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Tinybird is a data platform for data and engineering teams to solve complex real-time, operational, and user-facing analytics use cases at any scale. Tinybird makes it easy to import data from a variety of sources, use SQL to filter, aggregate, and join that data, and publish low-latency, high-concurrency RESTful API endpoints.
We assessed the ability of popular LLMs to generate accurate and efficient SQL from natural language prompts. Using a 200 million record dataset from the GH Archive uploaded to Tinybird, we asked the LLMs to generate SQL based on 50 prompts.
Benchmark that tests the Full-text search ClickHouse feature in Tinybird
Benchmarks brute-force vs HNSW-indexed vector search in ClickHouse/Tinybird using 2M synthetic TikTok post embeddings (768-dim). Compares cosine and L2 distance functions across accuracy (Precision@K, MRR) and latency (avg, p95).
Continuous profiling for analysis of CPU and memory usage, down to the line number and throughout time. Saving infrastructure cost, improving performance, and increasing reliability.
A small Docker Compose stack that runs pg_clickhouse inside Postgres 18 and points it at Tinybird's ClickHouse®-compatible HTTP interface for demonstration purposes.
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