Hi All, Since QoS 0 in MQTT is a performance oriented delivery mode, an in-memory based message store was implemented to bypass the message database when running MB in stand alone mode, however when running in cluster in-memory store will not be applicable, since in order to exchange messages across the cluster, it requires a mechanism to communicate between brokers (Which will be introduced in future MB versions)
In Stand-Alone mode the current message flow would be similar to the following, Publisher ---> Message ---> Deliver from In-Memory Store ---> Deliver from Andes Kernal Store ---> Message ---> Subscription In the above flow, the subscriptions with QoS 0 would receive the messages through the in-memory store and the also currently the published message would be sent to the Andes kernal as well. Since for the same topic there could be subscriptions which are > QoS 0. Making a call to Andes Kernal strore would not make much sense if there're no subscriptions for a particular topic which is > QoS 0, also it will cause additional operation overhead. Therefore, we're in the process of implementing a solution which would be able to identify whether there are subscriptions > QoS 0 for a given topic to identify the necessity to perform a call to the store. If we maintain the state of topics which has subscriptions > QoS 0, for each message published it would require to iterate through all the subscriptions O(n), since there will be cases where the published topic will not 1:1 map with the subscribed topic, (i.e hierarchical topics with wildcards). In order to minimize the iteration cycles one option would be to partition subscriptions based on its attributes such as hierarchy, inclusive of wild card characters etc so that we could omit odds and iterate only through the possibilities. WDYT ? Please do advice on most optimized ways of filtering out matching subscriptions. Thanks, Pamod -- *Pamod Sylvester * *Senior Software Engineer * Integration Technologies Team, WSO2 Inc.; http://wso2.com email: pa...@wso2.com cell: +94 77 7779495
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