Spanpruning Processor

Available in: contrib Maintainers: @portertech, @csmarchbanks 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. Optionally, the processor can detect duration outliers using statistical methods (IQR or MAD) and either annotate summary spans with outlier correlations or preserve outlier spans as individual spans for debugging while still aggregating normal 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."

    # Upper bounds for histogram buckets (latency distribution)
    # Default: [5ms, 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 2.5s, 5s, 10s]
    # Set to empty list to disable histogram attributes
    aggregation_histogram_buckets: [10ms, 50ms, 100ms, 500ms, 1s]

    # Enable attribute loss analysis during aggregation
    # Default: false
    # When enabled, records attribute-loss metrics and adds summary span attributes
    enable_attribute_loss_analysis: false

    # Attribute loss exemplar sampling rate
    # Fraction of attribute-loss metric recordings that include trace exemplars
    # Range: 0.0 (disabled) to 1.0 (always)
    # Default: 0 (disabled)
    attribute_loss_exemplar_sample_rate: 0.01

    # Enable IQR or MAD outlier detection and attribute correlation
    # When enabled, adds duration_median_ns and outlier_correlated_attributes
    # to summary spans
    # Default: false
    enable_outlier_analysis: false

    # Outlier analysis configuration (optional)
    outlier_analysis:
      # Statistical method for outlier detection
      # "iqr" (default): Interquartile Range method
      # "mad": Median Absolute Deviation method (more robust to extreme outliers)
      method: iqr

      # IQR multiplier for outlier detection threshold (when method=iqr)
      # Outliers are spans with duration > Q3 + (iqr_multiplier * IQR)
      # Common values: 1.5 (standard), 3.0 (extreme only)
      # Default: 1.5
      iqr_multiplier: 1.5

      # MAD multiplier for outlier detection threshold (when method=mad)
      # Outliers are spans with duration > median + (mad_multiplier * MAD * 1.4826)
      # Common values: 2.5-3.0 (standard), 3.5+ (extreme only)
      # Default: 3.0
      mad_multiplier: 3.0

      # Minimum group size for reliable IQR/MAD analysis
      # Groups smaller than this skip outlier analysis
      # Must be at least 4 (need quartiles)
      # Default: 7
      min_group_size: 7

      # Minimum fraction of outliers that must share an attribute value
      # for it to be reported as correlated
      # Range: (0.0, 1.0]
      # Default: 0.75 (75% of outliers must share the value)
      correlation_min_occurrence: 0.75

      # Maximum fraction of normal spans that can have the correlated value
      # Lower values mean stronger signal
      # Range: [0.0, 1.0)
      # Default: 0.25 (at most 25% of normal spans can have the value)
      correlation_max_normal_occurrence: 0.25

      # Maximum correlated attributes to report in summary span attribute
      # Default: 5
      max_correlated_attributes: 5

      # Preserve outlier spans as individual spans instead of aggregating
      # When true, only normal spans are aggregated; outliers remain in the trace
      # Default: false
      preserve_outliers: false

      # Maximum number of outlier spans to preserve per aggregation group
      # Spans are selected by most extreme duration first
      # 0 = preserve all detected outliers
      # Default: 2
      max_preserved_outliers: 2

      # Only preserve outliers when a strong attribute correlation is found
      # This avoids preserving outliers that are just random variance
      # Default: false
      preserve_only_with_correlation: false

      # Minimum percentage above median required to mark an outlier
      # Adds a floor to avoid overly sensitive detection when IQR/MAD is near zero
      # Range: [0.0, 1.0+]
      # Default: 0.1 (10%)
      min_outlier_threshold_percent: 0.1

