Temporal
Group documents by time windows and compute per-window aggregations with drift detection
On one real run
A Nine-Second Day Disappears Into a Two-Second Average: What Temporal Windows Show That a Mean Hides
Mixpeek writes every retriever execution into mxp_retriever_executions in your own namespace, so a retriever can read your own telemetry. Grouped by day, one window on 2026-09-04 averaged 9,397 ms against neighbours near 2,500, on only 10 executions. Averaged across the full period that day contributes almost nothing and disappears; bucketed by day it is the tallest bar on the chart.
See the full diagram
Configuration
REQUIRED. Field containing timestamps.
Time window size (e.g., '1h', '1d', '7d').
Aggregations to compute per window.
Output
No output yet
Configure the stage parameters and click "Run Stage" to see the simulated output.
Related to temporal
Pages on the same stages, extractors and patterns.
Aggregate
Compute aggregations (COUNT, SUM, AVG, etc.) on pipeline results
Group By
Group documents by field value for decompose/recompose workflows
Cluster
Cluster documents by embedding similarity to discover themes
Sample
Sample a subset of documents using random or stratified sampling
Limit
Truncate results to a maximum count with optional offset for pagination
Deduplicate
Remove duplicate documents by field match or content similarity