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12 changes: 9 additions & 3 deletions _observability-plugin/trace/index.md
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Expand Up @@ -6,12 +6,18 @@ has_children: true
has_toc: false
---

# Trace Analytics
# Trace analytics

Trace Analytics provides a way to ingest and visualize [OpenTelemetry](https://opentelemetry.io/) data in OpenSearch. This data can help you find and fix performance problems in distributed applications.
Trace analytics provides a way to ingest and visualize [OpenTelemetry](https://opentelemetry.io/) data in OpenSearch. This data can help you find and fix performance problems in distributed applications.

A single operation, such as a user clicking a button, can trigger an extended series of events. The front end might call a back end service, which calls another service, which queries a database, processes the data, and sends it to the original service, which sends a confirmation to the front end.
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Trace Analytics can help you visualize this flow of events and identify performance problems.
Trace analytics can help you visualize this flow of events and identify performance problems.

![Detailed trace view]({{site.url}}{{site.baseurl}}/images/ta-trace.png)
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## Trace analytics with Jaeger data

The trace analytics functionality in the OpenSearch Observability plugin now supports Jaeger trace data. If you use OpenSearch as the backend for Jaeger trace data, you can use the Trace analytics built-in analysis capabilities.
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To set up your environment to perform trace analytics, see [Analyze Jaeger trace data]({{site.url}}{{site.baseurl}}/observability-plugin/trace/trace-analytics-jaeger/).
249 changes: 249 additions & 0 deletions _observability-plugin/trace/trace-analytics-jaeger.md
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---
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layout: default
title: Analyze Jaeger trace data
parent: Trace analytics
nav_order: 55
---

# Analyze Jaeger trace data
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Introduced 2.5
{: .label .label-purple }
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The trace analytics functionality in the OpenSearch Observability plugin now supports Jaeger trace data. If you use OpenSearch as the backend for Jaeger trace data, you can use the trace analytics built-in analysis capabilities. This provides support for OpenTelemetry (OTEL) formatted trace data.
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When you perform trace analytics, you can select from two data sources:

- **Data Prepper** – Data ingested into OpenSearch through Data Prepper.
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- **Jaeger** – Trace data stored within OpenSearch as its backend.
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If you currently store your Jaeger trace data in OpenSearch, you can now use the capabilities built into trace analytics to analyze the error rates and latency. You can also filter the traces and look into the span details of a trace to pinpoint any service issues.
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When you ingest Jaeger data into OpenSearch, it gets stored in a different index than the OTEL-generated index that gets created when you run data through Data Prepper. Use the data source selector in Dashboards to indicate on which data source you want to perform trace analytics.
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Jaeger trace data that you can analyze includes span data, as well as service and operation endpoint data. <!-- Need more info for next release. add how to configure for span analysis. Jaeger span data analysis requires some configuration.-->
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By default, each time you ingest data for Jaeger, it creates a separate index for that day.

To learn more about Jaeger data tracing, see the [Jaeger](https://www.jaegertracing.io/) open source documentation.
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## Data ingestion requirements

To use trace analytics with Jaeger data, you need to configure error capability.
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Jaeger data that is ingested for OpenSearch needs to have the environment variable `ES_TAGS_AS_FIELDS_ALL` set to `true` for errors. If data is not ingested in this format it will not work for errors and error data will not be available for traces in trace analytics with OpenSearch.
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### About data ingestion with Jaeger indexes

Trace analytics for non-Jaeger data use OTEL indexes with the naming conventions `otel-v1-apm-span-*` or `otel-v1-apm-service-map*`.
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Jaeger indexes follow the naming conventions `jaeger-span-*` or `jaeger-service-*`.

## How to set up OpenSearch to use Jaeger data
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The following section provides a sample Docker compose file that contains the required configuration to enable errors for trace analytics.
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### Step 1: Run the Docker compose file
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Use the following Docker compose file to enable Jaeger data for trace analytics with the `ES_TAGS_AS_FIELDS_ALL` environment variable set to `true` to enable errors to be added to trace data.
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Copy the following Docker compose file contents and save it as `docker-compose.yml`.
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```
version: '3'
services:
opensearch-node1: # This is also the hostname of the container within the Docker network (i.e. https://opensearch-node1/)
image: opensearchproject/opensearch:latest # Specifying the latest available image - modify if you want a specific version
container_name: opensearch-node1
environment:
- cluster.name=opensearch-cluster # Name the cluster
- node.name=opensearch-node1 # Name the node that will run in this container
- discovery.seed_hosts=opensearch-node1,opensearch-node2 # Nodes to look for when discovering the cluster
- cluster.initial_cluster_manager_nodes=opensearch-node1,opensearch-node2 # Nodes eligible to serve as cluster manager
- bootstrap.memory_lock=true # Disable JVM heap memory swapping
- "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m" # Set min and max JVM heap sizes to at least 50% of system RAM
ulimits:
memlock:
soft: -1 # Set memlock to unlimited (no soft or hard limit)
hard: -1
nofile:
soft: 65536 # Maximum number of open files for the opensearch user - set to at least 65536
hard: 65536
volumes:
- opensearch-data1:/usr/share/opensearch/data # Creates volume called opensearch-data1 and mounts it to the container
ports:
- "9200:9200"
- "9600:9600"
networks:
- opensearch-net # All of the containers will join the same Docker bridge network

