Add design doc for processing query language.

docs/processing.md

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+# OpenTelemetry Collector Processor Exploration
+
+## Objective
+
+To describe a user experience and strategies for configuring processors in the OpenTelemetry collector.
+
+This work is being prototyped in opentelemetry-collector-contrib, the design doc is here for broader discussion.
+
+## Summary
+
+The OpenTelemetry (OTel) collector is a tool to set up pipelines to receive telemetry from an application and export it
+to an observability backend. Part of the pipeline can include processing stages, which executes various business logic
+on incoming telemetry before it is exported.
+
+Over time, the collector has added various processors to satisfy different use cases, generally in an ad-hoc way to
+support each feature independently. We can improve the experience for users of the collector by consolidating processing
+patterns in terms of user experience, and this can be supported by defining a querying model for processors
+within the collector core, and likely also for use in SDKs, to simplify implementation and promote the consistent user
+experience and best practices.
+
+## Goals and non-goals
+
+Goals:
+- List out use cases for processing within the collector
+- Consider what could be an ideal configuration experience for users
+
+Non-Goals:
+- Merge every processor into one. Many use cases overlap and generalize, but not all of them
+- Technical design or implementation of configuration experience. Currently focused on user experience.
+
+## Use cases for processing
+
+### Telemetry mutation
+
+Processors can be used to mutate the telemetry in the collector pipeline. OpenTelemetry SDKs collect detailed telemetry
+from applications, and it is common to have to mutate this into a way that is appropriate for an individual use case.
+
+Some types of mutation include
+
+- Remove a forbidden attribute such as `http.request.header.authorization`
+- Reduce cardinality of an attribute such as translating `http.target` value of `/user/123451/profile` to `/user/{userId}/profile`
+- Decrease the size of the telemetry payload by removing large resource attributes such as `process.command_line`
+- Filtering out signals such as by removing all telemetry with a `http.target` of `/health`
+- Attach information from resource into telemetry, for example adding certain resource fields as metric dimensions
+
+The processors implementing this use case are `attributesprocessor`, `filterprocessor`, `metricstransformprocessor`, 
+`resourceprocessor`, `spanprocessor`.
+
+### Metric generation
+
+The collector may generate new metrics based on incoming telemetry. This can be for covering gaps in SDK coverage of
+metrics vs spans, or to create new metrics based on existing ones to model the data better for backend-specific
+expectations.
+
+- Create new metrics based on information in spans, for example to create a duration metric that is not implemented in the SDK yet
+- Apply arithmetic between multiple incoming metrics to produce an output one, for example divide an `amount` and a `capacity` to create a `utilization` metric
+
+The processors implementing this use case are `metricsgenerationprocessor`, `spanmetricsprocessor`.
+
+### Grouping
+
+Some processors are stateful, grouping telemetry over a window of time based on either a trace ID or an attribute value,
+or just general batching.
+
+- Batch incoming telemetry before sending to exporters to reduce export requests
+- Group spans by trace ID to allow doing tail sampling
+- Group telemetry for the same path
+
+The processors implementing this use case are `batchprocessor`, `groupbyattrprocessor`, `groupbytraceprocessor`.
+
+### Metric temporality
+
+Two processors convert between the two types of temporality, cumulative and delta. The conversion is generally expected
+to happen as close to the source data as possible, for example within receivers themselves. The same configuration
+mechanism could be used for selecting metrics for temporality conversion as other cases, but it is expected that in
+practice configuration will be limited.
+
+The processors implementing this use case are `cumulativetodeltaprocessor` and `deltatorateprocessor`.
+
+### Telemetry enrichment
+
+OpenTelemetry SDKs focus on collecting application specific data. They also may include resource detectors to populate
+environment specific data but the collector is commonly used to fill gaps in coverage of environment specific data.
+
+- Add environment about a cloud provider to `Resource` of all incoming telemetry
+
+The processors implementing this use case are `k8sattributesprocessor`, `resourcedetectionprocessor`.
+
+## Telemetry query language
+
+When looking at the use cases, there are certain common features for telemetry mutation and metric generation.
