State[Int]) => (key, sum) • Input: key = b, value = Some(1), state = 1 • Output: key = b, sum = 2 54 / 79 updateStateByKey vs. mapWithState Example (3/3) ▶ The third micro batch contains a message b State[Int]) => (key, sum) • Input: key = b, value = Some(1), state = 1 • Output: key = b, sum = 2 54 / 79 updateStateByKey vs. mapWithState Example (3/3) ▶ The third micro batch contains a message b State[Int]) => (key, sum) • Input: key = b, value = Some(1), state = 1 • Output: key = b, sum = 2 54 / 79 Structured Streaming 55 / 79 Structured Streaming ▶ Treating a live data stream as a table

pyflink-docs, Release release-1.15 (continued from previous page) # -rw-r--r-- 1 dianfu staff 45K 10 18 20:54 flink-dianfu-python-B-7174MD6R-1908. ˓→local.log Besides, you could also check if the files of the ges/pyflink/log # You will see the log file as following: # -rw-r--r-- 1 dianfu staff 45K 10 18 20:54 flink-dianfu-python-B-7174MD6R-1908. ˓→local.log Execute PyFlink jobs in IDE You need firstly configure IterableLike$class.foreach(IterableLike.scala:72) at scala.collection.AbstractIterable.foreach(Iterable.scala:54) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.AbstractTraversable

pyflink-docs, Release release-1.16 (continued from previous page) # -rw-r--r-- 1 dianfu staff 45K 10 18 20:54 flink-dianfu-python-B-7174MD6R-1908. ˓→local.log Besides, you could also check if the files of the ges/pyflink/log # You will see the log file as following: # -rw-r--r-- 1 dianfu staff 45K 10 18 20:54 flink-dianfu-python-B-7174MD6R-1908. ˓→local.log Execute PyFlink jobs in IDE You need firstly configure IterableLike$class.foreach(IterableLike.scala:72) at scala.collection.AbstractIterable.foreach(Iterable.scala:54) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.AbstractTraversable

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3 ??? Vasiliki Kalavri | Boston University 2020 8 7 4 8 7 8 4 5 4 6 5 3 2 4 3 6 3 1 4 54 k=3 7 d(1, 4) = 1 d(4, 7) = 1 d(7, 8) = 1 5 d(4, 5) = 1 ??? Vasiliki Kalavri | Boston University

Apache Flink. (O’Reilly Media ’19). Lecture references ??? Vasiliki Kalavri | Boston University 2020 54 • Re-ordering • Shivnath Babu et. al. Adaptive Ordering of Pipelined Stream Filters. SIGMOD 2004