How to make bulk data changes in ClickHouse?

How to make bulk data changes in ClickHouse?


Bulk data changes in ClickHouse can be made using the INSERT INTO or the INSERT INTO SELECT statements. The INSERT INTO statement is used to insert data into a specific table, while the INSERT INTO SELECT statement is used to insert data into a table by selecting it from another table or a subquery. The COPY statement can also be used to make bulk data changes in ClickHouse. The COPY statement can be used to import data from a file in a specific format, such as CSV or TSV, into a table. To optimize performance when making bulk data changes in ClickHouse, it is recommended to use the SETTINGS insert_distributed_sync=1 statement, which will make the inserts to be sent to all replicas at the same time.

There are several ways to tune ClickHouse performance for bulk data changes. Some ways include:

  1. Using the merge tree engine for bulk data changes: The merge tree engine is optimized for bulk data changes, and it can be used to improve the performance of bulk data changes.
  2. Increasing the number of worker threads: ClickHouse uses worker threads to perform bulk data changes. By increasing the number of worker threads, you can improve the performance of bulk data changes.
  3. Using a large buffer size: ClickHouse uses a buffer to store data before it is written to disk. By increasing the buffer size, you can reduce the number of disk writes and improve the performance of bulk data changes.
  4. Using a large max_insert_block_size: ClickHouse uses a max_insert_block_size parameter to limit the number of rows that can be inserted in one block. By increasing the max_insert_block_size, you can improve the performance of bulk data changes.
  5. Using a large number of replicas: ClickHouse uses replicas to distribute the load and improve the performance of bulk data changes. By increasing the number of replicas, you can improve the performance of bulk data changes.
  6. Using a large number of shards: ClickHouse uses shards to divide data into smaller pieces. By increasing the number of shards, you can improve the performance of bulk data changes.
  7. Using a large number of partitions: ClickHouse uses partitions to divide data into smaller pieces. By increasing the number of partitions, you can improve the performance of bulk data changes.
  8. Using the right storage engine: ClickHouse has different storage engines, like MergeTree, CollapsingMergeTree, and ReplacingMergeTree. Each of these engines is optimized for a specific use case, and it is important to choose the right storage engine for your use case.

It’s also worth noting that it’s important to test different configurations with your specific workload and dataset to determine the best performance.

About Shiv Iyer 56 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.

Be the first to comment

Leave a Reply