You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: Growing volumes of data increase the demand to use it in analytical applications to make informed decisions. Unfortunately, object-oriented runtimes experience performance problems when dealing with large data volumes. Similar problems have been addressed by column-oriented in-memory databases, whose memory layout is tailored to analytical workloads. As a result, data storage and processing are often delegated to such a database. However, the more domain logic is moved to this separate system, the more benefits of object-orientation are lost. We propose modifications to dynamic object-oriented runtimes to store collections of objects in a column-oriented memory layout and leverage a jit to take advantage of the adjusted layout by mapping object traversal to array operations. We implemented our concept in PyPy, a Python interpreter equipped with a tracing jit. Finally, we show that analytical algorithms, expressed through object-oriented code, are up to three times faster due to our optimizations, without substantially impairing the paradigm. Hopefully, extending these concepts will mitigate some problems originating from the paradigm mismatch between object-oriented runtimes and databases.
[1] describe columnar objects, which is an implementation of an in-memory column store embedded into Python together with a seamless integration into the Python object system. With the help of the PyPy JIT compiler they produce efficient machine code for Python code that queries the data store. Compared to SQPyte their approach offers a much deeper integration of the database implementation into the host language, at the cost of having to implement the data store from scratch.