The solution is to either gamble on the server not sending any chunked responses and then setting protocol_version in the stream context that you pass to the file retrieving function (the list of HTTP wrapper settings (.. I don’t think it’s a good idea to define protocol_version as float, but .. well.)), or use cURL instead. The Solr pecl extension uses cURL internally, so it’s not affected by this issue.

One of the common themes that show up at the gearman IRC channel on freenode is an attempt to understand what gearman is and how everything fits together. I’ll try to explain the different concepts and what the different responsibilities of a working gearman infrastructure are. There’s also a “Getting Started” guide on the Gearman web site with a bit of example code and installation instructions, so you might want to keep that open in another tab. So here we go: a simple gearman tutorial explaining the concepts and not just throwing example code your way.

There are three core components of a gearman installation. These are a client (someone requesting a task to be performed), a worker (someone performing a task) and the server (which coordinates tasks between clients and workers). All these three components need to be running for you to be able to something useful with gearman. It’s worth noting that I’ll use name “task” for a single item to be performed, you’ll also see this named ‘function’ (which is the name of the actual function the task asks to be performed – a server offers several “functions” that a client can call). Some APIs might also refer to a “task” as a collection of functions to be called. I’ll use the first definition; a task is a call to a function on the server, together with the data for the task and a task identifier. Several subsequent tasks will call the same function.

I’ll go a bit more in detail about each of these components, but it’s important that you understand how everything is interconnected first. An exchange of messages between the different parts can be illustrated as follows:

client -> server: ask server to perform a task
server acknowledges request and assigns an identificator to the request
server -> all workers: tell workers registered for the task that there is work to be performed
worker -> server: I'll perform the task you just told us about
server -> worker: ok, go ahead, here's the information about the task.
worker -> server: here's the result of the task performed
server -> client: here's the result of the task you asked me to get someone to do for you

The idea behind the server telling all the workers that there are work available is to let the worker that responds fastest to actually get the task, as it’s assumed that this is the worker with the least load on the server it’s running on (as it responds quickly, the server is not busy doing other things). As I wrote above, the worker is the piece of code actually doing the work – the worker performs the task that a client has submitted to the gearman server.

You’ll find that most of Gearman is designed according to the same principle – keep stuff simple. The server only needs to keep track of which workers perform which functions, and then let the workers grab a task when it becomes available.

The Gearman Client

In Gearman the client is the piece of code that connects to the server and asks for a task to be performed. This can be a dynamic web page (running in python, ruby, PHP, perl or another language with a suitable Gearman library), a completely application that connects to Gearman, a worker (to submit a new task or to divide the current task into several smaller tasks to be performed by other workers) or a combination of the above. The important part is that this is simply a client – it has a task that needs to be handled, and it’ll ask the Gearman server to find someone who can perform the task.

The client can be run in synchronous (blocking) or asynchronous (non-blocking) mode. The first will make the client wait until the task has been performed by a worker (and if no worker is available, it’ll wait indefinitely or until reaching a timeout in the client), while the latter will simply fire-and-forget the task to the Gearman server (the server will confirm that the task has been received) and then go on its merry way afterwards. The Gearman server will provide a task identification value which the asynchronous client can use to query the current state of the task it asked to be performed (as long as the actual worker provide such updates).

A small example of how a client might work (using PHP):

This will submit a request to a Gearman server running on the same machine as the script, asking for the function “fetchURL” to be run, and including an array of arguments to the function (you could simply include just the URL, but I find that this way is easier to extend in the future – and using JSON for data exchange makes the worker code more programming language independent). This code uses addTaskBackground to submit the task to be performed in an asynchronous manner. We’re not interested in the result of this task in this particular piece of code – the worker will either provide the result through other means (storing it in a database, in memcache, call an API function telling us that it’s finished) or perhaps we’re not interested in the result at all, just that we’ve attempted to perform the task. If you’re using the synchronous interface, the data returned from the worker will be returned to your code as the return value from the client.

