Analytical database providers (e.g., Redshift, Databricks, BigQuery) have
rapidly added support for invoking Large Language Models (LLMs) through native
user-defined functions (UDFs) to help users perform natural language tasks,
such as classification, entity extraction, and translation, inside analytical
workloads. For instance, an analyst might want to extract customer sentiments
on millions of product reviews. However, LLM inference is highly expensive in
both computational and economic terms: for example, an NVIDIA L4 GPU running
Llama2-7B can only process 6 KB of text per second. In this paper, we explore
how to optimize LLM inference for analytical workloads that invoke LLMs within
relational queries. We show that relational queries present novel opportunities
for accelerating LLM inference, including reordering rows to maximize key-value
(KV) cache reuse within the LLM inference engine, reordering columns within a
row to further increase cache reuse, and deduplicating redundant inference
requests. We implement these optimizations in Apache Spark, with vLLM as the
model serving backend and achieve up to 4.4x improvement in end-to-end latency
on a benchmark of diverse LLM-based queries on real datasets. To the best of
our knowledge, this is the first work to explicitly address the problem of
optimizing LLM invocations within SQL queries.

本文探讨了如何优化在关系查询中调用大型语言模型的 LLM 推断，包括重排行以最大化 LLM 推断引擎内的键值（KV）缓存重用，重排列列以进一步增加缓存重用，并去重复冗余的推断请求。我们在 Apache Spark 中实现了这些优化，在真实数据集上的多样化 LLM 查询基准测试中，最终端到端延迟提高了 4.4 倍。据我们所知，这是第一个明确解决在 SQL 查询中优化 LLM 调用问题的研究。