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To load a large dataset into ClickHouse without overwhelming MergeTree tables with tiny inserts, batch rows into larger synchronous inserts when your client can buffer them. If it cannot, use asynchronous inserts and choose whether the client must wait for a successful flush. For recurring object-storage, CDC, or event-stream ingestion, evaluate ClickPipes. In every case, choose a format suited to the source and benchmark with your own schema and deployment.
Choose the ingestion path that fits the job
| Use case | Practical starting point | What to weigh |
|---|---|---|
| One-time file in object storage | Load a suitable Parquet or ORC file; compare the conversion and parsing work for your data. | Whether the source is already columnar, and how its schema maps to the destination. |
| ClickHouse-to-ClickHouse transfer | Consider piping Native-format output from the source to a Native-format insert on the destination. | Target schema, connection details, and security settings. |
| Application or client writes | Buffer rows on the client and send larger synchronous batches when the application can tolerate the delay and memory use. | Batch size, row width, partitioning, latency requirements, and available resources. |
| Many producers sending small writes | Consider asynchronous inserts with async_insert=1. |
Whether producers need confirmation after data is flushed and flush errors are returned. |
| Continuous object-storage, CDC, or event-stream ingestion | Evaluate ClickPipes for the relevant source and workflow. | Current connector availability and service constraints. |
Why tiny inserts cause trouble
For MergeTree-family tables, each insert creates at least one data part per affected partition. A stream of very small inserts can therefore create many parts, increasing the work of writing files, sorting and compressing data, and merging parts. It can also add Keeper overhead where applicable. Batching reduces this pressure by giving ClickHouse more rows to process in each insert.
Batch synchronous inserts on the client when practical
ClickHouse’s 2026 engineering guidance recommends at least 1,000 rows per synchronous insert and says 10,000 to 100,000 rows is an ideal range to target. Treat those figures as starting guidance, not a universal promise: row width, partitioning, latency needs, and server resources all affect the useful batch size. ClickHouse’s insert guidance explains the operational context.
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Do not optimize only for row count. A batch of wide rows can be substantially larger than the same number of narrow rows, and partitioning affects how many parts an insert creates. Test with representative data and the real target schema, while keeping an eye on application memory and the time spent buffering.
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Use asynchronous inserts when producers cannot batch
Asynchronous inserts let the server buffer small writes and flush them in larger groups. Enable them with async_insert=1. The acknowledgement setting determines what the client can infer from a successful response:
wait_for_async_insert=1: the client waits for the buffer to flush. This is the appropriate mode when the producer needs confirmation of a successful flush and should receive flush errors.wait_for_async_insert=0: the client is acknowledged after data enters memory, before the storage flush. This is a fire-and-forget response, not confirmation that the data has been flushed or is queryable; it also has reliability and error-visibility tradeoffs.
Buffered data is not yet queryable from the table. Flushes depend on configured thresholds and insert shape or settings, and ClickHouse settings and defaults can change between versions. Check the documentation for the version you operate rather than assuming a default. For troubleshooting, ClickHouse describes monitoring asynchronous insert outcomes, system tables, and logs in its asynchronous insert monitoring guide.
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Match the format to the source
Object-storage files
For object-storage loads, ClickHouse recommends columnar formats such as Parquet or ORC when they suit the data. A ClickHouse 2026 best-practices example loading the Amazon reviews dataset reported 79 seconds with Parquet and ORC, 94 seconds with Avro, and 105 seconds with JSON. Those are results for that particular example, not predicted load times for another dataset. The ClickHouse loading best practices article provides its context.
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For a server-to-server transfer, ClickHouse’s Native format is a binary option supported by clickhouse-client. The general pattern is to send SELECT ... FORMAT Native on the source into an INSERT ... FORMAT Native on the destination. Confirm the destination schema and connection and security settings before starting a large transfer.
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Separate a one-time load from continuous ingestion
A file that needs to be loaded once does not necessarily call for the same machinery as a continuing feed. For ongoing S3 or GCS ingestion, CDC, and event streams, evaluate ClickPipes if its connectors and service constraints fit your workflow. Confirm current availability for your source before implementation; do not assume a connector is supported just because the general ingestion category is mentioned. For a one-time file, first assess whether a direct file load using an appropriate format is simpler.
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ClickHouse reported a specific large-load experiment involving more than 600 billion rows and 100 parallel workers: throughput rose from 4 million to 8 million rows per second when a ClickHouse Cloud service grew from three servers to six. This is a vendor-reported result for that experiment, not a throughput target or scaling guarantee for other datasets, schemas, or configurations. See ClickHouse’s large-load account for the experiment’s context.
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Run a representative benchmark against the deployment you intend to use. Keep the data shape, schema, ingestion path, and concurrency close to production, then change concurrency or service capacity in controlled steps. Monitor active parts and, for asynchronous inserts, flush outcomes while the run is in progress. A benchmark that changes several of these variables at once is harder to use for capacity decisions.
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