I have a mesos / marathon system, and it is working well for the most part. There are upwards of 20 processes running, most of them using only part of a CPU. However, sometimes (especially during development), a process will spin up and start using as much CPU as is available. I can see on my system monitor that there is a pegged CPU, but I can't tell what marathon process is causing it.
Is there a monitor app showing CPU usage for marathon jobs? Something that shows it over time. This would also help with understanding scaling and CPU requirements. Tracking memory usage would be good, but secondary to CPU.
It seems that you haven't configured any isolation mechanism on your agent (slave) nodes. mesos-slave comes with an --isolation flag that defaults to posix/cpu,posix/mem. Which means isolation at process level (pretty much no isolation at all). Using cgroups/cpu,cgroups/mem isolation will ensure that given task will be killed by kernel if exceeds given memory limit. Memory is a hard constraint that can be easily enforced.
Restricting CPU is more complicated. If you have machine that offers 8 CPU cores to Mesos and each of your tasks is set to require cpu=2.0, you'll be able run there at most 4 tasks. That's easy, but at given moment any of your 4 tasks might be able to utilize all idle cores. In case some of your jobs is misbehaving, it might affect other jobs running on the same machine. For restricting CPU utilization see Completely Fair Scheduler (or related question How to understand CPU allocation in Mesos? for more details).
Regarding monitoring there are many possibilities available, choose an option that suits your requirements. You can combine many of the solutions, some are open-source other enterprise level solutions (in random order):
collectd for gathering stats, Graphite for storing, Grafana for visualization
Telegraf for gathering stats, InfluxDB for storing, Grafana for visualization
Prometheus for storing and gathering data, Grafana for visualization
Datadog for a cloud based monitoring solution
Sysdig platform for monitoring and deep insights
Related
My tests with standalone (single-threaded) Redis show that load from a number of parallel clients can drive Redis CPU usage to 100% (in my memory cache use case).
Starting it in cluster mode and sharding the content to multiple masters is a possible approach for speeding it up, if persistence is turned on.
I have a configuration without persistence (turned off RDB and AOF). Would starting multiple masters help performance (still using the same cummulative amount of RAM)?
Redis is single-threaded, so the performance of a standalone instance is limited by processing power of a single CPU core and the network bandwidth of a single machine. However, Redis is very very fast. So normally the bottleneck is network bandwidth, unless you run lots of slow commands/lua scripts.
If you deploy Redis cluster on multiple machines, the performance should be improved no matter whether the persistence is turned on or off. Since you have more CPU cores, and more network bandwidth.
If you deploy Redis cluster on a single machine (each node listen on a unique port), the performance might be improved. It depends... If the bottleneck is network bandwidth, it won't be improved. On the other hand, if the bottleneck is CPU processing power, the performance should be improved. So, in this case, you should do some benchmark with your specific data, specific environment, and specific commands/lua scripts.
I am working on a spark program that monitor each executors' performance such as mark down when one executor start to work and when it finishes its job. I am thinking two ways to do that:
First, develop programs so when the executor starts work, it mark down the current time to a file, when it finishes, mark down that time to the same file. In the ends, all "log" files will be spread the whole cluster networks except for the driver machine.
Second, since executors will report to driver periodically, each time the driver receives message from executors, if the message contains "start" and "finish" information, let the driver record everything.
Is that possible?
There are many ways to Monitor the executor performance as well as application performance
Best ways are to Monitor with the help of Spark Web UI and Other Monitoring tools available Open Source (Ganglia)
You Need to Monitor your application whether your cluster is under utilized or not how much resources are used by your application which you have created.
Monitoring can be done using various tools eg. Ganglia From Ganglia you can find CPU, Memory and Network Usage.Based on Observation about CPU and Memory Usage you can get a better idea what kind of tuning is needed for your application
Hope this Helps!!!....
I've been playing with Mesos cluster for a little bit, and thinking of utilizing Mesos cluster in our production environment. One problem I can't seem to find an answer to: how to properly schedule long running apps that will have varying load?
Marathon has "CPUs" property, where you can set weight for CPU allocation to particular app. (I'm planning on running Docker containers) But from what I've read, it is only a weight, not a reservation, allocation, or limitation that I am setting for the app. It can still use 100% of CPU on the server, if it's the only thing that's running. The problem is that for long running apps, resource demands change over time. Web server, for example, is directly proportional to the traffic. Coupled to Mesos treating this setting as a "reservation," I am choosing between 2 evils: set it too low, and it may start too many processes on the same host and all of them will suffer, with host CPU going past 100%. Set it too high, and CPU will go idle, as reservation is made (or so Mesos think), but there is nothing that's using those resources.
How do you approach this problem? Am I missing something in how Mesos and Marathon handle resources?
