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Joe.Ferraro avatar image
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How to safely redispatch an RsslMsgEvent to another thread

I'd like to offload the processing of a defaultMsgEventCallback to another thread to distribute the data received from the subscriptions among multiple threads rather than the thread on which rsslReactorDispatch is called to prevent being disconnect for buffer overruns in the Elektron Server

In order to due so, I'm assuming I'll need to make a copy of the RsslMsgEvent that is passed into the callback routine (unless it remains valid after the callback function returns)

The question is what is the safest way to copy the data in that structure.

Looking at it, I see it contains 5 pointers.

The last two pointers, to a 32 bit and 8 bit value should be fairly straight forward.

If I make a deep copy of each of the areas to which the other pointers in the structure point, will that be sufficient?

Assuming all the data is copied, will the data still be valid for processing? (decoding)

Is there a routine like: rsslCopyMsgEvent() ?

Or should I just create multiple reactors in separate threads and let the dispatch take take of it natively?

elektronrefinitiv-realtimeelektron-sdkrrteta-apielektron-transport-apimulti-threadingcallbackdispatch
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Jirapongse avatar image   Jirapongse

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chavalit-jintamalit avatar image   chavalit-jintamalit

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@Joe.Ferraro

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veerapath.rungruengrayubkul avatar image
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Events received in callback functions should be assumed to be invalid once the callback function returns. As far as I know, there is no function to make a deep copy of the RsslMsgEvent. Instead, you can made a deep copy of RsslMsg or Rssl provided in the event. To copy an RsslMsg, please see the rsslCopyMsg function in the Transport API C Developers Guide document.


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Jirapongse avatar image
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It depends on requirement of the application regarding performance. In normal situation, using only one reactor and copying the messages to be processed by other threads are enough.

However, in some extreme scenarios, multiple reactors are required. This allows users to quickly and easily horizontally scale the application to leverage multi-core systems or distribute content across multiple connections.

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