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log_reduce_train_fl()

  • [アーティクル]

Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer

The function log_reduce_train_fl() finds common patterns in semi structured textual columns, such as log lines, and clusters the lines according to the extracted patterns. The function's algorithm and most of the parameters are identical to log_reduce_fl(), but unlike log_reduce_fl() that outputs a patterns summary table, this function outputs the serialized model. The model can be used by the function log_reduce_predict_fl()/log_reduce_predict_full_fl() to predict the matched pattern for new log lines.

Prerequisites

  • The Python plugin must be enabled on the cluster. This is required for the inline Python used in the function.

Syntax

T | invoke log_reduce_train_fl(reduce_col, model_name [, use_logram [, use_drain [, custom_regexes [, custom_regexes_policy [, delimiters [, similarity_th [, tree_depth [, trigram_th [, bigram_th ]]]]]]]]])

Learn more about syntax conventions.

Parameters

The following parameters description is a summary. For more information, see More about the algorithm section.

Name Type Required Description
reduce_col string ✔️ The name of the string column the function is applied to.
model_name string ✔️ The name of the output model.
use_logram bool Enable or disable the Logram algorithm. Default value is true.
use_drain bool Enable or disable the Drain algorithm. Default value is true.
custom_regexes dynamic A dynamic array containing pairs of regular expression and replacement symbols to be searched in each input row, and replaced with their respective matching symbol. Default value is dynamic([]). The default regex table replaces numbers, IPs and GUIDs.
custom_regexes_policy string Either 'prepend', 'append' or 'replace'. Controls whether custom_regexes are prepend/append/replace the default ones. Default value is 'prepend'.
delimiters dynamic A dynamic array containing delimiter strings. Default value is dynamic([" "]), defining space as the only single character delimiter.
similarity_th real Similarity threshold, used by the Drain algorithm. Increasing similarity_th results in more refined databases. Default value is 0.5. If Drain is disabled, then this parameter has no effect.
tree_depth int Increasing tree_depth improves the runtime of the Drain algorithm, but might reduce its accuracy. Default value is 4. If Drain is disabled, then this parameter has no effect.
trigram_th int Decreasing trigram_th increases the chances of Logram to replace tokens with wildcards. Default value is 10. If Logram is disabled, then this parameter has no effect.
bigram_th int Decreasing bigram_th increases the chances of Logram to replace tokens with wildcards. Default value is 15. If Logram, then is disabled this parameter has no effect.

Function definition

You can define the function by either embedding its code as a query-defined function, or creating it as a stored function in your database, as follows:

Define the function using the following let statement. No permissions are required.

Important

A let statement can't run on its own. It must be followed by a tabular expression statement. To run a working example of log_reduce_fl(), see Example.

let log_reduce_train_fl=(tbl:(*), reduce_col:string, model_name:string,
              use_logram:bool=True, use_drain:bool=True, custom_regexes: dynamic = dynamic([]), custom_regexes_policy: string = 'prepend',
              delimiters:dynamic = dynamic(' '), similarity_th:double=0.5, tree_depth:int = 4, trigram_th:int=10, bigram_th:int=15)
{
    let default_regex_table = pack_array('(/|)([0-9]+\\.){3}[0-9]+(:[0-9]+|)(:|)', '<IP>', 
                                         '([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12})', '<GUID>', 
                                         '(?<=[^A-Za-z0-9])(\\-?\\+?\\d+)(?=[^A-Za-z0-9])|[0-9]+$', '<NUM>');
    let kwargs = bag_pack('reduced_column', reduce_col, 'delimiters', delimiters,'output_column', 'LogReduce', 'parameters_column', '', 
                          'trigram_th', trigram_th, 'bigram_th', bigram_th, 'default_regexes', default_regex_table, 
                          'custom_regexes', custom_regexes, 'custom_regexes_policy', custom_regexes_policy, 'tree_depth', tree_depth, 'similarity_th', similarity_th, 
                          'use_drain', use_drain, 'use_logram', use_logram, 'save_regex_tuples_in_output', True, 'regex_tuples_column', 'RegexesColumn', 
                          'output_type', 'model');
    let code = ```if 1:
        from log_cluster import log_reduce
        result = log_reduce.log_reduce(df, kargs)
    ```;
    tbl
    | extend LogReduce=''
    | evaluate python(typeof(model:string), code, kwargs)
    | project name=model_name, timestamp=now(), model
};
// Write your query to use the function here.

Example

The following example uses the invoke operator to run the function.

To use a query-defined function, invoke it after the embedded function definition.

//
// Finding common patterns in HDFS logs, export and store the trained model in ML_Models table
//
.set-or-append ML_Models <|
//
let log_reduce_train_fl=(tbl:(*), reduce_col:string, model_name:string,
              use_logram:bool=True, use_drain:bool=True, custom_regexes: dynamic = dynamic([]), custom_regexes_policy: string = 'prepend',
              delimiters:dynamic = dynamic(' '), similarity_th:double=0.5, tree_depth:int = 4, trigram_th:int=10, bigram_th:int=15)
{
    let default_regex_table = pack_array('(/|)([0-9]+\\.){3}[0-9]+(:[0-9]+|)(:|)', '<IP>', 
                                         '([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12})', '<GUID>', 
                                         '(?<=[^A-Za-z0-9])(\\-?\\+?\\d+)(?=[^A-Za-z0-9])|[0-9]+$', '<NUM>');
    let kwargs = bag_pack('reduced_column', reduce_col, 'delimiters', delimiters,'output_column', 'LogReduce', 'parameters_column', '', 
                          'trigram_th', trigram_th, 'bigram_th', bigram_th, 'default_regexes', default_regex_table, 
                          'custom_regexes', custom_regexes, 'custom_regexes_policy', custom_regexes_policy, 'tree_depth', tree_depth, 'similarity_th', similarity_th, 
                          'use_drain', use_drain, 'use_logram', use_logram, 'save_regex_tuples_in_output', True, 'regex_tuples_column', 'RegexesColumn', 
                          'output_type', 'model');
    let code = ```if 1:
        from log_cluster import log_reduce
        result = log_reduce.log_reduce(df, kargs)
    ```;
    tbl
    | extend LogReduce=''
    | evaluate python(typeof(model:string), code, kwargs)
    | project name=model_name, timestamp=now(), model
};
HDFS_log_100k
| take 100000
| invoke log_reduce_train_fl(reduce_col="data", model_name="HDFS_100K")

Output

ExtentId OriginalSize ExtentSize CompressedSize IndexSize RowCount
3734a525-cc08-44b9-a992-72de97b32414 10383 11546 10834 712 1