When I/O is the bottleneck of your application, more threads will increase the performance in opposite to when CPU is the bottleneck.
Stress Testing: Checking the throughput of an application by sending a huge amount of requests and examining the response times.
Isolate concurrent code by putting it in a few separate classes.
Always consider the concept of execution paths: The amount of possible interleaving of instructions that are processed by at least two threads. For example, objects with mutable states could unintentionally cause different results doing the same operation twice.
Atomic operation = operation which can not be interrupted by other threads. But for unsynchronized processes threads can put instructions between two atomic operations.
synchronized prevents unintended side-effects.
Server-based locking is preferred over client-based locking.
Server-based locking: The class used takes care of internal locking, so the user has nothing else to worry about.
Client-based locking: User has to manually implement locking. This approach error prone and hard to maintain.
If there is no access to the server an adapter class can be used instead. Even better would be thread-save collections using extended interfaces.
As little synchronized code (synchronized) as possible should be used. And if, then only for small, critical code sections.
Problems of testing multi-threaded methods
Very tricky which is why concurrency should be avoided in the first place.
In general this requires a lot of iteration which makes it resource intensive.
The outcome is architecture dependent (OS, hardware) which introduced randomness and make the error detection unreliable.
Solution approaches for testing multi-threaded methods
Monte-Carlo-Tests
Write flexible, adaptive tests.
Repeatedly run them on a test server and randomly vary the test settings.
If something fails, the code is defect and the applied settings should be logged.
Do this early to gain tests ASAP for your testing repertoire or CI-server.
Execute these tests on every platform over a long time to increase the probability that
the production code is correct or
the test code is bad.
Execute the tests on a computer using simulations of application loads when possible.
There are tools to test thread-based code like ConTest.
