Category Archives: Python

An Introduction To Asynchronous Programming and Twisted

Part 22: The End

This concludes the introduction started here. You can find an index to the entire series here.

All Done

Whew! Thank you for sticking with me. When I started this series I didn’t realize it was going to be this long, or take this much time to make. But I have enjoyed creating it and hope you have enjoyed reading it.

Now that I have finished, I will look into the possibility of generating a PDF format. No promises, though.

I would like to conclude with a few suggestions on how to continue your Twisted education.

Further Reading

First, I would recommend reading the Twisted online documentation. Although it is much-maligned, I think it’s better than it is often given credit for.

If you want to use Twisted for web programming, then Jean-Paul Calderone has a well-regarded series called “Twisted Web in 60 Seconds“. I suspect it will take a little longer than that to read, though.

There is also a Twisted Book, which I can’t say much about as I haven’t read it.

But more important than any of those, I think, is to read the Twisted source code. Since that code is written by people who know Twisted very well, it is an excellent source of examples for how do to things the “Twisted Way”.

Suggested Exercises

  1. Port a synchronous program you wrote to Twisted.
  2. Write a new Twisted program from scratch.
  3. Pick a bug from the Twisted bug database and fix it. Submit a patch to the Twisted developers. Don’t forget to read about the process for making your contribution.

The End, Really

Happy Hacking!

Figure 47: The End
Figure 47: The End

An Introduction To Asynchronous Programming and Twisted

Part 21: Lazy is as Lazy Doesn’t: Twisted and Haskell

This continues the introduction started here. You can find an index to the entire series here.

Introduction

In the last Part we compared Twisted with Erlang, giving most of our attention to some ideas they have in common. And that ended up being pretty simple, as asynchronous I/O and reactive programming are key components of the Erlang runtime and process model.

Today we are going to range further afield and look at Haskell, another functional language that is nevertheless quite different from Erlang (and, of course, Python). There won’t be as many parallels, but we will nevertheless find some asynchronous I/O hiding under the covers.

Functional with a Capital F

Although Erlang is also a functional language, its main focus is a reliable concurrency model. Haskell, on the other hand, is functional through and through, making unabashed use of concepts from category theory like functors and monads.

Don’t worry, we’re not going into any of that here (as if we could). Instead we’ll focus on one of Haskell’s more traditionally functional features: laziness. Like many functional languages (but unlike Erlang), Haskell supports lazy evaluation. In a lazily evaluated language the text of a program doesn’t so much describe how to compute something as what to compute. The details of actually performing the computation are generally left to the compiler and runtime system.

And, more to the point, as a lazily-evaluated computation proceeds the runtime may evaluate expressions only partially (lazily) instead of all at once. In general, the runtime will evaluate only as much of an expression as is needed to make progress on the current computation.

Here is a simple Haskell statement applying head, a function that retrieves the first element of a list, to the list [1,2,3] (Haskell and Python share some of their list syntax):

  head [1,2,3]

If you install the GHC Haskell runtime, you can try this out yourself:

[~] ghci
GHCi, version 6.12.1: http://www.haskell.org/ghc/  : ? for help
Loading package ghc-prim ... linking ... done.
Loading package integer-gmp ... linking ... done.
Loading package base ... linking ... done.
Prelude> head [1,2,3]
1
Prelude>

The result is the number 1, as expected.

The Haskell list syntax includes the handy ability to define a list from its first couple of elements. For example, the list [2,4 ..] is the sequence of even numbers starting with 2. Where does it end? Well, it doesn’t. The Haskell list [2,4 ..] and others like it represent (conceptually) infinite lists. You can see this if you try to evaluate one at the interactive Haskell prompt, which will attempt to print out the result of your expression:

Prelude> [2,4 ..]
[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126,128,130,132,134,136,138,140,142,144,146,
...

You’ll have to press Ctrl-C to stop that computation as it will never actually terminate. But because of lazy evaluation, it is possible to use these infinite lists in Haskell with no trouble:

Prelude> head [2,4 ..]
2
Prelude> head (tail [2,4 ..])
4
Prelude> head (tail (tail [2,4 ..]))
6

Here we are accessing the first, second, and third elements of this infinite list respectively, with no infinite loop anywhere in sight. This is the essence of lazy evaluation. Instead of first evaluating the entire list (which would cause an infinite loop) and then giving that list to the head function, the Haskell runtime only constructs as much of the list as it needs for head to finish its work. The rest of the list is never constructed at all, because it is not needed to proceed with the computation.

