Index sharding in ClickHouse Cloud: Petabyte-scale data needs petabyte-scale indexing

Index sharding in ClickHouse is a method designed to improve the efficiency of index analysis by distributing the analysis workload across multiple replicas, thereby reducing the working memory requirement for each replica and accelerating the analysis process. This approach partitions indexes across the fleet of replicas, allowing each to handle only a portion of the index and collectively covering the entire data set. As a result, it not only reduces memory usage significantly—especially crucial at massive scales involving billions of rows and petabytes of data—but also enhances performance by leveraging increased parallelism. This is particularly beneficial for workloads with extensive secondary indexes, where index analysis constitutes a substantial part of query execution time. Furthermore, ClickHouse's architecture, which separates compute and storage, facilitates this distribution without necessitating data movement, thereby allowing new replicas to integrate swiftly and efficiently. The introduction of index sharding enables horizontal scaling of index analysis, converting the previous single-node bottleneck into a distributed task, thus improving query speed and reducing resource overhead.