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Determining the settings for the MQTT Transmission History Store requires understanding the unique system properties at each Edge Node.These include the number of tags at the Edge Node and Device levels, the frequency at which these tags are changing, the required period for data storage and the RAM or disk based storage available on the Edge hardware There are a number of factors involved in determining how large the tag capacity can be including but not limited to system resources such as CPU, RAM (especially when using 'In-Memory'), Disk IOPS (if using 'Disk-Backed'), the nominal tag change rate (e.g. number of tags changing per second in the system), the flush rate, bandwidth availability, whether flushing in order vs asynchronously, etc.

Tip

Review the MQTT Transmission Transmitters and Tag Trees documentation for help in identifying your Edge Node(s) and Device(s).

If using UDTs you should include each member tag of a UDT in your count when determining the number of tags.

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Info

Large Tag Capacities

If using a large tag capacity it is highly recommended to test the system under load in a non-production environment on similar hardware and software that will be used in production. During testing is also important to get the system into a state where the store and forward cache becomes full before beginning to flush. This will ensure that the system is sized appropriately when deployed into a production environment. There are a number of factors involved in determining how large the tag capacity can be including but not limited to system resources such as CPU, RAM (especially when using 'In-Memory'), Disk IOPS (if using 'Disk-Backed'), the nominal tag change rate (e.g. number of tags changing per second in the system), the flush rate, bandwidth availability, whether flushing in order vs asynchronously, etc. Because of the complex interactions of these variables it is highly recommended to test in a controlled environment. Generally any capacity over 2,000,000 is considered large and should be tested before deploying to production.

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