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
`aggregation_histogram_buckets`	[]time.Duration	`[5ms, 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 2.5s, 5s, 10s]`	Upper bounds for latency histogram buckets
`enable_attribute_loss_analysis`	bool	false	Enable attribute loss analysis (records attribute-loss metrics and adds summary span loss attributes)
`attribute_loss_exemplar_sample_rate`	float64	0 (disabled)	Fraction of attribute-loss metric recordings with exemplars (0.0-1.0). Only applies when `enable_attribute_loss_analysis` is true.
`enable_outlier_analysis`	bool	false	Enable outlier detection and correlation analysis
`outlier_analysis.method`	string	”iqr”	Statistical method: “iqr” or “mad”
`outlier_analysis.iqr_multiplier`	float64	1.5	IQR threshold multiplier (when method=iqr)
`outlier_analysis.mad_multiplier`	float64	3.0	MAD threshold multiplier (when method=mad)
`outlier_analysis.min_group_size`	int	7	Minimum group size for outlier analysis
`outlier_analysis.correlation_min_occurrence`	float64	0.75	Minimum outlier occurrence fraction for correlation
`outlier_analysis.correlation_max_normal_occurrence`	float64	0.25	Maximum normal occurrence fraction for correlation
`outlier_analysis.max_correlated_attributes`	int	5	Maximum correlated attributes to report
`outlier_analysis.preserve_outliers`	bool	false	Keep outliers as individual spans instead of aggregating
`outlier_analysis.max_preserved_outliers`	int	2	Max outliers to preserve per group (0=preserve all)
`outlier_analysis.preserve_only_with_correlation`	bool	false	Only preserve outliers if a strong correlation is found
`outlier_analysis.min_outlier_threshold_percent`	float64	0.1	Minimum percentage above median required before a span is considered an outlier

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
`<prefix>histogram_bucket_bounds_s`	[]float64	Bucket upper bounds in seconds (excludes +Inf)
`<prefix>histogram_bucket_counts`	[]int64	Cumulative count per bucket (includes +Inf bucket)

Optional Attribute Loss Analysis

When enable_attribute_loss_analysis: true, the processor analyzes how attributes vary across aggregated spans and records two loss types:

Diversity loss: Attribute exists on all spans in the group, but values differ across spans.
Missing loss: Attribute is absent from some spans in the group.

These are added to the summary span as string attributes (with your configured aggregation_attribute_prefix):

Attribute	Type	Description
`<prefix>diverse_attributes`	string	Attributes with value diversity loss. Format: `key(lost_values),...`
`<prefix>missing_attributes`	string	Attributes missing on some spans. Format: `key(lost_values),...`

The processor also records leaf and parent histogram metrics for both loss types:

processor_spanpruning_leaf_attribute_diversity_loss
processor_spanpruning_leaf_attribute_loss
processor_spanpruning_parent_attribute_diversity_loss
processor_spanpruning_parent_attribute_loss

Use attribute_loss_exemplar_sample_rate to control how often those metric points include exemplars for trace correlation.

Optional Outlier Analysis Attributes

When enable_outlier_analysis: true, the following additional attributes are added:

Attribute	Type	Description
`<prefix>duration_median_ns`	int64	Median duration (more robust than average for skewed distributions)
`<prefix>outlier_correlated_attributes`	string	Attributes that distinguish outliers from normal spans (format: `key=value(outlier%/normal%), ...`)

Summary Span Attributes (When Preserving Outliers)

When outlier_analysis.preserve_outliers: true, the summary span also includes:

Attribute	Type	Description
`<prefix>preserved_outlier_count`	int64	Number of outlier spans preserved
`<prefix>preserved_outlier_span_ids`	[]string	SpanIDs of preserved outliers

Preserved Outlier Span Attributes

Preserved outlier spans are annotated with:

Attribute	Type	Description
`<prefix>is_preserved_outlier`	bool	Identifies span as a preserved outlier
`<prefix>summary_span_id`	string	SpanID of the associated summary span

A preserved outlier becomes a sibling of its summary span. Grouping is depth-aware — leaves and parents are never grouped with same-named ancestors at a different depth — so an outlier stays at its original depth and the summary it links to (via summary_span_id) sits where its group was.

