In-memory databases, which offer super fast transaction processing capabilities for OLTP systems and key-value store DBs, are gaining popularity. Some examples of well-known in-memory databases are SAP HANA, VoltDB, Oracle TimesTen, MSSQL In-Memory OLTP, and Memcached. Some less-known ones are GridGain, Couchbase, and Hazlecast. The demand for DRAM has skyrocketed due to the use of in-memory databases for SAP S/4 HANA, big data, generative AI, and data lakes. One of the main challenges in large computer clusters is the limited availability of main memory. For instance, the maximum DRAM for SAP HANA on AWS is 24TB, which costs 63000 USD per month or about 750000 USD per year. On-premises, the maximum DRAM is often 12–18TB.

Moore’s law, which states that the number of transistors in an IC doubles every two years, is no longer valid. This means that main memory is becoming more and more of a bottleneck for in-memory databases. A potential solution was Intel’s Optane memory, a non-volatile memory that had similar performance to DRAM at a lower cost, by enabling load/store access at a cache block granularity. However, Intel discontinued Optane, ending its effort to create and market a memory tier that was slightly slower than RAM but had the advantages of persistence and high IOPS.

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