When we bring the tail function into play, Haskell is forced to construct the list further, but again only as much as it needs to evaluate the next step of the computation. And once the computation is done, the (unfinished) list can be discarded.

Here’s some Haskell code that partially consumes three different infinite lists:

Prelude> let x = [1..]
Prelude> let y = [2,4 ..]
Prelude> let z = [3,6 ..]
Prelude> head (tail (tail (zip3 x y z)))
(3,6,9)

Here we zip all the lists together, then grab the head of the tail of the tail. Once again, Haskell has no trouble with this and only constructs as much of each list as it needs to finish evaluating our code. We can visualize the Haskell runtime “consuming” these infinite lists in Figure 46:

Figure 46: Haskell consuming some infinite lists
Figure 46: Haskell consuming some infinite lists

Although we’ve drawn the Haskell runtime as a simple loop, it might be implemented with multiple threads (and probably is if you are using the GHC version of Haskell). But the main point to notice is how this figure looks like a reactor loop consuming bits of data as they come in on network sockets.

You can think of asynchronous I/O and the reactor pattern as a very limited form of lazy evaluation. The asynchronous I/O motto is: “Only process as much data as you have”. And the lazy evaluation motto is: “Only process as much data as you need”. Furthermore, a lazily-evaluated language applies that motto almost everywhere, not just in the limited scope of I/O.

But the point is that, for a lazily-evaluated language, making use of asynchronous I/O is no big deal. The compiler and runtime are already designed to process data structures bit by bit, so lazily processing the incoming chunks of an I/O stream is just par for the course. And thus the Haskell runtime, like the Erlang runtime, simply incorporates asynchronous I/O as part of its socket abstractions. And we can show that by implementing a poetry client in Haskell.

Haskell Poetry

Our first Haskell poetry client is located in haskell-client-1/get-poetry.hs. As with Erlang, we’re going to jump straight to a finished client, and then suggest further reading if you’d like to learn more.

Haskell also supports light-weight threads or processes, though they aren’t as central to Haskell as they are to Erlang, and our Haskell client creates one process for each poem we want to download. The key function there is runTask which connects to a socket and starts the getPoetry function in a light-weight thread.

You’ll notice a lot of type declarations in this code. Haskell, unlike Python or Erlang, is statically typed. We don’t declare types for each and every variable because Haskell will automatically infer types not explicitly declared (or report an error if it can’t). A number of the functions include the IO type (technically a monad) because Haskell requires us to cleanly separate code with side-effects (i.e., code that performs I/O) from pure functions.

The getPoetry function includes this line:

poem <- hGetContents h

which appears to be reading the entire poem from the handle (i.e., the TCP socket) at once. But Haskell, as usual, is lazy. And the Haskell runtime includes one or more actual threads which perform asynchronous I/O in a select loop, thus preserving the possibilities for lazy evaluation of I/O streams.

Just to illustrate that asynchronous I/O is really going on, we have included a “callback” function, gotLine, that prints out some task information for each line in the poem. But it’s not really a callback function at all, and the program would use asynchronous I/O whether we included it or not. Even calling it “gotLine” reflects an imperative-language mindset that is out of place in a Haskell program. No matter, we’ll clean it up in a bit, but let’s take our first Haskell client out for a spin. Start up some slow poetry servers:

python blocking-server/slowpoetry.py --port 10001 poetry/fascination.txt
python blocking-server/slowpoetry.py --port 10002 poetry/science.txt
python blocking-server/slowpoetry.py --port 10003 poetry/ecstasy.txt --num-bytes 30

Now compile the Haskell client:

cd haskell-client-1/
ghc --make get-poetry.hs

This will create a binary called get-poetry. Finally, run the client against our servers:

./get-poetry 10001 10002 1000

And you should see some output like this:

Task 3: got 12 bytes of poetry from localhost:10003
Task 3: got 1 bytes of poetry from localhost:10003
Task 3: got 30 bytes of poetry from localhost:10003
Task 2: got 20 bytes of poetry from localhost:10002
Task 3: got 44 bytes of poetry from localhost:10003
Task 2: got 1 bytes of poetry from localhost:10002
Task 3: got 29 bytes of poetry from localhost:10003
Task 1: got 36 bytes of poetry from localhost:10001
Task 1: got 1 bytes of poetry from localhost:10001
...

