In Part 1 we showed how ClickHouse uses parallel processing power to collect aggregates. In Part 2, we’re showing how to make aggregations faster and more efficient.
Summarization is a powerful tool for understanding masses of data. Learn how to make this process efficient and fast through ClickHouse — in part 1 of our ClickHouse aggregation series.
Bloom filters are an important ClickHouse index type with mysterious parameters. Take a closer look at the theory behind bloom filters, parameter selection using queries on a test dataset, and effective tuning.