valyala

Try VictoriaLogs for your warehouse product next time. It provides efficient storing and querying petabytes of wide events with hundreds of fields - see https://docs.victoriametrics.com/victorialogs/keyconcepts/ , and it provides very easy scaling path from single-node setup to cluster setup, so you can start with an instance per customer and then gradually scale to many storage nodes as workload grows - https://docs.victoriametrics.com/victorialogs/cluster/#singl... .

The benchmark can be reproduced in your environment - see https://github.com/VictoriaMetrics/logs-benchmark . It measures memory usage, CPU usage, disk io usage and disk space usage at different ingestion rates for Kubernetes-like logs.

Because of unreviewed AI code at GitHub services?

You can add the following statements in the first example and click "Run" button in order to understand the underlying tables' structure and contents mentioned in the example:

    SELECT * FROM person;

    SELECT * FROM follows;

You can explore the database used in this example with any valid PostgreSQL statements.

You can add missing EXPLAIN statement to all the examples in the article - they are editable and interactive.

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You nailed it down!

VictoriaLogs could be a good alternative: it stores logs in column-oriented compressed format, so they occupy small amounts of disk space, and it consists of a single small executable without external dependencies, which stores the logs into a single directory on a local filesystem.