---
layout: default
title: Pipelines
parent: Data Prepper
nav_order: 2
---

# Pipelines

![Data Prepper Pipeline]({{site.url}}{{site.baseurl}}/images/data-prepper-pipeline.png)

To use Data Prepper, you define pipelines in a configuration YAML file. Each pipeline is a combination of a source, a buffer, zero or more processors, and one or more sinks. For example:

```yml
simple-sample-pipeline:
  workers: 2 # the number of workers
  delay: 5000 # in milliseconds, how long workers wait between read attempts
  source:
    random:
  buffer:
    bounded_blocking:
      buffer_size: 1024 # max number of records the buffer accepts
      batch_size: 256 # max number of records the buffer drains after each read
  processor:
    - string_converter:
        upper_case: true
  sink:
    - stdout:
```

- Sources define where your data comes from. In this case, the source is a random UUID generator (`random`).

- Buffers store data as it passes through the pipeline.

  By default, Data Prepper uses its one and only buffer, the `bounded_blocking` buffer, so you can omit this section unless you developed a custom buffer or need to tune the buffer settings.

- Processors perform some action on your data: filter, transform, enrich, etc.

  You can have multiple processors, which run sequentially from top to bottom, not in parallel. The `string_converter` processor transform the strings by making them uppercase.

- Sinks define where your data goes. In this case, the sink is stdout.

Starting from Data Prepper 2.0, you can define pipelines across multiple configuration YAML files, where each file contains the configuration for one or more pipelines. This gives you more freedom to organize and chain complex pipeline configurations. For Data Prepper to load your pipeline configuration properly, place your configuration YAML files in the `pipelines` folder under your application's home directory (e.g. `/usr/share/data-prepper`).
{: .note }

## Conditional Routing

Pipelines also support **conditional routing**  which allows you to route Events to different sinks based on specific conditions. To add conditional routing to a pipeline, specify a list of named routes under the `route` component and add specific routes to sinks under the `routes` property. Any sink with the `routes` property will only accept Events that match at least one of the routing conditions. 

In the following example, `application-logs` is a named route with a condition set to `/log_type == "application"`. The route uses [Data Prepper expressions](https://github.com/opensearch-project/data-prepper/tree/main/examples) to define the conditions. Data Prepper only routes events that satisfy the condition to the first OpenSearch sink. By default, Data Prepper routes all Events to a sink which does not define a route. In the example, all Events route into the third OpenSearch sink.

```yml
conditional-routing-sample-pipeline:
  source:
    http:
  processor:
  route:
    - application-logs: '/log_type == "application"'
    - http-logs: '/log_type == "apache"'
  sink:
    - opensearch:
        hosts: [ "https://opensearch:9200" ]
        index: application_logs
        routes: [application-logs]
    - opensearch:
        hosts: [ "https://opensearch:9200" ]
        index: http_logs
        routes: [http-logs]
    - opensearch:
        hosts: [ "https://opensearch:9200" ]
        index: all_logs
```


## Examples

This section provides some pipeline examples that you can use to start creating your own pipelines. For more information, see [Data Prepper configuration reference]({{site.url}}{{site.baseurl}}/clients/data-prepper/data-prepper-reference/) guide.

The Data Prepper repository has several [sample applications](https://github.com/opensearch-project/data-prepper/tree/main/examples) to help you get started.

### Log ingestion pipeline

The following example demonstrates how to use HTTP source and Grok prepper plugins to process unstructured log data.

```yml
log-pipeline:
  source:
    http:
      ssl: false
  processor:
    - grok:
        match:
          log: [ "%{COMMONAPACHELOG}" ]
  sink:
    - opensearch:
        hosts: [ "https://opensearch:9200" ]
        insecure: true
        username: admin
        password: admin
        index: apache_logs
```

This example uses weak security. We strongly recommend securing all plugins which open external ports in production environments.
{: .note}

### Trace analytics pipeline

The following example demonstrates how to build a pipeline that supports the [Trace Analytics OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/observability-plugin/trace/ta-dashboards/). This pipeline takes data from the OpenTelemetry Collector and uses two other pipelines as sinks. These two separate pipelines index trace and the service map documents for the dashboard plugin.

