In recent years, the hegemony of traditional relational database management systems (RDBMSs) has declined in favour of non-relational databases (NoSQL). These database technologies are better adapted to meet the requirements of large-scale (web) infrastructures handling Big Data by providing elastic and horizontal scalability. Each NoSQL technology however is suited for specific use cases and data models. As a consequence, NoSQL adopters are faced with tremendous heterogeneity in terms of data models, database capabilities and application programming interfaces (APIs). Opting for a specific NoSQL database poses the immediate problem of vendor or technology lock-in. A solution has been proposed in the shape of Object-NoSQL Database Mappers (ONDMs), which provide a uniform abstraction interface for different NoSQL technologies. Such ONDMs however come at a cost of increased performance overhead, which may have a significant economic impact, especially in large distributed setups involving massive volumes of data. In this paper, we present a benchmark study quantifying and comparing the performance overhead introduced by Object-NoSQL Database Mappers, for create, read, update and search operations. Our benchmarks involve five of the most promising and industry-ready ONDMs: Impetus Kundera, Apache Gora, EclipseLink, DataNucleus and Hibernate OGM, and are executed both on a single node and a 9-node cluster setup. Our main findings are summarised as follows: (i) the introduced overhead is substantial for database operations in-memory, however on-disk operations and high network latency result in a negligible overhead, (ii) we found fundamental mismatches between standardised ONDM APIs and the technical capabilities of the NoSQL database, (iii) search performance overhead increases linearly with the number of results, (iv) DataNucleus and Hibernate OGM's search overhead is exceptionally high in comparison to the other ONDMs.