As the image shows that, as the memory capacity increases the accessing time is also increasing.
Does it make sense that, accessing time is dependent on the memory capacity..???
No. The images show that technologies with lower cost in $ / GB are slower. Within a certain level (tier of the memory hierarchy), performance is not dependent on size. You can build systems with wider busses and so on to get more bandwidth out of a certain tier, but it's not inherently slower to have more.
Having more disks or larger disks doesn't make disk access slower, they're close to constant latency determined by the nature of the technology (rotating platter).
In fact, larger-capacity disks tend to have better bandwidth once they do seek to the right place, because more bits per second are flying under the read / write heads. And with multiple disks you can run RAID to utilize multiple disks in parallel.
Similarly for RAM, having multiple channels of RAM on a big many-core Xeon increases aggregate bandwidth. (But unfortunately hurts latency due to a more complicated interconnect vs. simpler quad-core "client" CPUs: Why is Skylake so much better than Broadwell-E for single-threaded memory throughput?) But that's a sort of secondary effect, and just using RAM with more bits per DIMM doesn't change latency or bandwidth, assuming you use the same number of DIMMs in the same system.
Related
What do I get by running multiple nodes on a single host? I am not getting availability, because if the host is down, the whole cluster goes with it. Does it make sense regarding performance? Doesn't one instance of ES take as many resources from the host as it needs?
Generally no, but if you have machines with ridiculous amounts of CPU and memory, you might want that to properly utilize the available resources. Avoiding big heaps with Elasticsearch is a good thing generally since garbage collection on bigger heaps can become a problem and in any case above 32 GB you lose the benefit of pointer compression. Mostly you should not need big heaps with ES. Most of the memory that ES uses is through memory mapped files, which relies on the OS cache. So just because you aren't assigning memory to the heap doesn't mean it is not being used: more memory available for caching means you'll be able to handle bigger shards or more shards.
So if you run more nodes, that advantage goes away and you waste memory on redundant heaps, and you'll have nodes competing for resources. Mostly, you should base these decisions on actual memory, cache, and cpu usage of course.
It depends on your host and how you configure your nodes.
For example, Elastic recommends allocating up to 32GB of RAM (because of how Java compresses pointers) to elasticsearch and have another 32GB for the operating system (mostly for disk caching).
Assuming you have more than 64GB of ram on your host, let's say 128, it makes sense to have two nodes running on the same machine, having both configured to 32GB ram each and leaving another 64 for the operating system.
I dig into Kubernetes resource restrictions and have a hard time to understand what CPU limits are for. I know Kubernetes passes requests and limits down to the (in my case) Docker runtime.
Example: I have 1 Node with 1 CPU and 2 Pods with CPU requests: 500m and limits: 800m. In Docker, this results in (500m -> 0.5 * 1024 = 512) --cpu-shares=512 and (800m -> 800 * 100) --cpu-quota=80000. The pods get allocated by Kube scheduler because the requests sum does not exceed 100% of the node's capacity; in terms of limits the node is overcommited.
The above allows each container to get 80ms CPU time per 100ms period (the default). As soon as the CPU usage is 100%, the CPU time is shared between the containers based on their weight, expressed in CPU shares. Which would be 50% for each container according to the base value of 1024 and a 512 share fo each. At this point - in my understanding - the limits have no more relevance because none of the containers can get its 80ms anymore. They both would get 50ms. So no matter how much limits I define, when usage reaches critical 100%, it's partitioned by requests anyway.
This makes me wonder: Why should I define CPU limits in the first place, and does overcommitment make any difference at all? requests on the other hand in terms of "how much share do I get when everything is in use" is completely understandable.
One reason to set CPU limits is that, if you set CPU request == limit and memory request == limit, your pod is assigned a Quality of Service class = Guaranteed, which makes it less likely to be OOMKilled if the node runs out of memory. Here I quote from the Kubernetes doc Configure Quality of Service for Pods:
For a Pod to be given a QoS class of Guaranteed:
Every Container in the Pod must have a memory limit and a memory request, and they must be the same.
Every Container in the Pod must have a CPU limit and a CPU request, and they must be the same.
