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Wednesday
Jul292015

A Well Known But Forgotten Trick: Object Pooling

This is a guest repost by Alex Petrov. Find the original article here.

Most problem are quite straightforward to solve: when something is slow, you can either optimize it or parallelize it. When you hit a throughput barrier, you partition a workload to more workers. Although when you face problems that involve Garbage Collection pauses or simply hit the limit of the virtual machine you're working with, it gets much harder to fix them.

When you're working on top of a VM, you may face things that are simply out of your control. Namely, time drifts and latency. Gladly, there are enough battle-tested solutions, that require a bit of understanding of how JVM works.

If you can serve 10K requests per second, conforming with certain performance (memory and CPU parameters), it doesn't automatically mean that you'll be able to linearly scale it up to 20K. If you're allocating too many objects on heap, or waste CPU cycles on something that can be avoided, you'll eventually hit the wall.

The simplest (yet underrated) way of saving up on memory allocations is object pooling. Even though the concept is sounds similar to just pooling objects and socket descriptors, there's a slight difference.

When we're talking about socket descriptors, we have limited, rather small (tens, hundreds, or max thousands) amount of descriptors to go through. These resources are pooled because of the high initialization cost (establishing connection, performing a handshake over the network, memory-mapping the file or whatever else). In this article we'll talk about pooling larger amounts of short-lived objects which are not so expensive to initialize, to save allocation and deallocation costs and avoid memory fragmentation.

Object Pooling

 

Object Pool is a design pattern, that works with a set of pre-initalized entities, instead of disposing and then re-creating them on demand. Whenever the client of the pool is done working with an object, it just returns it to the pool for recycling.

You may think about pooling like about the manual memory management: allocation and freeing of objects is explicit.

In garbage-collected langauges, such as Java, pooling is used to reduce the garbage collection pressure by handling memory management in critical parts of the program in an explicit manner.

If the objects contained in the pool have large initialization cost, or have to be created in large quantities at a time, pool can be used to pay this cost upfront (for example, during the application startup), and avoiding these expensive operations during the working mode.

Pooling objects is the first and most straightforward way to reduce the GC pressure. There are three easy-to-identify use-cases for object pooling. We're always talking about the objects sharing the same structure (often they're just instances of the same class). Also, these objects are mostly short-lived.

Here's a couple of examples of the allocation patterns that are nice candidates for pooling:

  • objects are allocated constantly at an unchanging rate (fast enough that it starts influencing application performance), the garbage collection times gradually increase until stabilizing, memory usage grows.
  • objects are allocated with bursts, every burst resulting in a system lag, followed by a noticable GC pause.

In the majority of cases such objects are created as either data containers or wrappers, that act as an envelope between an application and internal message bus, communication layer or some API.

You can see such things every day: when using Database Drivers that have Request and Response objects created for each request and response, Message or Event wrappers within favorite messaging system using. Parsers create objects of a certain type as a result of parsing, RPC libraries create protocol message instances. These objects are literally everywhere.

Object Pooling helps to preserve and reuse these constructed object instances. This might be a good solution for cases when your profiler tells you that you're constantly creating way too many objects of the same exact type.

There are two basic recycling patterns for pools are borrowing and reference-counting: borrowing and reference counting. They mostly borrowing is more explicit, reference counting implies automatic recycling when all interested parties are done with the object. Let's check out both of them.

Borrowing

Borrowing mostly looks like malloc / free on top of garbage-collected runtime, and you'll certainly face same issues as you were facing back in the days programming non-garbage collected languages.

If you have freed the object and returned it to the pool, any modifications to it or reading from it will lead to unpredictable results: other writers or readers may hold same object at the same time. In C, any operation on freed (dangling) pointer would result into segmentation fault. Here you just have to take care of it yourself, or build in some additional protection mechanisms.

Borrowing is good when the consumer operation has explicit begin/end. In majority of cases, it isn't used in cases when object could be accessed by multiple threads simultaneously. In this case synchronising access and exit point may just be too complicated.

The Big advantage of borrowing is that object may know absolutely nothing about pool or even existance of the pool. It has to have some reset mechanisms, but since the control over borrowing and return is completely up to consumer, object itself doesn't have to take care of it. This means that you may even pool the API objects of an external library.

Reference-counting

Reference counting is slightly more complex in terms of implementation, but it also offers more granular control over the data structure, and allows consumer to know nothing about pool itself by wrapping pool into a some functional interface, like: 

(pooledObject, pooledObjectConsumer) -> {
  pooledObject.retain();
  pooledObjectConsumer.accept(pooledObject);
  pooledObject.release();
  };

Each time the objects enters a block, caller has to retain the object, and release it after the execution block is done. Each object holds an internal counter and a reference to the pool. As soon as counter reaches zero, object returns itself to the pool.

