September 2019 Summaries

The upcoming SpringOne Platform 2019 conference in Austin, Texas, promises to be a must-attend event for cloud native, microservices, and Kubernetes enthusiasts, featuring Redis collaboration with Pivotal, key capabilities like Active-Active and Redis Streams, and numerous sessions on building cloud-native services and Kubernetes implementations. Attendees can sample local craft beers, participate in in-person demos and whiteboarding sessions, schedule 1:1 meetings with sales reps or SAs, and enjoy exclusive invitations to offsite events, including a Redis Happy Hour. The conference also features sessions on achieving data consistency with Redis Streams, talks from industry experts like Barbara Sanders and Jay Piskorik, and hands-on labs in the Pivotal booth, as well as free Redis University online training for those who can't attend in person.

At Oracle OpenWorld, Redis showcased its capabilities with a focus on performance, simplicity, and extensibility, highlighting its suitability as a complement to MySQL in areas such as high-performance operational and analytics use cases. The company demonstrated its ability to support various high-performance operational, analytics, and hybrid use cases, including those involving artificial intelligence, through its RedisAI module, which enables AI serving without requiring extensive knowledge of AI. Additionally, the event featured various activities and giveaways, including a "GOT Redis" t-shirt and other prizes, allowing attendees to engage with the Redis team and stay updated on upcoming events.

The text discusses the use of goto statements in programming and their potential benefits in certain situations. It provides an example where using goto can simplify code by allowing for a cleaner and less error-prone design when managing resources like memory allocation. Additionally, it explores the concept of exception handling in C, which lacks built-in support for this feature found in other high-level languages. The author presents various approaches to implement try-catch logic in C, ultimately settling on using longjmp as a solution that allows jumping from anywhere within the call stack and restoring the stack automatically. This approach is demonstrated through an example of error reporting in a RedisGraph query execution context.

This post discusses the basics of async/await in Python, focusing on improving program throughput by reducing idle time when performing I/O operations. The author chooses to use Python 3 as an example since its concurrency primitives are relatively recent and may not be familiar to many users. The main reason for using async/await is to improve performance in programs that do a lot of network communication, such as those connected to a Redis database. The author provides a code sample that tracks wins for a hypothetical game, updating a Redis Sorted Set that acts as the leaderboard, and then shows an equivalent non-blocking version of the code using aio-libs/aioredis. However, the initial non-blocking version does not improve performance due to overusing the await keyword, which blocks execution flow. The author addresses this by introducing asyncio.gather, a synchronization primitive that allows scheduling multiple tasks concurrently. By applying asyncio.gather, the program can now process each batch of events concurrently, improving throughput without significant changes to the code.