Bulk insert data into ClickHouse and deduplicate rows using ReplacingMergeTree

Steps

1. Create a table with the ReplacingMergeTree engine, specifying a version column (e.g., updated_at UInt64 or DateTime) as the engine parameter: ENGINE = ReplacingMergeTree(version); set the ORDER BY clause to your natural deduplication key (the combination of columns that identifies a unique logical row)
2. Insert data in large batches rather than row-by-row: ClickHouse is optimized for bulk inserts of at least 1,000–100,000 rows per INSERT statement; small frequent inserts create many small parts and degrade merge performance
3. Use the HTTP interface or native protocol with async_insert=1 for high-throughput streaming ingestion where batching at the client is impractical; ClickHouse will buffer and merge writes server-side
4. Understand that ReplacingMergeTree deduplication happens lazily during background merges — immediately after insert, duplicate rows exist and will be returned by SELECT; use FINAL modifier (SELECT ... FROM table FINAL) to force deduplication at query time, or use the argMax pattern for latest-value queries
5. Use INSERT with SETTINGS max_insert_block_size and adjust max_partitions_per_insert_block if inserting across many partitions; partition by a low-cardinality column like toYYYYMM(event_date) not by a high-cardinality field
6. Monitor part count via SELECT count() FROM system.parts WHERE table='<table>' AND active=1; a very high part count (thousands) indicates merges are falling behind inserts and query performance will degrade