[ML] Make ml_standard tokenizer the default for new categorization jobs #73605
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Categorization jobs created once the entire cluster is upgraded to version 7.14 or higher will default to using the new ml_standard tokenizer rather than the previous default of the ml_classic tokenizer, and will incorporate the new first_non_blank_line char filter so that categorization is based purely on the first non-blank line of each message. The difference between the ml_classic and ml_standard tokenizers is that ml_classic splits on slashes and colons, so creates multiple tokens from URLs and filesystem paths, whereas ml_standard attempts to keep URLs, email addresses and filesystem paths as single tokens. It is still possible to config the ml_classic tokenizer if you prefer: just provide a categorization_analyzer within your analysis_config and whichever tokenizer you choose (which could be ml_classic or any other Elasticsearch tokenizer) will be used. To opt out of using first_non_blank_line as a default char filter, you must explicitly specify a categorization_analyzer that does not include it. If no categorization_analyzer is specified but categorization_filters are specified then the categorization filters are converted to char filters applied that are applied after first_non_blank_line. Backport of elastic#72805
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Once elastic#73605 is merged this test should pass on master.
droberts195
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Once #73605 is merged this test should pass on master.
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Categorization jobs created once the entire cluster is upgraded to
version 7.14 or higher will default to using the new ml_standard
tokenizer rather than the previous default of the ml_classic
tokenizer, and will incorporate the new first_non_blank_line char
filter so that categorization is based purely on the first non-blank
line of each message.
The difference between the ml_classic and ml_standard tokenizers
is that ml_classic splits on slashes and colons, so creates multiple
tokens from URLs and filesystem paths, whereas ml_standard attempts
to keep URLs, email addresses and filesystem paths as single tokens.
It is still possible to config the ml_classic tokenizer if you
prefer: just provide a categorization_analyzer within your
analysis_config and whichever tokenizer you choose (which could be
ml_classic or any other Elasticsearch tokenizer) will be used.
To opt out of using first_non_blank_line as a default char filter,
you must explicitly specify a categorization_analyzer that does not
include it.
If no categorization_analyzer is specified but categorization_filters
are specified then the categorization filters are converted to char
filters applied that are applied after first_non_blank_line.
Backport of #72805