Reference counting is usually used when allocated objects are accessed by more than a one consumer at a time, object can only be recycled after all blocks have releases the reference. It is also good for pipelining or nested processing. In this case you can avoid explicit operation begin/end, and allow recycling after last consumer is done with the object.

Allocation Triggers

 

When working with pools, it important to identify pool growth strategies, allocation trigger conditions and whether pool will be bounded.

Allocation triggers are responsible for noticing that pool is low on elements, and needs to allocate new resources.

Empty Pool Trigger

The simplestway is to allocate objects whenever the pool is empty. In this case, you can use some Queue implementation, put elements into the queue, poll the queue each time you require a new object. If there're no objects available, the allocation step is triggered. 

Watermarks

Problem with this trigger strategy is of course that one of the poll operations will be paused to perform the allocation. To avoid it, you may use watermarks. Whenever the new object is requested from the pool, you check how many elements are still available in the pool. If the resources are at critically low level, allocation step is triggered. 

For example, you start with 100 elements, which corresponds to 100%, and objects get requested from the pool. After 75 elements are given, there are only 25 elements left in the pool, and pool is now at the cricically low resource level of 25%, so additional resources are allocated, and counter is adjusted accordingly.

Lease/Return Rate

Most of the time, watermarks are enough, although sometimes a bit more precision is required. In this case, you can use record lease and return rates, at which objects are being taken and returned to the pool. For that, you count how many items were allocated per second and find some statistically significant number representing the rate.

For example, if you have 100 items in the pool, and 20 items are taken form pool every second, but only 10 are returned, you will empty the pool after 9 seconds.

Using this information, you can "plan ahead" and allocate enough elements to satisfy the lease requirements.

Growth Strategies

 

Growth strategies identify what happens when the allocation trigger is fired, and bounds specify maximum allowed amount of allocated objects.

Fixed Size

The simplest pool implementation is of course a fixed-size pool. Elements are pre-allocated in one run, and pool is never grown afterwards.

Such an implementation is most useful when you need to make sure that only a certain amount of objects can be out simultaneously, and maybe throttle the application, forcing it to consume not more than a certain amount of resources.

Fixed size pool may cause resource starvation, in case the pool size isn't calculated optimally, but performance characteristics are easy to grasp, because allocations are done explicitly.

Most of the time, you'd allocate more resources than you usually need. This way, at peak times it might cause some latency when pool becomes empty and you have to wait for the next freed object, although you will have some free resources in the pool all the time.

Tiny-Step growth

If you can identify the size of the pool with a good degree of certanity, but think that during peak times you still may run in resource starvation problem, you can grow your pool with very small steps (say, one additional item at a time).

Of course you can allocate new item on-demand, which would add some latency for every item, allocated after the pool was empty, but it's easy to implement and maintain, and the pool size will correspond to the maximum amount of objects that may possibly exist in the system simultaneously.

Block Growth

If you can't afford allocation pauses, and would like to be able to get a ready element from the pool any time, you'd have to use a block-growth allocation strategy, combined with an allocation trigger of your preference. 

This way, every time you reach a 25% watermark, you can allocate the additional 25% of the pool size and see if it fits the application demand.

In such cases, you're almost always allocating more resources than may be possibly required, and large exponent values may result into running out of memory.

Using lease/return rates, you may get down to quite precise pool size. But it's always good to keep it flexible and allow pool to grow a little.

Pitfalls

Of course, as long as you start managing memory youself, you become fully responsible for pretty much everything that's going with the memory you're managing at all times. There are several ways things may go wrong:

