Have you tried lokijs.js with large amount data for desktop browsers and What is the maximum stoage size of lokijs.js?
On node.js the virtual limit is the amount of RAM utilizable by your process (because Loki works in memory so it can't store more data than it fits in memory). So on a normal dev machine that would be something in the region between 1gb and 3-4gb. In a browser, it's 5MB.
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We run MongoDB mongod processes inside Docker containers in Kubernetes with clear memory limits.
I am trying to configure the mongod processes correctly for the imposed memory limits.
These are the information I could collect from the docs:
The memory usage of MongoDB is correlated to the WiredTiger cache size. Its is calculated using the formula 50% of (RAM - 1 GB) or a minimum of 256 MB https://docs.mongodb.com/manual/core/wiredtiger/#memory-use
RAM is the total amount of ram available on the system. In the case of containerized nodes, it is the available memory to the container (since MongoDB 4.0.9) https://docs.mongodb.com/manual/faq/diagnostics/#must-my-working-set-size-fit-ram
“If you run mongod in a container (e.g. lxc, cgroups, Docker, etc.) that does not have access to all of the RAM available in a system, you must set storage.wiredTiger.engineConfig.cacheSizeGB to a value less than the amount of RAM available in the container.” https://docs.mongodb.com/manual/faq/diagnostics/#must-my-working-set-size-fit-ram
The docs state that increasing the WiredTiger cache size above the default should be avoided. https://docs.mongodb.com/manual/faq/diagnostics/#must-my-working-set-size-fit-ram
This information is a little unclear.
Do I leave the default values of the WiredTiger cache size or do I set it to "a value less than the amount of RAM available in the container"? How much lower should that value be? (a higher value than the default would also contradict the advice to not increase it above the default value)
The default is to allow the WiredTiger cache to use slightly less than half of the total RAM on the system.
The process normally determines the total RAM automatically by querying the underlying operating system.
In the case of a Docker container which has been allocated 16GB of RAM but is running on a host machine that has 128GB RAM, the system call will report 128GB. The default in this case would be 63GB, which obviously would cause a problem.
In general:
Use the default in situations where the system call reports the true memory available in the environment. This includes bare metal, most VMs, cloud providers, etc.
In containers where the amount of memory reported by the system call does not reflect the total amount available to the container, manually make the calculation for what the default would have been if it did, and use that value instead.
I'm using MongoDB on a 32 bit production system, which sucks but it's out of my control right now. The challenge is to keep the memory usage under ~2.5GB since going over this will cause 32 bit systems to crash.
According to the mongoDB team, the best way to track the memory usage is to use your operating system's process tracking system (i.e. ps or htop on Unix systems; Process Explorer on Windows.) for virtual memory size.
The DB mainly consists of one table which is continually cycling data, i.e. receiving data at regular intervals from sensors, and every day a cron job wipes all data from before the last 3 days. Over a period of time, the memory usage slowly increases. I took some notes over time using db.serverStats(), db.lectura.totalSize() and ps, shown in the chart below. Note that the size of the table in question has reduced in the last month but the memory usage increased nonetheless.
Now, there is some scope for adjustment in how many days of data I store. Today I deleted basically half of the data, and then restarted mongodb, and yet the mem virtual / mem mapped and most importantly memory usage according to ps have hardly changed! Why do these not reduce when I wipe data (and restart)? I read some other questions where people said that mongo isn't really using all the memory that it might appear to be using, and that you can't clear the cache or limit memory use. But then how can I ensure I stay under the 2.5GB limit?
Unless there is a way to stem this dataset-size-irrespective gradual increase in memory usage, it seems to me that the 32-bit version of Mongo is unuseable. Note: I don't mind losing a bit of performance if it solves the problem.
To answer regarding why the mapped and virtual memory usage does not decrease with the deletes, the mapped number is actually what you get when you mmap() the entire set of data files. This does not shrink when you delete records, because although the space is freed up inside the data files, they are not themselves reduced in size - the files are just more empty afterwards.
Virtual will include journal files, and connections, and other non-data related memory usage also, but the same principle applies there. This, and more, is described here:
http://www.mongodb.org/display/DOCS/Checking+Server+Memory+Usage
So, the 2GB storage size limitation on 32-bit will actually apply to the data files whether or not there is data in them. To reclaim deleted space, you will have to run a repair. This is a blocking operation and will require the database to be offline/unavailable while it was run. It will also need up to 2x the original size in terms of free disk space to be able to run the repair, since it essentially represents writing out the files again from scratch.
This limitation, and the problems it causes, is why the 32-bit version should not be run in production, it is just not suitable. I would recommend getting onto a 64-bit version as soon as possible.
By the way, neither of these figures (mapped or virtual) actually represents your resident memory usage, which is what you really want to look at. The best way to do this over time is via MMS, which is the free monitoring service provided by 10gen - it will graph virtual, mapped and resident memory for you over time as well as plenty of other stats.
If you want an immediate view, run mongostat and check out the corresponding memory columns (res, mapped, virtual).
In general, when using 64-bit builds with essentially unlimited storage, the data will usually greatly exceed the available memory. Therefore, mongod will use all of the available memory it can in terms of resident memory (which is why you should always have swap configured to the OOM Killer does not come into play).