As you can see, the client code is very, very simple. There is no actual work being performed here, we’re just telling the server that we’d like some work to be performed for us.

The Gearman Worker

The Gearman worker is where all the actual work (.. who’d guess) is performed. This is the application that receives a notice that it has to wake up and do a bit of hard work, and which actually goes out and does just that. What kind of work it does depends on what you’re using Gearman for, but a couple of use cases could be to resize an image into smaller sizes (such as thumbnails), to convert an uploaded video into another format for a specific device, sending notification emails, updating an internal search engine such a Solr and quite a few other tasks. As long as the task is not important for the application to continue running (no need for waiting for an E-mail to be delivered if you’re going to show a “Your information has been saved” message), then Gearman (and other alternative message queues) is a valid solution.

You’ll run each worker as its own process. A worker can perform several different functions (although you should (usually) stay away from multi-threading to perform them at the same time). This means starting several copies of the same worker if you want to allow for more than one worker performing a task at the same time (i.e., if you want to send 30 e-mails in parallel), you’ll start each worker as separate processes (30 workers in that case). There are several daemons and frameworks that can help you manage the number of processes available depending on server and task load, such as supervisord and GearmanManager (a PHP daemon). Another possible solution is to use screen to start several workers, which also will allow you to attach to the output of any worker at any time.

How the worker performs its work is up to the worker itself. In most cases you’ll have to write a bit of code to expose your code as a Gearman function (so that clients can submit tasks to perform that function), but this code will usually just instantiate the worker framework from the Gearman library you’re using, letting you register what functions you’ll be able to perform and attaching callbacks telling the library what part of your own code should be called when a request to perform a task arrives.

A simple example modified from the Gearman Getting Started guide:

The $worker->work() method call will wait until a work arrives, then execute the callback as defined in the addFunction call. addFunction instructs the worker to tell the gearman server that this worker is able to perform any tasks calling the “fetchURL” function. The callback provided to the library (“call this PHP function (‘fetch_url’) when tasks want to call ‘fetchURL’”) will then receive the job object containing information about the job (task) to be performed. The workload() method returns the workload – the information we included in addition to which function to call in the client example. The server receives the workload from the client and then sends it to the worker together with the task information.

Since our client calls the server using the asynchronous interface it’ll not wait for the worker to return the web page contents, but by using ->do() or one of the other foreground methods in the PHP Gearman library.

The Gearman Server

The Gearman Server used is usually the C version of the server. There’s also a PERL version, but these days the C server is the one being actively developed. There’s not much to say about the server, you usually just start it and let it run by itself, doing what it was supposed to do all along.

I’ve got one simple suggestion if you’re just playing around with Gearman for the first time: start the server with the -vvv option. This will make gearmand a lot noisier, and will allow you to see clients registering themselves with the server, pinging the server and getting a bit more information about what’s happening inside the server process.

You’ll also want to provide an IP address that the gearman server should bind to – by default it binds to all interfaces, and since gearmand does not have any authentication built in by default, you don’t want to expose your server to the whole world.

Here’s an example of how we start gearmand at one of our servers:

You can drop the part related to screen if you just want to play with gearmand:

If you have gearmand in your path and not in the same location as us, drop /usr/local/sbin :-) This will bind gearmand to your localhost and use the default port (earlier the default port was something other than 4730, so we provide it just in case).

Making it all come together

The easiest way to play around with gearman is to simply open three terminal windows: one for gearmand with logging turned on, one for your worker and its output and the last window for a client sending a task request to gearmand (you can use the ‘gearman’ binary for this, just be sure to include any data in an appropriate format). As you submit a task for a function that the worker has registered, you should see it pick it up and then start processing the task as soon as possible. After a while (depending on how you’ve implemented your worker and what function it performs) the result should appear in your client.

