. . . . . . . . . . . . . . . . . . . . . . . . 24 1.3.2.1 O1: How to prepare Python Virtual Environment . . . . . . . . . . . . . . . . . . . 24 1.3.2.2 O2: How to add Python Files . . . . . . . . --version Create a Python virtual environment Virtual environment gives you the ability to isolate the Python dependencies of different projects by creating a separate environment for each project. It is a directory standalone Python environment and also useful when deploying a PyFlink job to production when there are massive Python dependencies. It’s supported to use Python virtual environment in your PyFlink jobs

. . . . . . . . . . . . . . . . . . . . . . . . 24 1.3.2.1 O1: How to prepare Python Virtual Environment . . . . . . . . . . . . . . . . . . . 24 1.3.2.2 O2: How to add Python Files . . . . . . . . --version Create a Python virtual environment Virtual environment gives you the ability to isolate the Python dependencies of different projects by creating a separate environment for each project. It is a directory standalone Python environment and also useful when deploying a PyFlink job to production when there are massive Python dependencies. It’s supported to use Python virtual environment in your PyFlink jobs

f[j] = ci,j return min(f[1], f[2], …, f[p]) ??? Vasiliki Kalavri | Boston University 2020 24 Computing top-k ??? Vasiliki Kalavri | Boston University 2020 24 • Additional to the array of counter, we elements seen so far • a heap X* of up to k potential heavy hitters and their frequency estimations Computing top-k ??? Vasiliki Kalavri | Boston University 2020 24 • Additional to the array of counter, we frequency estimations • We use a frequency threshold f*=N/k to decide whether an element is popular Computing top-k ??? Vasiliki Kalavri | Boston University 2020 24 • Additional to the array of counter, we

discard by relying on the notion of a window-based concept drift. • The metric is defined by computing a similarity metric across windows. 18 ??? Vasiliki Kalavri | Boston University 2020 How many

University 2020 53 • Martin Hirzel et. al. A Catalog of Stream Processing Optimizations. (ACM Computing Surveys 2014). • Ron Avnur and Joseph M. Hellerstein. Eddies: continuously adaptive query processing

Synopses for massive data streams • Maintaining synopses is often the only means of providing interactive response times when exploring massive datasets or high speed data streams. • Queries are executed

-algorithm/ • A video lecture on global snapshots: https://www.coursera.org/lecture/ cloud-computing/1-2-global-snapshot-algorithm-hndGi 52

11 Challenges of reconfiguration ??? Vasiliki Kalavri | Boston University 2020 12 • Detect environment changes: external workload and system performance • Identify bottleneck operators, straggler Vasiliki Kalavri | Boston University 2020 Control: When and how much to adapt? 12 • Detect environment changes: external workload and system performance • Identify bottleneck operators, straggler Control: When and how much to adapt? Mechanism: How to apply the re-configuration? 12 • Detect environment changes: external workload and system performance • Identify bottleneck operators, straggler

Windows subsystem for Linux (WSL), Cygwin, or a Linux virtual machine to run Flink in a UNIX environment. • A Java 8.x installation. To develop Flink applications and use its DataStream API in Java

temperature”)
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 } Flink programs are defined in regular Scala/Java methods Set up the execution environment: local, cluster, I/O, time semantics, parallelism, … Example: Sensor Readings 9 Vasiliki