Accelerating Go Services with Redis and Probabilistic Data Structures

Users don’t want to waste time looking at loading spinners. When creating a user-facing service it’s extremely important to ensure it can perform adequately well, especially under heavy load.

Abstract

Users don’t want to waste time looking at loading spinners. When creating a user-facing service it’s extremely important to ensure it can perform adequately well, especially under heavy load. This concern becomes even more important in microservices architectures, where a long chain of internal network calls will inevitably have an impact on latency. Redis (https://redis.io) is a very popular open source in-memory database that offers many tools to help make applications faster. In this workshop we will talk about accelerating applications using Redis from three points of view: 1. Caching and distributed sessions, the most straightforward approach to application acceleration. (20 mins); 2. Using probabilistic data structures to keep track of data with seemingly impossibly-low resource requirements using HyperLogLog and Bloom Filters. (40 mins); 3. Using Redis with Go and extending Go Channels across processes using Pub/Sub (30 mins) Workshop requirements: We will provide a virtual lab that will allow anybody with a laptop and a web browser to do the hands-on part in [2] (probabilistic data structures), while to follow along with [3] you will need to have Go and Redis setup on your laptop.

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