Edge Delta Sample Processor

The Edge Delta sample processor takes a representative sample of incoming data items.

7 minute read

The Sample Processor enables you to apply consistent probabilistic sampling to telemetry data, primarily logs and traces, based on configurable conditions and percentage-based rules. It is designed to reduce data volume while preserving representative data characteristics, which is especially useful for controlling cost and improving observability signal quality.

Learn more: Consistent Probabilistic Sampling in Our Sample Processor Node

Configuration

In the example above, 50% of logs are sampled (let through) without regard to content. Sampling behavior is customizable based on field values, timestamp granularity, and dynamic override fields.

nodes:
- name: otlp_input_9cd0_multiprocessor
  type: sequence
  user_description: Multi Processor
  processors:
  - type: sample
    metadata: '{"id":"12456789","type":"sample","name":"Sample"}'
    data_types:
    - log
    percentage: 50
    pass_through_on_failure: true

Options

Select a telemetry type

You can specify, log, metric, trace or all. It is specified using the interface, which generates a YAML list item for you under the data_types parameter. This defines the data item types against which the processor must operate. If data_types is not specified, the default value is all. It is optional.

It is defined in YAML as follows:

- name: multiprocessor
  type: sequence
  processors:
  - type: <processor type>
    data_types:
    - log

condition

The condition parameter contains a conditional phrase of an OTTL statement. It restricts operation of the processor to only data items where the condition is met. Those data items that do not match the condition are passed without processing. You configure it in the interface and an OTTL condition is generated. It is optional.

Important: All conditions must be written on a single line in YAML. Multi-line conditions are not supported.

Comparison Operators

Operator	Name	Description	Example
`==`	Equal to	Returns `true` if both values are exactly the same	`attributes["status"] == "OK"`
`!=`	Not equal to	Returns `true` if the values are not the same	`attributes["level"] != "debug"`
`>`	Greater than	Returns `true` if the left value is greater than the right	`attributes["duration_ms"] > 1000`
`>=`	Greater than or equal	Returns `true` if the left value is greater than or equal to the right	`attributes["score"] >= 90`
`<`	Less than	Returns `true` if the left value is less than the right	`attributes["load"] < 0.75`
`<=`	Less than or equal	Returns `true` if the left value is less than or equal to the right	`attributes["retries"] <= 3`
`matches`	Regex match	Returns `true` if the string matches a regular expression (generates `IsMatch` function)	`IsMatch(attributes["name"], ".*\\.log$")`

Logical Operators

Important: Use lowercase and, or, not - uppercase operators will cause errors!

Operator	Description	Example
`and`	Both conditions must be true	`attributes["level"] == "ERROR" and attributes["status"] >= 500`
`or`	At least one condition must be true	`attributes["log_type"] == "TRAFFIC" or attributes["log_type"] == "THREAT"`
`not`	Negates the condition	`not regex_match(attributes["path"], "^/health")`

Functions

Function	Description	Example
`regex_match`	Returns `true` if string matches the pattern	`regex_match(attributes["message"], "ERROR\\|FATAL")`
`IsMatch`	Alternative regex function (UI generates this from “matches” operator)	`IsMatch(attributes["name"], ".*\\.log$")`

Field Existence Checks

Check	Description	Example
`!= nil`	Field exists (not null)	`attributes["user_id"] != nil`
`== nil`	Field doesn’t exist	`attributes["optional_field"] == nil`
`!= ""`	Field is not empty string	`attributes["message"] != ""`

Common Examples

- name: _multiprocessor
  type: sequence
  processors:
  - type: <processor type>
    # Simple equality check
    condition: attributes["request"]["path"] == "/json/view"
    
  - type: <processor type>
    # Multiple values with OR
    condition: attributes["log_type"] == "TRAFFIC" or attributes["log_type"] == "THREAT"
    
  - type: <processor type>
    # Excluding multiple values (NOT equal to multiple values)
    condition: attributes["log_type"] != "TRAFFIC" and attributes["log_type"] != "THREAT"
    
  - type: <processor type>
    # Complex condition with AND/OR/NOT
    condition: (attributes["level"] == "ERROR" or attributes["level"] == "FATAL") and attributes["env"] != "test"
    
  - type: <processor type>
    # Field existence and value check
    condition: attributes["user_id"] != nil and attributes["user_id"] != ""
    
