Modern databases can run application logic defined in stored procedures inside the database server to improve application speed. The SQL standard specifies how to call external stored routines implemented in programming languages, such as C, C++, or JAVA, to complement declarative SQL-based application logic. This is beneficial for scientific and analytical algorithms because they are usually too complex to be implemented entirely in SQL. At the same time, database applications like matrix calculations or data mining algorithms benefit from multi-threading to parallelize compute-intensive operations. Multi-threaded application code, however, introduces a resource competition between the threads of applications and the threads of the database task scheduler. In this paper, we show that multi-threaded application code can render the database's workload scheduling ineffective and decrease the core throughput of the database by up to 50%. We present a general approach to address this issue by integrating shared memory programming solutions into the task schedulers of databases. In particular, we describe the integration of OpenMP into databases. We implement and evaluate our approach using SAP HANA. Our experiments show that our integration does not introduce overhead, and can improve the throughput of core database operations by up to 15%.