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log_reduce_predict_full_fl()

  • Άρθρο

Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer

The function log_reduce_predict_full_fl() parses semi structured textual columns, such as log lines, and for each line it matches the respective pattern from a pretrained model or reports an anomaly if no matching pattern was found. The patterns are retrieved from a pretrained model, generated by log_reduce_train_fl(). The function is similar to log_reduce_predict_fl(), but unlike log_reduce_predict_fl() that outputs a patterns summary table, this function outputs a full table containing the pattern and parameters per each line.

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_predict_full_fl(models_tbl, model_name, reduce_col, pattern_col, parameters_col [, anomaly_str ])

Learn more about syntax conventions.

Parameters

Name Type Required Description
models_tbl table ✔️ A table containing models generated by log_reduce_train_fl(). The table's schema should be (name:string, timestamp: datetime, model:string).
model_name string ✔️ The name of the model that will be retrieved from models_tbl. If the table contains few models matching the model name, the latest one is used.
reduce_col string ✔️ The name of the string column the function is applied to.
pattern_col string ✔️ The name of the string column to populate the pattern.
parameters_col string ✔️ The name of the string column to populate the pattern's parameters.
anomaly_str string This string is output for lines that have no matched pattern in the model. Default value is "ANOMALY".

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_predict_full_fl=(tbl:(*), models_tbl: (name:string, timestamp: datetime, model:string), 
                           model_name:string, reduce_col:string, pattern_col:string, parameters_col:string, 
                           anomaly_str: string = 'ANOMALY')
{
    let model_str = toscalar(models_tbl | where name == model_name | top 1 by timestamp desc | project model);
    let kwargs = bag_pack('logs_col', reduce_col, 'output_patterns_col', pattern_col,'output_parameters_col', 
                          parameters_col, 'model', model_str, 'anomaly_str', anomaly_str, 'output_type', 'full');
    let code = ```if 1:
        from log_cluster import log_reduce_predict
        result = log_reduce_predict.log_reduce_predict(df, kargs)
    ```;
    tbl
    | evaluate hint.distribution=per_node python(typeof(*), code, kwargs)
};
// 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.

let log_reduce_predict_full_fl=(tbl:(*), models_tbl: (name:string, timestamp: datetime, model:string), 
                           model_name:string, reduce_col:string, pattern_col:string, parameters_col:string, 
                           anomaly_str: string = 'ANOMALY')
{
    let model_str = toscalar(models_tbl | where name == model_name | top 1 by timestamp desc | project model);
    let kwargs = bag_pack('logs_col', reduce_col, 'output_patterns_col', pattern_col,'output_parameters_col', 
                          parameters_col, 'model', model_str, 'anomaly_str', anomaly_str, 'output_type', 'full');
    let code = ```if 1:
        from log_cluster import log_reduce_predict
        result = log_reduce_predict.log_reduce_predict(df, kargs)
    ```;
    tbl
    | evaluate hint.distribution=per_node python(typeof(*), code, kwargs)
};
HDFS_log_100k
| extend Patterns='', Parameters=''
| take 10
| invoke log_reduce_predict_full_fl(models_tbl=ML_Models, model_name="HDFS_100K", reduce_col="data", pattern_col="Patterns", parameters_col="Parameters")

