Note that you can loose some data on crash due to their background syncing to disk. Network hops are faster than disk seeks but even still they will add a lot of overhead.
Ideally, your data should fit entirely in memory on one host. If you need to run on more than one host you should ensure that your data and requests are properly partitioned so that all the data necessary to service a given request is available locally. Low latency requires always having resources to process the request. Always have lots of head room for bursts and then some. Context switches are a sign that you are doing more compute work than you have resources for.
You will want to limit your number of threads to the number of cores on your system and to pin each thread to its own core. All forms of storage, wither it be rotational, flash based, or memory performs significantly better when used sequentially. When issuing sequential reads to memory you trigger the use of prefetching at the RAM level as well as at the CPU cache level.
If done properly, the next piece of data you need will always be in L1 cache right before you need it. The easiest way to help this process along is to make heavy use of arrays of primitive data types or structs.
Following pointers, either through use of linked lists or through arrays of objects, should be avoided at all costs. However, there is a misconception that this means the system should wait an arbitrary amount of time before doing a write.
Each write will batch all the data that arrived since the last write was issued. This makes for a very fast and adaptive system. With all of these optimizations in place, memory access quickly becomes a bottleneck. Beyond that, you should keep memory sizes down using primitive data types so more data fits in cache. Make friends with non blocking and wait free data structures and algorithms. Every time you use a lock you have to go down the stack to the OS to mediate the lock which is a huge overhead.
For instance if your high availability strategy includes logging transactions to disk and sending transactions to a secondary server those actions can happen in parallel. Read up on that and follow anything that Martin Thompson does. Thanks for the reply. While the Go memory model http: Benjamin, the Go memory model detailed here: The projects that you link use atomic. Add… which is a sequentially consistent atomic. I am not trying to knock Go down.
It takes minimal effort to write async IO and concurrent code that is sufficiently fast for most people. The std library too is highly tuned for performance. Golang also has support for structs which is missing in Java. But as it stands, I think the simplistic memory model and the go-routine runtime stand in the way of building the kind of systems you are talking about. Facebook showed us it can be done in PHP. It seems the tougher it is to write in a language, the faster it executes.
I would strongly recommend you look at the work being done in the projects and blogs that I linked to. The JVM is quickly becoming the hot spot for these types of systems because it provides a strong memory model and garbage collection which enable lock free programming which is nearly or completely impossible with a weak or undefined memory model and reference counters for memory management.
Garbage collection for lock free programming is a bit of a deus ex machina. There are also plenty of ways to do lock free programming without garbage collection and reference counting is not the only way. Hazard pointers, RCU, Proxy-Collectors etc all provide support for deferred reclamation and are usually coded in support of an algorithm not generic , hence they are usually much easier to build. Of course the trade-off lies in the fact that production quality GCs have a lot of work put into them and will help the less experienced programmer write lock-free algorithms should they be doing this at all?
Some links on work done in this field: GCC and other high quality compilers had compiler specific directives to do lock free programming on supported platforms for a really long time — it was just not standardized in the language. Linux and other platforms have provided these primitives for some time too. They already had the tools to build lock free code for their platform.
GC is a great tool but not a necessary one. You can even tune your code layout through linker scripts! A trade-off that makes sense for a lot of people but a trade-off none the less. Do not use garbadge collected languages. GC is a bottleneck in the worstcase. It likely halts all threads. It distracts the architect to manage one of the most crital resources CPU-near memory himself.
Actually a lot of this work comes directly from Java. If you know how to work with GC and not against it you can create low latency systems often with much more ease. I have to agree with Ben here. There has been a lot of progress in GC parallelism in the last decade or so with the G1 collector being the latest incantation.
It may take a little time to tune the heap and various knobs to get the GC to collect with almost no pause, but this pales in comparison to the developer time it takes to not have GC. You can even go one step further and create systems that produce so little garbage that you can easily push your GC outside of your operating window. This is how all of the high frequency trading shops do it when running on the JVM.
Hazard pointers, RCU, Proxy-Collectors etc all provide support for deferred reclamation and are coded in support of an algorithm not generic , hence they are much easier to build.
It has a cost both in terms of performance and in complexity all the tricks needed to delay and avoid STW GC. There are other costs which offset that, of course: Reblogged this on Java Prorgram Examples and commented: It goes beyond simply using memory barriers.
You have to consider freeing memory as well which gets particularly difficult when you are dealing with lock free and wait free algorithms. This is where GC adds a huge win. That said, I hear Rust has some very interesting ideas around memory ownership that might begin to address some of these issues. You are commenting using your WordPress. You are commenting using your Twitter account.
You are commenting using your Facebook account. Notify me of new comments via email. Choose the right language Scripting languages need not apply. Keep data and processing colocated Network hops are faster than disk seeks but even still they will add a lot of overhead.
Keep the system underutilized Low latency requires always having resources to process the request. Keep context switches to a minimum Context switches are a sign that you are doing more compute work than you have resources for.
Keep your reads sequential All forms of storage, wither it be rotational, flash based, or memory performs significantly better when used sequentially. Respect your cache With all of these optimizations in place, memory access quickly becomes a bottleneck. Non blocking as much as possible Make friends with non blocking and wait free data structures and algorithms.
Thank you for the in depth reply. I hope people find this back and forth useful. Thanks for pointing them out. On Mon, Mar 10, at Lock less — Lock free -Wait free Links mybookmarks. Baeldung Weekly Review 26 Baeldung. Reviving an old thread, but amazingly this has to be pointed out: Application Performance Improvement with Cache — Anil.
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