Starting from Data Prepper 2.0, Data Prepper no longer supports `otel_trace_raw_prepper` processor due to the Data Prepper internal data model evolution. 
Instead, users should use `otel_trace_raw`.

```yml
entry-pipeline:
  delay: "100"
  source:
    otel_trace_source:
      ssl: false
  buffer:
    bounded_blocking:
      buffer_size: 10240
      batch_size: 160
  sink:
    - pipeline:
        name: "raw-pipeline"
    - pipeline:
        name: "service-map-pipeline"
raw-pipeline:
  source:
    pipeline:
      name: "entry-pipeline"
  buffer:
    bounded_blocking:
      buffer_size: 10240
      batch_size: 160
  processor:
    - otel_trace_raw:
  sink:
    - opensearch:
        hosts: ["https://localhost:9200"]
        insecure: true
        username: admin
        password: admin
        index_type: trace-analytics-raw
service-map-pipeline:
  delay: "100"
  source:
    pipeline:
      name: "entry-pipeline"
  buffer:
    bounded_blocking:
      buffer_size: 10240
      batch_size: 160
  processor:
    - service_map_stateful:
  sink:
    - opensearch:
        hosts: ["https://localhost:9200"]
        insecure: true
        username: admin
        password: admin
        index_type: trace-analytics-service-map
```

To maintain similar ingestion throughput and latency, scale the `buffer_size` and `batch_size` by the estimated maximum batch size in the client request payload.
{: .tip}

### Metrics pipeline

Data Prepper supports metrics ingestion using OTel. It currently supports the following metric types:

* Gauge
* Sum
* Summary
* Histogram

Other types are not supported. Data Prepper drops all other types, including Exponential Histogram and Summary. Additionally, Data Prepper does not support Scope instrumentation.

To set up a metrics pipeline:

```yml
metrics-pipeline:
  source:
    otel_metrics_source:
  processor:
    - otel_metrics_raw_processor:
  sink:
    - opensearch:
      hosts: ["https://localhost:9200"]
      username: admin
      password: admin
```

### S3 log ingestion pipeline

The following example demonstrates how to use the S3 Source and Grok Processor plugins to process unstructured log data
from [Amazon Simple Storage Service](https://aws.amazon.com/s3/) (Amazon S3). This example uses Application Load 
Balancer logs. As the Application Load Balancer writes logs to S3, S3 creates notifications in Amazon SQS. Data Prepper 
reads those notifications and reads the S3 objects to get the log data and process it.

```yml
log-pipeline:
  source:
    s3:
      notification_type: "sqs"
      compression: "gzip"
      codec:
        newline:
      sqs:
        queue_url: "https://sqs.us-east-1.amazonaws.com/12345678910/ApplicationLoadBalancer"
      aws:
        region: "us-east-1"
        sts_role_arn: "arn:aws:iam::12345678910:role/Data-Prepper"

  processor:
    - grok:
        match:
          message: ["%{DATA:type} %{TIMESTAMP_ISO8601:time} %{DATA:elb} %{DATA:client} %{DATA:target} %{BASE10NUM:request_processing_time} %{DATA:target_processing_time} %{BASE10NUM:response_processing_time} %{BASE10NUM:elb_status_code} %{DATA:target_status_code} %{BASE10NUM:received_bytes} %{BASE10NUM:sent_bytes} \"%{DATA:request}\" \"%{DATA:user_agent}\" %{DATA:ssl_cipher} %{DATA:ssl_protocol} %{DATA:target_group_arn} \"%{DATA:trace_id}\" \"%{DATA:domain_name}\" \"%{DATA:chosen_cert_arn}\" %{DATA:matched_rule_priority} %{TIMESTAMP_ISO8601:request_creation_time} \"%{DATA:actions_executed}\" \"%{DATA:redirect_url}\" \"%{DATA:error_reason}\" \"%{DATA:target_list}\" \"%{DATA:target_status_code_list}\" \"%{DATA:classification}\" \"%{DATA:classification_reason}"]
    - grok:
        match:
          request: ["(%{NOTSPACE:http_method})? (%{NOTSPACE:http_uri})? (%{NOTSPACE:http_version})?"]
    - grok:
        match:
          http_uri: ["(%{WORD:protocol})?(://)?(%{IPORHOST:domain})?(:)?(%{INT:http_port})?(%{GREEDYDATA:request_uri})?"]
    - date:
        from_time_received: true
        destination: "@timestamp"


  sink:
    - opensearch:
        hosts: [ "https://localhost:9200" ]
        username: "admin"
        password: "admin"
        index: alb_logs
```

