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Add use case for anomaly detection with Data Prepper (#6140) 

* Add anomaly detection use case for Data Prepper

---------

Signed-off-by: Melissa Vagi <vagimeli@amazon.com>
Co-authored-by: Nathan Bower <nbower@amazon.com>
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---
layout: default
title: Anomaly detection
parent: Common use cases
nav_order: 30
---
# Anomaly detection
You can use Data Prepper to train models and generate anomalies in near real time on time-series aggregated events. You can generate anomalies either on events generated within the pipeline or on events coming directly into the pipeline, like OpenTelemetry metrics. You can feed these tumbling window aggregated time-series events to the [`anomaly_detector` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/anomaly-detector/), which trains a model and generates anomalies with a grade score. Then you can configure your pipeline to write the anomalies to a separate index to create document monitors and trigger fast alerting.
## Metrics from logs
The following pipeline receives logs from an HTTP source like FluentBit, extracts important values from the logs by matching the value in the `log` key against the [Grok Apache Common Log Format](https://httpd.apache.org/docs/2.4/logs.html#accesslog), and then forwards the grokked logs to both the `log-to-metrics-pipeline` pipeline and an OpenSearch index named `logs`.
The `log-to-metrics-pipeline` pipeline receives the grokked logs from the `apache-log-pipeline-with-metrics` pipeline, aggregates them, and derives histogram metrics based on the values in the `clientip` and `request` keys. It then sends the histogram metrics to an OpenSearch index named `histogram_metrics` as well as to the `log-to-metrics-anomaly-detector-pipeline` pipeline.
The `log-to-metrics-anomaly-detector-pipeline` pipeline receives the aggregated histogram metrics from the `log-to-metrics-pipeline` pipeline and sends them to the `anomaly_detector` processor to detect anomalies by using the Random Cut Forest algorithm. If the algorithm detects anomalies, it sends them to an OpenSearch index named `log-metric-anomalies`.
```json
apache-log-pipeline-with-metrics:
source:
http:
# Provide the path for ingestion. ${pipelineName} will be replaced with pipeline name configured for this pipeline.
# In this case it would be "/apache-log-pipeline-with-metrics/logs". This will be the FluentBit output URI value.
path: "/${pipelineName}/logs"
processor:
- grok:
match:
log: [ "%{COMMONAPACHELOG_DATATYPED}" ]
sink:
- opensearch:
...
index: "logs"
- pipeline:
name: "log-to-metrics-pipeline"
log-to-metrics-pipeline:
source:
pipeline:
name: "apache-log-pipeline-with-metrics"
processor:
- aggregate:
# Specify the required identification keys
identification_keys: ["clientip", "request"]
action:
histogram:
# Specify the appropriate values for each the following fields
key: "bytes"
record_minmax: true
units: "bytes"
buckets: [0, 25000000, 50000000, 75000000, 100000000]
# Pick the required aggregation period
group_duration: "30s"
sink:
- opensearch:
...
index: "histogram_metrics"
- pipeline:
name: "log-to-metrics-anomaly-detector-pipeline"
log-to-metrics-anomaly-detector-pipeline:
source:
pipeline:
name: "log-to-metrics-pipeline"
processor:
- anomaly_detector:
# Specify the key on which to run anomaly detection
keys: [ "bytes" ]
mode:
random_cut_forest:
sink:
- opensearch:
...
index: "log-metric-anomalies"
```
{% include copy-curl.html %}
## Metrics from traces
You can derive metrics from traces and find anomalies in those metrics. In this example, the `entry-pipeline` pipeline receives trace data from the OpenTelemetry Collector and forwards it to the following pipelines:
- `span-pipeline` –- Extracts the raw spans from the traces. The pipeline sends the raw spans to any indexes OpenSearch prefixed with `otel-v1-apm-span`.
