I am working on an analysis tool that reads output from a process and continuously converts this to an internal format. After the "logging phase" is complete, analysis is done on the data. The data is all held in memory.
However, due to the fact that all logged information is held in memory, there is a limit on the duration of the logging. For most use cases this is ok, but it should be possible to run for longer, even if this will hurt performance.
Ideally, the program should be able to start using hard drive space in addition to RAM once the RAM usage reaches a certain limit.
This leads to my question:
Are there any existing solutions for doing this? It has to work on both Unix and Windows.
To use the disk after memory is full, we use Cache technologies such as EhCache. They can be configured with the amount of memory to use, and to overflow to disk.
But they also have smarter algorithms you can configure as needed, such as sending to disk data not used in the last 10 minutes etc... This could be a plus for you.
Without knowing more about your application it is not possible to provide a perfect answer. However it does sound a bit like you are re-inventing the wheel. Have you considered using an in-process database library like sqlite?
If you used that or similar it will take care of moving the data to and from the disk and memory and give you powerful SQL query capabilities at the same time. Even if your logging data is in a custom format if each item has a key or index of some kind a small light database may be a good fit.
This might seem too obvious, but what about memory mapped files? This does what you want and even allows a 32 bit application to use much more than 4GB of memory. The principle is simple, you allocate the memory you need (on disk) and then map just a portion of that into system memory. You could, for example, map something like 75% of the available physical memory size. Then work on it, and when you need another portion of the data, just re-map. The downside to this is that you have to do the mapping manually, but that's not necessarily bad. The good thing is that you can use more data than what fits into physical memory and into the per-process memory limit. It works really great if you actually use only part of the data at any given time.
There may be libraries that do this automatically, like the one KLE suggested (though I do not know that one). Doing it manually means you'll learn a lot about it and have more control, though I'd prefer a library if it does exactly what you want with regard to how and when the disk is being used.
This works similar on both Windows on Unix. For Windows, here is an article by Raymond Chen that shows a simple example.
Related
I am building a ML application for binary classification using ML.NET. It will have multiple ML models of varying sizes (built using different training data) which will be stored in SQL server database as Blob. Clients will send items for classification to this app in random order and based on client ID, corresponding model is to be used for classification. To classify item, model needs be read from database and then loaded into memory. Loading model in memory is taking considerable time depending on size and I don't see any way to optimize it. Hence I am planning to cache models in memory. If I cache many heavy models, it may put pressure on memory hampering performance of other processes running on server. So there is no straightforward way to limit caching. So looking for suggestions to handle this.
Spawn a new process
In my opinion this is the only viable option to accomplish what you're trying to do. Spawn a complete new process that communicates (via IPC?) with your "main application". You could set a memory limit using this property https://learn.microsoft.com/en-us/dotnet/api/system.gcmemoryinfo.totalavailablememorybytes?view=net-5.0 or maybe even use a 3rd-party-library (e.g. https://github.com/lowleveldesign/process-governor), that kills your process if it reaches a specific amount of RAM. Both of these approaches are quite rough and will basically kill your process.
If you have control over your side car application running, it might make sense to really monitor the RAM usage with something like this Getting a process's ram usage and gracefully stop the process.
Do it yourself solution (not recommended)
Basically there is no built in way of limiting memory usage by thread or similar.
What counts towards the memory limit?
Shared resources
Since you have a running process, you need to define what exactly counts towards the memory limit. For example if you have some static Dictionary that is manipulated by the running thread - what did it occupy? Only the diff between the old value and the new value? The whole new value? The key and the value?
There are many more cases like this you'll have to take into consideration.
The actual measuring
You need some kind of way to count the actual memory usage. This will probably be hard/near impossible to "implement":
Reference counting needed?
If you have a hostile thread, it might spawn an infinite amount of references to one object, no new keyword used. For each reference you'd have to count 32/64 bits.
What about built in types?
