Spanpruning Processor

Available in: contrib Maintainers: @portertech Source: opentelemetry-collector-contrib

Supported Telemetry

Overview

The Span Pruning Processor identifies duplicate or similar leaf spans within a single trace, groups them, and replaces each group with a single aggregated summary span. When leaf spans are aggregated, the processor also recursively aggregates their parent spans if all children of those parents are being aggregated. Leaf spans are spans that are not referenced as a parent by any other span in the trace. They typically represent the last actions in an execution call stack (e.g., individual database queries, HTTP calls to external services). Spans are grouped by:

Span name - spans must have the same name
Span kind - spans must have the same kind (Internal, Server, Client, Producer, Consumer)
Status code - spans must have the same status (OK, Error, or Unset)
TraceState - spans must have identical TraceState values (for Consistent Probability Sampling compatibility)
Configured attributes - spans must have matching values for attributes specified in group_by_attributes
Parent span name - leaf spans must share the same parent span name to be grouped together

Parent spans are eligible for aggregation when all of their children are aggregated, they share the same name, kind, and status code, and they are not root spans. This processor is useful for reducing trace data volume while preserving meaningful information about repeated operations.

Use Cases

Database query optimization: When an application makes many similar database queries (e.g., N+1 queries), aggregate them into a single summary span
Batch operations: Consolidate many similar leaf operations into a single representative span
Cost reduction: Reduce trace storage costs by eliminating redundant span data

Configuration

processors:
  spanpruning:
    # Attributes to use for grouping similar leaf spans (supports glob patterns)
    # Spans with the same name AND same values for matching attributes will be grouped
    # Examples:
    #   - "db.*" matches db.operation, db.name, db.statement, etc.
    #   - "http.request.*" matches http.request.method, http.request.header, etc.
    #   - "db.operation" matches only the exact key "db.operation"
    group_by_attributes:
      - "db.*"
      - "http.method"

    # Minimum number of similar leaf spans required before aggregation
    # Default: 5
    min_spans_to_aggregate: 3

    # Maximum depth of parent span aggregation above leaf spans
    # 0 = only aggregate leaf spans (no parent aggregation)
    # -1 = unlimited depth
    # Default: 1
    max_parent_depth: 1

    # Prefix for aggregation statistics attributes
    # Default: "aggregation."
    aggregation_attribute_prefix: "batch."

Configuration Options

Field	Type	Default	Description
`group_by_attributes`	[]string	[]	Attribute patterns for grouping (supports glob patterns like `db.*`)
`min_spans_to_aggregate`	int	5	Minimum group size before aggregation occurs
`max_parent_depth`	int	1	Max depth of parent aggregation (0=none, -1=unlimited)
`aggregation_attribute_prefix`	string	”aggregation.”	Prefix for aggregation statistics attributes

Glob Pattern Support

The group_by_attributes field supports glob patterns for matching attribute keys:

Pattern	Matches
`db.*`	`db.operation`, `db.name`, `db.statement`, etc.
`http.request.*`	`http.request.method`, `http.request.header.content-type`, etc.
`rpc.*`	`rpc.method`, `rpc.service`, `rpc.system`, etc.
`db.operation`	Only the exact key `db.operation`

When multiple attributes match a pattern, they are all included in the grouping key (sorted alphabetically for consistency).

Summary Span

When spans are aggregated, the summary span includes:

Properties

Name: Original span name (e.g., SELECT)
TraceID: Same as original spans
SpanID: Newly generated unique ID
ParentSpanID: Same as original spans (common parent)
Kind: Same as template span (inherited from slowest span)
StartTimestamp: Earliest start time of all spans in the group
EndTimestamp: Latest end time of all spans in the group
Status: Same as original spans (spans are grouped by status code)
TraceState: Inherited from the template span (preserved for Consistent Probability Sampling compatibility)
Attributes: Inherited from the slowest span in the group

Note: The summary span’s duration (EndTimestamp - StartTimestamp) represents the total time window covered by all aggregated spans, which may exceed duration_max_ns. For example, if spans overlap or are staggered, the time range can be larger than any individual span’s duration. Use duration_max_ns to find the slowest individual operation.