- `service-map-pipeline` –- Aggregates and analyzes the traces to create documents that represent connections between services. The pipeline sends these documents to an OpenSearch index named `otel-v1-apm-service-map`. You can then see a visualization of the service map through the [Trace Analytics]({{site.url}}{{site.baseurl}}/observing-your-data/trace/index/) plugin for OpenSearch Dashboards.
- `trace-to-metrics-pipeline` -- Aggregates and derives histogram metrics from the traces based on the value of the `serviceName`. The pipeline then sends the derived metrics to an OpenSearch index named `metrics_for_traces` and to the `trace-to-metrics-anomaly-detector-pipeline` pipeline.
The `trace-to-metrics-anomaly-detector-pipeline` pipeline receives the aggregated histogram metrics from the `trace-to-metrics-pipeline` and sends them to the `anomaly_detector` processor to detect anomalies by using the Random Cut Forest algorithm. If the algorithm detects any anomalies, it sends them to an OpenSearch index named `trace-metric-anomalies`.
```json
entry-pipeline:
source:
otel_trace_source:
# Provide the path for ingestion. ${pipelineName} will be replaced with pipeline name configured for this pipeline.
# In this case it would be "/entry-pipeline/v1/traces". This will be endpoint URI path in OpenTelemetry Exporter
# configuration.
# path: "/${pipelineName}/v1/traces"
processor:
- trace_peer_forwarder:
sink:
- pipeline:
name: "span-pipeline"
- pipeline:
name: "service-map-pipeline"
- pipeline:
name: "trace-to-metrics-pipeline"
span-pipeline:
source:
pipeline:
name: "entry-pipeline"
processor:
- otel_trace_raw:
sink:
- opensearch:
...
index_type: "trace-analytics-raw"
service-map-pipeline:
source:
pipeline:
name: "entry-pipeline"
processor:
- service_map:
sink:
- opensearch:
...
index_type: "trace-analytics-service-map"
trace-to-metrics-pipeline:
source:
pipeline:
name: "entry-pipeline"
processor:
- aggregate:
# Pick the required identification keys
identification_keys: ["serviceName"]
action:
histogram:
# Pick the appropriate values for each the following fields
key: "durationInNanos"
record_minmax: true
units: "seconds"
buckets: [0, 10000000, 50000000, 100000000]
# Pick the required aggregation period
group_duration: "30s"
sink:
- opensearch:
...
index: "metrics_for_traces"
- pipeline:
name: "trace-to-metrics-anomaly-detector-pipeline"
trace-to-metrics-anomaly-detector-pipeline:
source:
pipeline:
name: "trace-to-metrics-pipeline"
processor:
- anomaly_detector:
# Below Key will find anomalies in the max value of histogram generated for durationInNanos.
keys: [ "max" ]
mode:
random_cut_forest:
sink:
- opensearch:
...
index: "trace-metric-anomalies"
```
{% include copy-curl.html %}
## OpenTelemetry metrics
You can create a pipeline that receives OpenTelemetry metrics and detects anomalies in those metrics. In this example, `entry-pipeline` receives metrics from the OpenTelemetry Collector. If a metric is of type `GAUGE` and the name of the metric is `totalApiBytesSent`, the processor sends it to the `ad-pipeline` pipeline.
The `ad-pipeline` pipeline receives the metrics from the entry pipeline and performs anomaly detection on the metric values by using the [`anomaly_detector` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/anomaly-detector/).
```json
entry-pipeline:
source:
otel_metrics_source:
processor:
- otel_metrics:
route:
- gauge_route: '/kind = "GAUGE" and /name = "totalApiBytesSent"'
sink:
- pipeline:
name: "ad-pipeline"
routes:
- gauge_route
- opensearch:
...
index: "otel-metrics"
ad-pipeline:
source:
pipeline:
name: "entry-pipeline"
processor:
- anomaly_detector:
# Use "value" as the key on which anomaly detector needs to be run
keys: [ "value" ]
mode:
random_cut_forest:
sink:
- opensearch:
...
index: otel-metrics-anomalies
```
{% include copy-curl.html %}