A lambda is just an anonymous function - a function defined with no name. In some languages, such as Scheme, they are equivalent to named functions. In fact, the function definition is re-written as binding a lambda to a variable internally. In other languages, like Python, there are some (rather needless) distinctions between them, but they behave the same way otherwise.
A closure is any function which closes over the environment in which it was defined. This means that it can access variables not in its parameter list. Examples:
def func(): return h
def anotherfunc(h):
return func()
This will cause an error, because func
does not close over the environment in anotherfunc
- h
is undefined. func
only closes over the global environment. This will work:
def anotherfunc(h):
def func(): return h
return func()
Because here, func
is defined in anotherfunc
, and in python 2.3 and greater (or some number like this) when they almost got closures correct (mutation still doesn't work), this means that it closes over anotherfunc
's environment and can access variables inside of it. In Python 3.1+, mutation works too when using the nonlocal
keyword.
Another important point - func
will continue to close over anotherfunc
's environment even when it's no longer being evaluated in anotherfunc
. This code will also work:
def anotherfunc(h):
def func(): return h
return func
print anotherfunc(10)()
This will print 10.
This, as you notice, has nothing to do with lambdas - they are two different (although related) concepts.
Some people would say that two threads is too many - I'm not quite in that camp :-)
Here's my advice: measure, don't guess. One suggestion is to make it configurable and initially set it to 100, then release your software to the wild and monitor what happens.
If your thread usage peaks at 3, then 100 is too much. If it remains at 100 for most of the day, bump it up to 200 and see what happens.
You could actually have your code itself monitor usage and adjust the configuration for the next time it starts but that's probably overkill.
For clarification and elaboration:
I'm not advocating rolling your own thread pooling subsystem, by all means use the one you have. But, since you were asking about a good cut-off point for threads, I assume your thread pool implementation has the ability to limit the maximum number of threads created (which is a good thing).
I've written thread and database connection pooling code and they have the following features (which I believe are essential for performance):
- a minimum number of active threads.
- a maximum number of threads.
- shutting down threads that haven't been used for a while.
The first sets a baseline for minimum performance in terms of the thread pool client (this number of threads is always available for use). The second sets a restriction on resource usage by active threads. The third returns you to the baseline in quiet times so as to minimise resource use.
You need to balance the resource usage of having unused threads (A) against the resource usage of not having enough threads to do the work (B).
(A) is generally memory usage (stacks and so on) since a thread doing no work will not be using much of the CPU. (B) will generally be a delay in the processing of requests as they arrive as you need to wait for a thread to become available.
That's why you measure. As you state, the vast majority of your threads will be waiting for a response from the database so they won't be running. There are two factors that affect how many threads you should allow for.
The first is the number of DB connections available. This may be a hard limit unless you can increase it at the DBMS - I'm going to assume your DBMS can take an unlimited number of connections in this case (although you should ideally be measuring that as well).
Then, the number of threads you should have depend on your historical use. The minimum you should have running is the minimum number that you've ever had running + A%, with an absolute minimum of (for example, and make it configurable just like A) 5.
The maximum number of threads should be your historical maximum + B%.
You should also be monitoring for behaviour changes. If, for some reason, your usage goes to 100% of available for a significant time (so that it would affect the performance of clients), you should bump up the maximum allowed until it's once again B% higher.
In response to the "what exactly should I measure?" question:
What you should measure specifically is the maximum amount of threads in concurrent use (e.g., waiting on a return from the DB call) under load. Then add a safety factor of 10% for example (emphasised, since other posters seem to take my examples as fixed recommendations).
In addition, this should be done in the production environment for tuning. It's okay to get an estimate beforehand but you never know what production will throw your way (which is why all these things should be configurable at runtime). This is to catch a situation such as unexpected doubling of the client calls coming in.
Best Answer
There is nothing wrong with this. The compiler is essentially doing automatically what you described as your alternative. It creates a class to hold the captured variables (test, s1 and s2) and passes a delegate instance to the lambda which is turned into a method on the anonymous class. In other words, if you went ahead with your alternative you would end up with soemthing very similar to what the compiler just generated for you.