Histogram Buckets

When aggregation_histogram_buckets is configured, summary spans include latency distribution data as cumulative histogram buckets. Cumulative means each bucket count includes all spans with duration less than or equal to that bucket boundary. Worked example with buckets [10ms, 50ms, 100ms] and span durations [5ms, 15ms, 25ms, 75ms, 150ms]:

histogram_bucket_bounds_s: [0.01, 0.05, 0.1]
histogram_bucket_counts: [1, 3, 4, 5]
- Bucket 0 (<=10ms): 1 span (5ms)
- Bucket 1 (<=50ms): 3 spans (5ms, 15ms, 25ms)
- Bucket 2 (<=100ms): 4 spans (5ms, 15ms, 25ms, 75ms)
- Bucket 3 (+Inf): 5 spans (all spans)

Outlier Analysis (Optional)

When enable_outlier_analysis: true, the processor detects duration outliers and identifies attributes that correlate with slow spans.

Detection Methods

The processor supports two statistical methods for outlier detection:

Method	Formula	Characteristics
IQR (default)	`threshold = Q3 + (multiplier × IQR)`	Standard method; sensitive to moderate outliers; uses quartiles
MAD	`threshold = median + (multiplier × MAD × 1.4826)`	More robust to extreme outliers; uses median

When to use each:

IQR: Best for typical distributions with moderate outliers. Standard choice for most use cases.
MAD: Better when you have extreme outliers that would skew IQR calculations, or when you need more stable detection thresholds.

How It Works

IQR (Interquartile Range) Method:

Sort spans by duration
Calculate Q1 (25th percentile) and Q3 (75th percentile)
Calculate IQR = Q3 - Q1
Flag spans with duration > Q3 + (iqr_multiplier × IQR) as outliers

MAD (Median Absolute Deviation) Method:

Sort spans by duration and find the median
Calculate |duration - median| for each span
MAD = median of those deviations
Flag spans with duration > median + (mad_multiplier × MAD × 1.4826) as outliers

Note: The 1.4826 scale factor makes MAD comparable to standard deviation for normal distributions. Attribute Correlation (same for both methods):

Compare attribute values between outliers and normal spans
Find attribute values that appear frequently in outliers but rarely in normal spans
Report the strongest correlations based on the configured thresholds

Configuration Example

processors:
  spanpruning:
    enable_outlier_analysis: true
    outlier_analysis:
      method: iqr                # or "mad" for more robustness
      iqr_multiplier: 1.5        # Standard outlier threshold (IQR method)
      mad_multiplier: 3.0        # Standard outlier threshold (MAD method)
      min_group_size: 7          # Skip groups with <7 spans
      correlation_min_occurrence: 0.75   # 75% of outliers must share value
      correlation_max_normal_occurrence: 0.25  # <25% of normal spans can have it
      max_correlated_attributes: 5       # Report top 5 correlations
      min_outlier_threshold_percent: 0.1 # Require at least 10% above median

Example Output

SELECT (summary, span_count: 20)
  aggregation.duration_avg_ns: 45000000
  aggregation.duration_median_ns: 8000000
  aggregation.outlier_correlated_attributes: "db.cache_hit=false(100%/0%), db.shard=7(80%/10%)"

Interpretation:

Median vs Avg: Large difference (8ms vs 45ms) indicates outliers are skewing the average
Primary correlation: All outliers (100%) had cache_hit=false, while 0% of normal spans did
Secondary correlation: 80% of outliers hit shard 7, but only 10% of normal spans did

This helps identify root causes of latency issues:

Cache misses
Specific database shards
Failed retries
Timeout scenarios

When to Use

Enable when you need to understand why some operations are slow
Disable (default) to minimize overhead when outlier analysis isn’t needed
Works best with groups of 10+ spans for statistical reliability

Performance Impact

Computational overhead: Sorts durations, calculates quartiles, counts attribute occurrences
Minimal when disabled: Zero overhead (no sorting or calculations)
Recommended: Use min_group_size: 7 or higher to skip analysis on small groups

Preserving Outlier Spans (Optional)

When outlier_analysis.preserve_outliers: true, detected outlier spans are kept as individual spans instead of being aggregated. This provides:

Full visibility into slow operations for debugging
Preserved context: Original attributes, events, and links remain intact
Selective aggregation: Only prune repetitive normal spans

Configuration

processors:
  spanpruning:
    enable_outlier_analysis: true
    outlier_analysis:
      preserve_outliers: true         # Keep outliers as individual spans
      max_preserved_outliers: 2       # Keep top 2 slowest outliers per group
      preserve_only_with_correlation: false  # Preserve even without correlation