+
+- Identify the type of signal (span, metric, log, resource), or apply to all signals
+- Navigate to a path within the telemetry to operate on it
+- Define an operation, and possibly operation arguments
+
+We can try to model these into a query language, in particular allowing the first two points to be shared among all
+processing operations, and only have implementation of individual types of processing need to implement operators that
+the user can use within an expression.
+
+Telemetry is modeled in the collector as [`pdata`](https://github.com/open-telemetry/opentelemetry-collector/tree/main/model/pdata)
+which is roughly a 1:1 mapping of the [OTLP protocol](https://github.com/open-telemetry/opentelemetry-proto/tree/main/opentelemetry/proto).
+This data can be navigated using field expressions, which are fields within the protocol separated by dots. For example,
+the status message of a span is `status.message`. A map lookup can include the key as a string, for example `attributes["http.status_code"]`.
+
+Operations can be scoped to the type of a signal (`span`, `metric`, `log`), with all of the flattened points of that
+signal being part of a query space. Virtual fields are added to access data from a higher level before flattening, for
+`resource`, `library_info`. For metrics, the structure presented for processing is actual data points, e.g. `NumberDataPoint`, 
+`HistogramDataPoint`, with the information from higher levels like `Metric` or the data type available as virtual fields.
+
+Navigation can then be used with a simple expression language for identifying telemetry to operate on.
+
+`... where name = "GET /cats"`
+`... from span where attributes["http.target"] = "/health"`
+`... where resource.attributes["deployment"] = "canary"`
+`... from metric where descriptor.metric_type = gauge`
+`... from metric where descriptor.metric_name = "http.active_requests"`
+
+Fields should always be fully specified - for example `attributes` refers to the `attributes` field in the telemetry, not
+the `resource`. In the future, we may allow shorthand for accessing scoped information that is not ambiguous.
+
+Having selected telemetry to operate on, any needed operations can be defined as functions. Known useful functions should
+be implemented within the collector itself, provide registration from extension modules to allow customization with
+contrib components, and in the future can even allow user plugins possibly through WASM, similar to work in 
+[HTTP proxies](https://github.com/proxy-wasm/spec). The arguments to operations will primarily be field expressions,
+allowing the operation to mutate telemetry as needed.
+
+### Examples
+
+Remove a forbidden attribute such as `http.request.header.authorization` from all telemetry.
+
+`delete(attributes["http.request.header.authorization"])`
+
+Remove a forbidden attribute from spans only
+
+`delete(attributes["http.request.header.authorization"]) from span`
+
+Remove all attributes except for some
+
+`keep(attributes, "http.method", "http.status_code") from metric`
+
+Reduce cardinality of an attribute
+
+`replace_wildcards("/user/*/list/*", "/user/{userId}/list/{listId}", attributes["http.target"])`
+
+Reduce cardinality of a span name
+
+`replace_wildcards("GET /user/*/list/*", "GET /user/{userId}/list/{listId}", name) from span`
+
+Decrease the size of the telemetry payload by removing large resource attributes
+
+`delete(resource.attributes["process.command_line"])`
+
+Filtering out signals such as by removing all telemetry with a `http.target` of `/health`
+
+`drop() where attributes["http.target"] = "/health"`
+
+Attach information from resource into telemetry, for example adding certain resource fields as metric attributes
+
+`set(attributes["k8s_pod"], resource.attributes["k8s.pod.name"]) from metric`
+
+Stateful processing can also be modeled by the language. The processor implementation would set up the state while
+parsing the configuration.
+
+Create duration_metric with two attributes copied from a span
+
+```
+create_histogram("duration", end_time_nanos - start_time_nanos) from span
+keep(attributes, "http.method") from metric where descriptor.metric_name = "duration"
+```
+
+Group spans by trace ID
+
+`group_by(trace_id, 2m) from span`
+
+Create utilization metric from base metrics. Because navigation expressions only operate on a single piece of telemetry,
+helper functions for reading values from other metrics need to be provided.
