Just-in-time (JIT) query compilation is a technique to improve analytical query performance in database management systems (DBMSs). But the cost of compiling each query can be significant relative to its execution time. This overhead prohibits the DBMS from employing well-known adaptive query processing (AQP) methods to generate a new plan for a query if data distributions do not match the optimizer’s estimations. The optimizer could eagerly generate multiple sub-plans for a query, but it can only include a few alternatives as each addition increases the query compilation time.

We present a method, called Permutable Compiled Queries (PCQ), that bridges the gap between JIT compilation and AQP. It allows the DBMS to modify compiled queries without needing to recompile or including all possible variations before the query starts. With PCQ, the DBMS structures a query’s code with indirection layers that enable the DBMS to change the plan even while it is running.

We implement PCQ in an in-memory DBMS and compare it against non-adaptive plans in a microbenchmark and against state-of-the-art analytic DBMSs. Our evaluation shows that PCQ outperforms static plans by more than 4x and yields better performance on an analytical benchmark by more than 2x against other DBMSs.