## Migrating from Logstash

Data Prepper supports Logstash configuration files for a limited set of plugins. Simply use the logstash config to run Data Prepper.

```bash
docker run --name data-prepper \
    -v /full/path/to/logstash.conf:/usr/share/data-prepper/pipelines/pipelines.conf \
    opensearchproject/opensearch-data-prepper:latest
```

This feature is limited by feature parity of Data Prepper. As of Data Prepper 1.2 release, the following plugins from the Logstash configuration are supported:

- HTTP Input plugin
- Grok Filter plugin
- Elasticsearch Output plugin
- Amazon Elasticsearch Output plugin

## Configure the Data Prepper server

Data Prepper itself provides administrative HTTP endpoints such as `/list` to list pipelines and `/metrics/prometheus` to provide Prometheus-compatible metrics data. The port that has these endpoints has a TLS configuration and is specified by a separate YAML file. By default, these endpoints are secured by Data Prepper docker images. We strongly recommend providing your own configuration file for securing production environments. Here is an example `data-prepper-config.yaml`:

```yml
ssl: true
keyStoreFilePath: "/usr/share/data-prepper/keystore.jks"
keyStorePassword: "password"
privateKeyPassword: "other_password"
serverPort: 1234
```

To configure the Data Prepper server, run Data Prepper with the additional yaml file.

```bash
docker run --name data-prepper \
    -v /full/path/to/my-pipelines.yaml:/usr/share/data-prepper/pipelines/my-pipelines.yaml \
    -v /full/path/to/data-prepper-config.yaml:/usr/share/data-prepper/data-prepper-config.yaml \
    opensearchproject/data-prepper:latest
```

## Configure the peer forwarder

Data Prepper provides an HTTP service to forward Events between Data Prepper nodes for aggregation. This is required for operating Data Prepper in a clustered deployment. Currently, peer forwarding is supported in `aggregate`, `service_map_stateful`, and `otel_trace_raw` processors. Peer forwarder groups events based on the identification keys provided by the processors. For `service_map_stateful` and `otel_trace_raw` it's `traceId` by default and can not be configured. For `aggregate` processor, it is configurable using `identification_keys` option. 

Peer forwarder supports peer discovery through one of three options: a static list, a DNS record lookup , or AWS Cloud Map. This option can be configured using `discovery_mode` option. Peer forwarder also supports SSL for verification and encrytion, and mTLS for mutual authentication in peer forwarding service.

To configure the peer forwarder, add configuration options to `data-prepper-config.yaml` mentioned in the previous [Configure the Data Prepper server](#configure-the-data-prepper-server) section:

```yml
peer_forwarder:
  discovery_mode: dns
  domain_name: "data-prepper-cluster.my-domain.net"
  ssl: true
  ssl_certificate_file: "<cert-file-path>"
  ssl_key_file: "<private-key-file-path>"
  authentication:
    mutual_tls:
```
