Comments (1)
Seconding this,
just adapted spell to my workflow and compared to my ibm/drain3 derived drain implementation it is insanely slow.
Drain parsing: 5 minutes, Spell parsing 2h.
Not only is the spell implementation not optimized for multi-processors, there are also many expensive and inefficient python function calls that add up.
The whole LCS implementation is questionable and I wonder how they did their experiments for their tools and benchmarks paper.
from logparser.
Related Issues (20)
- The difference in number of records between raw data and parsed data HOT 1
- logrevised和log_corrected区别
- Support for multiline HOT 1
- How does the logpai/Drain3 repository compare to the implementation in this repository? HOT 1
- Controlling verbosity of output HOT 1
- Known log formats in benchmarks HOT 2
- Use Spell to parser HDFS have some question HOT 1
- There are too many log templates, and the encoding is abnormal.
- raise KeyError in Drain HOT 2
- How to define log_format and regex[] for each dataset? HOT 1
- df_log always empty HOT 1
- bad escape \s in Drain.py HOT 7
- The regex is different HOT 3
- EventId in HDFS_template.csv
- What do precision and recall really mean in log parsing? HOT 2
- Configure logparser with python2.7
- I think there is a misspelling in '/logs/Andriod' HOT 1
- Unable to figure out how to use the logparser HOT 2
- running python Drain_demo.py , An error occurred
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from logparser.