Queuing theory models: for latency objectives • Control theory models: e.g., PID controller • Rule-based models, e.g. if CPU utilization > 70% => scale out • Analytical dataflow-based models Action coarse-grained and aggregates • CPU utilization, throughput, back- pressure signal • Policy • rule-based • If CPU utilization > 70% and back- pressure then scale up • Action • speculative, one coarse-grained and aggregates • CPU utilization, throughput, back- pressure signal • Policy • rule-based • If CPU utilization > 70% and back- pressure then scale up • Action • speculative, one

A(X>0) & (B(Y=10);[timespan:5] C(Z<5))[within:15] A, B, C are topics X, Y, Z are inner fields The rule fires when an item of type A having an attribute X > 0 enters the system and also an item of type SELECT CustomerID,‘pattern123’ FROM state WHERE sno = 3; } } Initialize state to 0 Check next event Pattern failed Order matched Refund and cancel matched Output success!

1.16 Python 3.6 to 3.9 PyFlink 1.15 Python 3.6 to 3.8 PyFlink 1.14 Python 3.6 to 3.8 You could check your Python version as following: 3 pyflink-docs, Release release-1.15 python3 --version Create apache-flink Installing from Source To install PyFlink from source, you could refer to Build PyFlink. Check the installed package You could then perform the following checks to make sure that the installed 1] # +I[be,--that, 1] # ... If there are any problems, you could perform the following checks. Check the logging messages in the log file to see if there are any problems: # Get the installation directory

1.16 Python 3.6 to 3.9 PyFlink 1.15 Python 3.6 to 3.8 PyFlink 1.14 Python 3.6 to 3.8 You could check your Python version as following: 3 pyflink-docs, Release release-1.16 python3 --version Create apache-flink Installing from Source To install PyFlink from source, you could refer to Build PyFlink. Check the installed package You could then perform the following checks to make sure that the installed 1] # +I[be,--that, 1] # ... If there are any problems, you could perform the following checks. Check the logging messages in the log file to see if there are any problems: # Get the installation directory

Heap increases significantly, this can usually be attributed to the size of your application state (check the checkpointing metrics5 for an estimated size of the on-heap state). The possible reasons for monitoring is disabled by default and requires additional dependencies on the classpath. Please check out the Flink system resource metrics documentation9 for additional guidance and details. System

is State? ▶ Accumulate and aggregate the results from the start of the streaming job. ▶ Need to check the previous state of the RDD in order to do something with the current RDD. ▶ Spark supports stateful is State? ▶ Accumulate and aggregate the results from the start of the streaming job. ▶ Need to check the previous state of the RDD in order to do something with the current RDD. ▶ Spark supports stateful

Periodic: periodically ask the user-defined function for the current watermark timestamp. Punctuated: check for a watermark in each passing record, e.g. if the stream contains special records that encode watermark

DROP Clases (without a ‘W’ grade) 4/3: Last Day to DROP Classes (with a ‘W’ grade) Make sure to check the Official Semester Dates 11 Vasiliki Kalavri | Boston University 2020 Final Project You will

Double)]): Unit = { // fetch the last temperature from state val lastTemp = lastTempState.value() // check if we need to emit an alert val tempDiff = (reading.temperature - lastTemp).abs if (tempDiff > threshold)

Vasiliki Kalavri | Boston University 2020 Dynamic resource allocation • Choose one among n workers • check the load of each worker and send the item to the least loaded one • load checking for every item