February 2021 Summaries

RediSearch 2.0 is now generally available, offering a powerful querying, indexing, and full-text search engine for all Redis users, making it more than twice as fast as its predecessor. It introduces a new architecture that supports Redis' Active-Active geo-distribution and Redis on Flash, allowing users to easily index their datasets and query the data in real-time at the speed of Redis. The tool provides several indexing strategies for the value part of the key, including full-text, geo-location, numbers, and tags, eliminating performance bottlenecks and simplifying data infrastructure. With RediSearch, developers can unlock the power of analytics in Redis without having to flip between multiple technologies or query languages, creating modern applications with rich search experiences. The new version is 2.4x faster than the previous one and supports full-text indexing and stemming-based query expansion in multiple languages, as well as auto-complete suggestions using 'fuzzy' search technology. RediSearch also makes it easier to scale, allowing customers to quickly grow to query and index billions of documents on hundreds of servers.

Redis Labs has introduced a new pricing model for its Redis Enterprise Cloud service, designed to be simple, flexible, and serverless. The updated model allows users to scale up or down instantly and pay only for the resources they need. Customers who choose an annual plan can enjoy significant discounts and use their existing public cloud commits. The Redis Enterprise Cloud service offers advanced capabilities like data persistence by default, five-nines availability, Active-Active multi-region deployment, and hosting large datasets on SSDs to reduce operating costs. The new pricing model includes four different plans: Free, Fixed, Flexible, and Annual, each with its own set of features and benefits.

The demonstration showcases a simplified version of a smart climate control system that collects sensor data from various IoT devices and sends it to a central location for analysis. The system uses Redis as the database to manage real-time IoT sensor data, specifically leveraging RedisTimeSeries to store and analyze time-series data. The application utilizes an NVIDIA Jetson Nano board, a Pimoroni BME680 breakout board, and Python scripts to read sensor data from the BME680 and send it to Redis for analysis. After setting up the Redis server, the application uses a transactional pipeline to populate the sensor data structure with temperature, pressure, humidity, and gas readings. The system also integrates with Grafana to create an informative dashboard that displays real-time sensor data.