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Optimizing Magma's Performance
Last updated at 8:40 pm UTC on 2 January 2012

Eliminate all wasteful processing

The key to good performance with Magma is to not do wasteful processing. Smalltalk favors dynamic agility over speed, which provides both the ability and need to avoid unnecessary processing.

Optimize Caching

When an application is running Magma code it is not running its own code. Magma code is run when objects which are not in memory (e.g., a Proxy) are accessed by the application. The MagmaSession does not strongly reference the persistent domain. It is left up to the application to decide which objects should be retained in memory and which should be allowed "fall out" back to being a Proxy. The worst-performing thing an application can do is frequently access the same objects but not cache them. This has a two-pronged negative-impact on performance: First, the application is spending time running Magma code to constantly rematerialize the same objects – very wasteful. Furthermore, by not keeping a strong reference, the garbage-collector is working overtime. It's a double-negative, important not to let it happen.

Designing an optimized cache is a balancing act. It is very important for the application to strongly reference the objects that will be frequently accessed, but also important to not cache too many objects. All of the special large-collections offer paged access to their elements, and so these large-collection objects themselves are useful to have available in the cache – they provide access to a lot of other objects with a brief, random access.

Large domain models that don't involve one of Magma's special collections will very likely need to manually stubOut: branches of the model that are no longer needed. Where and how-often this is done needs to be well-considered: If the objects are going to be needed again soon.

As an example, when a user logs into a web-application, he will be working with his portion of the domain model, so the objects he initiall-accesses (from Magma) should be cached until they are no longer needed. The application would wait to stubOut: the users cached objects until shortly after the user has logged out.

Do not frequently access an uncached root

Some applications have a high-frequency dependent processing that starts with a "top-down" access of the domain. This can be a viable design with Magma, but it is important to know that the root of a MagmaSession is not cached except when the session is in a transaction. Accessing the uncached #root of a session frequently, while outside a transaction, is wasteful and, therefore, will hurt performance.

Use WbArray's, WbOrderedCollections, WbSet's and WbDictionary's

For the new Closures, the Array class cannot be made uncompact, which means they cannot use #primitiveChangeClassTo:, which means it cannot be added to the WriteBarrier, which means they end up in Magma's readSet, which means commits will be slower. WbArray is just a subclass of Array which _can_ be compacted. WbOrderedCollection is just an OrderedCollection that uses an internal WbArray instead of an internal Array. Likewise for Dictionary and Set.

Therefore, using these WriteBarrier-capable versions will improve performance.

Statistics and Profiling

Profiling is the single-most revealing aspect of what ails a poorly-performing Magma application.

Additionally, Magma itself can report detailed timing information about where it is spending its time. A separate timing statistic is captured for every kind of request and virtually everything it does, both client and server. You may print a report of the performance statistics of the recent past with by sending #statistics or #serverStatistics to your MagmaSession.

Using ReadStrategies

Read strategies can be used to optimize how many objects are accessed within a single call to the server.

Use medium-sized requests

Commits should be put in your program as close to the mutations to the persistent model as possible. The Magma server handles requests serially, so large commits that take several seconds could cause requests to queue in the server (resulting in a pause for those clients).

At the same time, you don't want commits to be so microscopic that you end up smothering the network with requests. For example, if building an OrderedCollection of 100 medium-sized objects, you should do those in one commit instead of 100 commits. However, if the objects are very large and completely non-persistent, you may want to do 100 commits.

Keep your cachedObjectCount as low as possible

With a connected Magma session, evaluate:
 mySession cachedObjectCount
This number reprensents how many entries Magma has in its IdentityDictionaries. Magma tries to avoid the performance issues related to Squeak's IdentityDictionaries, but it can still slow down if you allow tens of thousands of objects to be cached in memory.

If you're not sure why your cachedObjectCount is growing, you can use cachedObjectCountByClass to see which ones are the most proliferate (they are sorted by most-occurrences at the top).

Manually trimming the cache

As you traverse parts of the model, you should stubOut: objects you no longer need. For example, after iterating a collection of large objects, stubOut: the collection object if you no longer need them. MagmaSession>>stubOut: chops off large branches of objects so the memory they consume can be reclaimed by the garbage collector.

But avoid too many calls to stubOut:. For example, after you've enumerated the collection of large objects, stubOut: the Collection object itself, not each object in the collection. This is due to unfortunate irony that stubOut: requires use of one of Squeak's most inefficienct methods; Dictionary>>#removeKey:. While fast in other Smalltalks, this method is VERY slow in Squeak but required for stubOut:.

Other optimizations

Use MagmaPreallocatedDictionary instead of MagmaDictionary

MagmaPreallocatedDictionary pre-allocates most of its slots from the very first commit, resulting in a large file (i.e., 160MB for a typical allocation-size) being created after just one entry. Subsequent entries, however, are placed within the preallocated file – it won't grow much further.

Use a MagmaHashTable instead of MagmaCollections

MagmaHashTable uses an internal MagmaSolHashTable, which, internally uses a MagmaArray. MagmaArray is the fastest large-collection possible with Magma (and what PreallocatedDictionary is based on).

About MagmaCollection Performance

By contrast, MagmaCollections are somewhat "tacked on" to Magma. They use a special file-structure on the server side that provides O(log n) (where n is the chosen record-size) access. However, they are the collection that provides server-side query processing. So if relational-style querying is absolutely needed, they can be one option.

MagmaCollections have fair read performance, but adding and removing objects is very slow. In theory, starting with an empty MagmaCollection, the rate-of-insertion will deteriorate somewhat before settling on a relatively fixed rate, IF you have a good key-dispersal.

If you put in a lot of duplicate keys, it will gradually get more costly to keep adding more of that key because a linear search for the "end" of that chain of keys is performed to find the point of insertion. So, for example, when you build a simple keyword index, consider eliminating prepositions such as "the" and "at".

Removing from MagmaCollections is even more expensive than insertions. Avoid using this operation for performance-intensive operations.

Understand how Magma works

Understanding how Magma works, even if just at a high-level, is the easiest way to build well-performing applications. Study these pages and the public API's of the packages – their individual responsibilities become clear and how Magma works.