Relational database management systems (DBMS) are the bedrock upon which modern data processing intensive applications are assembled. Critical to ensuring low-latency queries is the efficiency of the DBMSs query processor. Just-in-time (JIT) query compilation is a popular techniqueto improve analytical query processing performance. However, a compiled query cannot overcome poor choices made by the DBMSs optimizer. A lousy query plan results in lousy query code. Poor query plans often arise and for many reasons. Although there is a large body of work exploring how a query processor can adapt itself at runtime to compensate for inadequate plans, these techniques do not work in DBMSs that rely on compiling queries. This dissertation presents multiple effective, practical, and complementary techniques to build adaptive query processing into compilation-based engines with negligible overhead. First, we propose a method that intelligently blends two otherwise disparate query processing approaches (compilation and vectorization) into one engine. This necessary first step allows operators to optimizethemselves using a combination of software memory prefetching and single instruction, multiple data (SIMD) vectorization resulting in improved performance. Next, we present a framework that builds upon our previous work to allow the DBMS to modify compiled queries without recompiling the plan or generating code speculatively. This technique enables more extensive groups of operators in a query to coordinate their optimization process. Finally, we present a method that decomposes query plans into fragments that can be compiled and executed independently. This not only reduces compilation overhead but enables the DBMS to learn properties about data processed in an earlier phase of the query to hyper-optimize the code it generates for later phases. Collectively, the techniques proposed in this dissertation enable any compilation-based DBMS to achieve dynamic runtime robustness without succumbing to any of its overheads.