Dendrite matrix3/15/2023 ![]() See the Planning your Installation page for #dendrite-alerts: - Release notifications and important info, highly recommended for all Dendrite server admins.#dendrite-dev: - The place for developers, where all Dendrite development discussion happens.#dendrite: - General chat about the Dendrite project, for users and server admins alike.If you have further questions, please take a look at our FAQ or join us in: In the future, we will be able to scale up to gigantic servers (equivalent to ) via polylith mode. There is no sharding of microservices (although it is possible to run them on separate machines) and there is no high-availability/clustering support.Ĭurrently, we expect Dendrite to function well for small (10s/100s of users) homeserver deployments as well as P2P Matrix nodes in-browser or on mobile devices. Dendrite is ready for massive homeserver deployments.There may be client or federation APIs that are not implemented. It has not yet been battle-tested in the real world and so will be error prone initially. This means you should never lose your messages when upgrading Dendrite. Dendrite supports database schema upgrades between releases.We intend to release new versions as we fix bugs and land significant features. We recommend running in Monolith mode with a PostgreSQL database. Scalable: can run on multiple machines and eventually scale to massive homeserver deployments.Reliable: Implements the Matrix specification as written, using the.Efficient: A small memory footprint with better baseline performance than an out-of-the-box Synapse.It intends to provide an efficient, reliable and scalable alternative to Synapse: Print() #a new line to seperate output from the two blocks of codeDendrite is a second-generation Matrix homeserver written in Go. Print("After function call, id(x) = " + str(id(x))) Print("After function call, id(m) = " + str(id(m))) Print("After function call, m = " + str(m)) Print("Before function call, m = " + str(m)) Print("From inside the function, AFTER incrementing n, id(n) = " + str(id(n))) Print("From inside the function, n = " + str(n)) Print("From inside the function, BEFORE incrementing n, id(n) = " + str(id(n))) Where the data type is mutable, the output clearly identifies that the arguments have been passed by reference and have been successfully mutated without losing their object reference so that "By Reference" behaviour manifests. The upshot of this being, behaviour which seems to show that the argument is passed by value, when this is not strictly the case. Where the variable data type is immutable, a new object is created within the function with a NEW reference. However, in truth, the output of each block of code PROVES a reference is passed! The results of all the examples below suggest that python passes by value. Print("INSIDE modifying function, AFTER var incremented! id(var) AFTER incrementing = " + str(id(var))) Print("INSIDE modifying function, var incremented! Now, var = " + str(var)) Print("INSIDE modifying function, var TO BE incremented! id(var) BEFORE incrementing = " + str(id(var))) Print("AFTER function call, id(self.variable) = " + str(id(self.variable))) Print("AFTER function call, self.variable = " + str(self.variable)) Print("BEFORE function call, id(self.variable) = " + str(id(self.variable))) Print("BEFORE function call, self.variable = " + str(self.variable)) However, in truth, the output of the code PROVES a reference is passed!īut because the variable's data type is immutable, a new object is created within the function with a NEW reference. The results of this block of code suggest that python passes by value. Self.axonConnections = self.outputPotential Self.axonConnections = outputDendritesĪxonConns = įor dendrites, index in self.axonConnections:įor i in range(len(self.axonConnections)): # We have stored the value in outputDendrites, but we're not doing anything with it, we have to assign it Initial_axonConnections_list = self.axonConnectionsįor index, outputDendrites in enumerate(initial_axonConnections_list): # Store the axonConnections into a temporary list for parsing since we'll be changing the values WHILE interating
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