The output is slightly different than previous asynchronous clients because we are printing one line for each line of poetry instead of each arbitrary chunk of data. But, as you can see, the client is clearly processing data from all the servers together, instead of one after the other. You’ll also notice that the client prints out the first poem as soon as it’s finished, without waiting for the others, which continue on at their own pace.

Alright, let’s clean the remaining bits of imperative cruft from our client and present a version which just grabs the poetry without bothering with task numbers. You can find it in haskell-client-2/get-poetry.hs. Notice that it’s much shorter and, for each server, just connects to the socket, grabs all the data, and sends it back.

Ok, let’s compile a new client:

cd haskell-client-2/
ghc --make get-poetry.hs

And run it against the same set of poetry servers:

./get-poetry 10001 10002 10003

And you should see the text of each poem appear, eventually, on the screen.

You will notice from the server output that each server is sending data to the client simultaneously. What’s more, the client prints out each line of the first poem as soon as possible, without waiting for the rest of the poem, even while it’s working on the other two. And then it quickly prints out the second poem, which it has been accumulating all along.

And all of that happens without us having to do much of anything. There are no callbacks, no messages being passed back and forth, just a concise description of what we want the program to do, and very little in the way of how it should go about doing it. The rest is taken care of by the Haskell compiler and runtime. Nifty.

Discussion and Further Reading

In moving from Twisted to Erlang to Haskell we can see a parallel movement, from the foreground to the background, of the ideas behind asynchronous programming. In Twisted, asynchronous programming is the central motivating idea behind Twisted’s existence. And Twisted’s implementation as a framework separate from Python (and Python’s lack of core asynchronous abstractions like lightweight threads) keeps the asynchronous model front and center when you write programs using Twisted.

In Erlang, asynchronicity is still very visible to the programmer, but the details are now part of the fabric of the language and runtime system, enabling an abstraction in which asynchronous messages are exchanged between synchronous processes.

And finally, in Haskell, asynchronous I/O is just another technique inside the runtime, largely unseen by the programmer, for providing the lazy evaluation that is one of Haskell’s central ideas.

We don’t have any profound insight into this situation, we’re just pointing out the many and interesting places where the asynchronous model shows up, and the many different ways it can be expressed.

And if any of this has piqued your interest in Haskell, then we can recommend Real World Haskell to continue your studies. The book is a model of what a good language introduction should be. And while I haven’t read it, I’ve heard good things about Learn You a Haskell.

This brings us to the end of our tour of asynchronous systems outside of Twisted, and the penultimate part in our series. In Part 22 we will conclude, and suggest ways to learn more about Twisted.

Suggested Exercises for the Startlingly Motivated

  1. Compare the Twisted, Erlang, and Haskell clients with each other.
  2. Modify the Haskell clients to handle failures to connect to a poetry server so they download all the poetry they can and output reasonable error messages for the poems they can’t.
  3. Write Haskell versions of the poetry servers we made with Twisted.

An Introduction To Asynchronous Programming and Twisted

Part 20: Wheels within Wheels: Twisted and Erlang

This continues the introduction started here. You can find an index to the entire series here.

Introduction

One fact we’ve uncovered in this series is that mixing synchronous “plain Python” code with asynchronous Twisted code is not a straightforward task, since blocking for an indeterminate amount of time in a Twisted program will eliminate many of the benefits you are trying to achieve using the asynchronous model.

If this is your first introduction to asynchronous programming it may seem as if the knowledge you have gained is of somewhat limited applicability. You can use these new techniques inside of Twisted, but not in the much larger world of general Python code. And when working with Twisted, you are generally limited to libraries written specifically for use as part of a Twisted program, at least if you want to call them directly from the thread running the reactor.

But asynchronous programming techniques have been around for quite some time and are hardly confined to Twisted. There are in fact a startling number of asynchronous programming frameworks in Python alone. A bit of searching around will probably yield a couple dozen of them. They differ from Twisted in their details, but the basic ideas (asynchronous I/O, processing data in small chunks across multiple data streams) are the same. So if you need, or choose, to use an alternative framework you will already have a head start having learned Twisted.

