Posts in category 'smalltalk'
Dangers of Abstraction
One of the more impressive things about Pharo/Squeak is the level of depth in the core libraries, and how those libraries build upon each other to create larger, complex structures. One need only look at the Collection hierarchy for an example of this, where myriad collection types are supported in a deep hierarchy that allows for powerful language constructs like:
aCollection select: aPredicateBlock thenCollect: aMappingBlock
to work across essentially every type of collection available. Unfortunately, building these large software constructs can have negative consequences when one attempts to analyze performance or complexity, and in this post I’ll outline one particular case that bit me a few weeks back.
My problems all started while I was still experimenting with Magma. Magma, as you may or may not recall (depending on if you’ve read anything else I’ve posted… which you probably haven’t) is a pure-Smalltalk object-oriented database whose end goal is to provide the Smalltalk world with a free, powerful, transparent object store.
Now, among Magma’s features is a powerful set of collections, which implement the aforementioned collection protocols, while also providing a much-needed feature: querying. In order to make use of this facility, any column that you wish to generate queries over must have an index defined over it, which is really a glorified hash table on the column1. Whenever you create one of these indexes on a collection, the index itself is squirreled away in a file on disk alongside the database. And that’s where the problems come in.
In my application, a Go game repository, I had a fairly large number of collections sitting around holding references to Game objects (one per individual user, plus one per Go player), and I needed to be able to query each of these collections across a number of features (not the least of which, the tags applied to each game). That meant potentially many thousands of indexes in the system, at least2. And that meant thousands of files on disk for each of those indexes.
Well, when I first hit the site, I found something rather peculiar: initially accessing an individual collection took a very long time. On the order of a few seconds, at least. Naturally this dismayed me, and so I started profiling the code, in order to pin down the performance issues. And I was, frankly, a little shocked at the outcome.
It turns out that, deep in the bowels of the Magma index code, Magma makes use of the FileDirectory class to find the index file name for the index itself. Makes sense so far, right? As part of that, it uses some features of the FileDirectory class to identify files with a specific naming convention. And that code reads the entire directory, in order to identify the desired files.
On the face of it, this should be fine.
However, internally, that code does a bunch of work to translate those file names from Unicode to internal Squeak character/strings. And it turns out that little bit of code isn’t exactly snappy. Multiply that by thousands of files, and voila, you get horrible performance.
So believe it or not, the index performance issues had nothing to do with Magma. It was all due to inefficiencies deep in the bowels of Squeak. And hence the subject of this article. Deep abstraction and code reuse is a very good thing, don’t get me wrong. But any time you build up what I think of as a “cathedral” of code, it’s possible for rotting foundations to bite you later.
Where Pharo Falls Short
Well, as you might imagine from the title, I figured I’d take a bit of a break from my continual gushing about Seaside to examine some of the areas where I think Pharo/Squeak unfortunately falls behind as a development environment. Of course, keep in mind, I wouldn’t be using these tools if I didn’t think they were an overall win, despite their shortcomings. But perspective is always a good thing, and it’s important to see the bad as well as the good.
So, with that said, where to begin… well, as I’ve mentioned previously, in general, Smalltalk implementations make use of an image paradigm for storing and managing code. In this world, from the outside, the image is a monolithic blob of binary data, but contained within is essentially a snapshot of the entire Smalltalk environment. Open that image with a VM, and you’re presented with a completely self-contained world, including a windowing system, editors, file managers, and so forth. And in the case of Pharo or Squeak, that entire world is open to unlimited poking and prodding, as the deepest bowels of the system are themselves written in Smalltalk and available for inspection.