It might be "easy" to measure a byte[] included in your own type definition, but what about built in classes? If someone initializes a string with 100MB this might be an amount you need to keep track of.
... and many more ...
As you maybe noticed with previous samples, there is no easy definition of "RAM used by a thread". This is the reason there also is no easy to get the value of it.
In my opinion it's insanely complex to do such a thing and needs a lot of definition work to do on your side. It might be feasable with lots of effort but I'm not sure if that really is what you want. Even if you manage to - what will do you about it? Only killing the thread might not clean up the ressources.
Therefore I'd really think about having a OS managed, independent, process, that you can kill whenever you feel like it.
How big are your models? Even large models 100meg+ load pretty quickly off of fast/SSD storage. I would consider caching them on fast drives/SSDs, because pulling off of SQL Server is going to be much slower than raw disk. See if this helps your performance.
I have a large data set (200GB uncompressed, 9GB compressed in bz2 -9 ) of stock tick data.
I want to run some basic time series analysis on them.
My machine has 16GB of RAM.
I would prefer to:
keep all data, compressed, in memory
decompress that data on the fly, and stream it [so nothing ever hits disk]
do all analysis in memory
Now, I think there's nice interactions here with Clojure's laziness, and future objects (i.e. I can define objects s.t. when I try to access them, I'll decompress them on the fly.)
Question: what are the things I should keep in mind when doing high performance time series analysis in Clojure?
I'm particular interested in tricks involving:
efficiently storing tick data in memory
efficiently doing computation
weird convolutions to reduce # of passes over the data
Books / articles / research paper suggestions welcome. (I'm a CS PhD student).
Thanks.
Some ideas:
In terms of storing the compressed data, I don't think you will be able to do much better than your OS's own file system caching. Just make sure it s configured to use 11GB+ of RAM for file system caching and it should pull your whole compressed data set into memory as it is read the first time.
You should then be able to define your Clojure code to pull into the data lazily via a ZipInputStream, which will perform the decompression for you.
If you need to perform a second pass on the data, just create a new ZipInputStream on the same file. OS level caching should ensure that you don't hit the disk again.
I have heard of systems like that implemented in Java. It is possible. You'll certainly want to understand how to create your own lazy sequences in order to accomplish this. I also wouldn't hesitate to drop down into Java if you need to make sure that you're dealing with the primitive types that you want to deal with. e.g. Clojure won't generate code to do math on 32-bit ints, it will only generate code to work with longs, and if you don't want that it could be a pain.
It would also be worth some effort to make your in-memory format compatible with a disk format. That would give you the option of memory mapping files, or (at the very least) make your startup easy if your program were to crash. e.g. It could just read the files on disk to recover its previous state.
Our professor asked us to think of an embedded system design where caches cannot be used to their full advantage. I have been trying to find such a design but could not find one yet. If you know such a design, can you give a few tips?
Caches exploit the fact data (and code) exhibit locality.
So an embedded system wich does not exhibit locality, will not benefit from a cache.
Example:
An embedded system has 1MB of memory and 1kB of cache.
If this embedded system is accessing memory with short jumps it will stay long in the same 1kB area of memory, which could be successfully cached.
If this embedded system is jumping in different distant places inside this 1MB and does that frequently, then there is no locality and cache will be used badly.
Also note that depending on architecture you can have different caches for data and code, or a single one.
More specific example:
If your embedded system spends most of its time accessing the same data and (e.g.) running in a tight loop that will fit in cache, then you're using cache to a full advantage.
If your system is something like a database that will be fetching random data from any memory range, then cache can not be used to it's full advantage. (Because the application is not exhibiting locality of data/code.)
Another, but weird example
Sometimes if you are building safety-critical or mission-critical system, you will want your system to be highly predictable. Caches makes your code execution being very unpredictable, because you can't predict if a certain memory is cached or not, thus you don't know how long it will take to access this memory. Thus if you disable cache it allows you to judge you program's performance more precisely and calculate worst-case execution time. That is why it is common to disable cache in such systems.