What Gets Aggregated Away

When spans are aggregated into a summary span, the following data from non-template spans is lost:

Data	Behavior
Span Events	Events from the template (slowest) span are preserved
Span Links	Links from the template span are preserved
Attributes	Non-matching attribute values are lost
Individual Timestamps	Original start/end times replaced by the group’s time range
SpanIDs	Original SpanIDs are replaced by a single summary SpanID

Aggregation Attributes

The following attributes are added to the summary span (shown with default aggregation_attribute_prefix: "aggregation."):

Attribute	Type	Description
`<prefix>is_summary`	bool	Always `true` to identify summary spans
`<prefix>span_count`	int64	Number of spans that were aggregated
`<prefix>duration_min_ns`	int64	Minimum duration in nanoseconds
`<prefix>duration_max_ns`	int64	Maximum duration in nanoseconds
`<prefix>duration_avg_ns`	int64	Average duration in nanoseconds
`<prefix>duration_total_ns`	int64	Total duration in nanoseconds

Pipeline Placement

This processor is designed to work best when placed after processors that ensure complete traces are available:

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [groupbytrace, spanpruning, batch]
      exporters: [otlp]

Or with tail sampling:

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [tail_sampling, spanpruning, batch]
      exporters: [otlp]

Example

Basic Example

A trace with repeated database queries (some failing): Before Processing:

root-span (parent)
├── SELECT (leaf) - duration: 10ms, db.operation: select, status: OK
├── SELECT (leaf) - duration: 15ms, db.operation: select, status: OK
├── SELECT (leaf) - duration: 12ms, db.operation: select, status: OK
├── SELECT (leaf) - duration: 50ms, db.operation: select, status: Error
├── SELECT (leaf) - duration: 45ms, db.operation: select, status: Error
└── INSERT (leaf) - duration: 20ms, db.operation: insert, status: OK

After Processing (with min_spans_to_aggregate: 2):

root-span (parent)
├── SELECT (summary, status: OK)
│   - aggregation.is_summary: true
│   - aggregation.span_count: 3
│   - aggregation.duration_min_ns: 10000000
│   - aggregation.duration_max_ns: 15000000
│   - aggregation.duration_avg_ns: 12333333
├── SELECT (summary, status: Error)
│   - aggregation.is_summary: true
│   - aggregation.span_count: 2
│   - aggregation.duration_min_ns: 45000000
│   - aggregation.duration_max_ns: 50000000
│   - aggregation.duration_avg_ns: 47500000
└── INSERT (unchanged - only 1 span, below threshold)

Note: Spans with different status codes are grouped separately, preserving error information.

Recursive Parent Aggregation Example

When spans are aggregated, the processor also checks if their parent spans can be aggregated. Parent spans are eligible for aggregation when:

All of their children are being aggregated
They share the same name, kind, and status code with other eligible parents
They are not root spans (must have a parent)
At least 2 parents meet the criteria

Before Processing (with min_spans_to_aggregate: 2, group_by_attributes: ["db.op"]):

root
├── handler (status: OK)
│   └── SELECT (db.op=select, status: OK) ───┐
├── handler (status: OK)                      │ leaf group A: 3 OK SELECTs
│   └── SELECT (db.op=select, status: OK) ───┤
├── handler (status: OK)                      │
│   └── SELECT (db.op=select, status: OK) ───┘
├── handler (status: Error)
│   └── SELECT (db.op=select, status: Error) ┐ leaf group B: 2 Error SELECTs
├── handler (status: Error)                   │
│   └── SELECT (db.op=select, status: Error) ┘
├── handler (status: OK)
│   └── INSERT (db.op=insert, status: OK) ──── only 1, below threshold
└── worker (status: OK)
    └── SELECT (db.op=select, status: OK) ──── different parent name

