Speculative Ad-hoc Querying
Analyzing large datasets requires responsive query execution, but executing
SQL queries on massive datasets can be slow. This paper explores whether query
execution can begin even before the user has finished typing, allowing results
to appear almost instantly. We propose SpeQL, a system that leverages Large
Language Models (LLMs) to predict likely queries based on the database schema,
the user's past queries, and their incomplete query. Since exact query
prediction is infeasible, SpeQL speculates on partial queries in two ways: 1)
it predicts the query structure to compile and plan queries in advance, and 2)
it precomputes smaller temporary tables that are much smaller than the original
database, but are still predicted to contain all information necessary to
answer the user's final query. Additionally, SpeQL continuously displays
results for speculated queries and subqueries in real time, aiding exploratory
analysis. A utility/user study showed that SpeQL improved task completion time,
and participants reported that its speculative display of results helped them
discover patterns in the data more quickly. In the study, SpeQL improves user's
query latency by up to 289times and kept the overhead reasonable, at 4$
per hour.
67c6a803025b72f14ccb0939
Haoyu Li
extracted_pending
2025-03-04T07:13:08.306Z
6577437552f02732a463d97d
/avatars/8eb271ec249fa9b0d97dfe0eace6da88.svg
Haoyu Li
user
Haoyu0529
67c6a803025b72f14ccb093a
Srikanth Kandula
67c6a803025b72f14ccb093b
Maria Angels de Luis Balaguer
67c6a803025b72f14ccb093c
Aditya Akella
67c6a803025b72f14ccb093d
Venkat Arun
Speculative Ad-hoc Querying
Analyzing large datasets requires responsive query execution, but executing
SQL queries on massive datasets can be slow. This paper explores whether query
execution can begin even before the user has finished typing, allowing results
to appear almost instantly. We propose SpeQL, a system that leverages Large
Language Models (LLMs) to predict likely queries based on the database schema,
the user's past queries, and their incomplete query. Since exact query
prediction is infeasible, SpeQL speculates on partial queries in two ways: 1)
it predicts the query structure to compile and plan queries in advance, and 2)
it precomputes smaller temporary tables that are much smaller than the original
database, but are still predicted to contain all information necessary to
answer the user's final query. Additionally, SpeQL continuously displays
results for speculated queries and subqueries in real time, aiding exploratory
analysis. A utility/user study showed that SpeQL improved task completion time,
and participants reported that its speculative display of results helped them
discover patterns in the data more quickly. In the study, SpeQL improves user's
query latency by up to 289times and kept the overhead reasonable, at 4$
per hour.
SpeQL, a system leveraging Large Language Models, predicts and executes SQL queries in advance to significantly reduce latency and improve exploratory data analysis efficiency.
Large Language Models query prediction query structure speculative query execution temporary tables exploratory data analysis