However, this metaphor has influenced Smalltalk in ways that, I think, have proven a detriment to it’s adoption. For example, in general, there is no other way to edit Smalltalk code, save through the editor(s) provided by the environment. Now, granted, because that editor is deeply tied into the system, it’s capable of browsing code in some very impressive ways. But it means the user is given absolutely no flexibility to select a tool of his or her choice. And in the case of Pharo/Squeak, those tools can be a bit primitive (and occasionally buggy), providing little in the way of customizability (well, unless you want to hack the code, which you are, of course, free to do), while failing to provide facilitates that one often takes for granted (macro facilities, multiple copy/paste buffers, regexp-based search/replace… the list goes on). In fact, the Pharo/Squeak editor is little more than a basic Notepad-style editor with syntax highlighting and some primitive auto-indentation capability.
Additionally, much as there is no option for editors, the choice of code management tools is extremely limited. The current tool of choice for version control in Pharo/Squeak is Monticello, a form of distributed version control system. Unfortunately, compared to, say, git, Monticello is decidedly primitive. Now, granted, there are those attempting to implement Git for Pharo/Squeak, but those projects are only just beginning, and one will still be limited to working in the Pharo/Squeak environment, and the tools available there.
Lastly, the Pharo/Squeak VM itself can sometimes be rather… frustrating. The VM itself is single-threaded, which means that any long-running piece of code, if not invoked in a background process (implemented as green threads), will hang the VM. Fortunately, the Alt-. hotkey exists to interrupt such operations so they can be terminated, but if the operation is sufficiently nasty (say, accidentally looping and inspecting items in a collection, creating a very large number of windows), it can be very unpleasant to clean up. Moreover, the image itself is only saved upon demand (ie, there are no automatic saves to a separate backup file, like in many editors), and so if something catastrophic does happen to take down the VM, one’s work can be lost.
Unfortunately, in the end, despite all these shortcomings, the damn language and libraries are so good, I just can’t help but work with it. While I’m sure much time is wasted dealing with the aforementioned issues, so much is gained from sheer productivity that the win is clearly there. Plus, I must admit, Smalltalk is just plain fun to write. In fact, I haven’t had this much fun in years!
AJAX in Seaside
So, in yet another post on a series about Pharo and Seaside, I thought I’d highlight a great strength in Seaside: it’s incredibly powerful support for building rich, AJAX-enabled web applications.
As any web developer today knows, if you’re building rich web apps with complex user interactions, you’d be remiss not to look at AJAX for facilitating some of those interactions. AJAX makes it possible for a rendered web page, in a browser, to interact with the server and perform partial updates of the web page, in situ. This means that full page loads aren’t necessary to, say, update a list of information on the screen, and results in a cleaner, more seamless user experience (Gmail was really an early champion of this technique).
Now, traditionally, an AJAX workflow involves attaching Javascript functions to page element event handlers, and then writing those functions so that they call back to the web server using an XmlHttpRequest object, after which the results are inserted into an element on the screen. Of course, doing this in a cross-browser way is pretty complex, given various inconsistencies in the DOM and so forth, and so the web development world birthed libraries like jQuery and Prototype, and higher-level libraries like Script.aculo.us. But in the end, you still have to write Javascript, create server endpoints by hand, and so forth. Again, we’re back to gritty web development. And that makes me a sad panda.
Of course, this post wouldn’t exist if Seaside didn’t somehow make this situation a whole lot simpler, and boy does it ever. To illustrate this, I’m going to demonstrate an AJAX-enabled version of the counter program mentioned in my first post on Seaside. So, instead of doing a full page refresh to display the updated counter value, we’re simply going to update the heading each time the value changes. Now, again, imagine what it would take to do this is a more traditional web framework. Then compare it to this:
renderContentOn: html | id counter | counter := 0. id := html nextId. html heading id: id; with: counter. html anchor onClick: ( html scriptaculous updater id: id; callback: [ :ajaxHtml | counter := counter + 1. ajaxHtml text: counter. ] ); url: '#'; with: 'Increase'. html space. html anchor onClick: ( html scriptaculous updater id: id; callback: [ :ajaxHtml | counter := counter - 1. ajaxHtml text: counter. ] ); url: '#'; with: 'Decrease'.
That’s it. The full script.
