I have my RHEL linux server(VM) running a 4core processor and 8GB ram running the below applications
- an Apache Karaf container
- an Apache tomcat server
- an ActiveMQ server
- and the mongod server(either primary of secondary).
Often I see that mongo consumes nearly 80% of cpu. Now I see that my cpu and memory is overshooting most of the time and this has caused me to doubt whether my hardware config is too low for running these many components.
Please let me know if it is ok to run mongo like this on a shared server..
The question is to broad and the answer depends on too many variables, but I'll try to give you overall sense of it.
Can you use all these services together on the same machine at a minimum load? - for sure. It's not clear where other shards reside though, but it will work either way. You didn't provide your HDD specs which is quite important for a DB server, but again it will work at a minimum load.
Can you use this setup under heavy load - not the best idea. Perhaps it's better to have separate servers handling these services.
Monitor overall server load like: CPU, memory, IO. Check mongo logs for slow queries. If your queries supposed to run fast and they don't, you'll need more hardware.
Nobody would be really able to tell you how much load a specific server configuration can handle. You need at least 512Mb RAM and 1 CPU to get going these days but very soon you hit the limits. It all depends on how many users you have, what kinds of queries they run and how much data they cover.
Can you run MongoDB along other applications on a single server? Well it would appear that if you are having memory issues or CPU issues in your current configuration then you will likely need to address something. But "Can You?", well if it is not going to affect you then of course you can.
Should you, do this? Most people would firmly agree that you should not, and that would also stand for most of the other applications you are running on the one machine.
There are various reasons, process isolation, resource allocation, security, and far too many for a short topic response to go into why you should not have this kind of configuration. And certainly where it becomes a problem you should be addressing the issue by seeking a new configuration.
For Mongo alone, most people would not think twice about running their SQL database on dedicated hardware. The choice for Mongo should likely be no different.
Have also suggested this be moved to ServerFault, as it is not a programming question suited to stack overflow.
Related
From a performance perspective, is it a good choice to run Kafka in Docker containers ? Are there things which one should watch out for, tune specifically etc. ?
There is a good research paper from IBM on this topic - it is a bit dated by now, but I am sure the basic statements still hold true and have only been improved upon. The gist is, that the overhead introduced by Docker is quite small where it comes to cpu and memory, but for IO heavy applications you need to be a bit more careful. Depending on the workload I'd put Kafka squarely in the IO heavy group, so it is probably not a no-brainer.
Kafka benefits a lot from fast disc access, so if you run your containers in some sort of distributed platform with storage attached on a SAN or NFS share or something like that I'd assume, that you will notice a difference. But if you only chose containers to ease deployment and run them on one physical machine, I'd assume the difference to be negligible.
But as with all performance questions, it is hard to say this in general, you'll have to test your specific use case and environment to be sure.
I believe the performance would largely be effected by the type of machine you use. Linkedin and other large users of Kafka often recommend using spinning disks rather than SSDs because of the predominantly linear reads and writes done along with the the use of IBM's Zerocopy in the Kafka protocol. On a machine hosting many containers, you'd lose all the advantages that spinning disks give Kafka.
I'm in the early stages of planning out a virtualised environment for our production system (Moodle). The layers are relatively simple:
web - Apache 2.2
Database - MySQL 5
PHP 5.2
My question is this, what is the generally accepted approach for distributing the above layers amongst phycsical servers? In this case, we are planning to have 2 physical servers. Should I aim to keep my web server cluster on a single physical server and database cluster on another? Or, replicate a full stack on both servers, in case one fails? Any insights into this would be a great help to me.
thanks,
Cathal.
We use separate (virtual) servers, but do maintain separate stacks on each simply because the overheads are small and it allows for flexibility if we want to scale up/down. This is not for fallback however, because if one server is so broken that it's not web accessible, you probably won't be able to get data off it and onto the second server in order for it to be a useful replacement. Use proper backups for fallback and practice restoring from them regularly.
