# Filter by read status
As well as the poor selection of a partition key, this issue can manifest itself as a result of many small inserts. Each INSERT into ClickHouse results in an insert block being converted to a part. To keep the number of parts manageable, users should therefore buffer data client-side and insert data as batches - at a minimum 1,000 rows per insert, although batch sizes of 10,000 to 100,000 rows are optimal. If client-side buffering is not possible, users can defer this task to ClickHouse through async inserts. In this case, ClickHouse will buffer inserts in memory before flushing them as a single batched part into the underlying table. The flush is triggered when a configurable threshold is met: a buffer size limit (async_insert_max_data_size, default 1MB), a time threshold (async_insert_busy_timeout_ms, default 1 second), or a maximum number of queued queries (async_insert_max_query_number, default 100). Since data is held in memory until flush, it is important to set wait_for_async_insert=1 (the default) so that the client receives acknowledgement only after data has been safely written to disk, avoiding silent data loss in the event of a server crash before a flush.
。新收录的资料对此有专业解读
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Rails used to use SQLite with its default settings, which were optimized for safety and backward compatibility rather than performance. It was great in a development environment, but typically things started to fall apart the moment you tried to use it for production-like load. Specifically, you used to have to tweak various PRAGMA statements:
我发现我保存的大量 96kHz 和 192KHz 的音乐，都是普通的 CD 音乐强行升频的，感觉这个目前是高解析度音频的重灾区。当然不是所有这种音频的频谱都和上面一样，但是明显的特征是 21K 处有明显的边界，再以上的部分要么是全是静音，要么是静音和噪音的混合体，可能和使用的升频算法不一样导致的。