intelligentdds / nezha Goto Github PK
View Code? Open in Web Editor NEWThe implementation of multimodal observability data root cause analysis approach Nezha in FSE 2023
License: MIT License
The implementation of multimodal observability data root cause analysis approach Nezha in FSE 2023
License: MIT License
Dear Author,
I am interested in the baseline methods you used in your paper on this dataset.
Would it be possible for you to share the code implementation of the baseline method? It would be greatly appreciated if you could make it available for everyone to learn from and improve upon classic algorithms.
Thank you for your consideration.
Best regards,
Hi my idol @yuxiaoba ๐
I've been trying to reproduce the results of Nezha on Hister shop. I followed the instructions carefully to install the environment, but unable to reproduce the results. I have attached here the results I got.
I see other people also have the number exactly like me (#1)
I'm wondering whether you have met this problem before (and how did you solve it?). I know you're busy, but please help when you have time. Much appreciate! ๐
Hi,
I was wondering if you have made or can make public the complete replication package of your study. I was particularly interested in your implementation of MicroRCA and TraceAnomaly algorithms for your benchmark.
Thanks in advance!
Hi my idol @yuxiaoba ๐
I came across the Drain config and really interested in it. How did you obtain these numbers and regular expressions?
https://github.com/IntelligentDDS/Nezha/blob/main/log_template/drain3_hipster.ini
Thank you so much in advance ^^
Greetings.
First of all, I sincerely appreciate your generosity in providing the source code.
We plan to use Nezha as part of the baseline of our new platform, for which I need to reproduce Nezhe on five multi-modal datasets.
In this process, I've met two problem and had some tiny questions about Nezha.
The first problem is about the test environment. In requirements.txt, Orange3_Associate==1.1.9 was in demand, which also requires numpy==1.15.4. But Orange3_Associate1.1.9 requires Orange3>=3.25.0 and openTSNE>=0.6.1, which two requires numpy>=1.16.0.
The second problem may be caused by the first one: I've successfully run Nezha with Orange3_Associate==1.1.9 and numpy==1.15.4 on python3.6. But the outcome was awful. Here's part of the result:
[INFO]2023-11-13 21:04:39,357 pattern_ranker.py:650: --------AS@1 Result-------
[INFO]2023-11-13 21:04:39,357 pattern_ranker.py:651: 5.357143 %
[INFO]2023-11-13 21:04:39,357 pattern_ranker.py:652: --------AS@3 Result-------
[INFO]2023-11-13 21:04:39,357 pattern_ranker.py:653: 7.142857 %
[INFO]2023-11-13 21:04:39,357 pattern_ranker.py:654: --------AS@5 Result-------
[INFO]2023-11-13 21:04:39,357 pattern_ranker.py:655: 7.142857 %
I'd like to know what caused this.
And I noticed that your source code provide your own data, which contains both trace_id file and trace file. If my understanding was correct, the code only use traces when its id appreared in the trace_id file (reflected in data_integrate.py and in the function data_integrate()). I wonder why.
Please provide illuminate and dispell the darkness for us, amen.
This file has incorrect timestamps.
https://github.com/IntelligentDDS/Nezha/blob/main/construct_data/2022-08-22/metric/adservice-5f6585d649-fnmft_metric.csv
They all start with 18 when they should start with 16, e.g. 1861140279 when it should be 1661140279.
There could be other files as well. I am building a data loader for Nezha to https://github.com/EvoTestOps/LogLead so I just did raw string replacement and it did not check all files.
Hi,
I have gone through your paper and published code here on GitHub and I was wondering if you've made public the way you implemented MicroRCA and TraceAnomaly for the comparison with Nezha.
I inspected the available code here but I don't think I've seen their usage.
Thanks in advance!
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