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It is now beginning to be easy to pickle and unpickle aggregate data structures using a consistent format. For example, imagine the internal data structure is a list of integers and Booleans: type format = list<int32 * bool> let formatP = listP (tup2P int32P boolP) let formatU = listU (tup2U int32U boolU) open System.IO let writeData file data = use outStream = new BinaryWriter(File.OpenWrite(file)) formatP data outStream let readData file = use inStream = new BinaryReader(File.OpenRead(file)) formatU inStream You can now invoke the pickle/unpickle process as follows: > writeData "out.bin" [(102, true); (108, false)] ;; val it : unit > readData "out.bin";; val it : (int * bool) list = [(102, true); (108, false)]

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Speaking as a person who has been involved in many benchmarks, the benefits of this seem obvious. When running benchmarks, people frequently ask to run as many users as possible until the system breaks. One of the outputs of these benchmarks is always a chart that shows the number of concurrent users versus the number of transactions (see Figure 5-3).

Figure 5-3 Concurrent users vs transactions per second Initially, as you add concurrent users, the number of transactions increases At some point, however, adding additional users does not increase the number of transactions you can perform per second; the graph tends to flatten off The throughput has peaked and now response time starts to increase In other words, you are doing the same number of transactions per second, but the end users are observing slower response times As you continue adding users, you will find that the throughput will actually start to decline The concurrent user count before this drop-off is the maximum degree of concurrency you want to allow on the system Beyond this point, the system becomes flooded and queues begin forming to perform work Much like a backup at a tollbooth, the system can no longer keep up.

Combinator-based pickling is a powerful technique and can be taken well beyond what has been shown here. For example, it is possible to do the following: Ensure data is compressed and shared during the pickling process by keeping tables in the input and output states. Sometimes this requires two or more phases in the pickling and unpickling process. Build in extra-efficient primitives that compress leaf nodes, such as writing out all integers using BinaryWriter.Write7BitEncodedInt and BinaryReader.Read7BitEncodedInt. Build extra combinators for arrays, sequences, and lazy values and for lists stored in other binary formats than the 0/1 tag scheme used here. Build combinators that allow dangling references to be written to the pickled data, usually written as a symbolic identifier. When the data is read, the identifiers must be resolved and relinked, usually by providing a function parameter that performs the resolution. This can be a useful technique when processing independent compilation units. Combinator-based pickling is used mainly because it allows data formats to be created and read in a relatively bug-free manner. It is not always possible to build a single pickling library suitable for all purposes, and you should be willing to customize and extend code samples such as those listed previously in order to build a set of pickling functions suitable for your needs.

Not only does response time rise dramatically at this point, but throughput from the system may fall, too, as the overhead of simply context switching and sharing resources between too many consumers takes additional resources itself If we limit the maximum concurrency to the point right before this drop, we can sustain maximum throughput and minimize the increase in response time for most users Shared server allows us to limit the maximum degree of concurrency on our system to this number An analogy for this process could be a simple door The width of the door and the width of people limit the maximum people per minute throughput At low load, there is no problem; however, as more people approach, some forced waiting occurs (CPU time slice).

If a lot of people want to get through the door, we get the fallback effect there are so many people saying after you and so many false starts that the throughput falls Everybody gets delayed getting through Using a queue means the throughput increases, some people get through the door almost as fast as if there was no queue, while others (the ones put at the end of the queue) experience the greatest delay and might fret that this was a bad idea But when you measure how fast everybody (including the last person) gets through the door, the queued model (shared server) performs better than a free-for-all approach (even with polite people; but conjure up the image of the doors opening when a store has a large sale, with everybody pushing very hard to get through)..

Note Combinator-based parsing borders on a set of techniques that we don t cover in this book called