And moving outside of Python, there are plenty of other languages and systems that are either based around, or make use of, the asynchronous programming model. Your knowledge of Twisted will continue to serve you as you explore the wider areas of this subject.

In this Part we’re going to take a very brief look at Erlang, a programming language and runtime system that makes extensive use of asynchronous programming concepts, but does so in a unique way. Please note this is not meant as a general introduction to Erlang. Rather, it is a short exploration of some of the ideas embedded in Erlang and how they connect with the ideas in Twisted. The basic theme is the knowledge you have gained learning Twisted can be applied when learning other technologies.

Callbacks Reimagined

Consider Figure 6, a graphical representation of a callback. The principle callback in Poetry Client 3.0, introduced in Part 6, and all subsequent poetry clients is the dataReceived method. That callback is invoked each time we get a bit more poetry from one of the poetry servers we have connected to.

Let’s say our client is downloading three poems from three different servers. Looking at things from the point of view of the reactor (and that’s the viewpoint we’ve emphasized the most in this series), we’ve got a single big loop which makes one or more callbacks each time it goes around. See Figure 40:

Figure 40: callbacks from the reactor viewpoint
Figure 40: callbacks from the reactor viewpoint

This figure shows the reactor happily spinning around, calling dataReceived as the poetry comes in. Each invocation of dataReceived is applied to one particular instance of our PoetryProtocol class. And we know there are three instances because we are downloading three poems (and so there must be three connections).

Let’s think about this picture from the point of view of one of those Protocol instances. Remember each Protocol is only concerned with a single connection (and thus a single poem). That instance “sees” a stream of method calls, each one bearing the next piece of the poem, like this:

dataReceived(self, "When I have fears")
dataReceived(self, " that I may cease to be")
dataReceived(self, "Before my pen has glea")
dataReceived(self, "n'd my teeming brain")
...

While this isn’t strictly speaking an actual Python loop, we can conceptualize it as one:

for data in poetry_stream(): # pseudo-code
    dataReceived(data)

We can envision this “callback loop” in Figure 41:

Figure 41: A virtual callback loop
Figure 41: A virtual callback loop

Again, this is not a for loop or a while loop. The only significant Python loop in our poetry clients is the reactor. But we can think of each Protocol as a virtual loop that ticks around once each time some poetry for that particular poem comes in. With that in mind we can re-imagine the entire client in Figure 42:

Figure 42: the reactor spinning some virtual loops
Figure 42: the reactor spinning some virtual loops

In this figure we have one big loop, the reactor, and three virtual loops, the individual poetry protocol instances. The big loop spins around and, in so doing, causes the virtual loops to tick over as well, like a set of interlocking gears.

Enter Erlang

Erlang, like Python, is a general purpose dynamically typed programming language originally created in the 80’s. Unlike Python, Erlang is functional rather than object-oriented, and has a syntax reminiscent of Prolog, the language in which Erlang was originally implemented. Erlang was designed for building highly reliable distributed telephony systems, and thus Erlang contains extensive networking support.

One of Erlang’s most distinctive features is a concurrency model involving lightweight processes. An Erlang process is neither an operating system process nor an operating system thread. Rather, it is an independently running function inside the Erlang runtime with its own stack. Erlang processes are not lightweight threads because Erlang processes cannot share state (and most data types are immutable anyway, Erlang being a functional programming language). An Erlang process can interact with other Erlang processes only by sending messages, and messages are always, at least conceptually, copied and never shared.

So an Erlang program might look like Figure 43:

Figure 43: An Erlang program with three processes
Figure 43: An Erlang program with three processes

In this figure the individual processes have become “real”, since processes are first-class constructs in Erlang, just like objects are in Python. And the runtime has become “virtual”, not because it isn’t there, but because it’s not necessarily a simple loop. The Erlang runtime may be multi-threaded and, as it has to implement a full-blown programming language, it’s in charge of a lot more than handling asynchronous I/O. Furthermore, a language runtime is not so much an extra construct, like the reactor in Twisted, as the medium in which the Erlang processes and code execute.