  - type: <processor type>
    # Regex matching using regex_match
    condition: regex_match(attributes["path"], "^/api/") and not regex_match(attributes["path"], "^/api/health")
    
  - type: <processor type>
    # Regex matching using IsMatch
    condition: IsMatch(attributes["message"], "ERROR|WARNING") and attributes["env"] == "production"

Common Mistakes to Avoid

# WRONG - Cannot use OR/AND with values directly
condition: attributes["log_type"] != "TRAFFIC" OR "THREAT"

# CORRECT - Must repeat the full comparison
condition: attributes["log_type"] != "TRAFFIC" and attributes["log_type"] != "THREAT"

# WRONG - Uppercase operators
condition: attributes["status"] == "error" AND attributes["level"] == "critical"

# CORRECT - Lowercase operators
condition: attributes["status"] == "error" and attributes["level"] == "critical"

# WRONG - Multi-line conditions
condition: |
  attributes["level"] == "ERROR" and 
  attributes["status"] >= 500  

# CORRECT - Single line (even if long)
condition: attributes["level"] == "ERROR" and attributes["status"] >= 500

Pass through on failure

If enabled, logs are passed through the pipeline even when an error occurs during evaluation (e.g., malformed field, type mismatch). This prevents data loss due to misconfigurations.

nodes:
- name: otlp_input_9cd0_multiprocessor
  type: sequence
  processors:
  - type: sample
    pass_through_on_failure: true

Percentage

Defines the baseline percentage of data items to allow through the node. For example, setting this to 50 means 50% of matching logs or traces will pass through.

If a Sample Rate Override field is defined and present on an item, its value will override this percentage for that item.

nodes:
- name: otlp_input_9cd0_multiprocessor
  type: sequence
  processors:
  - type: sample
    percentage: 50

Timestamp granularity

Specifies the resolution for timestamp grouping when determining sample consistency.

Used to define sameness when no Field Paths are specified.
Default sampling keys for logs: (timestamp, service.name, body).
Granularity must be ≥ 1 millisecond.
Common use: 1s, 1m, 100ms

This value affects whether logs are considered “the same” during hashing for consistent sampling. If too coarse (e.g., 1m), many different logs may hash the same, leading to unintended sampling bias.

Sample Rate Override

A field path used to dynamically control the sampling percentage on a per-item basis. This field should contain a numeric value from 0 to 100 (as a string or number).

If present in the data item, this value overrides the default Percentage.

Field paths

A list of field paths that define what values to use when computing the hash for sampling. These values define what constitutes “sameness” across items and impact consistency.

If no field paths are specified, the default sampling fields are:

Logs: timestamp, service.name, body
Traces: trace_id

Use this option when sampling needs to be based on other attributes such as a cluster attribute or custom tags.

Final

Determines whether successfully processed data items should continue through the remaining processors in the same processor stack. If final is set to true, data items output by this processor are not passed to subsequent processors within the node—they are instead emitted to downstream nodes in the pipeline (e.g., a destination). Failed items are always passed to the next processor, regardless of this setting.

The UI provides a slider to configure this setting. The default is false. It is defined in YAML as follows:

- name: multiprocessor
  type: sequence
  processors:
    - type: <processor type>
    final: true

Known Limitation

If the Sample Rate Override field is used but the values in the item do not vary across logs (e.g., all are set to 100), then sampling behavior will default to allowing all items through. Additionally, consistent sampling relies on the sameness of hashed values. If timestamp granularity or default sampling fields (timestamp, service.name, body) don’t vary enough, this may result in sampling behavior that appears static.

Best Practices and Troubleshooting

Sample Rate Override Not Working?

Ensure the override field exists in all target items.
Its value is numeric (or a stringified number) between 0 and 100.
The timestamp granularity and field paths are appropriate for the log’s structure and rate of change.
The field is not empty, malformed, or missing from sampled logs.

Volume Doesn’t Fluctuate?

Consistent sampling hashes the same values for identical data. If the default sampling keys don’t vary enough, especially with a coarse Timestamp Granularity (like 1m), sampling will appear static.

Solutions:

Lower the Timestamp Granularity to 1s or 100ms.
Specify unique Field Paths (e.g., user_id attribute).
Confirm the rate value changes per event if using Sample Rate Override.