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Published: Nov 24, 2021 License: Apache-2.0


General Information

Processors are used at various stages of a pipeline. Generally, a processor pre-processes data before it is exported (e.g. modify attributes or sample) or helps ensure that data makes it through a pipeline successfully (e.g. batch/retry).

Some important aspects of pipelines and processors to be aware of:

Supported processors (sorted alphabetically):

The contrib repository has more processors that can be added to a custom build of the Collector.

By default, no processors are enabled. Depending on the data source, it may be recommended that multiple processors be enabled. Processors must be enabled for every data source: Not all processors support all data sources. In addition, it is important to note that the order of processors matters. The order in each section below is the best practice. Refer to the individual processor documentation for more information.

  1. memory_limiter
  2. any sampling processors
  3. Any processor relying on sending source from Context (e.g. k8sattributes)
  4. batch
  5. any other processors
  1. memory_limiter
  2. Any processor relying on sending source from Context (e.g. k8sattributes)
  3. batch
  4. any other processors

Data Ownership

The ownership of the pdata.Traces, pdata.Metrics and pdata.Logs data in a pipeline is passed as the data travels through the pipeline. The data is created by the receiver and then the ownership is passed to the first processor when ConsumeTraces/ConsumeMetrics/ConsumeLogs function is called.

Note: the receiver may be attached to multiple pipelines, in which case the same data will be passed to all attached pipelines via a data fan-out connector.

From data ownership perspective pipelines can work in 2 modes:

  • Exclusive data ownership
  • Shared data ownership

The mode is defined during startup based on data modification intent reported by the processors. The intent is reported by each processor via MutatesData field of the struct returned by Capabilities function. If any processor in the pipeline declares an intent to modify the data then that pipeline will work in exclusive ownership mode. In addition, any other pipeline that receives data from a receiver that is attached to a pipeline with exclusive ownership mode will be also operating in exclusive ownership mode.

Exclusive Ownership

In exclusive ownership mode the data is owned exclusively by a particular processor at a given moment of time, and the processor is free to modify the data it owns.

Exclusive ownership mode is only applicable for pipelines that receive data from the same receiver. If a pipeline is marked to be in exclusive ownership mode then any data received from a shared receiver will be cloned at the fan-out connector before passing further to each pipeline. This ensures that each pipeline has its own exclusive copy of data, and the data can be safely modified in the pipeline.

The exclusive ownership of data allows processors to freely modify the data while they own it (e.g. see attributesprocessor). The duration of ownership of the data by processor is from the beginning of ConsumeTraces/ConsumeMetrics/ConsumeLogs call until the processor calls the next processor's ConsumeTraces/ConsumeMetrics/ConsumeLogs function, which passes the ownership to the next processor. After that the processor must no longer read or write the data since it may be concurrently modified by the new owner.

Exclusive Ownership mode allows to easily implement processors that need to modify the data by simply declaring such intent.

Shared Ownership

In shared ownership mode no particular processor owns the data and no processor is allowed the modify the shared data.

In this mode no cloning is performed at the fan-out connector of receivers that are attached to multiple pipelines. In this case all such pipelines will see the same single shared copy of the data. Processors in pipelines operating in shared ownership mode are prohibited from modifying the original data that they receive via ConsumeTraces/ConsumeMetrics/ConsumeLogs call. Processors may only read the data but must not modify the data.

If the processor needs to modify the data while performing the processing but does not want to incur the cost of data cloning that Exclusive mode brings then the processor can declare that it does not modify the data and use any different technique that ensures original data is not modified. For example, the processor can implement copy-on-write approach for individual sub-parts of pdata.Traces/pdata.Metrics/pdata.Logs argument. Any approach that does not mutate the original pdata.Traces/pdata.Metrics/pdata.Logs is allowed.

If the processor uses such technique it should declare that it does not intend to modify the original data by setting MutatesData=false in its capabilities to avoid marking the pipeline for Exclusive ownership and to avoid the cost of data cloning described in Exclusive Ownership section.

Ordering Processors

The order processors are specified in a pipeline is important as this is the order in which each processor is applied to traces and metrics.


Path Synopsis
Package memorylimiterprocessor provides a processor for OpenTelemetry Service pipeline that drops data on the pipeline according to the current state of memory usage.
Package memorylimiterprocessor provides a processor for OpenTelemetry Service pipeline that drops data on the pipeline according to the current state of memory usage.

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