Modern CPUs keep integrating more cores and large size cache, which is beneficial for in-memory databases to improve parallel processing power and cache locality. While state-of-the-art CPUs have diverse architectures and roadmaps such as large core count and large cache size (AMD x86), moderate core count and cache size (intel x86), large core count and moderate cache size (ARM), exploring in-memory databases performance characteristics for different CPU architectures is important for in-memory database designs and optimizations. In this paper, we develop a fine-grained in-memory database benchmark to evaluate the performance of each operator on different CPUs to explore how CPU hardware architectures influence performance. Different from well known conclusions that more cores and larger cache size can achieve higher performance, we find out that the micro cache architectures play an important role opposite to core count and cache size, the shared monolithic L3 cache with moderate size beats large disaggregated L3 cache. The experiments also show that predicting operator performance on different CPUs is difficult according to diverse CPU architectures and micro cache architectures, and different implementations of each operator are not always high or low with interleaved strong and weak performance regions influenced by CPU hardware architectures. Intel x86 CPUs represent cache-centric processor design, while AMD x86 and ARM CPUs represent computing-centric processor design, the OLAP benchmark experiments of SSB discover that OmniSciDB and OLAP Accelerator with vector-wise processing model performs well on intel x86 CPUs compared to AMD x86 CPUs and the JIT compliant based Hyper prefers to AMD x86 CPUs rather than intel x86 CPUs. The CPU roadmaps of increasing cores or improving cache locality should be considered for in-memory database algorithm design and platform selection.

OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multi-dimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional surrogate key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35%, 365% and 169% performance improvements based on the Hyper, Vectorwise and MonetDB databases respectively, for the Star Schema Benchmark (SSB) with scale factor 100.

OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multidimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. This is achieved by mapping the relation tables into virtual multidimensional model and binding the multidimensional operations into a set of vector indexes to enable multidimensional computing on relation tables. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional surrogate key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35, 365, and 169 percent performance improvements based on the Hyper, Vectorwise, and MonetDB databases, respectively, for the Star Schema Benchmark (SSB) with scale factor 100.

Denormalization is a common tactic for enhancing performance of data warehouses. However, it is rarely used in main memory databases, which regards storage space as scarce resource. In this paper, we demonstrate that MMDB can actually benefit from the strategy of denormalization. We have created A-Store, a prototypical main-memory database system customized for star and snowflake schemas, which applies the strategy of denormalization to achieve highly efficient OLAP. Instead of resorting to fully materialized denormalization, A-Store applies a method called virtual denomalization, which allows query processing to be performed in a denormalized way, while without incurring additional space consumption.

Denormalization is a common tactic for enhancing performance of data warehouses, though its side-effect is quite obvious. Besides being confronted with update abnormality, denormalization has to consume additional storage space. As a result, this tactic is rarely used in main memory databases, which regards storage space, i.e., RAM, as scarce resource. Nevertheless, our research reveals that main memory database can benefit enormously from denormalization, as it is able to remarkably simplify the query processing plans and reduce the computation cost. In this paper, we present A-Store, a main memory OLAP engine customized for star/snowflake schemas. Instead of generating fully materialized denormalization, A-Store resorts to virtual denormalization by treating array indexes as primary keys. This design allows us to harvest the benefit of denormalization without sacrificing additional RAM space. A-Store uses a generic query processing model for all SPJGA queries. It applies a number of state-of-the-art optimization methods, such as vectorized scan and aggregation, to achieve superior performance. Our experiments show that A-Store outperforms the most prestigious MMDB systems significantly in star/snowflake schema based query processing.