I do not know what you background is but I suggest to read about what the "volatile" keyword does in the c language.
Think about how a cache works. For example if you want to defeat a cache, depending on the cache, you might try having your often accessed data at 0x10000000, 0x20000000, 0x30000000, 0x40000000, etc. It takes very little data at each location to cause cache thrashing and a significant performance loss.
Another one is that caches generally pull in a "cache line" A single instruction fetch may cause 8 or 16 or more bytes or words to be read. Any situation where on average you use a small percentage of the cache line before it is evicted to bring in another cache line, will make your performance with the cache on go down.
In general you have to first understand your cache, then come up with ways to defeat the performance gain, then think about any real world situations that would cause that. Not all caches are created equal so there is no one good or bad habit or attack that will work for all caches. Same goes for the same cache with different memories behind it or a different processor or memory interface or memory cycles in front of it. You also need to think of the system as a whole.
EDIT:
Perhaps I answered the wrong question. not...full advantage. that is a much simpler question. In what situations does the embedded application have to touch memory beyond the cache (after the initial fill)? Going to main memory wipes out the word full in "full advantage". IMO.
Caching does not offer an advantage, and is actually a hindrance, in controlling memory-mapped peripherals. Things like coprocessors, motor controllers, and UARTs often appear as just another memory location in the processor's address space. Instead of simply storing a value, those locations can cause something to happen in the real world when written to or read from.
Cache causes problems for these devices because when software writes to them, the peripheral doesn't immediately see the write. If the cache line never gets flushed, the peripheral may never actually receive a command even after the CPU has sent hundreds of them. If writing 0xf0 to 0x5432 was supposed to cause the #3 spark plug to fire, or the right aileron to tilt down 2 degrees, then the cache will delay or stop that signal and cause the system to fail.
Similarly, the cache can prevent the CPU from getting fresh data from sensors. The CPU reads repeatedly from the address, and cache keeps sending back the value that was there the first time. On the other side of the cache, the sensor waits patiently for a query that will never come, while the software on the CPU frantically adjusts controls that do nothing to correct gauge readings that never change.
In addition to almost complete answer by Halst, I would like to mention one additional case where caches may be far from being an advantage. If you have multiple-core SoC where all cores, of course, have own cache(s) and depending on how program code utilizes these cores - caches can be very ineffective. This may happen if ,for example, due to incorrect design or program specific (e.g. multi-core communication) some data block in RAM is concurrently used by 2 or more cores.
I'm developing a client/server application where the server holds large pieces of data such as big images or video files which are requested by the client and I need to create an in-memory client caching system to hold a few of those large data to speed up the process. Just to be clear, each individual image or video is not that big but the overall size of all of them can be really big.
But I'm faced with the "how much data should I cache" problem and was wondering if there are some kind of golden rules on Windows about what strategy I should adopt. The caching is done on the client, I do not need caching on the server.
Should I stay under x% of global memory usage at all time ? And how much would that be ? What will happen if another program is launched and takes up a lot of memory, should I empty the cache ?
Should I request how much free memory is available prior to caching and use a fixed percentage of that memory for my needs ?
I hope I do not have to go there but should I ask the user how much memory he is willing to allocate to my application ? If so, how can I calculate the default value for that property and for those who will never use that setting ?
Rather than create your own caching algorithms why don't you write the data to a file with the FILE_ATTRIBUTE_TEMPORARY attribute and make use of the client machine's own cache.
Although this approach appears to imply that you use a file, if there is memory available in the system then the file will never leave the cache and will remain in memory the whole time.
Some advantages:
You don't need to write any code.
The system cache takes account of all the other processes running. It would not be practical for you to take that on yourself.
On 64 bit Windows the system can use all the memory available to it for the cache. In a 32 bit Delphi process you are limited to the 32 bit address space.
Even if your cache is full and your files to get flushed to disk, local disk access is much faster than querying the database and then transmitting the files over the network.
It depends on what other software runs on the server. I would make it possible to configure it manually at first. Develop a system that can use a specific amount of memory. If you can, build it so that you can change that value while it is running.
If you got those possibilities, you can try some tweaking to see what works best. I don't know any golden rules, but I'd figure you should be able to set a percentage of total memory or total available memory with a specific minimum amount of memory to be free for the system at all times. If you save a miminum of say 500 MB for the server OS, you can use the rest, or 90% of the rest for your cache. But those numbers depend on the version of the OS and the other applications running on the server.
I think it's best to make the numbers configurable from the outside and create a management tool that lets you set the values manually first. Then, if you found out what works best, you can deduct formulas to calculate those values, and integrate them in your management tool. This tool should not be an integral part of the cache program itself (which will probably be a service without GUI anyway).
Questions:
One image can be requested by multiple clients? Or, one image can be requested by multiple times in a short interval?
How short is the interval?
The speed of the network is really high? Higher than the speed of the hard drive?? If you have a normal network, then the harddrive will be able to read the files from disk and deliver them over network in real time. Especially that Windows is already doing some good caching so the most recent files are already in cache.
The main purpose of the computer that is running the server app is to run the server? Or is just a normal computer used also for other tasks? In other words is it a dedicated server or a normal workstation/desktop?
but should I ask the user how much
memory he is willing to allocate to my
application ?
I would definitively go there!!!
If the user thinks that the server application is not a important application it will probably give it low priority (low cache). Else, it it thinks it is the most important running app, it will allow the app to allocate all RAM it needs in detriment of other less important applications.
Just deliver the application with that setting set by default to a acceptable value (which will be something like x% of the total amount of RAM). I will use like 70% of total RAM if the main purpose of the computer to hold this server application and about 40-50% if its purpose is 'general use' computer.
A server application usually needs resources set aside for its own use by its administrator. I would not care about others application behaviour, I would care about being a "polite" application, thereby it should allow memory cache size and so on to be configurable by the administator, which is the only one who knows how to configure his systems properly (usually...)
Defaults values should anyway take into consideration how much memory is available overall, especially on 32 bit systems with less than 4GB of memory (as long as Delphi delivers only 32 bit apps), to leave something free to the operating systems and avoids too frequent swapping. Asking the user to select it at setup is also advisable.
If the application is the only one running on a server, a value between 40 to 75% of available memory could be ok (depending on how much memory is needed beyond the cache), but again, ask the user because it's almost impossible to know what other applications running may need. You can also have a min cache size and a max cache size, start by allocating the lower value, and then grow it when and if needed, and shrink it if necessary.
On a 32 bit system this is a kind of memory usage that could benefit from using PAE/AWE to access more than 3GB of memory.
Update: you can also perform a monitoring of cache hits/misses and calculate which cache size would fit the user needs best (it could be too small but too large as well), and the advise the user about that.
To be honest, the questions you ask would not be my main concern. I would be more concerned with how effective my cache would be. If your files are really that big, how many can you hold in the cache? And if your client server app has many users, what are the chances that your cache will actually cache something someone else will use?
It might be worth doing an analysis before you burn too much time on the fine details.
I need to store large amounts of data on-disk in approximately 1k blocks. I will be accessing these objects in a way that is hard to predict, but where patterns probably exist.
Is there an algorithm or heuristic I can use that will rearrange the objects on disk based on my access patterns to try to maximize sequential access, and thus minimize disk seek time?
On modern OSes (Windows, Linux, etc) there is absolutely nothing you can do to optimise seek times! Here's why:
You are in a pre-emptive multitasking system. Your application and all it's data can be flushed to disk at any time - user switches task, screen saver kicks in, battery runs out of charge, etc.
You cannot guarantee that the file is contiguous on disk. Doing Aaron's first bullet point will not ensure an unfragmented file. When you start writing the file, the OS doesn't know how big the file is going to be so it could put it in a small space, fragmenting it as you write more data to it.
