rca_pyrca_epsilon_diagnosis
Identify anomalous metrics behind an SLI anomaly by comparing normal versus incident data windows with z-score thresholding, narrowing root cause candidates for deeper causal analysis.
Instructions
[Adapted from Salesforce PyRCA — BSD-3-Clause] Identify anomalous metrics contributing to a Service Level Indicator (SLI) anomaly by comparing metric distributions in normal vs. incident windows.
Uses z-score thresholding: metrics with |z| > epsilon in the incident window relative to the normal baseline are flagged as root cause candidates.
Best used as a FIRST STEP in RCA to narrow down candidate metrics before applying more compute-intensive causal methods like rca_pyrca_ht_diagnosis or rca_pyrca_random_walk -- this one needs no causal graph at all, just two data windows. Requires the pyrca feature (Starter+).
Args: params (EpsilonDiagnosisInput): - normal_data: baseline {metric: [values]} (min 3 per metric) - anomaly_data: incident window {metric: [values]} - sli_metric: the observed anomaly metric - epsilon: z-score threshold (default 3.0 = 3σ)
Returns: str: JSON with root_causes (anomalous metrics ranked by |z_score|), all_metrics, sli_z_score, epsilon_threshold
Attribution: Adapted from PyRCA EpsilonDiagnosis (Salesforce, BSD-3-Clause) Zhen et al. (2022) ε-Diagnosis
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |