waterdrop将hive导入clickhouse报错：Too many partitions for single INSERT block (more than 100).

waterdrop将hive导入clickhouse报错：Too many partitions for single INSERT block (more than 100). 一、问题描述

使用waterdrop导入数据进入clickhouse，然后日志报错：

Caused by: ru.yandex.clickhouse.except.ClickHouseException: ClickHouse exception, code: 252, host: 10.252.32.26, port: 8123; Code: 252, e.displayText() = DB::Exception: Too many partitions for single INSERT block (more than 100). The limit is controlled by 'max_partitions_per_insert_block' setting. Large number of partitions is a common misconception. It will lead to severe negative performance impact, including slow server startup, slow INSERT queries and slow SELECT queries. Recommended total number of partitions for a table is under 1000..10000. Please note, that partitioning is not intended to speed up SELECT queries (ORDER BY key is sufficient to make range queries fast). Partitions are intended for data manipulation (DROP PARTITION, etc). (version 20.3.10.75 (official build))

	at ru.yandex.clickhouse.except.ClickHouseExceptionSpecifier.specify(ClickHouseExceptionSpecifier.java:58)
	at ru.yandex.clickhouse.except.ClickHouseExceptionSpecifier.specify(ClickHouseExceptionSpecifier.java:28)
	at ru.yandex.clickhouse.ClickHouseStatementImpl.checkForErrorAndThrow(ClickHouseStatementImpl.java:680)
	at ru.yandex.clickhouse.ClickHouseStatementImpl.sendStream(ClickHouseStatementImpl.java:656)
	at ru.yandex.clickhouse.ClickHouseStatementImpl.sendStream(ClickHouseStatementImpl.java:639)
	at ru.yandex.clickhouse.ClickHousePreparedStatementImpl.executeBatch(ClickHousePreparedStatementImpl.java:382)
	at io.github.interestinglab.waterdrop.output.Clickhouse$$anonfun$process.apply(Clickhouse.scala:133)
	at io.github.interestinglab.waterdrop.output.Clickhouse$$anonfun$process.apply(Clickhouse.scala:115)
	at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$$anonfun$apply.apply(RDD.scala:926)
	at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$$anonfun$apply.apply(RDD.scala:926)
	at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:2069)
	at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:2069)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
	at org.apache.spark.scheduler.Task.run(Task.scala:108)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.Throwable: Code: 252, e.displayText() = DB::Exception: Too many partitions for single INSERT block (more than 100). The limit is controlled by 'max_partitions_per_insert_block' setting. Large number of partitions is a common misconception. It will lead to severe negative performance impact, including slow server startup, slow INSERT queries and slow SELECT queries. Recommended total number of partitions for a table is under 1000..10000. Please note, that partitioning is not intended to speed up SELECT queries (ORDER BY key is sufficient to make range queries fast). Partitions are intended for data manipulation (DROP PARTITION, etc). (version 20.3.10.75 (official build))

二、问题原因

clickhouse限制max_partitions_per_insert_block，即每个插入块的分区，解决办法就是，修改这个参数，然后重启clickhouse即可。

三、解决办法 1.修改users.xml配置

vi users.xml

添加

5000

2.重启

sudo systemctl restart clickhouse-server

四、参考 1.https://www.cnblogs.com/xibuhaohao/p/13856360.html