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Then there are parameters related to spilling data to disk, which are essential for gracefully handling queries that exceed in-memory capacity: `max_bytes_before_external_sort` and `max_bytes_before_external_group_by`. When a query needs to sort or group data that exceeds these limits, ClickHouse will _transparently write intermediate data to disk_ instead of keeping it all in RAM. This prevents OOM errors but comes at the cost of performance, as disk I/O is significantly slower than RAM access. The default values for these are often quite high (e.g., 0, meaning no external sort/group by, or a large number like 100GB), which implies ClickHouse will try to do everything in memory first. You might want to *lower* these values if you frequently encounter OOM errors and prefer slower but completing queries over fast but failing ones. Alternatively, if your server has plenty of RAM and fast NVMe SSDs, you might increase them to encourage more in-memory processing before resorting to disk. These are powerful tools for **ClickHouse memory optimization**, allowing you to control the trade-off between speed and robustness. Also, consider `max_concurrent_queries` which, while not a direct memory parameter, heavily influences the total memory footprint by controlling how many queries can execute at once. More concurrent queries mean a higher chance of hitting `max_memory_usage_for_all_queries`, so you might need to adjust them in tandem. Carefully adjusting these parameters in your `config.xml` or `users.xml` files is key to ensuring your ClickHouse instance is both performant and stable, tailored precisely to your specific workload and hardware resources.