  • Reference Leaks: object is registered somewhere within your system, although did not get returned to the pool. This happens quite often, and leads to out of memory errors that are hard to track down. It gets particularly hard when you have references leaking under just one subtle scenario.
  • Premature Recycling: this happens where you decide to return the object to the pool, but still hold the reference to it, and try to perform write or read. In C/C++ you would usually get a Segmentation Fault under similar circumstances. Basically, it means that you're trying to access memory that does not belong to you, whether it is for reading or for writing.
  • Implicit Recycling: may occur when you're working with reference counts. Because of the concurrent access, or error in one of the consumers the object may be implicitly recycled, while you would expect that reference should still be valid. To avoid it, it is important to keep all the operations explicit, never leak references to untrusted consumers, have control means (such as interrupts) over the consumers that break internal contracts.
  • Sizing Errors: this is quite usual for Byte Buffers and arrays: when objects should have different sizes / lengths, and are constructed in a tailored mode, although returned to the pool and reused as "one size fits all" objects. Usually you would get an IndexOutOfBounds error or similar, whenever trying to write or read to/from location that's outside of range of the generated object. If you're very (very, very) lucky, you may end up just carrying a memory overhead around (whenever the first tailored object is the largest one, and all smaller ones just fit in).
  • Double Booking: whenever two objects are aware of the fact that the object they received should be recycled after use, but reference to only of of them was actually known by the object itself. This is a variation of a reference leak, although that one happens more often, especially when there's any multiplexing involved: object gets dispatched to multiple destinations that have different performance, and one of them frees the object. Object eventually gets reused, and the remaining reference is now pretty much garbage for the reader.
  • In-place modification: it is always good idea to use immutable objects, but if conditions do not allow you do do that, you may run into problem of modification object while it's content is being read.
  • Shrinking: it's a good idea to shink the pool whenever there's a large amount of objects sit unused. Never shrinking a pool (freeing the objects from the pool), may result in an oversized pool.
  • Object re-initialization: if pool implementation doesn't have clear semantics of cleaning up the object, it might be unclear whose responsibility it is to bring the object back to the "clean" state. Make sure you can always guarantee that objects obtained from pool have a clean state and contain no dirty fields from previous usages.

Closing words

Object pooling is not for everyone. It doesn't make sense to start pooling objects in early application development stages, since you don't know what exactly to poll and can't clearly decide how to poll it. 

There are ways to work around every problem, but they are often either too complex, or too expensive to implement. However, it's always good to have a couple of trick up your sleeve.

 

On HackerNews

Reader Comments (6)

"liearly" should have been "linearly"

July 29, 2015 | Unregistered Commenterdilip

I am not a fan of this article due to it's incomplete handling of the topic of taming GC related bottlenecks. Not that it there is anything incorrect (although I didn't read it with intent to correct).

It's more than I want to go into in a comment, but you have to start by explaining what causes the allocation wall and at least mention alternatives to object pooling. For instance in an application with essentially zero promotion there is no allocation wall in any practical sense.

In some cases you can get to zero promotion by moving data off heap and in the process you can implement better memory management and data layout that your application needed anyways. The tragedy for most software is that it all starts on-heap and the transition is painful, but doing it right the first time would not have been so bad had the knowledge been out there.

You also have to mention the downsides to object pooling, apologies if some of these are already mentioned, like locality, no opportunity for escape analysis, coherence overhead, fragmentation, and probably some more things that haven't come to mind yet. You also can't talk about memory management for potentially long lived objects without also discussing different threading models and the single writer principle and the impact that can have.

Object pooling is one tool in the toolbox of dealing with memory management, but it is not the only tool. You don't have to present the other approaches, but you do need to put it into context so people know when object pooling is the right choice.

July 29, 2015 | Unregistered CommenterAriel Weisberg

The article has somewhat mixed-up terminology and makes some dubious claims.

For example it repeatedly mentions the cost of "allocation", which is strange, because it is typically close to zero. In GC environment, typically the new space is contiguous so the allocation path is very optimized (well, except for collectors like G1). Initialization is a different matter and possibly a valid reason to cache an object. "Deallocation" as such is not applicable to a GC environment.

For all the successful use cases I've seen, cbject caching is not a way to optimize the cost of allocation/init/deallocation, but to avoid Garbage Collection altogether.

Another point worth mentioning is that typically the pause time for most GC algorithms is proportional to the number of live references, so if an application uses aggressive object pooling, but doesn't reach zero-allocation can actually experience increased GC pauses.

July 29, 2015 | Unregistered CommenterDimitar Dimitrov

Without numbers this article is completely worthless : (

August 1, 2015 | Unregistered CommenterSigi

I wasn't clear what environment this was aimed at. For commonly used objects that don't need state in c# we use static to avoid GC, at the expense of testability, when performance is critical. It sure is simpler, if you can refactor in that way. Interesting description of object pooling, but I'm wondering what would make one pick this pattern? What scenarios, patterns of memory usage, etc.? In c# the other way of avoiding GC is to wrap disposable object creation/use in a using block, to trigger immediate disposal (or manual dispose, but that's not preferred).

August 2, 2015 | Unregistered CommenterBrian White

How did you come up with the formula for PoolWillBeEmptyIn duration? The numerator of the quotient has size (which is a scalar say of Qty type) subtracting rate which is of "qty/sec". You cannot perform such operation of incompatible unit types.
And further the result of Rate divided by Rate is not of Time units!

March 21, 2017 | Unregistered Commenterjeff

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