Once that is used, the OS does not stop allocating memory, it will just have the oldest items paged out to make room for the new data (LRU). In other words, the recycling of memory will be done for you, and the resident memory level will remain fairly constant.
Your options for stretching 32-bit are limited, but you can try some things. The thing that you run out of is address space, and the increases in the sizes of additional database files mean that you would like to avoid crossing over the boundary from "n" files to "n+1". It may be worth structuring your data into more or fewer databases so that you can get the maximum amount of actual data into memory and as little as possible "dead space".
For example, if your database named "mydatabase" consists of the files mydatabase.ns (the namespace file) at 16 MB, mydatabase.0 at 64 MB, mydatabase.1 at 128 MB and mydatabase.2 at 256 MB, then the next file created for this database will be mydatabase.3 at 512 MB. If instead of adding to mydatabase you instead created an additional database "mynewdatabase" it would start life with mynewdatabase.ns at 16 MB and mynewdatabase.0 at 64 MB ... quite a bit smaller than the 512 MB that adding to the original database would be. In fact, you could create 4 new databases for less space than would be consumed by adding a new file to the original database, and because the files are smaller they would be easier to fit into contiguous blocks of memory.
It is a well-known message that 32-bit should not be used for production.
Use 64-bit systems.
Point.
I am considering developing an application with a Cassandra backend. I am hoping that I will be able to run each cassandra node on commodity hardware with the following specs:
Quad Core 2GHz i7 CPU
2x 750GB disk drives
16 GB installed RAM
Now, I have been reading online that the available disk-space for Cassandra should be double the amount that is stored on the disks, which would mean that each node (set up in a RAID-1 configuration) would be able to store 375 GB of data, which is acceptable.
My question is this if 16GB RAM is enough to efficiently serve 375 GB of data per node. The data in the application developed will also be fairly time-dependant, such that recent data will be the data most read from the database. In fact, most of the data will be deleted after about 6 months.
Also, would I assign Cassandra a Heap (-Xmx) close to 16 GB, or does Cassandra utilize off-heap memory ?
You should not set the Cassandra heap to more than 8GB; bigger than that, and garbage collection will kill you with large pauses. Cassandra will use the buffer cache (like other applications) so the remaining memory isn't wasted.
16GB of RAM will be enough to serve the data if your hot set will all fit in RAM, or if serving rate can be served off disk. Disks can do about 100 random IO/s, so with your setup if you need more than 200 reads / second you will need to make sure the data is in cache. Cassandra exports good cache statistics (cassandra-cli show keyspaces) so you should easily be able to tell how effective your cache is being.
Do bear in mind, with only two disks in RAID-1, you will not have a dedicated commit log. This could hamper write performance quite badly. You may want to consider turning off the commit log if it does affect performance, and forgo durable writes.
Although it is probably wise not to use a really huge heap with Cassandra, at my company we have used 10GB to 12GB heaps without any issues so far. Our servers typically have at least 48 GB of memory (RAM is cheap -- so why not :-)) and so we may try expanding the heap a bit more and see what happens.
or when index larger then RAM?
Yes it can work. To what level it will perform is more of an "It Depends"
The key thing is to ensure your working set can fit in RAM. So if you have 16GB of RAM and 20GB database (inc. indexes) for example, if you need to only access half of all the data as the other half is older/never actually queried then you'll be fine as only half of your database needs to be in RAM (10GB).
Working set is key here. For example, if you have a logging application outputting to MongoDB, it may be that your working set is the amount of data (and indexes) from the past 3 months and that all data before that you don't access.
When your working set exceeds the amount of RAM, then it will carry on working but with noticeably degraded performance as things will then be constantly having to go to disk which is far less performant. If you're in this situation of exceeding RAM constraints on a machine, then this is where sharding comes into play - so you can balance the data out over a number of machines therefore increasing the amount of data that can be kept in RAM.
Nicely explained here: http://blog.boxedice.com/2010/12/13/mongodb-monitoring-keep-in-it-ram/
I am opening files using memory map. The files are apparently too big (6GB on a 32-bit PC) to be mapped in one ago. So I am thinking of mapping part of it each time and adjusting the offsets in the next mapping.
Is there an optimal number of bytes for each mapping or is there a way to determine such a figure?
Thanks.
There is no optimal size. With a 32-bit process, there is only 4 GB of address space total, and usually only 2 GB is available for user mode processes. This 2 GB is then fragmented by code and data from the exe and DLL's, heap allocations, thread stacks, and so on. Given this, you will probably not find more than 1 GB of contigous space to map a file into memory.
The optimal number depends on your app, but I would be concerned mapping more than 512 MB into a 32-bit process. Even with limiting yourself to 512 MB, you might run into some issues depending on your application. Alternatively, if you can go 64-bit there should be no issues mapping multiple gigabytes of a file into memory - you address space is so large this shouldn't cause any issues.
You could use an API like VirtualQuery to find the largest contigous space - but then your actually forcing out of memory errors to occur as you are removing large amounts of address space.
EDIT: I just realized my answer is Windows specific, but you didn't which platform you are discussing. I presume other platforms have similar limiting factors for memory-mapped files.
Does the file need to be memory mapped?
I've edited 8gb video files on a 733Mhz PIII (not pleasant, but doable).