k6 Cloud to DuckDB Integration

How Datrise loads k6 Cloud into DuckDB

Datrise syncs k6 Cloud's test runs, thresholds, checks, metrics, virtual users, and response times into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

k6 Cloud: Load testing runs and performance thresholds.

DuckDB: In-process analytics database for fast local OLAP.

How k6 Cloud entities map to DuckDB

k6 Cloud entity	DuckDB object	Notes
test runs	`k6_cloud_test_runs`	id PK · custom fields → JSON or STRUCT columns
thresholds	`k6_cloud_thresholds`	id PK · linked to k6_cloud_test_runs
checks	`k6_cloud_checks`	id PK · linked to k6_cloud_test_runs
metrics	`k6_cloud_metrics`	id PK · linked to k6_cloud_test_runs

FAQ

How does Datrise handle k6 Cloud's custom fields in DuckDB?

Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.

How does the k6 Cloud to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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