naru-project/naru
Neural Relation Understanding: neural cardinality estimators for tabular data
This tool helps database administrators and performance engineers improve SQL query performance. It takes your existing tabular data and SQL queries as input, and outputs highly accurate estimates of how many rows a query will return. This is crucial for the query optimizer to choose the most efficient execution plan, preventing slow queries.
104 stars. No commits in the last 6 months.
Use this if you need to accurately predict the number of rows (cardinality) that complex SQL queries will return on large, tabular datasets to optimize database performance.
Not ideal if you are looking for a general-purpose data analysis tool or if your database queries are simple and perform well without advanced optimization.
Stars
104
Forks
33
Language
Python
License
Apache-2.0
Category
Last pushed
Jun 07, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/naru-project/naru"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
transformerlab/transformerlab-app
The open source research environment for AI researchers to seamlessly train, evaluate, and scale...
neurocard/neurocard
State-of-the-art neural cardinality estimators for join queries
danielzuegner/code-transformer
Implementation of the paper "Language-agnostic representation learning of source code from...
salesforce/CodeTF
CodeTF: One-stop Transformer Library for State-of-the-art Code LLM
salcc/QuantumTransformers
Quantum Transformers for High Energy Physics Analysis at the Large Hadron Collider