Structured Streaming VS Flink

再记录下spark以及flink的一些差异

实时处理方面

1. spark 这里只关注structured streaming,因为工作中以sss为主，sss通常还是micro batch， 默认100 milliseconds，
   但是在since Spark 2.3引入新的Continuous Processing，这个老实说感觉挺鸡勒的

2. sss在多流join方面也是有些限制的,Support matrix for joins in streaming queries

3. 贴下官方的描述Continuous Processing
   Internally, by default, Structured Streaming queries are processed using a micro-batch processing engine, which processes data streams as a
   series of small batch jobs thereby achieving end-to-end latencies as low as 100 milliseconds and exactly-once fault-tolerance guarantees. However,
   since Spark 2.3, we have introduced a new low-latency processing mode called Continuous Processing, which can achieve end-to-end latencies
   as low as 1 millisecond with at-least-once guarantees. Without changing the Dataset/DataFrame operations in your queries, you will be able to
   choose the mode based on your application requirements
4. 另外： 对的支持也是相当有限的，即使在2.4版本
   

批处理

spark

1. spark目前在离线批处理方面应该比flink应用的更加广泛了，即便是用的hive引擎页大多是spark
2. flink已经整合了hive,当然也在整合delta lake, hudi， iceberg等的，具备流批处理的能力