We are using mirth connect for message transformation from hl7 to text and storing the transformed messages to azure sql database. Our current performance is 45000 messages per hour .
machine configuration is 8 GB RAM and 2 core CPU. Memory assigned to mirth is -XMS = 6122MB
We don't have any idea about what could be performance parameters for Mirth with above configurations. Anyone have idea about performance benchmarks for Mirth connect?
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We run the same process. Mirth -> Azure SQL Database. We're running through performance testing right now and have been stuck at 12 - 15 messages/second (43000 - 54000 per hour).
We've run tests on each channel and found this: 1 channel source: file reader -> destination: Azure SQL DB was about 36k per hour 2 channel source: file reader -> destination: Azure SQL DB was about 59k per hour 3 channel source: file reader -> destination: Azure SQL DB was about 80k per hour
We've added multi-threading (2,4,8) to both the source and destination on 1 channel with no performance increase. Mirth is running on 8GB mem and 2 Cores with heap size set to 2048MB.
We are now going to run through a few tests with mirth running on similar "hardware" as a C4.4xlarge which in Azure is 16 cores and 32GB mem. There is 200gb of SSD available as well.
Our goal is 100k messages per hour per channel.
I'd recommend looking into the Max Processing Threads option in version 3.4 and above. It's configurable in the Source Settings (Source tab). By default it's set to 1, which means only one message can process through the channel's main processing thread at any given time. This is important for certain interfaces where order of messages is paramount, but obviously it limits throughput.
Note that whatever client is sending your channel messages also needs to be reconfigured to send multiple messages in parallel. For example if you have a single-threaded process that is sending your channel messages via TCP/MLLP one after another in sequence, increasing the max processing threads isn't necessarily going to help because the client is still single-threaded. But, for example, if you stand up 10 clients all sending to your channel simultaneously, then you'll definitely reap the benefits of increasing the max processing threads.
If your source connector is a polling type, like a File Reader, you can still benefit from this by turning the Source Queue on and increasing the Max Processing Threads. When the source queue is enabled and you have multiple processing threads, multiple queue consumers are started and all read and process from the source queue at the same time.
Another thing to look at is destination queuing. In the Advanced (wrench icon) queue settings, there is a similar option to increase the number of Destination Queue Threads. By default when you have destination queuing enabled, there's just a single queue thread that processes messages in a FIFO sequence. Again, good for message order but hampers throughput.
If you do need messages to be ordered and want to maximize parallel throughput (AKA have your cake and eat it too), you can use the Thread Assignment Variable in conjunction with multiple destination Queue Threads. This allows you to preserve order among messages with the same unique identifier, while messages pertaining to different identifiers can process simultaneously. A common use-case is to use the patient MRN for this, so that all messages for a given patient are guaranteed to process in the order they were received, but messages longitudinally across different patients can process simultaneously.