December 2022 Summaries

The tech industry experienced economic swings in 2022, slowing its long winning streak and bringing it to a halt in some cases. The industry is feeling the effects of a recession, with over 90,000 tech employees laid off and companies continuing to make difficult decisions about navigating choppy waters ahead. Despite this, many businesses will continue their digital transformation initiatives into 2023, focusing on areas like real-time personalization, fraud detection, and AI. Digital channels drive new revenue channels, meet user demands, and foster innovation, but require performance beyond the capabilities of legacy technology and introduce new market competition. Real-time performance enables businesses to provide customers with a seamless and personalized shopping experience, while AI has advanced to the point where it can perform tasks like writing essays, negotiating utility bills, and creating works of art. To deliver on the promise of artificial intelligence and machine learning, vast quantities of data must be stored in real-time.

This article discusses the importance of data durability and availability in various industries, including financial services and streaming media. Data durability refers to the process of safeguarding data from loss or corruption in the event of an outage or failure, while data availability means that the data is operational and can be accessed when requested. The article highlights the consequences of vulnerable or slow data, citing examples such as a 73-hour outage at Roblox that resulted in an estimated $25 million in lost bookings, and how data durability and availability work together to create a perfect equation: uncompromised data plus uptime and accessibility. The article also touches on the importance of persistence configuration, database type, and other factors in measuring a database's resiliency against corruption from bit decay or loss.

Redis Enterprise powers applications by providing a single system for caching and enterprise-grade solutions, expanding its capacity with Redis on Flash (RoF) to minimize latency issues and reduce data expenditures. Senior Group Product Manager Brad Ascar explains how RoF serves containerized applications in detail. Will Johnston discusses the benefits of Active-Active Geo-Distribution, including 99.999% uptime and sub-millisecond latency, and provides insights into managing real-time consistency across geographic zones. He also highlights the flexibility of Redis Enterprise for cloud migration, multicloud, and hybrid cloud capabilities. The system's modern components, tools, and modules are showcased in a popular video series, offering practical advice on various Redis topics.

Design patterns are recommended practices that solve recurring design problems in software systems, with four parts: name, problem description, solution, and consequences. The master data-lookup pattern is a popular technique used to improve application performance, especially for long-lived, infrequently changing data such as country lists, genres, and movie languages. This pattern involves accessing master data quickly and efficiently, often using Redis, which can speed up access and manipulation operations while preserving data consistency. By applying this pattern, developers can create faster response times and improve the overall performance of their MEAN and MERN stack applications.

Redis, a popular in-memory data store, has had a notable year in 2022 with several significant announcements and developments. The company's Redis Stack product was introduced in March, consolidating various Redis modules into a single offering, which attracted both existing users and new ones. Later in the year, the latest version of Redis Stack was released, featuring additional features. Additionally, Redis joined forces with RESP.app to integrate its features into RedisInsight, generating interest from the open-source community. The company also published several popular blog posts, including a guide on installing Redis on Windows and a discussion on Redis's design philosophy, as well as a tutorial on using the Redis command line interface for efficient development. Overall, 2022 has been a productive year for Redis, with a focus on expanding its product offerings and enhancing user experience.

Kubernetes has become a widely adopted platform for building cloud-native services, with its popularity growing alongside the adoption of cloud-native applications. The platform's hybrid cloud capabilities, team-friendly portability, and cost-consciousness have made it an attractive choice for developers. Since its introduction in 2014, Kubernetes has undergone significant technical upgrades, including the release of OpenAPI in 2016, which enabled API providers to automate their tools. Today, Kubernetes is used by large organizations and has become a go-to platform for building platforms, with over 2.8 million contributions to its codebase. The platform's automations make it easier for development teams to get started, and its flexibility makes scaling applications a simple process. Cloud computing continues to gain traction, while on-prem Kubernetes deployments have taken a hit year over year, according to VMware Tanzu's The State of Kubernetes 2022 report. Most Kubernetes deployments are in multicloud or hybrid clouds, and the platform's growth is thanks in part to its continued investment in software development and infrastructure efficiency.

Redis has released its latest version of Redis Stack, which extends its open-source capabilities with data processing engines and modern data models. This new release provides several features that developers have requested, including improved search queries, multi-value indexing, and probabilistic data structures. The update also enhances the graph capabilities, time series data, and RedisInsight, a developer companion tool for visualizing and optimizing Redis data. The latest version is available in two versions: Redis Stack 6.2.6 and Redis Stack 7.0.6, built on top of Redis 6.2 and Redis 7.0 respectively, and can be installed through various channels including Redis Enterprise Cloud and Redis Enterprise Software.

RedisTimeSeries is a time-series database module that stores large amounts of streaming data in-memory, allowing for fast processing thanks to the Gorilla compression/decompression algorithm. The algorithm achieves a 10X compression ratio and was developed by Meta for its large-scale and distributed Time-Series Database (TSDB). Intel and Redis are collaborating to investigate potential performance optimizations for the RedisTimeSeries database. They have explored vectorizing the compression/decompression computation, but found that it would not pay off in terms of performance and code complexity. Instead, they focused on optimizing the decompression routine for floating-point values and discovered that using a dictionary and/or run-length encoding could lead to better performance and/or compression ratio. The team also considered alternative approaches, such as storing data as integers instead of floating points, but concluded that Gorilla remains the best compression ratio option.