This number is actually the total number of channels and patterns the client is still subscribed to. Pieter Noordhuis provided a great example using EventMachine and Redis to create a multi user high performance web chat.
Because all the messages received contain the original subscription causing the message delivery the channel in the case of message type, and the original pattern in the case of pmessage type client libraries may bind the original subscription to callbacks that can be anonymous functions, blocks, function pointers , using a hash table.
When a message is received an O 1 lookup can be done in order to deliver the message to the registered callback. The first element is the kind of message: subscribe : means that we successfully subscribed to the channel given as the second element in the reply.
Publishing on db 10, will be heard by a subscriber on db 1. Messages received as a result of pattern matching are sent in a different format: The type of the message is pmessage : it is a message received as result of a PUBLISH command issued by another client, matching a pattern-matching subscription.
This function publishes a message m on channel t using the publish connection to Redis. In InitHub function, we are creating two connections to Redis: one for subscribing to the channels that this web node is interested in, and the other to publish messages.
Once the connections are established, we start a new Go routine with a loop running forever waiting to receive messages through the subscriber connection to Redis. Every time it receives a message, it emits it locally i. And finally, HandleSocket is where we wait for messages to come through WebSockets or clean up after the connection closes:. On the client side, we are listening for any message coming in through WebSocket.
Since glue transmits all messages as strings, we encode all information in it using specfic patterns:. This is done using the create method on the backbone collection for messages:. Web nodes receiving this string iterate over all the WebSockets relevant to the channel and sends the string to the client via their WebSocket connections.
As a result, the string gets distributed to all the users looking at the uploaded image. The client, upon receiving this string parses it to extract the coordinates and renders a growing-fading circle to highlight the click location momentarily.
In this article we have seen a glimpse of how the publish-subscribe pattern can help solve the problem of scaling real-time web apps to a great extent and with relative ease. But, as mentioned earlier, the ideas can be implemented in almost any other popular programming language.
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Accept Cookies. Subscriber: An application or other entity that receives messages from one or more publishers. Topic: A channel that contains messages about a particular subject. Instead, messages are delivered instantaneously, which is a major advantage for real-time applications. This makes it easier to scale very large-scale systems, and to make changes to only part of the system. Well, the set of clients is another linked list!
But this is not true: each string has a separate allocation. This is a character array prefixed by its length and the number of free bytes. We can draw it like this:. Redis chooses to not use a linked list here; instead, Redis uses another hash table.
The channel names are the keys of the table:. Let us know if you have insights on this. Only the keys are used when using a hash table to represent a set. The memory waste is okay compared to the code reuse we gain. Iterate over the hash chain, comparing each channel name green to our target channel name. Iterate over the linked list of clients, sending the published message to each client purple.
Append the new client to the end of the linked list. Actually, this is a constant-time operation , because the linked lists have a tail pointer. Notice that the hash tables are different sizes, roughly proportional to how many elements they have. Redis resizes hash tables in response to their number of elements. But Redis is built for low latency, and resizing a hash table is a time-consuming operation.
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