+
+`create_gauge("pod.cpu.utilized", read_gauge("pod.cpu.usage") / read_gauge("node.cpu.limit") from metric`
+
+A lot of processing. Queries are executed in order. While initially performance may degrade compared to more specialized
+processors, the expectation is that over time, the query processor's engine would improve to be able to apply optimizations 
+across queries, compile into machine code, etc.
+
+```yaml
+receivers:
+  otlp:
+
+exporters:
+  otlp:
+
+processors:
+  query:
+    # Assuming group_by is defined in a contrib extension module, not baked into the "query" processor
+    extensions: [group_by]
+    expressions:
+      - drop() where attributes["http.target"] = "/health"
+      - delete(attributes["http.request.header.authorization"])
+      - replace_wildcards("/user/*/list/*", "/user/{userId}/list/{listId}", attributes["http.target"])
+      - set(attributes["k8s_pod"], resource.attributes["k8s.pod.name"]) from metric
+      - group_by(trace_id, 2m) from span
+
+pipelines:
+  - receivers: [otlp]
+    exporters: [otlp]
+    processors: [query]
+```
+
+The expressions would be executed in order, with each expression either mutating an input telemetry, dropping input
+telemetry, or adding additional telemetry (usually for stateful processors like batch processor which will drop telemetry
+for a window and then add them all at the same time). One caveat to note is that we would like to implement optimizations
+in the query engine, for example to only apply filtering once for multiple operations with a shared filter. Functions
+with unknown side effects may cause issues with optimization we will need to explore.
+
+## Declarative configuration
+
+The telemetry query language presents an SQL-like experience for defining telemetry transformations - it is made up of
+the three primary components described above, however, and can be presented declaratively instead depending on what makes
+sense as a user experience.
+
+```yaml
+- type: span
+  filter:
+    match:
+      path: status.code
+      value: OK
+  operation:
+    name: drop
+- type: all
+  operation:
+    name: delete
+    args:
+      - attributes["http.request.header.authorization"]
+```
+
+An implementation of the query language would likely parse expressions into this sort of structure so given an SQL-like
+implementation, it would likely be little overhead to support a YAML approach in addition.
+
+## Implementing a processor function
+
+The `replace_wildcards` function may look like this.
+
+```go
+
+package replacewildcards
+
+import "regexp"
+
+import "github.com/open-telemetry/opentelemetry/processors"
+
+// Assuming this is not in "core"
+processors.register("replace_wildcards", replace_wildcards)
+
+func replace_wildcards(pattern regexp.Regexp, replacement string, path processors.TelemetryPath) processors.Result  {
+    val := path.Get()
+	if val == nil {
+		return processors.CONTINUE
+    }
+
+	// replace finds placeholders in "replacement" and swaps them in for regex matched substrings.
+	replaced := replace(val, pattern, replacement)
+	path.Set(replaced)
+	return processors.CONTINUE
+}
+```
+
+Here, the processor framework recognizes the first parameter of the function is `regexp.Regexp` so will compile the string
+provided by the user in the config when processing it. Similarly for `path`, it recognizes properties of type `TelemetryPath`
+and will resolve it to the path within a matched telemetry during execution and pass it to the function. The path allows
+scalar operations on the field within the telemetry. The processor does not need to be aware of telemetry filtering,
+the `where ...` clause, as that will be handled by the framework before passing to the function.
+
+## Embedded processors
+
+The above describes a query language for configuring processing logic in the OpenTelemetry collector. There will be a
+single processor that exposes the processing logic into the collector config; however, the logic will be implemented
+within core packages rather than directly inside a processor. This is to ensure that where appropriate, processing
+can be embedded into other components, for example metric processing is often most appropriate to execute within a
+receiver based on receiver-specific requirements.
+
+## Limitations
+
+There are some known issues and limitations that we hope to address while iterating on this idea.
+
+- Handling array-typed attributes
+- Working on a array of points, rather than a single point
+- Metric alignment - for example defining an expression on two metrics, that may not be at the same timestamp
+- The collector has separate pipelines per signal - while the query language could apply cross-signal, we will need
+to remain single-signal for now