# Batch Processor

<!-- status autogenerated section -->
| Status        |           |
| ------------- |-----------|
| Stability     | [beta]: traces, metrics, logs   |
| Distributions | [core], [contrib], [k8s] |
| Issues        | [![Open issues](https://img.shields.io/github/issues-search/open-telemetry/opentelemetry-collector?query=is%3Aissue%20is%3Aopen%20label%3Aprocessor%2Fbatch%20&label=open&color=orange&logo=opentelemetry)](https://github.com/open-telemetry/opentelemetry-collector/issues?q=is%3Aopen+is%3Aissue+label%3Aprocessor%2Fbatch) [![Closed issues](https://img.shields.io/github/issues-search/open-telemetry/opentelemetry-collector?query=is%3Aissue%20is%3Aclosed%20label%3Aprocessor%2Fbatch%20&label=closed&color=blue&logo=opentelemetry)](https://github.com/open-telemetry/opentelemetry-collector/issues?q=is%3Aclosed+is%3Aissue+label%3Aprocessor%2Fbatch) |

[beta]: https://github.com/open-telemetry/opentelemetry-collector/blob/main/docs/component-stability.md#beta
[core]: https://github.com/open-telemetry/opentelemetry-collector-releases/tree/main/distributions/otelcol
[contrib]: https://github.com/open-telemetry/opentelemetry-collector-releases/tree/main/distributions/otelcol-contrib
[k8s]: https://github.com/open-telemetry/opentelemetry-collector-releases/tree/main/distributions/otelcol-k8s
<!-- end autogenerated section -->

The batch processor accepts spans, metrics, or logs and places them into
batches. Batching helps better compress the data and reduce the number of
outgoing connections required to transmit the data. This processor supports
both size and time based batching.

It is highly recommended to configure the batch processor on every collector.
The batch processor should be defined in the pipeline after the `memory_limiter`
as well as any sampling processors. This is because batching should happen after
any data drops such as sampling.

Please refer to [config.go](./config.go) for the config spec.

The following configuration options can be modified:
- `send_batch_size` (default = 8192): Number of spans, metric data points, or log
records after which a batch will be sent regardless of the timeout. `send_batch_size`
acts as a trigger and does not affect the size of the batch. If you need to
enforce batch size limits sent to the next component in the pipeline
see `send_batch_max_size`.
- `timeout` (default = 200ms): Time duration after which a batch will
be sent regardless of size.  If set to zero, `send_batch_size` is
ignored as data will be sent immediately, subject to only `send_batch_max_size`.
- `send_batch_max_size` (default = 0): The upper limit of the batch size.
  `0` means no upper limit of the batch size.
  This property ensures that larger batches are split into smaller units.
  It must be greater than or equal to `send_batch_size`.
- `metadata_keys` (default = empty): When set, this processor will
  create one batcher instance per distinct combination of values in
  the `client.Metadata`.
- `metadata_cardinality_limit` (default = 1000): When `metadata_keys` is 
  not empty, this setting limits the number of unique combinations of 
  metadata key values that will be processed over the lifetime of the
  process.

See notes about metadata batching below.

Examples:

This configuration contains one default batch processor and a second
with custom settings.  The `batch/2` processor will buffer up to 10000
spans, metric data points, or log records for up to 10 seconds without
splitting data items to enforce a maximum batch size.

```yaml
processors:
  batch:
  batch/2:
    send_batch_size: 10000
    timeout: 10s
```

This configuration will enforce a maximum batch size limit of 10000
spans, metric data points, or log records without introducing any
artificial delays.

```yaml
processors:
  batch:
    send_batch_max_size: 10000
    timeout: 0s
```

Refer to [config.yaml](./testdata/config.yaml) for detailed
examples on using the processor.

## Batching and client metadata

Batching by metadata enables support for multi-tenant OpenTelemetry
Collector pipelines with batching over groups of data having the same
authorization metadata.  For example:

```yaml
processors:
  batch:
    # batch data by tenant-id
    metadata_keys:
    - tenant_id

    # limit to 10 batcher processes before raising errors
    metadata_cardinality_limit: 10
```

Receivers should be configured with `include_metadata: true` so that
metadata keys are available to the processor.

Note that each distinct combination of metadata triggers the
allocation of a new background task in the Collector that runs for the
lifetime of the process, and each background task holds one pending
batch of up to `send_batch_size` records.  Batching by metadata can
therefore substantially increase the amount of memory dedicated to
batching.

The maximum number of distinct combinations is limited to the
configured `metadata_cardinality_limit`, which defaults to 1000 to
limit memory impact.

Users of the batching processor configured with metadata keys should
consider use of an Auth extension to validate the relevant
metadata-key values.

The number of batch processors currently in use is exported as the
`otelcol_processor_batch_metadata_cardinality` metric.
