Principle:Mlflow Mlflow Trace Search and Analysis
| Knowledge Sources | |
|---|---|
| Domains | ML_Ops, LLM_Observability |
| Last Updated | 2026-02-13 20:00 GMT |
Overview
Querying, retrieving, and analyzing captured execution traces to support debugging, monitoring, and quality evaluation of AI systems.
Description
Trace Search and Analysis is the principle that collected trace data must be queryable and retrievable to be useful. Capturing traces is only the first half of an observability system; the second half is providing structured access to that data so that developers and operators can answer questions about system behavior. This includes both point lookups (retrieving a specific trace by its ID) and aggregate queries (searching across traces by experiment, time range, model, or custom filters).
Effective trace search supports multiple access patterns. A developer debugging a single failed request needs to retrieve one trace by ID and inspect its full span tree. A quality engineer evaluating a new prompt needs to search all traces for a specific model over the past week and compare output distributions. A monitoring system needs to query recent traces to compute latency percentiles and error rates. Each of these use cases requires different query capabilities: exact-match retrieval, filter-based search with ordering, field extraction for tabular analysis, and pagination for large result sets.
The analysis dimension of this principle extends beyond simple retrieval. By supporting structured return formats such as tabular DataFrames alongside native trace objects, the search interface bridges the gap between observability and data science workflows. Developers can retrieve traces as structured data, join them with assessment scores, and perform statistical analysis using familiar tools. This makes traces not just a debugging aid but a first-class data source for model evaluation and improvement.
Usage
Use trace search and analysis when debugging specific requests (retrieve by trace ID), conducting evaluation campaigns (search and filter traces for scoring), monitoring production quality (aggregate query traces over time windows), or building training datasets from production data (extract input/output fields from trace spans into tabular format).
Theoretical Basis
The search and analysis principle is grounded in the observability pillar of query-driven debugging, where the ability to ask arbitrary questions of telemetry data is more valuable than any predefined dashboard. The filter-and-order query model follows the relational algebra approach used in SQL, providing composable predicates and sort expressions. The dual return type design (native objects vs. tabular DataFrames) reflects the impedance mismatch between hierarchical trace data and the flat tabular structures required for statistical analysis, and provides explicit bridges between these representations. Pagination support implements the cursor-based iteration pattern necessary for operating over result sets that exceed memory capacity.