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Complex analytics queries often involve expensive operations that may require large computational runtimes leading to slow query responsiveness and hampering real-time performance. Moreover, running these expensive analytics queries inside traditional online transaction processing (OLTP) systems for real-time analytics can affect the performance of mission-critical OLTP queries. On the other hand, support for real-time analytics is considered vital for important business insights and improved market responsiveness. In this paper, we try to address the needs of real-time analytics by enabling hardware acceleration of complex database query operations such as predicate evaluation, sort and projection. While projection helps reduce the amount of data being processed by subsequent query operations, sort is central to most database queries, even those not involving an explicit sort operation. Our system involves FPGA-based composable accelerator for offloading the analytics queries from the host CPU running the OLTP workload. The FPGA-accelerated database system contains accelerator kernels for various database operations and automatic transformation of query operations into calls to these hardware kernels for seamless integration of the accelerator into the database system. Based on the query semantics, each accelerator kernel can be tailored by software to execute specific database operations and different kernels can be fused together to compose a query accelerator. Our query transformation algorithm creates a query-specific control block to customize the accelerator without requiring FPGA-reconfiguration. [ABSTRACT FROM AUTHOR]