Languages and Frameworks 94. Polars Trial Polars is an in-memory data frame library implemented in Rust. Unlike other data frames (such as pandas), Polars is multithreaded, supports lazy execution and is safe for parallel operations. The in- memory data is organized in the Apache Arrow format for efficient analytic operations and to enable interoperability with other tools. If you’re familiar with pandas, you can quickly get started with Polars’ Python bindings. We believe Polars, with Rust implementation and Python bindings, is a performant in-memory data frame for your analytical needs. Our teams continue to have a good experience with Polars which is why we’re moving it to Trial. 95. Pushpin Trial Pushpin is a reverse proxy that acts as an intermediary between clients and back-end servers handling long-lived connections, such as WebSockets and Server-Sent Events. It provides a way to terminate long-lived connections from clients and means the rest of the system can be abstracted from this complexity. It effectively manages a large number of persistent connections and automatically distributes them across multiple back-end servers, optimizing performance and reliability. Our experience using this for real-time communication with mobile devices using WebSockets has been good, and we’ve been able to scale it horizontally for millions of devices. 96. Snowpark Trial Snowpark is a library for querying and processing data at scale in Snowflake. Our teams use it for writing manageable code for interacting with data residing in Snowflake — it’s akin to writing Spark code but for Snowflake. Ultimately, it’s an engine that translates code into SQL that Snowflake understands. You can build applications that process data in Snowflake without moving data to the system where your application code runs. One drawback: unit testing support is suboptimal; our teams compensate for that by writing other types of tests. 97. Baseline Profiles Assess Baseline Profiles — not to be confused with Android Baseline profiles — are Android Runtime profiles that guide ahead-of-time compilation. They’re created once per release on a development machine and are shipped with the application, making them available faster than relying on Cloud Profiles, an older, related technology. The run time uses the Baseline Profile in an app or library to optimize important code paths, which improves the experience for new and existing users when the app is downloaded or updated. Creating Baseline Profiles is relatively straightforward and can lead to significant (up to 30%) performance boosts according to its documentation. © Thoughtworks, Inc. All Rights Reserved. 43