Our production setups usually use a web application (PHP or python/django) as the client in the above scenario. The functions are usually long running tasks, such as analysing GPS paths, encoding videos and downloading files or internal web site analytics (where we just want to get things logged and not wait for the actual logging to complete). The web application submits a request to gearmand as soon as a file has been received, with a payload of the path to the file to be processed. The workers perform their function and then store the information back into the database or to disk, then usually call a web service to tell the web application that the work has been performed and any internal state can be updated to include (and show) the result of the task.

Message queues (such as Gearman) has become one of the core technologies behind many modern web applications (and non-web applications for that matter), so there’s really no reason to avoid at least playing around a bit with it and adding another possible tool to your future options.

The returned dictionary contains one entry for each of the entries in the DSN.

Update: helge also submitted a simplified version of the above:

The reasoning behind making the service is that there are several factors that are in play when deciding whether to order a product from the US or from the UK: the exchange rate for GBP and USD, the shipping cost, the delivery situation for the item and whether the item is sold in the store at all.

The user enters a list of the URLs to the products they’re considering purchasing from an Amazon-store, press submit and get a list back of which items are in stock, where the item is the cheapest and what the total sum of an order placed at the store would be. In addition I added a alpha stage feature just before Christmas which will also tell you the “optimum” set of items for the orders – “order item 1,4,7,9 from .com, item 2,3,5,6,8 from .co.uk”. This took quite a bit of hacking – you also have to consider the initial price of shipping, shipping for each item and other fun things.

Feel free to play with it over at mismi.e-mats.org. It’s in Norwegian, but it should be easy to understand anyhow with the description above.

After removing the apc.shm_size again, things went back to working as normal.

A bit of psychic debugging and reading phpinfo() output revealed the culprit:

Do NOT include a “M” specifier when providing a size for apc.shm_size. Leave it off. It will assume MBs anyways. This solved the issue.

This is in contrast with the manual page, where it has been documented with a default value of “32M”, while phpinfo() says “32″. This behaviour change with APC 3.1.4, so if your version is older than that you’ll have to use the format without the M.

The patch also contains three tests to test the max attribute.

The patch is available here: smarty.foreach.max.patch. The patch is against the current SVN trunk of 2010-08-08.

Example:

{foreach item=x from=[0,1,2,3,4,5,6,7,8,9] max=5}{$x}{/foreach}

Output:

01234

$len allows you do change how many bits it will print (starting from lsb – least significant bit), $blockSize adjust the amount of bits before throwing in a space.

An SOAP request contains an Id attribute – and an element with the exact name in the response (directly beneath the element containing the attribute – an immediate child):

The problem is that the generated result object from the SoapClient (at least of PHP 5.2.12) contains the attribute value, and not the element value. In our case we could ignore the z:Id attribute, as it was simply an Id to identify the element in the response (this might be something that ASP.NET or some other .NET component does).

Our solution is to subclass the internal SoapClient and handle the __doRequest method, stripping out the part of the request that gives the wrong value for the Id field:

This removes the attribute from all the values (there is no danger that the string will be present in any other of the elements. If there is – be sure to adjust the regular expression). And voilá, it works!

The solution is to either remove the space from the escape list in the previous function – and use another function for escaping values where you actually should escape the spaces – or break up the string into “escapable” parts.

The code included beneath performs the last task; it splits the string into different parts delimited by space and then escapes each part of the query by itself.

The function has two required parameters – the first one is provided automagically for you by smarty (it’s the value of the variable you’re applying the modifier to). This should be an array of objects containing the value you want to graph. The only required argument you have to provide to the modifier is the method to use for fetching the values for graphing.

Usage:
{$objects|googlechart:”getValue”}

This will dynamically load your plugin from the file modifier.googlechart.php in your Smarty plugins directory, or you can register the plugin manually by calling register_modifier on the template object after you’ve created it.

The function does not support the short version of the Google Chart API Just Yet ™ as it is an simple proof of concept hack made a few months ago.