Still not enough, we were forced to profile the java code and make some big changes. (from part 1 )
Profiling
You either profile an application for speed and/or memory usage and/or memory leaks. Our application is fast enough at the moment. Our major concern is optimizing memory usage and thus avoiding disk memory swapping.
Some words about architecture
It is not possible to profile any applications without having a deep understanding of the architecture behind. The Product Catalog is an innovative product which is a meta model for storing insurances product in a database, a Product is read only and can derivate instance that we call Policies. Policies are users data holder, containing no text, just values, and sharing a similar structure as the Product. This let the product know everything about (cross) default values, (cross) validations, multiple texts, attributes order/length/type etc and thus separate definition (Products) from implementation (Policies). Products and Policies can be fully described with Bricks, Attributes in a tree manner.
Reduce the number of object created
Looking at the code, we have seen that too many Products (17 Products has 15000 objects either Attributes/Bricks/Texts/Value/ValueRange) were loaded in memory. While this is clearly giving a speed advantage on an application server, it is simply killing the offline platform with it 1GB RAM (remember memory really free is 500Mb) The problem is that Attributes and Brick are using/can use a lot of fields/meta data in the database which translate into simple java type (String for UUID, and meta data keys and values) in memory. We start looking at the profiler and the 100 MB used by the product cache. Reducing this amount of object was the first priority, a lot of them are meta data which are common and spread across the Product Tree in memory. Since avoiding creation of unneeded object is always recommended, we decide to remove duplicate element in the tree by singularizing them. This is possible because the product is Read Only and made of identical meta data keys, meta keys value.
Entropy and cardinality of meta data An Attribute may have an unlimited number of meta text (among other things), common meta data keys are “long”, and “short” and “help” text description in 4 languages (en_us, fr_ch, de_ch, it_ch), while this is not a problem in the database, this make the Product object tree size quite huge in the Product cache (containing Products Value Object). ..Counting some of them in database for example return stunning result. We found 60000 “long” texts which translate into 60000 String text keys and 60000 String text values (worst case scenario since texts values may not be all reusable). Reducing this number of Objects is done quite easily by not creating new instance of String, Decimal, Integer object and returning always the right and same Object instance. (we keep them in a MAP and return either a new instance or a previously cache one).
Large objects cardinality but a poor entropy By running two or three SQL statement and trying to distinguish real distinct values, we found that a lot of these meta data are made of a relative small number of different values. By just storing a limited number of String like “”, “1”, “2” to “99”, “default”, “long”, “short”, “de_ch”, “fr_ch” we have reach a cache efficiency and reuse of object instance of 99% After that “small” change in the way value objects (VO) are created and connected, a java String object containing before “de_ch” and existing 10000 times in memory is now replaced across all Attributes/Bricks by the same instance!
The gain is simply phenomenal. Memory gain is bigger than 50%.
Reducing the number of objects in memory Instead of storing thousands of Products Text String in memory, we decided to allocate them on disk using java reflection API and a Dynamic Proxy.
The idea is to save all String in one or more files on disk, the position of each text and length being saved in the corresponding Value Object. So basically we gain the space used by a String in memory at the expense of a long (String position in file relative to start of file) and an int (length of String) primitive type
References: Proxy – InvocationHandler Resume: Java String disk based allocation Code snippet: soon
Use better data structures Java has a lot of quality library, commons collections from apache are well known. Javalution is a real time library with real time and reduce garbage collector impact. We have use FastTable and FastMap where it make sense.
For example the class FastTable
has the following advantages over the widely used java.util.ArrayList:
- No large array allocation (for large collections multi-dimensional arrays are employed). The garbage collector is not stressed with large chunk of memory to allocate (likely to trigger a full garbage collection due to memory fragmentation).
- Support concurrent access/iteration without synchronization if the collection values are not removed/inserted
Different caching strategy By design the ProductCatalog is able to use many caching strategy. One is named “Nocache” limit number of object in memory to the bare minimum, and redirect all access to product to database. In a mono user environment, and since products reside in 4 tables only (so only 4 select to read all data from DB and some VO to rebuild the tree are needed), the through output is more than enough.
More to come
References
- Java Performance Tuning Tips, resources, and monthly newsletter on improving the performance of applications.
- Java Tuning White Paper The Java Tuning White Paper is the reference for Java Performance Tuning information, techniques and pointers.
- Tomcat WIKI
- Apache MyFaces WIKI
