ClickHouse

1. Why ClickHouse Is Fast

   Columnar storage, vectorized execution, compression and parallelism.

2. Data Types

   LowCardinality, Nullable, Array, Tuple, Map, Enum and DateTime64.

3. MergeTree Engine

   Parts, merges, ORDER BY keys, sorting key and sparse indexing.

4. Primary Index and Sparse Indexing

   Granules, sparse primary index, ordering key design and index usage.

5. Table Partitions

   PARTITION BY, partition management and when partitioning helps.

6. Data Skipping Indexes

   minmax, set, bloom filter indexes and granule skipping.

7. Specialized MergeTree Engines

   ReplacingMergeTree, SummingMergeTree, AggregatingMergeTree and CollapsingMergeTree.

8. Materialized Views

   Incremental materialized views, insert-time transformations and aggregation.

9. Projections

   Alternate sort orders and pre-aggregation.

10. Insert Strategy and Ingestion

    Batching inserts, async inserts, parts creation and ingestion performance.

11. Joins

    Join algorithms, join types and join performance.

12. Dictionaries

    In-memory key-value lookups from external sources.

13. Compression and Codecs

    Compression codecs and storage efficiency.

14. Replication and ClickHouse Keeper

    ReplicatedMergeTree, Keeper coordination and fault tolerance.

15. Sharding and Distributed Tables

    Distributed engine, shards, replicas and scaling out.

16. TTL and Data Lifecycle

    Time-to-live rules, data movement and expiry.

Each section links to the official page. Titles and summaries by Extra Credit; the docs belong to their authors.

Source: endoflife.date