84 lines
6.1 KiB
Markdown
84 lines
6.1 KiB
Markdown
---
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layout: default
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title: Get Started
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parent: Trace analytics
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nav_order: 1
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---
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# Get started with Trace Analytics
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OpenSearch Trace Analytics consists of two components---Data Prepper and the Trace Analytics OpenSearch Dashboards plugin---that fit into the OpenTelemetry and OpenSearch ecosystems. The Data Prepper repository has several [sample applications](https://github.com/opensearch-project/data-prepper/tree/main/examples) to help you get started.
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## Basic flow of data
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1. Trace Analytics relies on you adding instrumentation to your application and generating trace data. The [OpenTelemetry documentation](https://opentelemetry.io/docs/) contains example applications for many programming languages that can help you get started, including Java, Python, Go, and JavaScript.
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(In the [Jaeger HotROD](#jaeger-hotrod) example below, an extra component, the Jaeger agent, runs alongside the application and sends the data to the OpenTelemetry Collector, but the concept is similar.)
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1. The [OpenTelemetry Collector](https://opentelemetry.io/docs/collector/getting-started/) receives data from the application and formats it into OpenTelemetry data.
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1. [Data Prepper]({{site.url}}{{site.baseurl}}/monitoring-plugins/trace/data-prepper/) processes the OpenTelemetry data, transforms it for use in OpenSearch, and indexes it on an OpenSearch cluster.
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1. The [Trace Analytics OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/monitoring-plugins/trace/ta-dashboards/) displays the data in near real-time as a series of charts and tables, with an emphasis on service architecture, latency, error rate, and throughput.
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## Jaeger HotROD
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One Trace Analytics sample application is the Jaeger HotROD demo, which mimics the flow of data through a distributed application.
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Download or clone the [Data Prepper repository](https://github.com/opensearch-project/data-prepper). Then navigate to `examples/jaeger-hotrod/` and open `docker-compose.yml` in a text editor. This file contains a container for each element from [Basic flow of data](#basic-flow-of-data):
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- A distributed application (`jaeger-hot-rod`) with the Jaeger agent (`jaeger-agent`)
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- The [OpenTelemetry Collector](https://opentelemetry.io/docs/collector/getting-started/) (`otel-collector`)
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- Data Prepper (`data-prepper`)
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- A single-node OpenSearch cluster (`opensearch`)
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- OpenSearch Dashboards (`opensearch-dashboards`).
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Close the file and run `docker-compose up --build`. After the containers start, navigate to `http://localhost:8080` in a web browser.
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Click one of the buttons in the web interface to send a request to the application. Each request starts a series of operations across the services that make up the application. From the console logs, you can see that these operations share the same `trace-id`, which lets you track all of the operations in the request as a single *trace*:
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```
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jaeger-hot-rod | http://0.0.0.0:8081/customer?customer=392
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jaeger-hot-rod | 2020-11-19T16:29:53.425Z INFO frontend/server.go:92 HTTP request received {"service": "frontend", "trace_id": "12091bd60f45ea2c", "span_id": "12091bd60f45ea2c", "method": "GET", "url": "/dispatch?customer=392&nonse=0.6509021735471818"}
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jaeger-hot-rod | 2020-11-19T16:29:53.426Z INFO customer/client.go:54 Getting customer{"service": "frontend", "component": "customer_client", "trace_id": "12091bd60f45ea2c", "span_id": "12091bd60f45ea2c", "customer_id": "392"}
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jaeger-hot-rod | 2020-11-19T16:29:53.430Z INFO customer/server.go:67 HTTP request received {"service": "customer", "trace_id": "12091bd60f45ea2c", "span_id": "252ff7d0e1ac533b", "method": "GET", "url": "/customer?customer=392"}
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jaeger-hot-rod | 2020-11-19T16:29:53.430Z INFO customer/database.go:73 Loading customer{"service": "customer", "component": "mysql", "trace_id": "12091bd60f45ea2c", "span_id": "252ff7d0e1ac533b", "customer_id": "392"}
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```
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These operations also have a `span_id`. *Spans* are units of work from a single service. Each trace contains some number of spans. Shortly after the application starts processing the request, you can see the OpenTelemetry Collector starts exporting the spans:
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```
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otel-collector | 2020-11-19T16:29:53.781Z INFO loggingexporter/logging_exporter.go:296 TraceExporter {"#spans": 1}
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otel-collector | 2020-11-19T16:29:53.787Z INFO loggingexporter/logging_exporter.go:296 TraceExporter {"#spans": 3}
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```
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Then Data Prepper processes the data from the OpenTelemetry Collector and indexes it:
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```
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data-prepper | 1031918 [service-map-pipeline-process-worker-2-thread-1] INFO com.amazon.dataprepper.pipeline.ProcessWorker – service-map-pipeline Worker: Processing 3 records from buffer
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data-prepper | 1031923 [entry-pipeline-process-worker-1-thread-1] INFO com.amazon.dataprepper.pipeline.ProcessWorker – entry-pipeline Worker: Processing 1 records from buffer
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```
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Finally, you can see the OpenSearch node responding to the indexing request.
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```
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node-0.example.com | [2020-11-19T16:29:55,064][INFO ][o.e.c.m.MetadataMappingService] [9fb4fb37a516] [otel-v1-apm-span-000001/NGYbmVD9RmmqnxjfTzBQsQ] update_mapping [_doc]
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node-0.example.com | [2020-11-19T16:29:55,267][INFO ][o.e.c.m.MetadataMappingService] [9fb4fb37a516] [otel-v1-apm-span-000001/NGYbmVD9RmmqnxjfTzBQsQ] update_mapping [_doc]
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```
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In a new terminal window, run the following command to see one of the raw documents in the OpenSearch cluster:
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```bash
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curl -X GET -u 'admin:admin' -k 'https://localhost:9200/otel-v1-apm-span-000001/_search?pretty&size=1'
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```
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Navigate to `http://localhost:5601` in a web browser and choose **Trace Analytics**. You can see the results of your single click in the Jaeger HotROD web interface: the number of traces per API and HTTP method, latency trends, a color-coded map of the service architecture, and a list of trace IDs that you can use to drill down on individual operations.
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If you don't see your trace, adjust the timeframe in OpenSearch Dashboards. For more information on using the plugin, see [OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/monitoring-plugins/trace/ta-dashboards/).
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