Streaming JSON query engines achieve high throughputs by avoiding full document parsing and exploiting local parallelism. However, they struggle with point queries accessing only a small fraction of the data. Conversely, approaches based on a Document Object Model (DOM) excel at point queries through direct indexing, yet suffer from excessive memory usage and costly preprocessing. We propose a hybrid solution: a lightweight structural index based on Minimal Perfect Hash Functions that map opening bracket positions to their corresponding closing brackets, enabling efficient subtree skipping directly on the raw JSON stream. Thanks to our optimizations based on statistical analysis of real-world JSON datasets, the resulting index requires only a fraction of the JSON’s size. Our solution significantly accelerates query processing and closes the gap between streaming and DOM-based approaches. We achieve a mean speedup of 1.49 × and up to 20 × on point queries.

Research advancements in storage formats continuously produce more efficient encodings and better compression rates. Despite this, new formats are not adopted due to high implementation cost and existing formats cannot evolve because they need to maintain compatibility across systems. Can this problem be solved by introducing a new abstraction? We answer affirmatively with AnyBlox, a framework for reading arbitrary datasets using lightweight WebAssembly decoders bundled with the data. By decoupling decoders from both systems and file format specifications, AnyBlox allows transparent format evolution, instance-optimized encodings, and enables mainstream adoption of research advancements. It integrates seamlessly with modern systems like DuckDB, Spark, and Umbra, while delivering solid performance and security guarantees.

Harnessing the power of SIMD can bring tremendous performance gains in data processing. In querying streamed JSON data, the state of the art leverages SIMD to fast forward significant portions of the document. However, it does not provide support for descendant, which excludes many real-life queries and makes formulating many others hard. In this work, we aim to change this: we consider the fragment of JSONPath that supports child, descendant, wildcard, and labels. We propose a modular approach based on novel depth-stack automata that process a stream of events produced by a state-driven classifier, allowing fast forwarding parts of the input document irrelevant at the current stage of the computation. We implement our solution in Rust and compare it with the state of the art, confirming that our approach allows supporting descendants without sacrificing performance, and that reformulating natural queries using descendants brings impressive performance gains in many cases.