PixelsDB: Serverless and NL-Aided Data Analytics with Flexible Service Levels and Prices
- URL: http://arxiv.org/abs/2405.19784v2
- Date: Mon, 23 Dec 2024 06:44:10 GMT
- Title: PixelsDB: Serverless and NL-Aided Data Analytics with Flexible Service Levels and Prices
- Authors: Haoqiong Bian, Dongyang Geng, Haoyang Li, Yunpeng Chai, Anastasia Ailamaki,
- Abstract summary: PixelsDB is an open-source data analytic system that allows users to explore data efficiently.
The queries are executed by a serverless query engine that offers varying prices for different performance service levels (SLAs)
We demonstrate that the combination of a serverless paradigm, a natural-language-aided interface, and flexible SLAs and prices will substantially improve the usability of cloud data analytic systems.
- Score: 17.048398987952332
- License:
- Abstract: Serverless query processing has become increasingly popular due to its advantages, including automated resource management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces the cost of owning a data analytic system. However, it is still a significant challenge for non-expert users to transform their complex and evolving data analytic needs into proper SQL queries and select a serverless query service that delivers satisfactory performance and price for each type of query. This paper presents PixelsDB, an open-source data analytic system that allows users who lack system or SQL expertise to explore data efficiently. It allows users to generate and debug SQL queries using a natural language interface powered by fine-tuned language models. The queries are then executed by a serverless query engine that offers varying prices for different performance service levels (SLAs). The performance SLAs are natively supported by dedicated architecture design and heterogeneous resource scheduling that can apply cost-efficient resources to process non-urgent queries. We demonstrate that the combination of a serverless paradigm, a natural-language-aided interface, and flexible SLAs and prices will substantially improve the usability of cloud data analytic systems.
Related papers
- AnDB: Breaking Boundaries with an AI-Native Database for Universal Semantic Analysis [11.419119182421964]
AnDB is an AI-native database that supports traditional O workloads and AI-driven tasks.
AnDB allows users to perform semantic queries using intuitive-like statements without requiring AI expertise.
AnDB future-proofs data management infrastructure, empowering users to effectively and efficiently harness the full potential of all kinds of data without starting from scratch.
arXiv Detail & Related papers (2025-02-19T15:15:59Z) - UQE: A Query Engine for Unstructured Databases [71.49289088592842]
We investigate the potential of Large Language Models to enable unstructured data analytics.
We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
arXiv Detail & Related papers (2024-06-23T06:58:55Z) - Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL [0.0]
We utilize OpenAI's GPT-4 model within a retrieval-augmented generation (RAG) framework.
Business context document is enriched with a business context document, to transform NLQs into Structured Query Language queries.
Performance achieved a maximum of 85% when high complexity queries are excluded.
arXiv Detail & Related papers (2024-06-15T17:07:31Z) - CHESS: Contextual Harnessing for Efficient SQL Synthesis [1.9506402593665235]
We introduce CHESS, a framework for efficient and scalable text-to- queries.
It comprises four specialized agents, each targeting one of the aforementioned challenges.
Our framework offers features that adapt to various deployment constraints.
arXiv Detail & Related papers (2024-05-27T01:54:16Z) - Optimizing LLM Queries in Relational Workloads [58.254894049950366]
We show how to optimize Large Language Models (LLMs) inference for analytical workloads that invoke LLMs within relational queries.
We implement these optimizations in Apache Spark, with vLLM as the model serving backend.
We achieve up to 4.4x improvement in end-to-end latency on a benchmark of diverse LLM-based queries on real datasets.
arXiv Detail & Related papers (2024-03-09T07:01:44Z) - JoinGym: An Efficient Query Optimization Environment for Reinforcement
Learning [58.71541261221863]
Join order selection (JOS) is the problem of ordering join operations to minimize total query execution cost.
We present JoinGym, a query optimization environment for bushy reinforcement learning (RL)
Under the hood, JoinGym simulates a query plan's cost by looking up intermediate result cardinalities from a pre-computed dataset.
arXiv Detail & Related papers (2023-07-21T17:00:06Z) - SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs)
With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses.
With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
arXiv Detail & Related papers (2023-05-26T21:39:05Z) - Outsourcing Training without Uploading Data via Efficient Collaborative
Open-Source Sampling [49.87637449243698]
Traditional outsourcing requires uploading device data to the cloud server.
We propose to leverage widely available open-source data, which is a massive dataset collected from public and heterogeneous sources.
We develop a novel strategy called Efficient Collaborative Open-source Sampling (ECOS) to construct a proximal proxy dataset from open-source data for cloud training.
arXiv Detail & Related papers (2022-10-23T00:12:18Z) - AskYourDB: An end-to-end system for querying and visualizing relational
databases using natural language [0.0]
We propose a semantic parsing approach to address the challenge of converting complex natural language into SQL.
We modified state-of-the-art models, by various pre and post processing steps which make the significant part when a model is deployed in production.
To make the product serviceable to businesses we added an automatic visualization framework over the queried results.
arXiv Detail & Related papers (2022-10-16T13:31:32Z) - Graph Enhanced BERT for Query Understanding [55.90334539898102]
query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information.
In recent years, pre-trained language models (PLMs) have advanced various natural language processing tasks.
We propose a novel graph-enhanced pre-training framework, GE-BERT, which can leverage both query content and the query graph.
arXiv Detail & Related papers (2022-04-03T16:50:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.