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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^® 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. Full article

The ceramic industry has been vital to the city’s development and prosperity in Jingdezhen, but the development of the ceramics industry in Jingdezhen has been unclear since China’s economic reforms, which will become a bottleneck limiting the sustainable development of the city. This study explored the spatial agglomeration and spatiotemporal evolution of the ceramics industry in Jingdezhen from 1980 to 2020 using enterprise directory data. The study opted for a microscopic perspective and employed kernel density estimation and exploratory spatial data analysis to obtain the necessary results. It also analyzed the influencing factors using a Geodetector. The results show that the temporal evolution of the ceramics industry in Jingdezhen went through two stages from 1980 to 2020. The number of enterprises experienced exponential growth, with fluctuations. The spatial evolution of the ceramics industry transitioned from a “single-center” to a “double-center” model and further evolved into a “multi-center” model. Moreover, the spatial agglomeration of the ceramics industry underwent the process of “agglomeration-diffusion-polarization”, ultimately developing into four ceramic industrial agglomeration patterns in six hotspots. Agglomeration, historical, technological, policy, and transportation factors had positive effects on the evolution of the ceramics industry in Jingdezhen, with agglomeration being the top contributor. Likewise, there were obvious interactions between the factors. This study can provide a basis for formulating policies to support urban spatial planning for urban revitalization, and provide foundation for the development of the national ceramic culture inheritance and innovation pilot zone in Jingdezhen. Full article