clickhouse-olap

What problem does it solve?

This Skill helps you design ClickHouse OLAP schemas and queries that stay fast under real analytical workloads by choosing the right MergeTree engine, sorting/indexing strategy, and lifecycle settings.

Core Features & Use Cases

• MergeTree engine selection for OLAP semantics: model append-only facts, upserts/deduplication (ReplacingMergeTree), pre-aggregation storage (AggregatingMergeTree), simple numeric rollups (SummingMergeTree), and sign-based mutability (CollapsingMergeTree).
• Performance-oriented table design: implement effective ORDER BY / PARTITION BY / PRIMARY KEY rules, add data skipping indexes (minmax/set/bloom_filter), and use FINAL/argMax patterns appropriately.
• Operationalizing ingestion and lifecycle: configure TTL for tiered storage, build real-time materialized views and aggregations, optimize scans using PREWHERE, and support batch inserts and common streaming patterns via Kafka engine and Python clients.

Quick Start

Tell me how to design a ClickHouse OLAP table for user event analytics that supports fast filtering by event type and user id, uses an appropriate MergeTree engine, includes data skipping indexes, and adds a materialized view for real-time minute-level aggregates.