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Cap’n Proto RPC employs TIME TRAVEL! If you want to use the results for anything else, you must wait. With any traditional RPC system, this will require two network round trips. With Cap’n Proto, it takes only one. However, Cap’n Proto promises support an additional feature: pipelining. Moreover, a pipelined promise can be used in the parameters to another call without waiting. But isn’t that just syntax sugar?
Now we’ve eliminated the round trip, without inventing a whole new RPC protocol. The problem is, this kind of arbitrary combining of orthogonal features quickly turns elegant object-oriented protocols into ad-hoc messes. This is a very clean interface for interacting with a file system. But say you are using this interface over a satellite link with 1000ms latency. Now you have a problem: simply reading the file foo in directory bar takes four round trips! In such a high-latency scenario, making your interface elegant is simply not worth 4x the latency. So now you’re going to change it.
Merge the File and Directory interfaces into a single Filesystem interface, where every call takes a path as an argument. We now have to implement path string manipulation, which is always a headache. But what if they are buggy and have hard-coded some path other than the one we specified? Or what if we don’t trust them, and we really want them to access only one particular File or Directory and not have permission to anything else. Now we have to implement authentication and authorization systems!
Essentially, in our quest to avoid latency, we’ve resorted to using a singleton-ish design, and singletons are evil. Promise Pipelining solves all of this! With pipelining, our 4-step example can be automatically reduced to a single round trip with no need to change our interface at all. We keep our simple, elegant, singleton-free interface, we don’t have to implement path strings, caching, authentication, or authorization, and yet everything performs as well as we can possibly hope for. Example code The calculator example uses promise pipelining.
Take a look at the client side in particular. Distributed Objects As you’ve noticed by now, Cap’n Proto RPC is a distributed object protocol. You can pass a capability as a parameter to a method or embed it in a struct or list. Didn’t CORBA prove this doesn’t work? CORBA failed for many reasons, with the usual problems of design-by-committee being a big one. However, the biggest reason for CORBA’s failure is that it tried to make remote calls look the same as local calls. API involving promises, and accounts for the presence of a network introducing latency and unreliability.
As shown above, promise pipelining is absolutely critical to making object-oriented interfaces work in the presence of latency. If remote calls look the same as local calls, there is no opportunity to introduce promise pipelining, and latency is inevitable. When this happens, the client will need to create a new connection and try again. Security Cap’n Proto interface references are capabilities. That is, they both designate an object to call and confer permission to call it. When a new object is created, only the creator is initially able to call it.
Such patterns tend to be much more adaptable than traditional ACL-based security, making it easy to keep security tight and avoid confused-deputy attacks while minimizing pain for legitimate users. That said, you can of course implement ACLs or any other pattern on top of capabilities. Protocol Features Cap’n Proto’s RPC protocol has the following notable features. Level 1: Object references and promise pipelining, as described above. B will form a new connection to machine C so that Bob can call Carol directly without proxying through machine A. If you receive a set of capabilities from different parties which should all point to the same underlying objects, you can verify securely that they in fact do. Specification The Cap’n Proto RPC protocol is defined in terms of Cap’n Proto serialization schemas.
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Cap’n Proto’s RPC protocol is based heavily on CapTP, the distributed capability protocol used by the E programming language. Lots of useful material for understanding capabilities can be found at those links. The protocol is complex, but the functionality it supports is conceptually simple. Just as TCP is a complex protocol that implements the simple concept of a byte stream, Cap’n Proto RPC is a complex protocol that implements the simple concept of objects with callable methods. Cap’n Proto is a project of Sandstorm. It assumes that the most recent major version of the client is used and the reader is familiar with the basics. JDK 8, both for compilation and at runtime.
On Android, this means only Android 7. 0 or later versions are supported. JDK 6 and Android versions prior to 7. This means that the user can consider the library to be licensed under any of the licenses from the list above. For example, the user may choose the Apache Public License 2. 0 and include this client into a commercial product. Codebases that are licensed under the GPLv2 may choose GPLv2, and so on.
