GlobalStreamExchangeMode 这几种交换模式的不同和使用场景是什么?哪些适合流式作业,哪些适合批式作业? Flink Remote Shuffle Service的推出是不是意味着可以在生产环境使用Flink处理批式作业?谢谢!
package org.apache.flink.streaming.api.graph;
import org.apache.flink.annotation.Internal;
@Internal
public enum GlobalStreamExchangeMode {
ALL_EDGES_BLOCKING,
FORWARD_EDGES_PIPELINED,
POINTWISE_EDGES_PIPELINED,
ALL_EDGES_PIPELINED,
ALL_EDGES_PIPELINED_APPROXIMATE;
private GlobalStreamExchangeMode() {
}
} *来自志愿者整理的flink
这个是可以直接控制内部连边的方式,可以参考一下这个的Java doc。不过这个是一个内部接口,还是建议使用 env.setRuntimeMode(RuntimeExecutionMode.BATCH),这个可以参考一下这个文档: https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/datastream/execution_mode/ 。
public enum GlobalStreamExchangeMode { /** Set all job edges to be {@link ResultPartitionType#BLOCKING}. */ ALL_EDGES_BLOCKING,
/** * Set job edges with {@link ForwardPartitioner} to be {@link * ResultPartitionType#PIPELINED_BOUNDED} and other edges to be {@link * ResultPartitionType#BLOCKING}. */ FORWARD_EDGES_PIPELINED,
/** * Set job edges with {@link ForwardPartitioner} or {@link RescalePartitioner} to be {@link * ResultPartitionType#PIPELINED_BOUNDED} and other edges to be {@link * ResultPartitionType#BLOCKING}. */ POINTWISE_EDGES_PIPELINED,
/** Set all job edges {@link ResultPartitionType#PIPELINED_BOUNDED}. */ ALL_EDGES_PIPELINED,
/** Set all job edges {@link ResultPartitionType#PIPELINED_APPROXIMATE}. */ ALL_EDGES_PIPELINED_APPROXIMATE }*来自志愿者整理的flink
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