###### Abstract  
Recent advancements in memory technology have opened up a wealth of possibilities for innovation in data structures. The emergence of byte-addressable persistent memory (PM) with its impressive capacity and low latency has accelerated the adoption of PM in existing hashing-based indexes. As a result, several new hashing schemes utilizing emulators have been proposed. However, these schemes have proven to be suboptimal, lacking scalability when implemented on real devices. Only a handful of hash table designs have successfully addressed critical properties such as load factor, scalability, efficient memory utilization, and recovery. One of the main challenges in redesigning data structures for an effective hashing scheme in PM is minimizing the overhead associated with dynamic hashing operations in the hash table. To tackle this challenge, this paper introduces ESH, an efficient and scalable hashing scheme that significantly improves memory efficiency, scalability, and overall performance on PM. The ESH scheme maximizes the utilization of the hash table's available space, thus reducing the frequency of full-table rehashing and improving performance. Consequently, this scheme achieves a high load factor while minimizing the need for rehashing. To evaluate the effectiveness of the ESH scheme, we compare it to widely used dynamic hashing schemes employing similar techniques on Intel Optane(r) DC persistent memory (DCPMM). The experimental results demonstrate that ESH outperforms CCEH and Dash in terms of data insertion performance, exhibiting a 30% improvement over CCEH and a 4% improvement over Dash. Furthermore, ESH improves the lookup operation by nearly 10% compared to Dash, while achieving a load factor of up to 91%, surpassing its competitors. p 202313 11528 2023  
OLTP [3,4]. However, data consistency and hardware limitations pose challenges for earlier applications that rely on indexing techniques, which were originally tailored for DRAM environments. Therefore, various persistent indexing techniques have been developed and selected to effectively manage data in persistent memory. The rapid expansion of data centers often pushes hardware utilization to its maximum capacity, necessitating innovations to redefine existing hardware. This has resulted in the emergence of devices like DCPMM that have addressed these limitations. The limitations of hardware and the need for data consistency pose challenges for earlier applications that utilize indexing techniques [3,4] originally designed for the DRAM environment. As a result, various persistent indexing techniques have been developed and selected to effectively store data in persistent memory. To enable a constant lookup time for metadata operations, numerous file systems such as ZFS [5], GPFS [6], and GFS/GFS2 [7,8] rely on hash-based indexing. Nevertheless, employing hashing in these systems necessitates a predictable data size, fixed entry size, and pre-allocation of hash buckets from the applications. In comparison to efforts made towards enhancing tree-based indexing structures for persistent memory, there have been relatively fewer initiatives targeting improvements in hash-based indexing structures. Nevertheless, the current indexing methods do not yield the same benefits on persistent memory (PM) due to issues such as uneven read/write performance and challenges in maintaining crash consistency. Merely adapting existing indexing structures to function on PM without necessary adjustments to align with the new architecture will not yield the anticipated performance enhancements. Consequently, there arises a compelling need to re-engineer index structures specifically tailored for PM environments. The development of high-performance and scalable indexing structures holds paramount importance for storage systems in achieving rapid query processing capabilities. Consequently, there is a need to redesign index structures specifically tailored for PM.