A high-performance one-pass in-memory streaming analytics engine for temporal and streaming data.
Trill is a high-performance one-pass in-memory streaming analytics engine from Microsoft Research that processes temporal and streaming data. It provides a unified query model for both real-time and offline data analytics, capable of handling trillion-event-per-day workloads. The engine can function as a streaming processor, lightweight relational engine, and progressive query processor for early results on partial data.
Data engineers and developers building real-time analytics systems, streaming data pipelines, and temporal data applications that require high-performance query processing.
Developers choose Trill for its unique combination of streaming performance with relational query expressiveness, its temporal data model built-in support, and its ability to handle both real-time and historical data through a single processing engine.
Trill is a single-node query processor for temporal or streaming data.
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Processes data in a single pass with in-memory efficiency, capable of handling trillion events per day as highlighted in the announcement blog post.
Built-in support for time-aware queries enables seamless analytics across both real-time and historical data, as emphasized in its temporal data model.
Functions as a streaming engine, lightweight relational engine, and progressive query processor, allowing for early results on partial data per the README.
Handles both live streaming and historical datasets, providing a unified query model for diverse processing needs as described in the key features.
Requires Visual Studio 2017 and .NET, limiting accessibility for teams using modern or non-Microsoft development environments.
As a single-node processor, it lacks built-in distributed scaling, which may hinder large-scale deployments without additional engineering.
Examples are in a separate GitHub repository, increasing the learning curve and setup complexity for new users.