Message Handling with Boolean Algebra Ben Deane / CppCon / 2024-09-16 1Alternative Title "The Unreasonable Effectiveness of Boolean Algebra in Software Design, Showing the Particular Application of of a Message Handling Library, with an Excursion into the Roots of Programming" 2Frontmatter No AI/LLM was used in the creation of this talk. Code is simplified for slides; may have some errors in this talk is about The workings of a message-handling library. How messages and the fields in them are specified. Efficiently identifying (with matchers) a message coming off the wire. The role of Boolean

Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 1/23: Stream Processing Fundamentals Vasiliki Kalavri | Boston University University 2020 What is a stream? • In traditional data processing applications, we know the entire dataset in advance, e.g. tables stored in a database. A data stream is a data set that is produced incrementally incrementally over time, rather than being available in full before its processing begins. • Data streams are high-volume, real-time data that might be unbounded • we cannot store the entire stream

Processing XML and Spreadsheet in Go 续 日 Gopher China Conference Beijing 2021 6/26 - 6/27 Self Introduction The author of the Excelize - Go language spreadsheet library. Familiar with Go language Complex XML 02 • Partial Load • Namespace & Entity • Ser/Deserialize Idempotence High Performance Processing 03 • XML Schema Definition • DOM or SAX OOXML Spreadsheets 04 • Excel XML Specification • work:addr="WORK"> High Performance Processing XML Components Data Model Tom

Scalable Stream Processing - Spark Streaming and Flink Amir H. Payberah payberah@kth.se 05/10/2018 The Course Web Page https://id2221kth.github.io 1 / 79 Where Are We? 2 / 79 Stream Processing Systems Design Design Issues ▶ Continuous vs. micro-batch processing ▶ Record-at-a-Time vs. declarative APIs 3 / 79 Outline ▶ Spark streaming ▶ Flink 4 / 79 Spark Streaming 5 / 79 Contribution ▶ Design issues issues • Continuous vs. micro-batch processing • Record-at-a-Time vs. declarative APIs 6 / 79 Spark Streaming ▶ Run a streaming computation as a series of very small, deterministic batch jobs. • Chops

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 vkalavri@bu.edu Spring 2020 4/16: Skew mitigation ??? Vasiliki Kalavri | Uddin Nasir et. al. The power of both choices: Practical load balancing for distributed stream processing engines. ICDE 2015. • Mitzenmacher, Michael. The power of two choices in randomized load balancing

Vasiliki Kalavri | Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 2/25: State Management Vasiliki Kalavri | Boston operator. Keyed state can only be used by functions that are applied on a KeyedStream: • When the processing method of a function with keyed input is called, Flink’s runtime automatically puts all keyed fare, Collector> out) throws Exception {
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Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/14: Stream processing optimizations ??? Vasiliki Kalavri | Boston University serialization cost • if operators are separate, throughput is bounded by either communication or processing cost • if fused, throughput is determined by operator cost only Operator fusion A B A B is statically configured with a certain number of processing slots that defines the maximum number of concurrent tasks it can execute. • A processing slot can execute one slice of an application, i.e

Vasiliki Kalavri | Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 2/11: Windows and Triggers Vasiliki Kalavri | Boston windowing use cases: • They assign an element based on its event-time timestamp or the current processing time to windows. • Time windows have a start and an end timestamp. • All built-in window assigners assigners provide a default trigger that triggers the evaluation of a window once the (processing or event) time passes the end of the window. • A window is created when the first element is assigned

Vasiliki Kalavri | Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 1/21: Introduction Vasiliki Kalavri | Boston University course, you will hopefully: • know when to use stream processing vs other technology • be able to comprehensively compare features and processing guarantees of streaming systems • be proficient in using end-to-end, scalable, and reliable streaming applications • have a solid understanding of how stream processing systems work and what factors affect their performance • be aware of the challenges and trade-offs