Memory mapping the file only works as long as the file size is less than the available address range in your application. On Win32, the amount of address space available is about 2Gb - memory used by application. Mapping larger files usually involves un-mapping and re-mapping portions of the file, which won't be the best of things to do.
Putting data in the centre of the file is no help as, for all you know, the central portion of the file could be the most fragmented bit.
To paraphrase Raymond Chen, if you have to ask about OS limits, you're probably doing something wrong. Treat your filesystem as an immutable black box, it just is what it is (I know, you can use RAID and so on to help).
The first step you must take (and must be taken whenever you're optimising) is to measure what you've currently got. Never assume anything. Verify everything with hard data.
From your post, it sounds like you haven't actually written any code yet, or, if you have, there is no performance problem at the moment.
The only real solution is to look at the bigger picture and develop methods to get data off the disk without stalling the application. This would usually be through asynchronous access and speculative loading. If your application is always accessing the disk and doing work with small subsets of the data, you may want to consider reorganising the data to put all the useful stuff in one place and the other data elsewhere. Without knowing the full problem domain it's not possible to to be really helpful.
Depending on what you mean by "hard to predict", I can think of a few options:
If you always seek based on the same block field/property, store the records on disk sorted by that field. This lets you use binary search for O(log n) efficiency.
If you seek on different block fields, consider storing an external index for each field. A b-tree gives you O(log n) efficiency. When you seek, grab the appropriate index, search it for your block's data file address and jump to it.
Better yet, if your blocks are homogeneous, consider breaking them down into database records. A database gives you optimized storage, indexing, and the ability to perform advanced queries for free.
Use memory-mapped file access rather than the usual open-seek-read/write pattern. This technique works on Windows and Unix platforms.
In this way the operating system's virtual memory system will handle the caching for you. Accesses of blocks that are already in memory will result in no disk seek or read time. Writes from memory back to disk are handled automatically and efficiently and without blocking your application.
Aaron's notes are good too as they will affect initial-load time for a chunk that's not in memory. Combine that with the memory-mapped technique -- after all it's easier to reorder chunks using memcpy() than by reading/writing from disk and attempting swapouts etc.
The most simple way to solve this is to use an OS which solves that for you under the hood, like Linux. Give it enough RAM to hold 10% of the objects in RAM and it will try to keep as many of them in the cache as possible reducing the load time to 0. The recent server versions of Windows might work, too (some of them didn't for me, that's why I'm mentioning this).
If this is a no go, try this algorithm:
Create a very big file on the harddisk. It is very important that you write this in one go so the OS will allocate a continuous space on disk.
Write all your objects into that file. Make sure that each object is the same size (or give each the same space in the file and note the length in the first few bytes of of each chunk). Use an empty harddisk or a disk which has just been defragmented.
In a data structure, keep the offsets of each data chunk and how often it is accessed. When it is accessed very often, swap its position in the file with a chunk that is closer to the start of the file and which has a lesser access count.
[EDIT] Access this file with the memory-mapped API of your OS to allow the OS to effectively cache the most used parts to get best performance until you can optimize the file layout next time.
Over time, heavily accessed chunks will bubble to the top. Note that you can collect the access patterns over some time, analyze them and do the reorder over night when there is little load on your machine. Or you can do the reorder on a completely different machine and swap the file (and the offset table) when that's done.
That said, you should really rely on a modern OS where a lot of clever people have thought long and hard to solve these issues for you.
That's an interesting challenge. Unfortunately, I don't know how to solve this out of the box, either. Corbin's approach sounds reasonable to me.
Here's a little optimization suggestion, at least: Place the most-accessed items at the center of your disk (or unfragmented file), not at the start of end. That way, seeking to lesser-used data will be closer by average. Err, that's pretty obvious, though.
Please let us know if you figure out a solution yourself.