So an even better picture of an Erlang program might be Figure 44:

Figure 44: An Erlang program with several processes
Figure 44: An Erlang program with several processes

Of course, the Erlang runtime does have to use asynchronous I/O and one or more select loops, because Erlang allows you to create lots of processes. Large Erlang programs can start tens or hundreds of thousands of Erlang processes, so allocating an actual OS thread to each one is simply out of the question. If Erlang is going to allow multiple processes to perform I/O, and still allow other processes to run even if that I/O blocks, then asynchronous I/O will have to be involved.

Note that our picture of an Erlang program has each process running “under its own power”, rather than being spun around by callbacks. And that is very much the case. With the job of the reactor subsumed into the fabric of the Erlang runtime, the callback no longer has a central role to play. What would, in Twisted, be solved by using a callback would, in Erlang, be solved by sending an asynchronous message from one Erlang process to another.

An Erlang Poetry Client

Let’s look at an Erlang poetry client. We’re going to jump straight to a working version instead of building up slowly like we did with Twisted. Again, this isn’t meant as a complete Erlang introduction. But if it piques your interest, we suggest some more in-depth reading at the end of this Part.

The Erlang client is listed in erlang-client-1/get-poetry. In order to run it you will, of course, need Erlang installed. Here’s the code for the main function, which serves a similar purpose as the main functions in our Python clients:

main([]) ->
    usage();

main(Args) ->
    Addresses = parse_args(Args),
    Main = self(),
    [erlang:spawn_monitor(fun () -> get_poetry(TaskNum, Addr, Main) end)
     || {TaskNum, Addr} <- enumerate(Addresses)],
    collect_poems(length(Addresses), []).

If you’ve never seen Prolog or a similar language before then Erlang syntax is going to seem a little odd. But some people say that about Python, too. The main function is defined by two separate clauses, separated by a semicolon. Erlang chooses which clause to run by matching the arguments, so the first clause only runs if we execute the client without providing any command line arguments, and it just prints out a help message. The second clause is where all the action is.

Individual statements in an Erlang function are separated by commas, and all functions end with a period. Let’s take each line in the second clause one at a time. The first line is just parsing the command line arguments and binding them to a variable (all variables in Erlang must be capitalized). The second line is using the Erlang self function to get the process ID of the currently running Erlang process (not OS process). Since this is the main function you can kind of think of it as the equivalent of the __main__ module in Python. The third line is the most interesting:

[erlang:spawn_monitor(fun () -> get_poetry(TaskNum, Addr, Main) end)
     || {TaskNum, Addr} <- enumerate(Addresses)],

This statement is an Erlang list comprehension, with a syntax similar to that in Python. It is spawning new Erlang processes, one for each poetry server we need to contact. And each process will run the same function (get_poetry) but with different arguments specific to that server. We also pass the PID of the main process so the new processes can send the poetry back (you generally need the PID of a process to send a message to it).

The last statement in main calls the collect_poems function which waits for the poetry to come back and for the get_poetry processes to finish. We’ll look at the other functions in a bit, but first you might compare this Erlang
main function to the equivalent main in one of our Twisted clients.

Now let’s look at the Erlang get_poetry function. There are actually two functions in our script called get_poetry. In Erlang, a function is identified by both name and arity, so our script contains two separate functions, get_poetry/3 and get_poetry/4 which accept three and four arguments respectively. Here’s get_poetry/3, which is spawned by main:

get_poetry(Tasknum, Addr, Main) ->
    {Host, Port} = Addr,
    {ok, Socket} = gen_tcp:connect(Host, Port,
                                   [binary, {active, false}, {packet, 0}]),
    get_poetry(Tasknum, Socket, Main, []).

This function first makes a TCP connection, just like the Twisted client get_poetry. But then, instead of returning, it proceeds to use that TCP connection by calling get_poetry/4, listed below:

get_poetry(Tasknum, Socket, Main, Packets) ->
    case gen_tcp:recv(Socket, 0) of
        {ok, Packet} ->
            io:format("Task ~w: got ~w bytes of poetry from ~s\n",
                      [Tasknum, size(Packet), peername(Socket)]),
            get_poetry(Tasknum, Socket, Main, [Packet|Packets]);
        {error, _} ->
            Main ! {poem, list_to_binary(lists:reverse(Packets))}
    end.