Now, a little explanation. The script begins with a little preamble, initializing our counter, and allocating an ID, which we then associate with the header when we first render it. Pretty standard fare so far. The really interesting bit comes in the anchor definition, and in particular the definition of the onClick handler. Of course, this bit bares a little explanation.
The various tag objects in Seaside respond to selectors that correspond to the standard DOM events. When sending such a message, the parameter is an instance of a JSFunction object, which encapsulates the actual javascript that will be rendered into the document. Now, in this particular example, we’re actually using part of the Scriptaculous library wrapper to create an “updater” object, a type of JSFunction, which takes the ID of a page element, and a callback, and when invoked, causes the callback to be triggered. Upon invocation, this callback is passed an HTML canvas, and when the callback terminates, the contents of that canvas are used to replace the contents of the indicated page element. Neat!
So in this particular case, we have two anchor tags, each of which has an onClick event registered which, when invoked, updates the counter value and then updates the heading on the page.
By the way, there’s also a little bit of extra magic going on here. You’ll notice the ‘counter’ variable is local, while in the original example it was an instance variable. But this works, here, because those callbacks are actually lexical closures, and so the ‘counter’ variable sticks around, referenced by those closures, even though the function itself has returned, and the variable technically has gone out of scope.
To me, the really amazing thing, here, is that never once do I, as a developer, have to even touch HTML or Javascript. The entire thing is written in clean, readable Smalltalk, and it’s the underlying infrastructure that translates my high-level ideas into a functional, cross-browser implementation. Once again, Seaside let’s me forget about all those annoying, gritty little details. I just write clean, expressive Smalltalk code, and it Just Works, exactly as I would expect it should.
Update:
If you want to see the above application running live, you can find it here.
Glorp - Early Impressions
Well, this was meant to be a shorter post, but alas, I’ve failed miserably. Oh well, suck it up. Well, assuming anyone’s out there and actually reading this…
Anyway, the topic today is… well, it should be evident from the post title: my initial impressions of Glorp. No, Glorp is not just the sound I make in the back of my throat while considering whether or not to ride the kiddie rollercoaster at West Edmonton Mall. It is, in fact, an object-relational mapping package for Smalltalk, which attempts to bridge the rather deep divide between the object-oriented and relational data modeling worlds.
Now, generally speaking, I tend to be a fan of ORM’s. Of course, that’s probably because I’ve never really used one heavily in a production environment. But, generally speaking, the idea of describing the relationship between objects and their tables in code, and then having the code do all the work to generate a schema seems like a really nice thing to me. Of course, the real question then becomes, how hard is it to set up those mappings? And it turns out, in Glorp, the answer is: well, it’s a pain in the ass.
Okay, to be fair, there’s a reason it’s a pain in the ass: Glorp is designed to be incredibly flexible, and so it’s designed for the general case. Unfortunately, that means added complexity. What kind of complexity, you ask? Well, allow me to demonstrate, using my little toy project as an example. This little project of mine is an online Go game record repository. As such, I need to store information about users, games, players, and so forth (well, there’s not much more forth… other than tags, that’s actually it). So, suppose we want to define a Game object and a User object, such that a Game contains a reference to the User that submitted it.
Now, before I begin, you need to understand that a database is generally represented by a single Repository class of some kind. That Repository class, which must be a subclass of DescriptorSystem, defines the tables in the database schema, their relationships, and how those tables map to the various objects in your system. This information is encapsulated in methods with a standard naming convention (how very Rails-esque), so if some of this looks a tad funny, it’s not me, it’s the naming convention.
So, let’s begin by defining a User. First, we need to describe the table schema where the User objects will come from:
tableForUSERS: aTable aTable createFieldNamed: 'UserID' type: platform sequence; createFieldNamed: 'Name' type: platform text; createFieldNamed: 'Password' type: platform text. (aTable fieldNamed: 'UserID') bePrimaryKey.
