June 2021
Intermediate to advanced
264 pages
7h 19m
English
Content preview from 97 Things Every Data Engineer Should Know
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Chapter 81. Threading and Concurrency in Data Processing
Matthew Housley, PhD
In a typical modern environment, data flows through complex distributed systems at each stage of a data pipeline, and servers must reliably maintain numerous connections to their peers. The Amazon Kinesis outage in late 2020 illustrates the consequences of ignoring concurrency problems.
The outage, which occurred on November 25, took down a large swath of the internet. In this discussion, I’ll assume that you’ve read Amazon’s official report. The report uncovers several engineering blunders, but we will focus primarily on the limits of thread-based concurrency.
Operating System Threading
Each frontend server creates operating system threads for each of the other servers in the frontend fleet.
In Linux and most other modern operating systems, threads are a mechanism for allowing a CPU to execute a number of concurrent tasks far exceeding the number of available CPU cores. A thread gets access to a CPU core during an allotted time; when time runs out, thread state is dumped to system memory, and the next thread is loaded into the core. Rapid cycling allows the operating system to maintain the illusion that all threads are running simultaneously. Threads provide an easy mechanism for managing many network connections.
Threading Overhead
The new capacity had caused all of the servers in the fleet to ...
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