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CHAPTER 18 RPC
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Recovering From Network Errors
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Of course, there is one reality of life on the network that RPC services cannot easily hide: the network can be down or even go down in the middle of a particular RPC call. You will find that most RPC mechanisms simply raise an exception if a call is interrupted and does not complete. Note that an error, unfortunately, is no guarantee that the remote end did not process the request maybe it actually did finish processing it, but then the network went down right as the last packet of the reply was being sent. In this case, your call would have technically happened and the data would have been successfully added to the database or written to a file or whatever the RPC call does. However, you will think the call failed and want to try it again possibly storing the same data twice. Fortunately, there are a few tricks you can use when writing code that delegates some function calls across the network. First, be careful to distinguish exceptions in the remote code from problems with the protocol and network itself. The former often have to be fatal errors, whereas the latter can sometimes be recovered from by re-trying automatically; good RPC libraries will use different Python exceptions for these two cases, so that you can easily distinguish between them. Second, take this advice offered in 5: instead of littering your code with a try except everywhere that an RPC call is made, try wrapping larger pieces of code that have a solid semantic meaning and can more cleanly be re-attempted or recovered from. If you guard each and every call with an exception handler, after all, you will have lost most of the benefit of RPC: that your code is supposed to be convenient to write, and not make you constantly attend to the fact that function calls are actually being forwarded over the network! In cases where you decide your program should re-try a failed call, you might want to try using something like the exponential back-off algorithm you saw for UDP in 3. This approach lets you avoid hammering an overloaded service and making the situation worse. Finally, be careful about working around the loss of exception detail across the network. Unless you are using a Python-aware RPC mechanism, you will probably find that what would normally be a familiar and friendly KeyError or ValueError on the remote side becomes some sort of RPC-specific error whose text or numeric error code you have to inspect in order to have any chance of telling what happened.
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Binary Options: Thrift and Protocol Buffers
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The first two RPC mechanisms we looked at were textual: XML-RPC and JSON-RPC require raw data to be turned into strings for transmission, and then parsed and decoded again on the remote end. The Python-specific systems that we then discussed supported less verbose forms of data interchange, but without any ability to operate between different programming languages. It is possible you will want both features: a compact and efficient binary format and support across several different languages. Here are a few options: Some JSON-RPC libraries support the BSON protocol, which provides a tight binary transport format and also an expanded range of data types beyond those supported by JSON. The Apache Foundation is now incubating Thrift, an RPC system developed several years ago at Facebook and released as open source. The parameters and data types supported by each service and method are pre-defined in files that are then shared by the developers programming the clients and servers.
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CHAPTER 18 RPC
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Google Protocol Buffers are popular with many programmers, but strictly speaking they are not a full RPC system; instead, they are a binary data serialization protocol. At the time of writing, Google has not released the additional pieces that they have written on top of Protocol Buffers to support a full-fledged RPC round-trip. To perform an actual remote procedure call, you might have to roll your own convention.
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And, of course, systems that I have never even heard of and perhaps some that have not yet been invented will come in to vogue over the years that this book is in print. But whatever RPC system you deploy, the basic principles discussed here should help you use it effectively.
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Remote procedure calls let you write what look like normal Python function calls that actually reach across the network and call a function on another server. They do this by serializing the parameters so that they can be transmitted; they then do the same with the return value that is sent back. All RPC mechanisms work pretty much the same way: you set up a network connection, and then make calls on the proxy object you are given in order to invoke code on the remote end. The old XMLRPC protocol is natively supported in the Python Standard Library, while good third-party libraries exist for the sleeker and more modern JSON-RPC. Both of these mechanisms allow only a small handful of data types to pass between the client and server. If you want a much more complete array of the Python data types available, then you should look at the Pyro system, which can link Python programs together across the network with extensive support for native Python types. The RPyC system is even more extensive, and it allows actual objects to be passed between systems in such a way that method calls on those objects are forwarded back to the system on which the object actually lives.
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