September 2020 Summaries

The text discusses various applications of Redis Data Source for Grafana, including building a weather dashboard using OpenWeatherMap API and storing data in RedisTimeSeries, and creating a dynamic dashboard to monitor queues for data processing using RedisGears. The author shares their experience with these projects, highlighting the benefits of using Redis Data Source for visualizing data in Grafana. The text also mentions the potential of combining Grafana streaming capabilities with interactivity to enhance its functionality, as well as the popularity of the Redis Data Source plug-in, which has been downloaded over 10,000 times since its publication in the Grafana repository.

Jane Paek, Director of Regional Solution Architects at Redis, shares her journey from electrical engineering to technical sales, highlighting key skills such as listening, urgency, and a desire to learn. She emphasizes the importance of networking, salary anchoring, and overcoming imposter syndrome. Paek also discusses the misconceptions about technical roles, including the idea that engineers are stuck in labs or offices all day, when in reality there are many customer-facing roles available. Her experiences and advice provide valuable insights for those considering a career in tech sales.

The author used Redis as a database deployed on Kubernetes to demonstrate its power during the California wildfires in 2020. They re-interpreted their previous work on exposing geospatial data with two new guiding principles: using Redis' geospatial features for partitioning and deploying the whole application on Kubernetes. The author applied a methodology called PAN (Partition, Annotate, and Name) to organize air-quality sensor data collected by PurpleAir. They used Python, Redis, and a Kubernetes deployment to collect, store, and use air quality data.

The Redis community is constantly inspiring developers to use the platform in innovative ways. Carlos Justiniano, Matthew Goos, and Dan Pipe-Mazo are three individuals who have successfully applied Redis to their respective fields of biotech, medtech, and robotics. Carlos uses RedisGraph to model human physiology, while Matthew utilizes RedisJSON and RedisGraph for analytics in his interactive data analytics platform MDmetrix. Dan Pipe-Mazo leverages Redis Streams to power the robotic software stack at Elementary Robotics, which has been running 100% on Redis Streams for over two years. These individuals have showcased their expertise through presentations at RedisConf and other events, highlighting the versatility and power of Redis in real-world applications.

RediSearch, a real-time secondary index with full-text search capabilities for Redis, has introduced the public preview of its latest version, RediSearch 2.0. This new version is designed to improve the developer experience and be the most scalable version of Redisearch yet. It supports Redis' Active-Active geo-distribution technology, is scalable without downtime, and includes Redis on Flash support (currently in private preview). The re-architecture for RediSearch 2.0 brings two main benefits: easier creation of secondary indices on top of existing data and increased scalability through the removal of the index from the keyspace. Additionally, RediSearch 2.0 delivers up to a 2.4X speedup compared to the previous version, making it the fastest and most scalable version for all Redis users.

RediSearch 2.0 has been released into public preview with new features driven by user feedback, focusing on improving developer experience and scalability. The new data indexing capabilities allow users to create an index associated with their data and use the query engine to search and query the data using secondary indices. With RediSearch, developers can easily add an index to an existing application without changing code, making it easy to manage and query complex queries. The full-text search engine allows for powerful queries à la Google, including prefix matches, fuzzy search, unions, and tag field search. Additionally, RediSearch includes data aggregation capabilities that are covered in the documentation, tutorial, and online course.

The Redis Data Source for Grafana is a new plug-in that connects the open-source application monitoring tool to Redis, allowing users to monitor Redis data in Grafana dashboards. The plug-in provides an out-of-the-box predefined dashboard and lets users build customized dashboards tailored to their specific needs. It can be installed using grafana-cli, Docker, or built from scratch following GitHub instructions. The plug-in allows users to track downloads of the plugin over time by using RedisTimeSeries, a Redis module that adds a time-series data structure to Redis. Users can collect data on plugin downloads and visualize it in Grafana dashboards, providing valuable insights into application performance and usage patterns.