---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 8935fa047c9750132f8e14188e8cc32cba61a96884a999fd7718766a1f0b9eb2
  pageDirectory: concepts
  sources:
    - low-level-client-apis-advanced-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - mlflowclient-trace-lifecycle-management
    - MTLM
  citations:
    - file: low-level-client-apis-advanced-databricks-on-aws.md
title: MlflowClient Trace Lifecycle Management
description: Low-level API for explicit, fine-grained control over trace creation, span management, and trace completion using the MlflowClient class.
tags:
  - mlflow
  - tracing
  - observability
timestamp: "2026-06-19T19:17:48.784Z"
---

# MlflowClient Trace Lifecycle Management

**MlflowClient Trace Lifecycle Management** refers to the explicit, fine-grained control over creating, managing, and completing traces and spans using the `MlflowClient` API. This approach is essential for advanced scenarios where the high-level [Function Decorator APIs](/concepts/mlflowtrace-function-decorator.md) are insufficient, such as custom trace ID generation, integration with existing observability systems, and complex span hierarchies. ^[low-level-client-apis-advanced-databricks-on-aws.md]

## Core Concepts

Every trace follows a strict lifecycle: a trace must be started, spans can be added and ended in a last-in-first-out order, and finally the trace must be ended. Each `start_trace` or `start_span` call requires a corresponding `end_trace` or `end_span` call to avoid incomplete traces. Key identifiers include the trace’s `request_id` and each span’s `span_id`, which are used to link spans to their parent and to the overall trace. ^[low-level-client-apis-advanced-databricks-on-aws.md]

## Getting Started

Initialize an `MlflowClient` with a default or custom tracking URI. To start a trace, call `client.start_trace(name, inputs, attributes)`, which returns a root span containing a `request_id`. Child spans are created with `client.start_span(name, request_id, parent_id, ...)`. Spans must be ended in reverse order using `client.end_span(request_id, span_id, outputs, status)`, and the trace is completed by calling `client.end_trace(request_id, outputs, status)` on the root span. ^[low-level-client-apis-advanced-databricks-on-aws.md]

## Practical Examples

### Error Handling

Proper error handling ensures that traces are always ended, even when exceptions occur. A common pattern is to use try/except blocks that end the current span with an error status and propagate the exception, while also ending the trace if the root span exists. ^[low-level-client-apis-advanced-databricks-on-aws.md]

### Custom Trace Management

A `CustomTraceManager` class can generate business‑specific trace IDs and manage active traces. The example in the source material shows generating IDs containing user ID, operation, timestamp, and a UUID fragment, then using those IDs as the trace name. ^[low-level-client-apis-advanced-databricks-on-aws.md]

### Batch Processing with Nested Spans

Batch processing workflows can create a hierarchy of spans: a root trace for the entire batch, item‑level spans, and validation/transform sub‑spans. Each item span is started, its child sub‑spans are managed, and the item span is ended before moving to the next item. The root trace is ended after all items are processed. ^[low-level-client-apis-advanced-databricks-on-aws.md]

## Best Practices

- **Use context managers** to guarantee spans are closed. A custom `@contextmanager` decorator can start a span, yield for the user’s code, and end the span in a finally block. ^[low-level-client-apis-advanced-databricks-on-aws.md]
- **Implement trace state management** for complex applications. A `TraceStateManager` can maintain a stack of active traces, pushing a new span (or root trace) and popping it when the operation completes. ^[low-level-client-apis-advanced-databricks-on-aws.md]
- **Add meaningful attributes** that aid debugging, such as model name, temperature, prompt template, or user tier, instead of generic labels like `"step": 1`. ^[low-level-client-apis-advanced-databricks-on-aws.md]

## Common Pitfalls

Common mistakes include forgetting to end spans, providing incorrect parent‑child span IDs, mixing high‑level decorator APIs with the client API (they do not interoperate), hardcoding trace IDs instead of generating unique values, and ignoring thread safety (the client APIs are not thread‑safe by default). ^[low-level-client-apis-advanced-databricks-on-aws.md]

## Related Concepts

- MlflowClient
- Trace (MLflow)
- Span
- [Function Decorator APIs](/concepts/mlflowtrace-function-decorator.md)
- [Manual Tracing](/concepts/manual-tracing.md)
- Trace State Management

## 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)