Configuration Options

Field	Type	Default	Description
`preserve_outliers`	bool	false	Keep outliers as individual spans instead of aggregating
`max_preserved_outliers`	int	2	Max outliers to preserve per group (0=preserve all detected)
`preserve_only_with_correlation`	bool	false	Only preserve outliers if a strong attribute correlation is found

Example Output

Before (10 similar SELECT spans, 2 are outliers):

handler
├── SELECT - 5ms (normal)
├── SELECT - 6ms (normal)
├── SELECT - 7ms (normal)
├── SELECT - 8ms (normal)
├── SELECT - 9ms (normal)
├── SELECT - 10ms (normal)
├── SELECT - 11ms (normal)
├── SELECT - 12ms (normal)
├── SELECT - 500ms (outlier, cache_hit=false)
└── SELECT - 600ms (outlier, cache_hit=false)

After (with preserve_outliers: true, max_preserved_outliers: 2):

handler
├── SELECT (summary, span_count=8)      ← Normal spans aggregated
│   - aggregation.preserved_outlier_count: 2
│   - aggregation.outlier_correlated_attributes: "cache_hit=false(100%/0%)"
├── SELECT - 500ms                       ← Outlier preserved
│   - aggregation.is_preserved_outlier: true
│   - aggregation.summary_span_id: "abc123"
│   - cache_hit: false
└── SELECT - 600ms                       ← Outlier preserved
    - aggregation.is_preserved_outlier: true
    - aggregation.summary_span_id: "abc123"
    - cache_hit: false

Behavior Notes

Parent aggregation: Parents can still be aggregated if all their children are either aggregated or preserved as outliers
Skip aggregation: If preserving outliers leaves too few normal spans (below min_spans_to_aggregate), the entire group is left unchanged
Selection order: Outliers are preserved starting with the most extreme (longest duration) first

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
`otelcol_processor_spanpruning_outliers_detected`	Total spans identified as outliers by analysis (when `enable_outlier_analysis: true`)
`otelcol_processor_spanpruning_outliers_preserved`	Total outlier spans kept as individual spans (when `preserve_outliers: true`)
`otelcol_processor_spanpruning_outliers_correlations_detected`	Total aggregation groups where outliers had correlated attributes

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."
  aggregation_histogram_buckets:
    - "10ms"
    - "50ms"
    - "100ms"
    - "500ms"
    - "1s"

spanpruning/attribute_loss:
  group_by_attributes:
    - "db.operation"
  min_spans_to_aggregate: 4
  aggregation_attribute_prefix: "aggregation."
  enable_attribute_loss_analysis: true
  attribute_loss_exemplar_sample_rate: 0.25

Last generated: 2026-07-06

​Spanpruning Processor

​Supported Telemetry

​Overview

​Overview

​Use Cases

​Configuration

​Configuration Options

​Glob Pattern Support

​Summary Span

​Properties

​What Gets Aggregated Away

​Aggregation Attributes

​Optional Attribute Loss Analysis

​Optional Outlier Analysis Attributes

​Summary Span Attributes (When Preserving Outliers)

​Preserved Outlier Span Attributes

​Histogram Buckets

​Outlier Analysis (Optional)

​Detection Methods

​How It Works

​Configuration Example

​Example Output

​When to Use

​Performance Impact

​Preserving Outlier Spans (Optional)

​Configuration

​Configuration Options

​Example Output

​Behavior Notes

​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

Optional Attribute Loss Analysis

Optional Outlier Analysis Attributes

Summary Span Attributes (When Preserving Outliers)

Preserved Outlier Span Attributes

Histogram Buckets

Outlier Analysis (Optional)

Detection Methods

How It Works

Configuration Example

Example Output

When to Use

Performance Impact

Preserving Outlier Spans (Optional)

Configuration

Configuration Options

Example Output

Behavior Notes

Pipeline Placement

Example

Basic Example

Recursive Parent Aggregation Example

Limitations

Consistent Probability Sampling (CPS) Compatibility

Telemetry

Counters

Histograms

Configuration

Example Configuration