> ## Documentation Index
> Fetch the complete documentation index at: https://otel.fyi/llms.txt
> Use this file to discover all available pages before exploring further.

# Spanpruning

> OpenTelemetry processor for Spanpruning

# Spanpruning Processor

![Status](https://img.shields.io/badge/status-alpha-red)

**Available in:** `contrib`

**Maintainers:** [@portertech](https://github.com/portertech), [@csmarchbanks](https://github.com/csmarchbanks)

**Source:** [opentelemetry-collector-contrib](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/spanpruningprocessor)

## Supported Telemetry

![Traces](https://img.shields.io/badge/traces-alpha-orange)

## Overview

## 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:

1. **Span name** - spans must have the same name
2. **Span kind** - spans must have the same kind (Internal, Server, Client, Producer, Consumer)
3. **Status code** - spans must have the same status (OK, Error, or Unset)
4. **TraceState** - spans must have identical TraceState values (for Consistent Probability Sampling compatibility)
5. **Configured attributes** - spans must have matching values for attributes specified in `group_by_attributes`
6. **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

```yaml theme={null}
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:**

1. Sort spans by duration
2. Calculate Q1 (25th percentile) and Q3 (75th percentile)
3. Calculate IQR = Q3 - Q1
4. Flag spans with duration > Q3 + (iqr\_multiplier × IQR) as outliers

**MAD (Median Absolute Deviation) Method:**

1. Sort spans by duration and find the median
2. Calculate |duration - median| for each span
3. MAD = median of those deviations
4. 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

```yaml theme={null}
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

```yaml theme={null}
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:

```yaml theme={null}
service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [groupbytrace, spanpruning, batch]
      exporters: [otlp]
```

Or with tail sampling:

```yaml theme={null}
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:

1. All of their children are being aggregated
2. They share the same name, kind, and status code with other eligible parents
3. They are not root spans (must have a parent)
4. 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](https://opentelemetry.io/docs/specs/otel/trace/tracestate-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

```yaml theme={null}
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*