aggregation: Integrate multiple Telegraf instances across different environments (development, testing, production) and funnel all metrics to a central Sumo Logic instance using this plugin. This aggregation enables comprehensive analysis across environments, facilitating better monitoring and informed decision-making across the software development lifecycle.

4. Custom Metrics with Dimensions Tracking: Use the Sumo Logic plugin to send customized metrics that include dimensions identifying various aspects of your infrastructure (e.g., environment, service type). This granular tracking allows for more tailored analytics, enabling your team to dissect performance across different application layers or business functions.

Feedback

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