---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 222cc2ec870d4dc21722b16aab7458891b791e556e7645356899e64d3187170a
  pageDirectory: concepts
  sources:
    - low-level-client-apis-advanced-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - custom-trace-id-generation
    - CTIG
    - Trace generation
  citations:
    - file: low-level-client-apis-advanced-databricks-on-aws.md
title: Custom Trace ID Generation
description: Technique for generating business-specific trace IDs by integrating MLflow tracing with existing observability systems using custom naming schemes.
tags:
  - tracing
  - customization
  - integration
timestamp: "2026-06-19T19:18:21.943Z"
---

# Custom Trace ID Generation

**Custom Trace ID Generation** refers to the practice of defining application-specific trace identifiers when instrumenting applications with [MLflow Tracing](/concepts/mlflow-tracing.md), rather than relying on system-generated IDs. This approach is essential when integrating [MLflow Tracing](/concepts/mlflow-tracing.md) with existing observability systems or when trace IDs must encode business context such as user identifiers, operation types, or timestamps. ^[low-level-client-apis-advanced-databricks-on-aws.md]

## Overview

Custom trace ID generation is a capability of the MlflowClient low-level APIs. While the high-level function decorator APIs (`@mlflow.trace`) handle trace ID generation automatically, the client APIs give developers explicit control over trace creation and identification. This control is necessary for advanced scenarios such as integration with existing observability systems, complex trace lifecycle management, or custom trace state management. ^[low-level-client-apis-advanced-databricks-on-aws.md]

## When to Use Custom Trace IDs

Custom trace ID generation is appropriate when:

- You need to integrate [MLflow Tracing](/concepts/mlflow-tracing.md) with an existing observability or monitoring system that uses its own ID format.
- Trace IDs must encode business-relevant information such as user IDs, operation types, or timestamps for easier debugging and correlation.
- You require a consistent naming convention across distributed components of an application.
- You are implementing complex trace lifecycle management with custom state tracking. ^[low-level-client-apis-advanced-databricks-on-aws.md]

Custom trace IDs should be avoided for simple function tracing, quick prototyping, or local Python applications where the high-level APIs are sufficient. ^[low-level-client-apis-advanced-databricks-on-aws.md]

## Implementation

### Basic Pattern

Custom trace ID generation is implemented by creating a trace name that encodes the desired information. The trace name is passed to `client.start_trace()` and can be stored as an attribute for later retrieval. ^[low-level-client-apis-advanced-databricks-on-aws.md]

```python
import uuid
from datetime import datetime
from mlflow import MlflowClient

# Generate a custom trace ID
timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
custom_trace_id = f"user123_report_generation_{timestamp}_{uuid.uuid4().hex[:8]}"

# Start a trace with the custom ID as the trace name
client = MlflowClient()
root_span = client.start_trace(
    name=custom_trace_id,
    attributes={
        "user_id": "user123",
        "operation": "report_generation",
        "custom_trace_id": custom_trace_id
    }
)
```

### Custom Trace Manager Pattern

For production applications, a dedicated trace manager class can encapsulate custom ID generation and trace lifecycle management: ^[low-level-client-apis-advanced-databricks-on-aws.md]

```python
import uuid
from datetime import datetime
from mlflow import MlflowClient

class CustomTraceManager:
    """Custom trace manager with business-specific trace IDs"""
    def __init__(self):
        self.client = MlflowClient()
        self.active_traces = {}

    def generate_trace_id(self, user_id: str, operation: str) -> str:
        """Generate custom trace ID based on business logic"""
        timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
        return f"{user_id}_{operation}_{timestamp}_{uuid.uuid4().hex[:8]}"

    def start_custom_trace(self, user_id: str, operation: str, **kwargs):
        """Start trace with custom ID format"""
        trace_name = self.generate_trace_id(user_id, operation)
        root_span = self.client.start_trace(
            name=trace_name,
            attributes={
                "user_id": user_id,
                "operation": operation,
                "custom_trace_id": trace_name,
                **kwargs
            }
        )
        self.active_traces[trace_name] = root_span
        return root_span

    def get_active_trace(self, trace_name: str):
        """Retrieve active trace by custom name"""
        return self.active_traces.get(trace_name)

# Usage
manager = CustomTraceManager()
trace = manager.start_custom_trace(
    user_id="user123",
    operation="report_generation",
    report_type="quarterly"
)
```

## Best Practices

### Encode Meaningful Context

Custom trace IDs should encode information that aids debugging and correlation. Good examples include user IDs, operation types, timestamps, and unique identifiers. Avoid generic or unhelpful IDs that provide no additional context. ^[low-level-client-apis-advanced-databricks-on-aws.md]

### Store the Custom ID as an Attribute

When using a custom trace ID as the trace name, also store it as an attribute on the root span. This ensures the custom ID is preserved in the trace metadata and can be queried programmatically. ^[low-level-client-apis-advanced-databricks-on-aws.md]

### Ensure Uniqueness

Always include a unique component (such as a UUID fragment or timestamp) in custom trace IDs to avoid collisions. Hardcoding trace IDs is a common pitfall that should be avoided. ^[low-level-client-apis-advanced-databricks-on-aws.md]

### Manage Active Traces

For complex applications, maintain a registry of active traces (such as the `active_traces` dictionary in the `CustomTraceManager` example) to allow different components to retrieve and update the correct trace. ^[low-level-client-apis-advanced-databricks-on-aws.md]

## Common Pitfalls

- **Hardcoding trace IDs** — Always generate unique IDs dynamically.
- **Mixing high-level and low-level APIs** — The function decorator and client APIs do not interoperate.
- **Forgetting to end spans** — Every `start_trace` or `start_span` call must have a corresponding `end_trace` or `end_span` call.
- **Ignoring thread safety** — Client APIs are not thread-safe by default. ^[low-level-client-apis-advanced-databricks-on-aws.md]

## Related Concepts

- MlflowClient — The client class providing low-level trace management APIs.
- [MLflow Tracing](/concepts/mlflow-tracing.md) — The overall tracing framework for instrumenting applications.
- Trace Lifecycle Management — Managing the creation, modification, and completion of traces.
- [Function Decorator APIs](/concepts/mlflowtrace-function-decorator.md) — The simpler alternative for most tracing use cases.
- [Span Hierarchies](/concepts/trace-span-hierarchy.md) — Creating parent-child relationships between spans within a trace.

## Sources

- low-level-client-apis-advanced-databricks-on-aws.md

# Citations

1. [low-level-client-apis-advanced-databricks-on-aws.md](/references/low-level-client-apis-advanced-databricks-on-aws-881056bc.md)
