Keep clickhouse integration prometheus scrapeable

Prometheus and ClickHouse use different data models, so reliable integrations require explicit metric schemas, bounded labels, efficient rollups, and a clear choice of the system that owns each alert. Four main approaches are described: exposing SQL-computed Tinybird Pipes in Prometheus exposition format for scraping, querying ClickHouse directly from Grafana, remote-writing Prometheus or OpenTelemetry samples into ClickHouse for long-term SQL analytics, and scraping Tinybird workspace telemetry for platform monitoring. Prometheus-format Pipe endpoints must return numeric values with metric names, optional help and type metadata, and carefully controlled label maps, while scrape authentication, endpoint validity, and query latency should be tested before deployment. Direct Grafana-to-ClickHouse queries simplify operations when Prometheus-native features are unnecessary, whereas remote write supports long retention but requires adapters, deduplication, batching, backpressure controls, and materialized rollups. The guidance emphasizes excluding high-cardinality identifiers such as user IDs, trace IDs, and raw URLs from labels, using aggregated data rather than raw samples for long dashboard ranges, and avoiding duplicate definitions of the same KPI across systems. It recommends keeping infrastructure metrics and short-term PromQL rules in Prometheus, computing metrics that require SQL joins in ClickHouse or Tinybird, selecting one canonical source for each alert, and validating schemas, format, authentication, cardinality, load behavior, failures, and configuration drift through acceptance tests.

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