I can't start or stop 2 processors: PutHiveQL and ListHDFS after I had to restart NiFi because of memory overload. Other processors react fine.
Even after 4 hours the processors don't react. I tried stopping the processors by stopping the Process Group but it failed.
Check the nifi-app.log file, you can also make a tail -f to the file to see what's going on in real time.
Also check this values in the processors:
Concurrent tasks.
Run schedule.
Run duration.
The default configuration is not so good and can give problems.
Related
Nifi flow has stopped working. Today, on starting the flow all the processors icon turns green, but there is no input or output as GenerateFlowFile shows active threads and terminated threads. This count keeps on increasing or decreasing if I terminate manually or start flow. I also tried to remove the GenerateFlowFile processor and create once again, but same result.
On disabling all processors are disabled but not the GenerateFlowFile.
What is the reason behind this active thread and how to run the flow once again.
Opening the nifi-app.log file and clearing it resolved this issue for me.
I installed elasticsearch logstash and kibana in the ubuntu server. Before I starting these services the CPU utilization is less than 5% and after starting these services in the next minute the CPU utilization crossing 85%. I don't know why it is happening. Can anyone help me with this issue?
Thanks in advance.
There is not enough information in your question to give you a specific answer, but i will point out few possible scenarios and how to deal with them.
Did you wait long enough? sometimes there is a warmpup which is consuming higher CPU until all services are registered and finish to boot. if you have a fairly small machine it might consume higher CPU and take longer to finish.
folder write permissions. if any of the components of the ELK fails due to restricted access on needed directories either for logging, creating sub folders for sinceDB files or more it can cause it to go into an infinity loop and try again and again while it is consuming high CPU.
connection issues. ES should be the first component to start, if it fails, Kibana and Logstash will go and try to connect to the ES again and again until successful connection- which can cause high CPU.
bad logstash configuration. if logstash fails to read the file from the configurations or if you have a bad parsing, excessive parsing for example- your first "match" in the filter part will include the least common option it might consume high CPU.
For further investigation:
I suggest you to not start all of them together. start ES first. if everything goes well start Kibana and lastly start Logstash.
check the logs of all the ELK components to find error messages, failures, etc.
for a better answer I will need the yaml of all 3 components (ES, Kibana, Logstash)
I will need the logstash configuration file.
Would recommend you to analyse the CPU cycles consumed by each of the elasticsearch, logstash and kibana process.
Check specifically which process among the above is consuming the most memory/cpu via top command for example.
Start only ES first and allow it to settle and the node to be started completely before starting kibana and may be logstash after that.
Send me the logs for each and I can assist if there are any errors.
It works fine these days. Suddenly Processors still have task running even been stopped, and the running task need to terminate manually.
Any thoughts?
update 1
I use nipyapi to manipulate some processors to start and stop over and over again. There are the APIs I used
nipyapi.canvas.get_processor(identifier=p_id, identifier_type='id')
nipyapi.canvas.get_process_group(identifier=pg_id, identifier_type='id')
nipyapi.canvas.schedule_processor(processor=p_id, scheduled=True, refresh=True)
I restart NiFi and problem solved, but after executing those APIs many times (about 10000 times, grep processor id | wc -l) problem occurred.
I reckon those APIs create a lot web connections and not being stopped.
Stopping a processor is really just telling the scheduler not to trigger any more executions. It is often the case that an already-triggered thread is still executing after the processor has been stopped, which is why the Terminate option was added.
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 have created a test plan for creating userprofile.
I want to run my test plan for 100 users but when i run it for 10 users then it is running successfully with rump up time of 2 sec; but when i try it for 100 users & more than that it is getting failed I am giving rump uptime of 40 sec for 100 users.
I am not able to understand what may be the problem with it.
In my test plan the thread user are differentiated with id
Thanks in Advance.
It's a wide question, this behavior can be caused by
Your application under test can't handle load of 100 threads. Check logs for errors and make sure that application/web server and/or database configuration allow 100+ concurrent connections. Also you can check "Latency" metric to see if there is a problem with infrastructure or application itself.
Your load generator machine can't create 100 concurrent threads. If so - you'll need to consider JMeter Distributed Testing
Your script isn't optimized. I.e. using memory-consuming listeners like "View Results Tree", any graph listeners, regular expression extractors. Try following JMeter Performance and Tuning Tips guide and see whether it resolves your issue.
Agree with Dmitri, reason could be one of the above three.
One more thing you can try.
You can run your jmeter in ui mode for validation of your script and after validation you can run it in non-ui mode which will save lot of memory and cpu processing (basically UI is heaviest part in jmeter).
you can run your jmeter script in non-ui mode like this,
Jmeter -n -t -H proxy -P port
generally on a single dual core machine with 2 GB ram (Load Generator in your case) 100 user test can be carried out successfully.
some more things you can look at to find out the actual bottleneck
1.check application server logs (server on which your application is hosted)
if there are any failures in that then see performance counters on server (CPU, Memory, network etc) to see anything is overloaded.
(if server is windows then check using perfmon if linux then try sar)
if something is overloaded then reason is your app server cant take load of 100 users
probably try tuning it more.
2.check load generator system performance counters (JVM heap usage,CPU,Memory etc)
if JVM heap size is small enough try increasing it but if other counters are overloaded then try distributed load testing.
3.remove unwanted/heavy listeners, assertion from script.
maybe this will help :)