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There are also command line tools that used to be shipped with the Java client. The client API is closely modelled on the AMQP 0-9-1 protocol model, with additional abstractions for ease of use. AMQP 0-9-1 connection and channel, respectively. All of these parameters have sensible defaults for a RabbitMQ node running locally. Note that user guest can only connect from localhost by default. This is to limit well-known credential use in production systems. The channel can now be used to send and receive messages, as described in subsequent sections.
Note that closing the channel may be considered good practice, but isn’t strictly necessary here – it will be done automatically anyway when the underlying connection is closed. The underlying protocol is designed and optimized for long running connections. That means that opening a new connection per operation, e. Closing and opening new channels per operation is usually unnecessary but can be appropriate.
When in doubt, consider reusing channels fist. Channel-level exceptions such as attempts to consume from a queue that does not exist will result in channel closure. These must be declared before they can be used. This will actively declare the following objects, both of which can be customised by using additional parameters. Here neither of them have any special arguments. The above function calls then bind the queue to the exchange with the given routing key.
There are also longer forms with more parameters, to let you override these defaults as necessary, giving full control where needed. This “short form, long form” pattern is used throughout the client API uses. A passive declare simply checks that the entity with the provided name exists. If it does, the operation is a no-op. Ready state in the queue response. Therefore if the method returns and no channel exceptions occurs, it means that the exchange does exist.
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It is possible to delete a queue only if it is empty: channel. We have not illustrated all the possibilities here. While some operations on channels are safe to invoke concurrently, some are not and will result in incorrect frame interleaving on the wire, double acknowledgements and so on. Concurrent publishing on a shared channel can result in incorrect frame interleaving on the wire, triggering a connection-level protocol exception and immediate connection closure by the broker. Sharing channels between threads will also interfere with Publisher Confirms.
Concurrent publishing on a shared channel is best avoided entirely, e. It is possible to use channel pooling to avoid concurrent publishing on a shared channel: once a thread is done working with a channel, it returns it to the pool, making the channel available for another thread. Channel pooling can be thought of as a specific synchronization solution. It is recommended that an existing pooling library is used instead of a homegrown solution. Channels consume resources and in most cases applications very rarely need more than a few hundreds open channels in the same JVM process. JVM is already a fair amount of overhead that likely can be avoided.
A classic anti-pattern to be avoided is opening a channel for each published message. Channels are supposed to be reasonably long-lived and opening a new one is a network round-trip which makes this pattern extremely inefficient. Consuming in one thread and publishing in another thread on a shared channel can be safe. The dispatch mechanism uses a java. When manual acknowledgements are used, it is important to consider what thread does the acknowledgement. Acknowledging a single message at a time can be safe. The messages will then be delivered automatically as they arrive, rather than having to be explicitly requested.
Consumer tags are used to cancel consumers. Duplicate consumer tags on a connection is strongly discouraged and can lead to issues with automatic connection recovery and confusing monitoring data when consumers are monitored. Just like with publishers, it is important to consider concurrency hazard safety for consumers. To explicitly retrieve messages, use Channel.
If the client has not configured a return listener for a particular channel, then the associated returned messages will be silently dropped. A return listener will be called, for example, if the client publishes a message with the “mandatory” flag set to an exchange of “direct” type which is not bound to a queue. Those objects always end up in the closed state, regardless of the reason that caused the closure, like an application request, an internal client library failure, a remote network request or network failure. The following code depends on the channel being in open state. Instead, we should normally ignore such checking, and simply attempt the action desired. The overhead is initially minimal and the total thread resources allocated are bounded, even if a burst of consumer activity may occasionally occur. This is entirely equivalent to repeatedly setting host and port on a factory, calling factory.
Combined with automatic recovery, the client can automatically connect to nodes that weren’t even up when it was first started. This can be useful for simple DNS-based load balancing or failover. The search is implemented as a DNS SRV request. Heartbeat Timeout See the Heartbeats guide for more information about heartbeats and how to configure them in the Java client. Below is an example for Google App Engine.
With the default blocking IO mode, each connection uses a thread to read from the network socket. You should use fewer threads than with the default blocking mode. With the appropriate number of threads set, you shouldn’t experiment any decrease in performance, especially if the connections are not so busy. The NIO mode uses reasonable defaults, but you may need to change them according to your own workload.