Moodle generally blocks on the PHP side rather than the DB side and we see roughly 3.5:1 PHP:MySQL CPU loads when they are on separate machines. With that in mind, you need to consider what the maximum capacity of one server is: you will get best performance if you have no network overhead between the machines at all, so bigger is better. If you can't do it with one, then making 2 VMS, one larger for PHP and one smaller for MySQL is the best option, but do benchmark the differences under load for your particular setup (use Apache JMeter for this).
Our largest installs involve 70,000 users or so and we have two 4-CPU/8GB VMs, one for PHP and one for MySQL (although the DB one rarely goes above 30% CPU). This allows for about 400 concurrent connections via Apache. However, we are using a large farm of VMs and can scale up and down between 2 and 16 CPUs easily, so you may wish to consider one monster machine if you want flexibility.
For more information on Moodle performance, look here, particularly under 'scalability'.
although I've much experience writing code. I don't really have much experience deploying things. I am writing a project that uses mongodb for persistence, redis for meta-caching, and play for serving pages. I am deciding whether to buy a dedicated server vs buying multiple small/medium instance from amazon/linode (one for each, mongo, redis, play). I have thought of the trade-offs as below, I wonder if anyone can add to the list or provide further insights. I am leaning toward (b) buying two sets of instances from linode and amazon, so if one of them have an outage it will fail over to the other provider. Also if anyone has any tips for deploying scala/maven cluster or tools to do so, much appreciated.
A. put everything in one instance
Pros:
faster speed between database and page servlet (same host).
cheaper.
less end points to secure.
Cons:
harder to manage. (in my opinion)
harder to upgrade a single module. if there are installation issues, it might bring down the whole system.
B. put each module (mongo,redis,play) in different instances
Pros:
sharding is easier.
easier to create cluster for a single purpose. (i.e. cluster of redis)
easier to allocate resources between module.
less likely everything will fail at once.
Cons:
bandwidth between modules -> $
secure each connection and end point.
I can only comment about the technical aspects (not cost, serviceability, etc ...)
It is not mentioned whether the dedicated instance is a physical box, or simply a large VM. If the application generates a lot of roundtrips to MongoDB or Redis, then the difference will be quite significant.
With a VM, the cost of I/Os, OS scheduling and system calls is higher. These elements tend to represent an important part in the performance cost of efficient remote data stores like MongoDB or Redis, and the virtualization toll is higher for them.
From a system point of view, I would not put MongoDB and Redis/Play on the same box if the MongoDB database is expected to be larger than the available memory. MongoDB maps data files in memory, and relies on the OS to perform memory swapping. It is designed for this. The other processes are not. Swapping induced by MongoDB will have catastrophic consequences on Redis and Play response time if they are all on the same box. So I would at least separate MongoDB from Redis/Play.
If you plan to use Redis for caching, it makes sense to keep it on the same box than the Play server. Redis will use memory, but low CPU. Play will use CPU, but not much memory. So it seems a good fit. Also, I'm not sure it is possible from Play, but if you use a unix domain socket to connect to Redis instead of the TCP loopback, you can achieve about 50% more throughput for free.
So, I have a what I call huge mongo database which is about 30Gb (about 30 millions documents). I tried to run mongod on the server shared with another application and it was completely slowed down. So I have to look for a dedicated server but have no idea how much RAM do I need.
I understand that I probably need to have amount of RAM enough to put all indexes there. But, if I'm correct, it would be about 13Gb of RAM which makes the price for the server very-very expensive (my app isn't making any money yet).
I tried to look into mongoHQ, but their cheapest dedicated plan is $600/month.
Any ideas? Is it really that expensive to host heavy mongo databases like that?
Build your own server and colocate it instead of renting someone's server. You have full control over the hardware, higher startup costs, but lower long-term costs. You are also liable for hardware malfunctions, so watch out for that.
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You're probably familiar with virtualization which takes a single host and is able to "emulate" many instances by sharing the resources among them all. You probably heard about XEN.
Is it completely insane to imagine the "opposite" of XEN : a layer that would abstract several hosts in a single running instance? I believe this would allow building apps which wouldn't need to really care much about a "clustering" layer themselves.
