oom | Open Optical Monitoring - http : //www
kandi X-RAY | oom Summary
kandi X-RAY | oom Summary
Open Optical Monitoring - This is a project to make the contents of optical module EEPROMs accessible to python programmers. This allows a python programmer to query the value of dozens of keys (serial Number, module type, temperature, transmit power, ...), for the optical module in each port of a switch. In addition to key/value read access, the OOM project also supports key/value write to a limited number of EEPROM locations, and raw read/write access directly to EEPROM. NEWS: August 1, 2019 The optoe driver has been updated to support CMIS type devices. These include QSFP-DD, OSFP, COBO and SFP-DD at least. They conform to the "Common Management Interface Specification", which defines a different EEPROM layout from either SFP (sff_8472) or QSFP (sff_8636) type devices. See the 'optoe_doc' file in the optoe directory for details on how to specify a CMIS device (hint, it is 'optoe3'). An OOM keyfile for CMIS devices has also been added (cmis.py). Consider it a sample. The keys are believed to be correct, but only a limited number of keys have been implemented. Users can add their own keyfile defining more keys, or contact the maintainer to discuss adding keys to the cmis.py file.
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QUESTION
I have a large gziped csv file (.csv.gz) uploaded to a dataset that's about 14GB in size and 40GB when uncompressed. Is there a way to decompress, read, and write it out to a dataset using Python Transforms without causing the executor to OOM?
...ANSWER
Answered 2022-Mar-09 at 20:44I'm going to harmonize a few tactics in answering this question.
First, I want to write this using test-driven development using the method discussed here since we are dealing with raw files. The iteration speed on raw files using full checks + build will be far too long, so I'll start off by creating a sample .csv file and compressing it for much faster development.
My sample .csv file looks like the following:
I then compressed it using command-line utilities and added it to my code repository by cloning the repository to my local machine, adding the file to my development branch, and pushing the result back up into my Foundry instance.
I also made a test directory in my repository as I want to ensure my parsing logic is properly verified.
This resulted in my repository looking like the following:
Protip: don't forget to modify your setup.py and build.gradle files to enable testing and specifically package up your small test file.
I also need to make my parsing logic sit outside my my_compute_function method so that its available to my test methods, so parse_gzip.py looks like the following:
QUESTION
I need to persist the heap dump when the java process gets OOM and the pod is restarted.
I have following added in the jvm args
...ANSWER
Answered 2022-Feb-16 at 01:33You will have to persists the heap dumps on a shared network location between the pods. In order to achieve this, you will need to provide persistent volume claims and in EKS, this could be achieved using an Elastic File System mounted on different availability zones. You can start learning about it by reading this guide about EFS-based PVCs.
QUESTION
I am requesting an API using the python requests library:
My python script is run once a day by the scheduler, Once the python script gets run, I am getting this error and the PID of the python script is getting killed showing OOM. I am not getting whether it's a DNS issue or an OOM (Out of memory) issue as the process is getting killed.
Previously script was running fine.
Any clues/help will be highly appreciable.
...ANSWER
Answered 2021-Sep-27 at 10:41I found the issue, in my case it was not DNS issue. The issue is related to the OOM(Out of memory) of the ec2 instance which is killing the process of a python script due to which the "Instance reachability check failed" and I was getting "Failed to establish a new connection: [Errno -3] Temporary failure in name resolution".
After upgrading ec2 instance, the instance reachability didn't fail and able to run python script containing api.
https://aws.amazon.com/premiumsupport/knowledge-center/system-reachability-check/
The instance status check failure indicates an issue with the reachability of the instance. This issue occurs due to operating system-level errors such as the following:
Failure to boot the operating system Failure to mount the volumes correctly Exhausted CPU and memory- This is happening in our case. Kernel panic
QUESTION
try {
// If no exception was thrown from map0, the address is valid
addr = map0(imode, mapPosition, mapSize);
} catch (OutOfMemoryError x) {
// An OutOfMemoryError may indicate that we've exhausted memory
// so force gc and re-attempt map
System.gc();
try {
Thread.sleep(100);
} catch (InterruptedException y) {
Thread.currentThread().interrupt();
}
try {
addr = map0(imode, mapPosition, mapSize);
} catch (OutOfMemoryError y) {
// After a second OOME, fail
throw new IOException("Map failed", y);
}
}
ANSWER
Answered 2021-Dec-23 at 06:25In the give example, the problem is insufficient memory. Thus, running the garbage collector to free up memory might remedy this. But the call to System.gc() is only a suggestion to the JVM. It is not guaranteed that that the garbage collector frees any memory through this call.
So this approach is somewhat of a heuristic.
QUESTION
I'm using multiprocessing in a larger code base where some of the import statements have side effects. How can I run a function in a background process without having it inherit global imports?
...ANSWER
Answered 2021-Dec-07 at 18:14# helper.py:
print('This message should only print once!')
QUESTION
ANSWER
Answered 2021-Nov-24 at 06:14Have you set log in loggingInterceptor or restadapter ?
if yes then try to set it NONE.
QUESTION
After updating the environment from Wildfly 13 to Wildfly 18.0.1 we experienced an
ANSWER
Answered 2021-Nov-05 at 14:19Probably its a Xnio problem. Look at this issue https://issues.redhat.com/browse/JBEAP-728
QUESTION
I am using node js to download large files(300MB) from a server and pipe the response to a file write stream. As far as I understand pipes in nodejs, the data flow is managed by node and I don't have to consider draining and other events. The issue I face is that the memory usage of the docker where my application is running increases in the same amount as the file being downloaded (i.e It seems the file is being saved in memory). This memory usage persists even when I delete the file in the docker. I am attaching the code used for creating request and piping, below for reference. The code is running fine but causing performance issues like huge memory/CPU usage and crashes with OOM error. I am not able to understand what I am doing wrong.
...ANSWER
Answered 2021-Nov-08 at 13:32The problem is that you are using the requestretry package, which does not really support streaming. It does always call request with a callback and will provide a promise that is resolved with the full response. The request library will read the entire response body when such a callback is provided, which indeed does buffer the complete response in memory. This is not what you want.
I don't see a way to do streaming-only with requestretry, so you should use the request package directly (or, given its deprecation, one of its successor libraries) and handle the retry logic yourself.
QUESTION
I notice when I run the same code as my example over here but with a union or unionByName or unionAll instead of the join, my query planning takes significantly longer and can result in a driver OOM.
Code included here for reference, with a slight difference to what occurs inside the for() loop.
ANSWER
Answered 2021-Aug-16 at 17:48This is a known limitation of iterative algorithms in Spark. At the moment, every iteration of the loop causes the inner nodes to be re-evaluated and stacked upon the outer df variable.
This means your query planning process is taking O(exp(n)) where n is the number of iterations of your loop.
There's a tool in Palantir Foundry called Transforms Verbs that can help with this.
Simply import transforms.verbs.dataframes.union_many and call it upon the total set of dataframes you wish to materialize (assuming your logic will allow for it, i.e. one iteration of the loop doesn't depend upon the result of a prior iteration of the loop.
The code above should instead be modified to:
QUESTION
I am trying to use Address Sanitizer, but the kernel keeps killing my process due to excessive memory usage. Without Address Sanitizer the process runs just fine.
The program is compiled for arm-v7a using gcc-8.2.1 with
...ANSWER
Answered 2021-Oct-14 at 09:45You could reduce some Asan features (or enable them one by one in separate runs):
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