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This artifact contains: the SQPyte prototype, a JIT for executing SQLite queries; and PyPy-SQPyte, a version of the PyPy Python VM which embeds SQPyte. In addition, a benchmark suite is included, which allows performance comparison against standard SQLite and the Java embedded database H2.

While database management systems (DBMSs) are highly optimized, interactions across the boundary between the programming language (PL) and the DBMS are costly, even for in-process embedded DBMSs. In this paper, we show that programs that interact with the popular embedded DBMS SQLite can be significantly optimized -- by a factor of 3.4 in our benchmarks -- by inlining across the PL / DBMS boundary. We achieved this speed-up by replacing parts of SQLite's C interpreter with RPython code and composing the resulting meta-tracing virtual machine (VM) -- called SQPyte -- with the PyPy VM. SQPyte does not compromise stand-alone SQL performance and is 2.2% faster than SQLite on the widely used TPC-H benchmark suite.

While database management systems (DBMSs) are highly optimized, interactions across the boundary between the programming language (PL) and the DBMS are costly, even for in-process embedded DBMSs. In this paper, we show that programs that interact with the popular embedded DBMS SQLite can be significantly optimized -- by a factor of 3.4 in our benchmarks -- by inlining across the PL / DBMS boundary. We achieved this speed-up by replacing parts of SQLite's C interpreter with RPython code and composing the resulting meta-tracing virtual machine (VM) -- called SQPyte -- with the PyPy VM. SQPyte does not compromise stand-alone SQL performance and is 2.2% faster than SQLite on the widely used TPC-H benchmark suite.

This artifact contains: the SQPyte prototype, a JIT for executing SQLite queries; and PyPy-SQPyte, a version of the PyPy Python VM which embeds SQPyte. In addition, a benchmark suite is included, which allows performance comparison against standard SQLite and the Java embedded database H2.