This code should be pretty self-explanatory (a side-effect of Smalltalk’s lovely syntax). This method takes a blank DatabaseTable instance and populates it with the fields that define the User table. Additionally, it sets the PK for the table to be UserID. Easy peasy. Now, assuming the Users table maps to a class called GRUser, we define the class model that this table will map to.
classModelGRUser: model model newAttributeNamed: #userid; newAttributeNamed: #name; newAttributeNamed: #password; newAttributeNamed: #games collectionOf: GRGame.
Also straightforward. This specifies the various attributes that make up the GRUser class. Incidentally, you still need to declare a real GRUser class… all this code does is tell Glorp what attributes it should be aware of, and what they are.
Lastly, we need to defined a “descriptor” for the Users -> GRUser mapping. The descriptor basically defines how the various attributes in the model map to fields in the table. Additionally, it defines the relations between the tables. So, here we go:
descriptorForGRUser: description | table | table := self tableNamed: 'Users'. description table: table. (description newMapping: DirectMapping) from: #userid to: (table fieldNamed: 'UserID'). (description newMapping: DirectMapping) from: #name to: (table fieldNamed: 'Name'). (description newMapping: DirectMapping) from: #password to: (table fieldNamed: 'Password'). (description newMapping: ToManyMapping) attributeName: #games; referenceClass: GRGame; collectionType: OrderedCollection; orderBy: #additionTime.
So, for each field, we define a mapping. A DirectMapping instance maps an attribute to a field… err… directly. The ToManyMapping, on the other hand, sets up a relation, and maps the #games attribute of the GRUser class to the GRGame class. But how does it figure out how to do the join? That’s in the table and descriptor definitions for the Games table and GRGame object (note, I’m going to leave out the extra junk):
descriptorForGRUser: description | table | table := self tableNamed: 'Users'. description table: table. (description newMapping: DirectMapping) from: #userid to: (table fieldNamed: 'UserID'). (description newMapping: DirectMapping) from: #name to: (table fieldNamed: 'Name'). (description newMapping: DirectMapping) from: #password to: (table fieldNamed: 'Password'). (description newMapping: ToManyMapping) attributeName: #games; referenceClass: GRGame; collectionType: OrderedCollection; orderBy: #additionTime.
So as you can see, in the table definition, we establish a foreign key from the Games table to the Users table, and then in the descriptor, we define a RelationshipMapping (which is a synonym for a OneToOneMapping) from GRGame -> GRUser.
I hope at this point you can see the one big problem with Glorp: It’s really really complicated. Worse, it’s not particularly well documented, which makes it a bit of a challenge to work with, and means that if you want to do something “interesting” it can be a bit of a challenge. As a quick example, in my schema, the Games table has two references to the Players table, one for the white player, and one for the black player. This greatly confuses Glorp, which means I had to do a bit of manual work to get the relationships set up. Here’s how the black player relation is established (there may be a better way, but I don’t know what it would be):
blackField := table fieldNamed: 'Black'. playerIdField := (self tableNamed: 'Players') fieldNamed: 'PlayerID'. mapping := (description newMapping: RelationshipMapping) attributeName: #black; referenceClass: GRPlayer. mapping join: ( self joinFor: mapping toTables: { self tableNamed: 'Players' } fromConstraints: { } toConstraints: { ForeignKeyConstraint sourceField: blackField targetField: playerIdField } ).
And then it’s basically the same thing for the white player. Mmmm… ugly.
But, all that said, once the mappings are set up, suddenly Glorp can be a real joy to work with. Here’s the code necessary to add a user, and then query him back out:
| user | user := GRUser withName: 'shyguy' andPassword: 'secret'. self session inUnitOfWorkDo: [ self session register: aGRUser ]. self session readOneOf: GRUser where: [ :each | each name = 'shyguy' ].
The query is of particular interest. That looks an awful lot like a straight select block, but it is, in fact, translated into an SQL query, which is then run against the database. And that is pretty darn cool. It almost looks like a pure object store, ala Magma, and that’s mighty impressive.