Another benefit of using the Guaranteed QoS class is that it allows you to lock exclusive CPUs for the pod, which is critical for certain kinds of low-latency programs. Quote from Control CPU Management Policies:
The static CPU management policy allows containers in Guaranteed pods with integer CPU requests access to exclusive CPUs on the node. ... Only containers that are both part of a Guaranteed pod and have integer CPU requests are assigned exclusive CPUs.
According to the Motivation for CPU Requests and Limits section of the Assign CPU Resources to Containers and Pods Kubernetes walkthrough:
By having a CPU limit that is greater than the CPU request, you
accomplish two things:
The Pod can have bursts of activity where it makes use of CPU resources that happen to be available.
The amount of CPU resources a Pod can use during a burst is limited to some reasonable amount.
I guess that might leave us wondering why we care about limiting the burst to "some reasonable amount" since the very fact that it can burst seems to seems to suggest there are no other processes contending for CPU at that time. But I find myself dissatisfied with that line of reasoning...
So first off I checked out the command line help for the docker flags you mentioned:
--cpu-quota int Limit CPU CFS (Completely Fair Scheduler) quota
-c, --cpu-shares int CPU shares (relative weight)
Reference to the Linux Completely Fair Scheduler means that in order to understand the value of CPU limit/quota we need to undestand how the underlying process scheduling algorithm works. Makes sense, right? My intuition is that it's not as simple as time-slicing CPU execution according to the CPU shares/requests and allocating whatever is leftover at the end of some fixed timeslice on a first-come, first-serve basis.
I found this old Linux Journal article snippet which seems to be a legit description of how CFS works:
The CFS tries to keep track of the fair share of the CPU that would
have been available to each process in the system. So, CFS runs a fair
clock at a fraction of real CPU clock speed. The fair clock's rate of
increase is calculated by dividing the wall time (in nanoseconds) by
the total number of processes waiting. The resulting value is the
amount of CPU time to which each process is entitled.
As a process waits for the CPU, the scheduler tracks the amount of
time it would have used on the ideal processor. This wait time,
represented by the per-task wait_runtime variable, is used to rank
processes for scheduling and to determine the amount of time the
process is allowed to execute before being preempted. The process with
the longest wait time (that is, with the gravest need of CPU) is
picked by the scheduler and assigned to the CPU. When this process is
running, its wait time decreases, while the time of other waiting
tasks increases (as they were waiting). This essentially means that
after some time, there will be another task with the largest wait time
(in gravest need of the CPU), and the currently running task will be
preempted. Using this principle, CFS tries to be fair to all tasks and
always tries to have a system with zero wait time for each
process—each process has an equal share of the CPU (something an
“ideal, precise, multitasking CPU” would have done).
While I haven't gone as far as to dive into the Linux kernel source to see how this algorithm actually works, I do have some guesses I would like to put forth as to how shares/requests and quotas/limits play into this CFS algorithm.
First off, my intuition leads me to believe that different processes/tasks accumulate wait_runtime at different relative rates based on their assigned CPU shares/requests since Wikipedia claims that CFS is an implementation of weighted fair queuing and this seems like a reasonable way to achieve a shares/request based weighting in the context of an algorithm that attempts to minimize the wait_runtime for all processes/tasks. I know this doesn't directly speak to the question that was asked, but I want to be sure that my explanation as a whole has a place for both concepts of shares/requests and quotas/limits.
Second, with regard to quotas/limits I intuit that these would be applicable in situations where a process/task has accumulated a disproportionately large wait_runtime while waiting on I/O. Remember that the quoted description above CFP prioritizes the process/tasks with the largest wait_runtime? If there were no quota/limit on a given process/task then it seems to me like a burst of CPU usage on that process/task would have the effect of, for as long as it takes for its wait_runtime to reduce enough that another task is allowed to preempt it, blocking all other processes/tasks from execution.
So in other words, CPU quotas/limits in Docker/Kubernetes land is a mechanism that allows the given container/pod/process to burst in CPU activity to play catch up to other processes after waiting on I/O (rather than CPU) without in the course of doing so unfairly blocking other processes from also doing work.
There is no upper bound with just cpu shares. If there are free cycles, you are free to use them. limit is imposed so that one rogue process is not holding up the resource forever.
There should be some fair scheduling. CFS imposes that using cpu quota and cpu period via the limit attribute configured here.
To conclude, this kind of property ensures that when I schedule your task you get a minimum of 50 milliseconds to finish it. If you need more time, then if no one is waiting in the queue I would let you run for few more but not more than 80 milliseconds.
