for each value of B 20 Vasiliki Kalavri | Boston University 2020 What kind of queries can we express and support on data streams? 21 Vasiliki Kalavri | Boston University 2020 Non-blocking (monotonic) monotonic constructs: • EXCEPT, NOT EXIST, NOT IN and ALL • all standard blocking aggregates Can we express all streaming (monotonic queries) with NB-SQL? 30 Vasiliki Kalavri | Boston University 2020 Some union operators and non-blocking UDAs on data streams are complete, in the sense that they can express every monotonic function on their input. 49 Vasiliki Kalavri | Boston University 2020 Consider

ctured-streaming-in-apache-spark.html] 60 / 79 Structured Streaming Example (2/3) ▶ We could express it as the following SQL query. SELECT action, WINDOW(time, "1 hour"), COUNT * FROM events GROUP the time embedded in the data, not the time Spark receives them. ▶ Use groupBy() and window() to express windowed aggregations. // count words within 10 minute windows, updating every 5 minutes. // streaming

the final graph state back to disk 12 ??? Vasiliki Kalavri | Boston University 2020 13 • We express the computation from the view of a single vertex • Vertices communicate through messages

operators and edges are data channels • operators can accumulate state, have multiple inputs, express event- time custom window-based logic • some systems, like Timely Dataflow support cyclic dataflows