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EventStats: compiled queries endpoint for server-side aggregations #116
Description
Feature Description
Extend the EventStats Lambda with a compiled queries endpoint — a curated list of predefined, server-executed queries (e.g. "failed jobs in the last 7 days", "job count aggregated by catalog") that consumers can invoke by name. Rather than pushing aggregation logic into the client or BI tool, the heavy lifting runs inside PostgreSQL and only the final result set is returned over the wire.
Problem / Opportunity
The current POST /stats/{topic_name} endpoint returns raw paginated rows. Consumers (dashboards, reports, downstream tools) re-derive the same aggregations repeatedly on the client side — wasting bandwidth, duplicating logic, and making results inconsistent across consumers. Predefined server-side queries centralise that logic, reduce payload sizes significantly, and allow PostgreSQL query planning to optimise repeated patterns.
Acceptance Criteria
- A new route (e.g.
POST /stats/{topic_name}/query/{query_name}) accepts a query identifier and an optional parameter bag (e.g. time window, tenant filter). - A
SUPPORTED_QUERIESregistry maps eachquery_nameto its SQL template and accepted parameters — unknown names return400. - Queries are executed server-side; only the aggregated result is returned (no raw row streaming).
- The endpoint is protected by the same JWT auth and per-topic ACL as the rest of EventStats.
- Unknown or unsupported
query_namevalues produce a clear400error, not a500. - Unit tests cover routing, registry lookup, and each query's result shaping.
- Integration tests validate at least one aggregation query end-to-end against a seeded testcontainer database.
- All quality gates pass (
./ci_local.sh).
Note for implementer: the concrete set of queries and their SQL definitions must be identified and agreed upon as part of this issue's implementation. The examples below are starting points only — validate with stakeholders which aggregations are actually needed before writing SQL.
Proposed Solution
Introduce a CompiledQueryRegistry (or extend ReaderPostgres) that maps query names to parameterised SQL templates (using psycopg2 %s / sql.SQL composition — never string interpolation). Each entry declares its accepted input parameters and output schema.
Example candidate queries to evaluate with stakeholders:
failed_jobs_last_7d— count and list of jobs with a failure status in the last N days, grouped by pipeline/tenant.aggregation_by_catalog— job count, success rate, and average elapsed time grouped by catalog/source.run_status_summary— distribution of run statuses (running, completed, failed) over a configurable time window.
Route dispatch follows the existing ROUTE_HANDLERS pattern in event_stats_lambda.py. A new HandlerCompiledQuery (or an extension of HandlerStats) handles validation and delegates to the registry.
Dependencies / Related
- Builds on the EventStats Lambda introduced in PR EventStats lambda with Stats endpoint #113.
- Connection pooling (PostgreSQL connection pooling for WriterPostgres and ReaderPostgres #115) should be resolved first to avoid per-query connection overhead under aggregation load.
Activity
tmikula-dev commentedon Jun 2, 2026
The issue is currently waiting for the input from the business, which will help us define, which compiled queries should be set.
lukasmatta commentedon Sep 30, 2026
As a starting point, I would recommend adding simple aggregations for a given time window. So the endpoint
query/runs_jobs_detailwould, in addition to the currently returned paginated job runs, also return aggregated data for the given time window:total_runs- count across the full windowtotal_tenants- number of distinct tenantstotal_catalogs- number of distinct catalogssucceeded- count of succeeded runsfailed- count of failed runssuccess_rate- percentage (this could also be calculated on the FE side if it's onlysucceeded/total_runs)avg_duration_ms- average run durationtimeline- a bit more tricky, explained below:Timeline
Timeline breaks the selected window into equal time buckets so we can see when runs happened, not just the totals. It should be the source for a heatmap chart.
bucket- how wide each slot is ("hour" or "day"). We use hourly for short ranges (last day/week) and daily for longer ones (last month).entries- one per bucket that actually had runs (empty ones can be skipped):bucket_start- when the bucket starts (epoch ms); we read the date and hour from it.total,succeeded,failed- how many runs happened in that slot, and how they turned out.Entries are ordered oldest-first, and all the totals add up to total_runs.
From there, we can add additional data/aggregations based on specific business needs.