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a) OS Physical Memory – Total amount of memory available on physical host. However, total memory allocation limit for SAP HANA usage is approximate 93-95% (without specify the global allocation limit)

b) Virtual Memory – Reserve memory for SAP HANA processes from Linux OS, and this entire reserved memory footprint of a program is referred to Virtual Memory. SAP HANA virtual memory is the maximal amount that the process has been allocated, including its reservation for code, stack, data, and memory pool under program control. SAP HANA Virtual Memory dynamically growth when more memory needed (eg: table growth, temp computation and etc). When current pool memory can’t satisfy the request, memory manager requesting more memory from OS to this pool memory, up to its predefined memory allocation limit.

c) Resident Memory – the physical memory actually in operational use by a process.

d) Pool Memory – When SAP HANA started, a significant number of memory is requested from OS to this memory pool to store all the in memory data and system tables, thread stacks, temporary computation and other structures that need for managing HANA Database.

d1) only part of pool memory is used initially. When more memory is required for table growth or temporary computations, SAP HANA Manager obtain it from this pool. When the pool cannot satisfy the request, the memory manage will increase the pool size by requesting more memory from the OS, up to the pre-defined Allocation Limit.  Once computations completed or table dropped, freed memory is returned to the memory manager, who recycles it to its pool.

e) SAP HANA Used Memory – total amount of memory currently used by SAP HANA processes, including the currently allocated Pool Memory. The value will dropped when freed memory after each temporary computation and increase when more memory is needed and requested from Pool.

Hope above provide a clearer picture for SAP HANA Memory on Linux and hopefully if time permits, i'll post more on how to analyze current memory usage by SAP HANA (code, stack, shared and heap memory) next based on my analysis done.

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