After Processing:

root
├── handler (summary, status: OK, span_count: 3)
│   └── SELECT (summary, status: OK, span_count: 3)
├── handler (summary, status: Error, span_count: 2)
│   └── SELECT (summary, status: Error, span_count: 2)
├── handler (status: OK)
│   └── INSERT (status: OK) ─────────────────────────── unchanged
└── worker (status: OK)
    └── SELECT (status: OK) ─────────────────────────── unchanged

Why each span was handled this way:

Span	Result	Reason
3x handler (OK) with SELECT children	Aggregated	All children aggregated, same name+kind+status
3x SELECT (OK) under handler	Aggregated	Same name + kind + status + attributes + parent name
2x handler (Error) with SELECT children	Aggregated	All children aggregated, same name+kind+status
2x SELECT (Error) under handler	Aggregated	Same name + kind + status + attributes + parent name
handler (OK) with INSERT child	Unchanged	Child not aggregated (only 1 INSERT)
INSERT (OK)	Unchanged	Below threshold (only 1 span)
worker (OK)	Unchanged	Child not aggregated
SELECT (OK) under worker	Unchanged	Different parent name than other SELECTs

Limitations

Requires complete traces for accurate leaf detection
Summary span inherits attributes from the slowest span in the group
Parent spans are only aggregated when ALL their children are aggregated

Consistent Probability Sampling (CPS) Compatibility

The processor is designed to be compatible with Consistent Probability Sampling (CPS). CPS uses TraceState to carry sampling metadata (ot=th:...;rv:...) where:

th (threshold) indicates the sampling probability threshold
rv (randomness value) provides consistent randomness for sampling decisions

Why TraceState matters for aggregation: Spans with different TraceState values represent different sampling populations with different “adjusted counts” (weights). Aggregating them together would produce statistically incorrect summaries and break downstream sampling decisions. The processor uses exact TraceState matching (not just the th value) because:

The rv value affects sampling decisions
Vendor-specific keys may have semantic meaning
Key ordering may be significant

Telemetry

The processor emits the following metrics to help monitor its operation:

Counters

Metric	Description
`otelcol_processor_spanpruning_spans_received`	Total number of spans received by the processor
`otelcol_processor_spanpruning_spans_pruned`	Total number of spans removed by aggregation
`otelcol_processor_spanpruning_aggregations_created`	Total number of aggregation summary spans created
`otelcol_processor_spanpruning_traces_processed`	Total number of traces processed

Histograms

Metric	Description
`otelcol_processor_spanpruning_aggregation_group_size`	Distribution of the number of spans per aggregation group
`otelcol_processor_spanpruning_processing_duration`	Time taken to process each batch of traces (in seconds)

These metrics can be used to:

Monitor the effectiveness of span pruning (compare spans_received vs spans_pruned)
Track the compression ratio achieved by aggregation
Identify processing bottlenecks via processing_duration
Understand aggregation patterns via aggregation_group_size

Configuration

Example Configuration

spanpruning:
  group_by_attributes:
    - "db.operation"
  min_spans_to_aggregate: 5
  aggregation_attribute_prefix: "aggregation."

spanpruning/custom:
  group_by_attributes:
    - "db.operation"
    - "db.name"
  min_spans_to_aggregate: 3
  aggregation_attribute_prefix: "batch."

Last generated: 2026-04-13

Collector components

​Spanpruning Processor

​Supported Telemetry

​Overview

​Overview

​Use Cases

​Configuration

​Configuration Options

​Glob Pattern Support

​Summary Span

​Properties

​What Gets Aggregated Away

​Aggregation Attributes

​Pipeline Placement

​Example

​Basic Example

​Recursive Parent Aggregation Example

​Limitations

​Consistent Probability Sampling (CPS) Compatibility

​Telemetry

​Counters

​Histograms

​Configuration

​Example Configuration

Spanpruning Processor

Supported Telemetry

Overview

Overview

Use Cases

Configuration

Configuration Options

Glob Pattern Support

Summary Span

Properties

What Gets Aggregated Away

Aggregation Attributes

Pipeline Placement

Example

Basic Example

Recursive Parent Aggregation Example

Limitations

Consistent Probability Sampling (CPS) Compatibility

Telemetry

Counters

Histograms

Configuration

Example Configuration