This Erlang function is doing the work of the PoetryProtocol from our Twisted client, except it does so using blocking function calls. The gen_tcp:recv function waits until some data arrives on the socket (or the socket is closed), however long that might be. But a “blocking” function in Erlang only blocks the process running the function, not the entire Erlang runtime. That TCP socket isn’t really a blocking socket (you can’t make a true blocking socket in pure Erlang code). For each of those Erlang sockets there is, somewhere inside the Erlang runtime, a “real” TCP socket set to non-blocking mode and used as part of a select loop.

But the Erlang process doesn’t have to know about any of that. It just waits for some data to arrive and, if it blocks, some other Erlang process can run instead. And even if a process never blocks, the Erlang runtime is free to switch execution from that process to another at any time. In other words, Erlang has a non-cooperative concurrency model.

Notice that get_poetry/4, after receiving a bit of poem, proceeds by recursively calling itself. To an imperative language programmer this might seem like a recipe for running out of memory, but the Erlang compiler can optimize “tail” calls (function calls that are the last statement in a function) into loops. And this highlights another curious parallel between the Erlang and Twisted clients. In the Twisted client, the “virtual” loops are created by the reactor calling the same function (dataReceived) over and over again. And in the Erlang client, the “real” processes running (get_poetry/4) form loops by calling themselves over and over again via tail-call optimization. How about that.

If the connection is closed, the last thing get_poetry does is send the poem to the main process. That also ends the process that get_poetry is running, as there is nothing left for it to do.

The remaining key function in our Erlang client is collect_poems:

collect_poems(0, Poems) ->
    [io:format("~s\n", [P]) || P <- Poems];
collect_poems(N, Poems) ->
    receive
        {'DOWN', _, _, _, _} ->
            collect_poems(N-1, Poems);
        {poem, Poem} ->
            collect_poems(N, [Poem|Poems])
    end.

This function is run by the main process and, like get_poetry, it recursively loops on itself. It also blocks. The receive statement tells the process to wait for a message to arrive that matches one of the given patterns, and
then extract the message from its “mailbox”.

The collect_poems function waits for two kinds of messages: poems and “DOWN” notifications. The latter is a message sent to the main process when one of the get_poetry processes dies for any reason (this is the monitor part of spawn_monitor). By counting DOWN messages, we know when all the poetry has finished. The former is a message from one of the get_poetry processes containing one complete poem.

Ok, let’s take the Erlang client out for a spin. First start up three slow poetry servers:

python blocking-server/slowpoetry.py --port 10001 poetry/fascination.txt
python blocking-server/slowpoetry.py --port 10002 poetry/science.txt
python blocking-server/slowpoetry.py --port 10003 poetry/ecstasy.txt --num-bytes 30

Now we can run the Erlang client, which has a similar command-line syntax as the Python clients. If you are on a Linux or other UNIX-like system, then you should be able to run the client directly (assuming you have Erlang installed and available in your PATH). On Windows you will probably need to run the escript program, with the path to the Erlang client as the first argument (with the remaining arguments for the Erlang client itself).

./erlang-client-1/get-poetry 10001 10002 10003

After that you should see output like this:

Task 3: got 30 bytes of poetry from 127:0:0:1:10003
Task 2: got 10 bytes of poetry from 127:0:0:1:10002
Task 1: got 10 bytes of poetry from 127:0:0:1:10001
...

This is just like one of our earlier Python clients where we print a message for each little bit of poetry we get. When all the poems have finished the client should print out the complete text of each one. Notice the client is switching back and forth between all the servers depending on which one has some poetry to send.

Figure 45 shows the process structure of our Erlang client:

Figure 45: Erlang poetry client
Figure 45: Erlang poetry client

This figure shows three get_poetry processes (one per server) and one main process. You can also see the messages that flow from the poetry processes to main process.

So what happens if one of those servers is down? Let’s try it:

./erlang-client-1/get-poetry 10001 10005

The above command contains one active port (assuming you left all the earlier poetry servers running) and one inactive port (assuming you aren’t running any server on port 10005). And we get some output like this:

Task 1: got 10 bytes of poetry from 127:0:0:1:10001

=ERROR REPORT==== 25-Sep-2010::21:02:10 ===
Error in process <0.33.0> with exit value: {{badmatch,{error,econnrefused}},[{erl_eval,expr,3}]}

Task 1: got 10 bytes of poetry from 127:0:0:1:10001
Task 1: got 10 bytes of poetry from 127:0:0:1:10001
...