I was thinking of an ideal way of doing this:
Specify weight for CPU for different apps (on the order of, say, 0.1 through 1), so that when going gets tough, higher priority gets more (as is right now)
Have Mesos slave report "Available LA" with its status (e.g. if 10 minute LA is 2, with 8 CPUs available, report 6 "Available LA")
Configure Marathon to require "Available LA" resource on the slave to schedule a task (e.g. don't start on particular host if Available LA is < 2)
When available LA goes to 0 (due to influx of traffic at the same time as some job was started on the same server before the influx) - have Marathon move jobs to another slave, one that has more "Available LA"
Is there a way to achieve any of this?
So far, I gather that I can possible write a custom isolator module that will run on slaves, and report this custom metric to the master. Then I can use it in resource negotiation. Is this true?
I wasn't able to find anything on Marathon rescheduling tasks on different nodes if one becomes overloaded. Any suggestions?
As of Mesos 0.23.0 oversubscription is supported. Unfortunately it is not yet implemented in Marathon: https://github.com/mesosphere/marathon/issues/2424
In order to dynamically do allocation, you can use the Mesos slave metrics along with the Marathon HTTP API to scale, for example, as I've done here, in a different context. My colleague Niklas did related work with nibbler, which might also be of help.
I created test with JMeter to test performance of Ghost blogging platform. Ghost written in Node.js and was installed in cloud server with 1Gb RAM, 1 CPU.
I noticed after 400 concurrent users JMeter getting errors. Till 400 concurrent users load is normal. I decide increase CPU and added 1 CPU.
But errors reproduced and added 2 CPUs, totally 4 CPUs. The problem is occuring after 400 concurrent users.
I don't understand why 1 CPU can handle 400 users and the same results with 4 CPUs.
During monitoring I noticed that only one CPU is busy and 3 other CPUs idle. When I check JMeter summary in console there were errors, about 5% of request. See screenshot.
I would like to know is it possible to balance load between CPUs?
Are you using cluster module to load-balance and Node 0.10.x?
If that's so, please update your node.js to 0.11.x.
Node 0.10.x was using balancing algorithm provided by an operating system. In 0.11.x the algorithm was changed, so it will be more evenly distributed from now on.
Node.js is famously single-threaded (see this answer): a single node process will only use one core (see this answer for a more in-depth look), which is why you see that your program fully uses one core, and that all other cores are idle.
The usual solution is to use the cluster core module of Node, which helps you launch a cluster of Node processes to handle the load, by allowing you to create child processes that all share the same server ports.
However, you can't really use this without fixing Ghost's code. An option is to use pm2, which can wrap a node program, by using the cluster module for you. For instance, with four cores:
$ pm2 start app.js -i 4
In theory this should work, except if Ghost relies on some global variables (that can't be shared by every process).
Use cluster core and for load balancing nginx. Thats bad part about node.js. Fantastic framework, but developer has to enter into load balancing mess. While java and other runtimes makes is seamless. Anyway, nothing is perfect.
I am looking for a performance monitoring tool for my application which will collect/visualize in realtime the CPU and cache usage on single Linux box like IBM System or HP ProLiant with typical configuration 8 processors / 80 cores.
Application is the home-grown multithreaded C+ code which uses OpenMP.
This monitoring tool should not run 24 hours per day; it should not do e-mail notification.
I will run this tool just before sending commands to my apps, the apps will execute the command (it may take as a maximum few minutes only). During this time interval I need to analyze:
- usage of cores
- data movement between processors
- usage of L1, L2, L3 caches
- some other metrics (help me here) which can help to find bottleneck in application
performance and resource utilization
I guess that tools like Nagios / Zabbix are too heavy for this task.
From another side using the command-line tools like "top" and "sar" for 80 cores not very convenient and plotting (not necessary real-time) would be nice to have...
While getting the per core usage is rather easy - the other values might prove to be not practical, not at least without running that application within a profiler of some sorts.
Measuring QPI utilization is something highly non-trivial if at all possible. Intel's vTune might be able to acquire such things but only when running instrumented version of your binaries.
Also on x86 there is no way to figure out L1,L2,L3 usage of any kind - you can grab the low level CPU counters to measure cache misses though (but would probably need to use instrumented/profiled binaries and always withan something like vTune or PAPI).
You could "easily" setup something to pull all the lower level performance counters into SNMP and grab the SNMP values via standard SNMP capable monitoring tools but be aware that SNMP pulling is something that you don't want to occur more than 1-2/s max. Or pull that info into something like collectd.
I'm also having the impression that you don't understand the problem domain of monitoring tools. They are not ment to be used as low level analysis probes for finding application level/system bottlenecks - at best you could get some hints which resource (from a 10K feet view) is running under full utilization. Monitoring and alterting tools are something that operations staff needs to use to understand which part of their IT system is currently used and how, to gather historical data and predict future resource utilization and to be alerted when something breaks.
SiteScope, Hyperic or any combination of shell scripts, native OS utilities and a DB to store the results may do the job.