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Choose an ingestion path for the workload
| Workload | Practical starting point | What to verify |
|---|---|---|
| One-time files in object storage | Use Parquet or ORC when the data is available in a suitable columnar representation. | Measure parsing and any conversion work against your target schema. |
| ClickHouse-to-ClickHouse transfer | Consider streaming Native format from the source query into an insert on the destination with clickhouse-client. |
Confirm the target schema, destination connection, and security settings. |
| Application or client writes | Buffer rows in the client and send larger synchronous batches when the application can accommodate the delay and memory use. | Account for row width, partitioning, latency needs, and available resources. |
| Many producers that send small writes | Consider asynchronous inserts to let ClickHouse batch data on the server. | Choose acknowledgement behavior deliberately and monitor flush outcomes. |
| Ongoing object-storage, CDC, or event-stream ingestion | Evaluate ClickPipes for the relevant supported source and workflow. | Check current connector availability and service constraints before implementation. |
Batch synchronous inserts to limit part creation
For MergeTree-family tables, each insert creates at least one part per affected partition. A large number of tiny inserts can therefore produce many small parts, adding file, sorting, compression, and merge work, and potentially Keeper overhead.
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ClickHouse’s 2026 engineering guidance recommends at least 1,000 rows per synchronous insert, with 10,000 to 100,000 rows as an ideal starting range. These are guidelines, not a universal target: row width, the number of affected partitions, latency requirements, and available resources all influence a useful batch size. Increase or reduce batch size based on representative runs rather than treating a row count as a throughput promise.
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Use asynchronous inserts when client-side batching is impractical
With async_insert=1, ClickHouse buffers small writes on the server and flushes them according to configured thresholds and insert shape or settings. Data still in the buffer is not yet queryable from the table, so acknowledgement mode matters.
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Wait for the flush
Set wait_for_async_insert=1 when the client should wait for the flush and receive flush errors. This is the more appropriate choice when the producer needs confirmation that the buffered insert was successfully flushed.
Acknowledge before the flush
With wait_for_async_insert=0, the client is acknowledged once data enters memory, before the storage flush. That is not equivalent to confirmed persistence, and it changes reliability and error-visibility trade-offs. Use this mode only when the application can accept those trade-offs.
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Settings and defaults can change between ClickHouse versions. Check the documentation for the version you run, and monitor asynchronous insert outcomes during a representative load. ClickHouse describes relevant system tables and logs for diagnosing flush behavior and errors in its async insert monitoring guide.
Match the data format to its source
Object-storage files
ClickHouse recommends columnar formats such as Parquet or ORC when they suit the source data. In a ClickHouse 2026 example loading the Amazon reviews dataset, ClickHouse reported 79 seconds for Parquet and ORC, 94 seconds for Avro, and 105 seconds for JSON. Those times describe that example only; they do not predict load times for a different dataset, schema, or deployment. See ClickHouse’s data-modelling best practices article for the example and format discussion.
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For server-to-server transfers, Native is a binary format supported by clickhouse-client. A basic streaming pattern is:
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Scale only from measured results
Use representative data and the real target schema to benchmark. Change concurrency or service capacity in controlled steps, and observe active parts and asynchronous insert outcomes while the load runs. A vendor-reported experiment can illustrate a result without establishing a general rate: ClickHouse reported loading more than 600 billion rows with 100 parallel workers, with throughput rising from 4 million to 8 million rows per second as a ClickHouse Cloud service grew from three servers to six. That was a specific experiment, not a throughput guarantee for other data or configurations; see ClickHouse’s large-load experiment.
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Run a large load in a controlled sequence
- Identify the workload. Decide whether this is a one-time file load, a ClickHouse transfer, application writes, many small producer writes, or continuous ingestion.
- Choose a suitable format and path. For object-storage files, assess Parquet or ORC; for ClickHouse transfers, consider Native; for continuous ingestion, check whether ClickPipes supports the source and workflow.
- Set the batching strategy. Buffer synchronous client writes where practical, using ClickHouse’s row-count guidance as a starting point. If buffering is impractical, configure asynchronous inserts and select acknowledgement behavior based on the required confirmation and error handling.
- Test with representative data. Use the intended schema, data shape, partitioning, producer concurrency, and deployment. Include conversion costs when formats must be transformed.
- Monitor and adjust. Watch active parts and asynchronous insert outcomes. Change batch size, concurrency, or capacity in measured increments, and verify the deployed ClickHouse version’s settings and defaults.
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