Output

data Patterns Parameters
081110 215858 15485 INFO dfs.DataNode$PacketResponder: Received block blk_5080254298708411681 of size 67108864 from /10.251.43.21 081110 <NUM> <NUM> INFO dfs.DataNode$PacketResponder: Received block blk_<NUM> of size <NUM> from <IP> {"parameter_0": "215858", "parameter_1": "15485", "parameter_2": "5080254298708411681", "parameter_3": "67108864", "parameter_4": "/10.251.43.21"}
081110 215858 15494 INFO dfs.DataNode$DataXceiver: Receiving block blk_-7037346755429293022 src: /10.251.43.21:45933 dest: /10.251.43.21:50010 081110 <NUM> <NUM> INFO dfs.DataNode$DataXceiver: Receiving block blk_<NUM> src: <IP> dest: <IP> {"parameter_0": "215858", "parameter_1": "15494", "parameter_2": "-7037346755429293022", "parameter_3": "/10.251.43.21:45933", "parameter_4": "/10.251.43.21:50010"}
081110 215858 15496 INFO dfs.DataNode$PacketResponder: PacketResponder 2 for block blk_-7746692545918257727 terminating 081110 <NUM> <NUM> INFO dfs.DataNode$PacketResponder: PacketResponder <NUM> for block blk_<NUM> terminating {"parameter_0": "215858", "parameter_1": "15496", "parameter_2": "2", "parameter_3": "-7746692545918257727"}
081110 215858 15496 INFO dfs.DataNode$PacketResponder: Received block blk_-7746692545918257727 of size 67108864 from /10.251.107.227 081110 <NUM> <NUM> INFO dfs.DataNode$PacketResponder: Received block blk_<NUM> of size <NUM> from <IP> {"parameter_0": "215858", "parameter_1": "15496", "parameter_2": "-7746692545918257727", "parameter_3": "67108864", "parameter_4": "/10.251.107.227"}
081110 215858 15511 INFO dfs.DataNode$DataXceiver: Receiving block blk_-8578644687709935034 src: /10.251.107.227:39600 dest: /10.251.107.227:50010 081110 <NUM> <NUM> INFO dfs.DataNode$DataXceiver: Receiving block blk_<NUM> src: <IP> dest: <IP> {"parameter_0": "215858", "parameter_1": "15511", "parameter_2": "-8578644687709935034", "parameter_3": "/10.251.107.227:39600", "parameter_4": "/10.251.107.227:50010"}
081110 215858 15514 INFO dfs.DataNode$DataXceiver: Receiving block blk_722881101738646364 src: /10.251.75.79:58213 dest: /10.251.75.79:50010 081110 <NUM> <NUM> INFO dfs.DataNode$DataXceiver: Receiving block blk_<NUM> src: <IP> dest: <IP> {"parameter_0": "215858", "parameter_1": "15514", "parameter_2": "722881101738646364", "parameter_3": "/10.251.75.79:58213", "parameter_4": "/10.251.75.79:50010"}
081110 215858 15517 INFO dfs.DataNode$PacketResponder: PacketResponder 2 for block blk_-7110736255599716271 terminating 081110 <NUM> <NUM> INFO dfs.DataNode$PacketResponder: PacketResponder <NUM> for block blk_<NUM> terminating {"parameter_0": "215858", "parameter_1": "15517", "parameter_2": "2", "parameter_3": "-7110736255599716271"}
081110 215858 15517 INFO dfs.DataNode$PacketResponder: Received block blk_-7110736255599716271 of size 67108864 from /10.251.42.246 081110 <NUM> <NUM> INFO dfs.DataNode$PacketResponder: Received block blk_<NUM> of size <NUM> from <IP> {"parameter_0": "215858", "parameter_1": "15517", "parameter_2": "-7110736255599716271", "parameter_3": "67108864", "parameter_4": "/10.251.42.246"}
081110 215858 15533 INFO dfs.DataNode$DataXceiver: Receiving block blk_7257432994295824826 src: /10.251.26.8:41803 dest: /10.251.26.8:50010 081110 <NUM> <NUM> INFO dfs.DataNode$DataXceiver: Receiving block blk_<NUM> src: <IP> dest: <IP> {"parameter_0": "215858", "parameter_1": "15533", "parameter_2": "7257432994295824826", "parameter_3": "/10.251.26.8:41803", "parameter_4": "/10.251.26.8:50010"}
081110 215858 15533 INFO dfs.DataNode$DataXceiver: Receiving block blk_-7771332301119265281 src: /10.251.43.210:34258 dest: /10.251.43.210:50010 081110 <NUM> <NUM> INFO dfs.DataNode$DataXceiver: Receiving block blk_<NUM> src: <IP> dest: <IP> {"parameter_0": "215858", "parameter_1": "15533", "parameter_2": "-7771332301119265281", "parameter_3": "/10.251.43.210:34258", "parameter_4": "/10.251.43.210:50010"}