I wonder what are the technical limits to this, because I'm pretty sure some people are already working on it somewhere :)
The goal is NOT to achieve any kind of failure recovery. I believe this can (and should?) be handled at a higher level. For example, if someone is able to run a MySQL server on a gigantic instance (made of say 50 hosts), then one can easily use MySQL's replication features to replicate the database over a similar virtual instance.
Good question. Microsoft Azure is attempting to address this by allowing you to put applications "in the cloud" and not have to be as concerned with scalability up/down, redundancy, data storage, etc. But this is not accomplished at the hypervisor level.
http://www.microsoft.com/windowsazure/
Hardware-wise, there are some downsides to having everything be one big VM rather than many smaller ones. For one thing, software doesn't always understand how to handle all the resources. For example, some applications still can't handle multiple processor cores. I've seen informal benchmarks showing that IIS performs better spreading the same resources over multiple instances rather than one giant instance.
From a management perspective, it is probably better to have multiple VMs in certain cases. Imagine that a bad deployment corrupts a node. If that were your one and only (albeit giant) node, now your whole application is down.
You're probably talking about the concept Single System Image.
There used to be a Linux implementation, openMosix that since closed down. I don't know of any replacements. openMosix made it very easy to create and use SSI on a standard Linux kernel; too bad it got overtaken by events.
I do not know enough about Xen to know if it is possible but with VMware you can create pools of resources which come from many physical hosts. Then you can assign the resources to your VMs. That could be many VMs or just one VM.
Aggregation: Transform Isolated Resources into Shared Pools
Simulating a single core over multiple physical cores is very inefficient. You can do it, but it'll be slower than a cluster. Two physical cores can talk to each other in near-real-time, if they're on separate machines then you're doing something like say clocking down your motherboard speed by factors of 10 or more if these two physical cores (and RAM) are communicating even over a fibre optic network.
Dual cores can communicate faster than two distinct CPUs on the same motherboard, if they are on separate machines, thats slower again, if there are multiple machines, slower even again.
Basically you can, but there is net performance loss compared to the net performance gain you would be hoping to achieve.
Real life example, I had a bunch of VMs on a dual quad core server (~2.5Ghz/core) performing way, way below what they should have been. On closer inspection, it turned out that the hypervisor was emulating a single 3.5-4Ghz core when the load on an individual VM was more than 2.5Ghz -- after limiting each VM to 2.5Ghz performance went back to what was expected.
I agree with saidimu, you are talking about the Single System Image concept. In addition to the OpenMosix project, there have been several commercial implementations of the same idea (one contemporary example is ScaleMP). It's not a new idea.
I just wanted to elaborate on some of the technical points of SSI.
Basically, the reason it's not done is because the performance is generally absolutely unpredictable or terrible. There is a concept in computer systems known as [NUMA][3], which basically means that the cost of accessing different pieces of memory is not uniform. This can apply to huge systems where CPUs may have some memory accesses routed around to different chips, or in cases where memory is accessed remotely over a network (such as in SSI). Typically, the operating system will attempt to compensate for this by laying out programs and data in memory in such a way that a program can run as quickly as possible. I.e., the code and data will all be placed in the same NUMA "region", and be scheduled on the closest possible CPU.
However, in cases where you are running big applications (attempting to use all the memory in your SSI), there is little the operating system can do to reduce the impact of remote memory fetches. MySQL is not aware that accessing page 0x1f3c will cost 8 nanoseconds, while accessing page 0x7f46 will stall it for hundreds of microseconds, possibly milliseconds while the memory is fetched over the network. This means that non-NUMA aware applications will run like crap (seriously, very bad) in this kind of environment. As far as I know, most comtemporary SSI products rely on the fastest possible interconnects (such as Infiniband) between machines to achieve even a passable performance.
This is also why frameworks that expose the true cost of accessing data to the programmer (such as MPI: message passing interface) have achieved more traction than SSI or DSM (distributed shared memory) approaches. In fact, there is basically no way for a programmer to optimize an application to run in an SSI environment, which just sucks.