Jérôme Schneider
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Jérôme Schneider
Announcing support for GROUP BY, SUM, and other aggregation queries in R2 SQL
When you’re dealing with large amounts of data, it’s helpful to get a quick overview — which is exactly what aggregations provide in SQL. Aggregations, known as “GROUP BY queries”, provide a bird’s eye view, so you can quickly gain insights from vast volumes of data.
That’s why we are excited to announce support for aggregations in R2 SQL, Cloudflare's serverless, distributed, analytics query engine, which is capable of running SQL queries over data stored in R2 Data Catalog. Aggregations will allow users of R2 SQL to spot important trends and changes in the data, generate reports and find anomalies in logs.
This release builds on the already supported filter queries, which are foundational for analytical workloads, and allow users to find needles in haystacks of Apache Parquet files.
In this post, we’ll unpack the utility and quirks of aggregations, and then dive into how we extended R2 SQL to support running such queries over vast amounts of data stored in R2 Data Catalog.
Aggregations, or “GROUP BY queries”, generate a short summary of the underlying data.
A common use case for aggregations is generating reports. Consider a table called “sales”, which contains Continue reading
Building Vectorize, a distributed vector database, on Cloudflare’s Developer Platform
Vectorize is a globally distributed vector database that enables you to build full-stack, AI-powered applications with Cloudflare Workers. Vectorize makes querying embeddings — representations of values or objects like text, images, audio that are designed to be consumed by machine learning models and semantic search algorithms — faster, easier and more affordable.
In this post, we dive deep into how we built Vectorize on Cloudflare’s Developer Platform, leveraging Cloudflare’s global network, Cache, Workers, R2, Queues, Durable Objects, and container platform.
A vector database is a queryable store of vectors. A vector is a large array of numbers called vector dimensions.
A vector database has a similarity search query: given an input vector, it returns the vectors that are closest according to a specified metric, potentially filtered on their metadata.
Vector databases are used to power semantic search, document classification, and recommendation and anomaly detection, as well as contextualizing answers generated by LLMs (Retrieval Augmented Generation, RAG).
Conventional data structures like B-trees, or binary search trees expect the data they index to be cheap to compare and to follow Continue reading