And eventually the client finishes downloading the poem from the active server, prints out the poem, and exits. So how did the main function know that both processes were done? That error message is the clue. The error happens when get_poetry tries to connect to the server and gets a connection refused error instead of the expected value ({ok, Socket}). The resulting exception is called badmatch because Erlang “assignment” statements are really pattern-matching operations.

An unhandled exception in an Erlang process causes the process to “crash”, which means the process stops running and all of its resources are garbage collected. But the main process, which is monitoring all of the get_poetry processes, will receive a DOWN message when any of those processes stops running for any reason. And thus our client exits when it should instead of running forever.

Discussion

Let’s take stock of some of the parallels between the Twisted and Erlang clients:

  1. Both clients connect (or try to connect) to all the poetry servers at once.
  2. Both clients receive data from the servers as soon as it comes in, regardless of which server delivers the data.
  3. Both clients process the poetry in little bits, and thus have to save the portion of the poems received thus far.
  4. Both clients create an “object” (either a Python object or an Erlang process) to handle all the work for one particular server.
  5. Both clients have to carefully determine when all the poetry has finished, regardless of whether a particular download succeeded or failed.

And finally, the main functions in both clients asynchronously receive poems and “task done” notifications. In the Twisted client this information is delivered via a Deferred while the Erlang client receives inter-process messages.

Notice how similar both clients are, in both their overall strategy and the structure of their code. The mechanics are a bit different, with objects, deferreds, and callbacks on the one hand and processes and messages on the other. But the high-level mental models of both clients are quite similar, and it’s pretty easy to move from one to the other once you are familiar with both.

Even the reactor pattern reappears in the Erlang client in miniaturized form. Each Erlang process in our poetry client eventually turns into a recursive loop that:

  1. Waits for something to happen (a bit of poetry comes in, a poem is delivered, another process finishes), and
  2. Takes some appropriate action.

You can think of an Erlang program as a big collection of little reactors, each spinning around and occasionally sending a message to another little reactor (which will process that message as just another event).

And if you delve deeper into Erlang you will find callbacks making an appearance. The Erlang gen_server process is a generic reactor loop that you “instantiate” by providing a fixed set of callback functions, a pattern repeated elsewhere in the Erlang system.

So if, having learned Twisted, you ever decide to give Erlang a try I think you will find yourself in familiar mental territory.

Further Reading

In this Part we’ve focused on the similarities between Twisted and Erlang, but there are of course many differences. One particularly unique feature of Erlang is its approach to error handling. A large Erlang program is structured as a tree of processes, with “supervisors” in the higher branches and “workers” in the leaves. And if a worker process crashes, a supervisor process will notice and take some action (typically restarting the failed worker).

If you are interested in learning more Erlang then you are in luck. Several Erlang books have either been published recently, or will be published shortly:

  • Programming Erlang — written by one of Erlang’s inventors. A great introduction to the language.
  • Erlang Programming — this complements the Armstrong book and goes into more detail in several key areas.
  • Erlang and OTP in Action — this hasn’t been published yet, but I am eagerly awaiting my copy. Neither of the first two books really addresses OTP, the Erlang framework for building large apps. Full disclosure: two of the authors are friends of mine.

Well that’s it for Erlang. In the next Part we will look at Haskell, another functional language with a very different feel from either Python or Erlang. Nevertheless, we shall endeavor to find some common ground.

Suggested Exercises for the Highly Motivated

  1. Go through the Erlang and Python clients and identify where they are similar and where they differ. How do they each handle errors (like a failure to connect to a poetry server)?
  2. Simplify the Erlang client so it no longer prints out each bit of poetry that comes in (so you don’t need to keep track of task numbers either).
  3. Modify the Erlang client to measure the time it takes to download each poem.
  4. Modify the Erlang client to print out the poems in the same order as they were given on the command line.
  5. Modify the Erlang client to print out a more readable error message when we can’t connect to a poetry server.
  6. Write Erlang versions of the poetry servers we made with Twisted.