#Azure file storage vs blob driver#
This is a result of how the data requests are translated by the filesystem driver or network protocol into hardware level operations. Because of the way different technology layers handle data, what appears to be a single IOP to a client application may be viewed as multiple IOPs by the storage service. When considering the pricing dimension, it is also important to consider that some tiers will also charge for IOPs/transactions as well as for the at-rest data size.Īn important note at this point is that not all IOPs are the same. Storage services typically have a limit on the maximum IOP rate for different scopes (file, client, storage instance), limiting the total data throughput achievable for small transactions. This can also be the case for workloads that perform large numbers of metadata operations. IOP Rateįor workloads that must access many small files or perform small random reads into large files, the limiting factor for performance will usually be the I/O operations (IOPs) rate. As a result, the overall performance will normally be limited by the bulk data transfer capacity of either the network layer or the underlying storage devices, rather than by the performance of metadata operations or transaction rate limits.
This incurs very few metadata operations and allows efficient streaming of data from the underlying storage medium.
Accessing this type of data typically requires loading contiguous chunks of data from a small number of relatively large files.
Total read bandwidth is the key performance driver for data-intensive workloads which consume large blocks of data. Fortran Library for SMP & Multicore Versions.Software Optimization and Code Modernization.
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