opensearch-node2:
image: opensearchproject/opensearch:latest # This should be the same image used for opensearch-node1 to avoid issues
container_name: opensearch-node2
environment:
- cluster.name=opensearch-cluster
- node.name=opensearch-node2
- discovery.seed_hosts=opensearch-node1,opensearch-node2
- cluster.initial_cluster_manager_nodes=opensearch-node1,opensearch-node2
- bootstrap.memory_lock=true
- "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m"
ulimits:
memlock:
soft: -1
hard: -1
nofile:
soft: 65536
hard: 65536
volumes:
- opensearch-data2:/usr/share/opensearch/data
networks:
- opensearch-net
opensearch-dashboards:
image: opensearchproject/opensearch-dashboards:latest # Make sure the version of opensearch-dashboards matches the version of opensearch installed on other nodes
container_name: opensearch-dashboards
ports:
- 5601:5601 # Map host port 5601 to container port 5601
expose:
- "5601" # Expose port 5601 for web access to OpenSearch Dashboards
environment:
OPENSEARCH_HOSTS: '["https://opensearch-node1:9200","https://opensearch-node2:9200"]' # Define the OpenSearch nodes that OpenSearch Dashboards will query
networks:
- opensearch-net

jaeger-collector:
image: jaegertracing/jaeger-collector:latest
ports:
- "14269:14269"
- "14268:14268"
- "14267:14267"
- "14250:14250"
- "9411:9411"
networks:
- opensearch-net
restart: on-failure
environment:
- SPAN_STORAGE_TYPE=opensearch
- ES_TAGS_AS_FIELDS_ALL=true
- ES_USERNAME=admin
- ES_PASSWORD=admin
- ES_TLS_SKIP_HOST_VERIFY=true
command: [
"--es.server-urls=https://opensearch-node1:9200",
"--es.tls.enabled=true",
]
depends_on:
- opensearch-node1

jaeger-agent:
image: jaegertracing/jaeger-agent:latest
hostname: jaeger-agent
command: ["--reporter.grpc.host-port=jaeger-collector:14250"]
ports:
- "5775:5775/udp"
- "6831:6831/udp"
- "6832:6832/udp"
- "5778:5778"
networks:
- opensearch-net
restart: on-failure
environment:
- SPAN_STORAGE_TYPE=opensearch
depends_on:
- jaeger-collector

hotrod:
image: jaegertracing/example-hotrod:latest
ports:
- "8080:8080"
command: ["all"]
environment:
- JAEGER_AGENT_HOST=jaeger-agent
- JAEGER_AGENT_PORT=6831
networks:
- opensearch-net
depends_on:
- jaeger-agent

volumes:
opensearch-data1:
opensearch-data2:

networks:
opensearch-net:
```

### Step 2: Start the cluster

Run the following command to deploy the Docker compose YAML file.

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Run the following command to deploy the Docker compose YAML file.
Run the following command to deploy the Docker Compose YAML file:


```
docker compose up -d
```
To stop the cluster, run the following command:

```
docker compose down
```

### Step 3: Generate sample data

Use the sample app provided with the Docker file to generate data. After you run the Docker compose file, it runs the sample app in your local host port 8080. To open the app, go to http://localhost:8080.
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![Services list]({{site.url}}{{site.baseurl}}/images/trace-analytics/sample-app.png)

In the sample app, Hot R.O.D., select any one of the buttons to generate data. Now you can view trace data in Dashboards.
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### Step 4: View trace data in OpenSearch Dashboards

After you generate Jaeger trace data you can go to OpenSearch Dashboards to view your trace data.
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Go to Dashboards **Trace analytics** at [http://localhost:5601/app/observability-dashboards#/trace_analytics/home](http://localhost:5601/app/observability-dashboards#/trace_analytics/home).
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## Use trace analytics in OpenSearch Dashboards
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To analyze the Jaeger trace data in Dashboards, first set up the trace analytics functionality. To get started, see [Get started with trace analytics]({{site.url}}{{site.baseurl}}/observability-plugin/trace/get-started/).

### Data sources

You can specify either Data Prepper or Jaeger as the data source when you perform trace analytics.
From Dashboards, go to **Observability > Trace analytics** and select Jaeger.

![Select data source]({{site.url}}{{site.baseurl}}/images/trace-analytics/select-data.png)

## Dashboard view

After you select Jaeger for the data source, you can view all of the indexed data in **Dashboard** view, including **Error rate** and **Throughput**.
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### Error rate

You can view the trace error count over time in the **Dashboard** view and also see the top five combinations of services and operations that have a non-zero error rate.
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![Error rate]({{site.url}}{{site.baseurl}}/images/trace-analytics/error-rate.png)

### Throughput

With **Throughput** selected, you can see the throughput of traces on Jaeger indexes that are coming in over time.
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You can select an individual trace from **Top 5 Service and Operation Latency** list and view the detailed trace data.
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![Throughput]({{site.url}}{{site.baseurl}}/images/trace-analytics/throughput.png)

You can also see the combinations of services and operations that have the highest latency.

If you select one of the entries for Service and Operation Name and go to the **Traces** column to select a trace, it will automatically add the service and operation as filters.

## Traces

In **Traces**, you can see the latency and errors for the filtered service and operation for each individual Trace ID in the list.
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![Select data source]({{site.url}}{{site.baseurl}}/images/trace-analytics/service-trace-data.png)

If you select an individual Trace ID, you can see more detailed information about the trace, such as time spent by the service and each span for the service and operation. You can also view the payload that you get from the index in JSON format.
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![Select data source]({{site.url}}{{site.baseurl}}/images/trace-analytics/trace-details.png)

## Services

You can also look at individual error rates and latency for each individual service. Go to **Observability > Trace analytics > Services**. In **Services**, you can see the average latency, error rate, throughput and trace for each service in the list.
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![Services list]({{site.url}}{{site.baseurl}}/images/trace-analytics/services-jaeger.png)
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