I think it's correct that, during periods where the Node's CPU is being fully utilized, it's the requests (CPU shares) that will determine how much CPU time each container gets, rather than the limits (which are effectively moot at that point). In that sense, a rogue process can't do unlimited damage (by depriving another of its requests).
However, there are still two broad uses for limits:
If you don't want a container to be able to use more than a fixed amount of CPU even if extra CPU is available on the Node. It might seem weird that you wouldn't want excess CPU to be utilized, but there are use cases for this. Some that I've heard:
You're charging customers for the right to use up to x amount of compute resources (a limit), so you don't want to give them more sometimes for free (which might dissuade them from paying for a higher tier on your service).
You're trying to figure out how a service will perform under load, but this gets complicated/unpredictable, because the performance during your load testing depends on how much spare CPU is lying around that the service is able to utilize (which might be a lot more than the spare CPU that'll actually be on the Node during a real-world high-load situation). This is mentioned here as a big risk.
If the requests on all the containers aren't set especially accurately (as is often the case; devs might set the values upfront and forget to update them as the service evolves, or not even set them very carefully initially). In these cases, things sometimes still function well enough if there's enough slack on the Node; limits can then be useful to prevent a buggy workload from eating all the slack and forcing the other pods back to their incorrectly-set(!) requested amounts.
I am using the official elasticsearch docker image. Since ES requires a certain level of memory mapped regions, (as documented), I increased it using
docker-machine ssh <NAME> sudo sysctl -w vm.max_map_count=262144
I also read here that the memory allocated should be around 50% of the total system memory.
I am confused about how these two play together. How does allocating more memory mapped regions affect the RAM allocated. Is it the part of the RAM, or is it taken above the RAM allocation for elasticsearch?
To sum it up very shortly, the heap is used by Elasticsearch only and Lucene will use the rest of the memory to map index files directly into memory for blazing fast access.
That's the main reason why the best practice is to allocate half the memory to the ES heap to let the remaining half to Lucene. However, there's also another best practice to not allocate more than 32-ish GB of RAM to the ES heap (and sometimes it's even less than 30B).
So, if you have a machine with 128GB of RAM, you won't allocate 64GB to ES but still a maximum 32-ish GB and Lucene will happily gobble up all the remaining 96GB of memory to map its index files.
Tuning the memory settings is a savant mix of giving enough memory (but not too much) to ES and making sure Lucene can have a blast by using as much as the remaining memory as possible.
Is Couchbase a kind of storage that address GroupBy-based read and write of 4TB worth of data with low latency? If not, what size of data Couchbase is good for for low latency access ?
Couchbase can definitely handle 4TB of data. It will be fast to the degree you can keep your working set in RAM. So you can have disk greater than memory, but you want to have a really small # of cache-miss rates, which we let you monitor. If you see that % get too high, it is time to grow your cluster so that more ram becomes available.
4TB should be a few tens of nodes. At that scale, disk throughput starts to be the limiting factor (eg slow disks take too long to warm up lots of ram). So for really hot stuff, people use SSDs, but for the majority of apps EC2 is plenty fine.
I'm working on a desktop application that will produce several in-memory datasets as an intermediary before being committed to a database.
Obviously I'm going to try to keep the size of these to a minimum, but are there any guidelines on thresholds I shouldn't cross for good functionality on an 'average' machine?
Thanks for any help.
There is no "average" machine. There is a wide range of still-in-use computers, including those that run DOS/Win3.1/Win9x and have less than 64MB of installed RAM.
If you don't set any minimum hardware requirements for your application, at least consider the oldest OS you're planning to support, and use the official minimum hardware requirements of that OS to gain a lower-bound assesment.
Generally, if your application is going to consume a considerable amount of RAM, you may want to let the user configure the upper bounds of the application's memory management mechanism.
That said, if you decide to dynamically manage the upper bounds based on realtime data, there are quite a few things you can do.
If you're developing a windows application, you can use WMI to get the system's total memory amount, and base your limitations on that value (say, use up to 5% of the total memory).
In .NET, if your data structures are complex and you find it hard to assess the amount of memory you consume, you can query the Garbage Collector for the amount of allocated memory using GC.GetTotalMemory(false), or use a System.Diagnostics.Process object.