Torch Log Service
Torch Log Service
Torch Log Service
Document
Product News
Product Overview
Product Billing
User Guide
TLS Agent
AgentLoop
Resource management
Host group
Data collection
LogCollector collection
Introduction to LogCollector
Installation and upgrade
Collect host logs
Collect container logs
LogCollector plugins
Manage LogCollector
general reference
Data import
Cloud product log collection
TOS log collection
DBW log collection
VPN Log collection
VKE log collection
CLB log collection
CloudTrail log collection
CFW log collection
NTA log collection
ALB log collection
APMPlus log collection
veFaaS log collection
VPC log collection
veImageX log collection
TrafficRoute DNS log collection
DDoS log collection
WAF log collection
DCDN log collection
Multi-cloud security platform log collection
APIG log collection
Machine learning task log collection
CR log collection
Volcano Ark application lab log collection
FileNAS log collection
LLM Application Firewall Service log collection
vePFS log collection
Cloud security center log collection
EFS log collection
Data storage
Query and analysis
Configure log indexes
Query log
Analyze logs
Query and analyze logs using third-party tools
Associate external data source for log query
SQL function
SQL syntax
Visualization
Query and analysis charts
Summaries of query and analysis charts
Create dashboard
Use dashboard
Add dashboard filters and variables
Use other systems to analyze logs
Log application
Log applications for product observability
Log applications for large models observability
Coding Agent log app
ModelProvider log app
No-coding Agent log app
Log applications for basic service monitoring
Data processing
Data transformation
Syntax and functions
Global operation function
expression function
Data processing best practices
general reference
Data write processor
Data consumer processor
Data consumption and shipping
Ship logs to TOS
Ship logs to Kafka
Ship logs to Splunk
Alarm
Alarm policy
Alarm notification content
Alarm notification content template
alarm notification channel
Tracing service
Monitor Log Service
API reference
Project management API
Topic management API
Shard management API
Index management API
Log management API
Host group management API
Collection configuration management API
Data import API
Data processor API
Data transformation API
Data backflow API
SQL analysis API
Alarm management API
Consumer group management API
Kafka protocol consumption API
Tag management API
Trace storage API
Account management API
SDK reference
Go SDK
Java SDK
Python SDK
iOS SDK
C++ SDK
CLI reference
Best practices
Data collection
Query and analysis
permission management
Consumption and delivery
Visualization
Alarm
FAQs
Billing and cost FAQ
Data collection FAQ
Data storage FAQ
Index configuration FAQ
Query and analysis FAQ
Scheduled SQL tasks FAQ
Alarm FAQ
- Documentation
Torch Log ServiceUser GuideQuery and analysisMachine learning functionDifference pattern statistical functions
Copy page
Download PDFMachine learning function
Difference pattern statistical functions
Copy pageDownload PDFDifference pattern statistical functions
This topic describes the syntax of difference pattern statistical functions supported by TLS and provides usage examples for common scenarios.
Notice
Currently, machine learning functions are in public beta.
Function list
Function | Description |
Function | Description |
Based on given field samples and judging criteria, analyze the set of differential patterns that affect the specified criteria. |
pattern_diff
Function syntax
- Syntax format
- pattern_diff(array_char_value, array_char_name, array_numeric_value, array_numeric_name, condition, supportScore,posSampleRatio,negSampleRatio )
- Parameters description
- ParametersData typeDescriptionParametersData typeDescriptionarray_char_valueArrayInput column for character data.NoticeThe array_char_value and array_numeric_value can not both be empty.array_char_nameArrayName of the input column for character data.array_numeric_valueArrayInput column for numeric data.array_numeric_nameArrayName of input column for numeric data.conditionStringCriteria for filtering data. If the condition is True, it is a positive sample; if it is False, it is a negative sample.supportScoreDoubleSupport degree of positive and negative samples during pattern mining. Valid values: (0,1].posSampleRatioDoubleSampling rate of positive samples. The default value is 0.5, which means only 50% of the positive sample set is selected. Valid values: (0,1].negSampleRatioDoubleSampling rate of negative samples. The default value is 0.5, which means only 50% of the negative sample set is selected. Valid values: (0,1].
- Return values
- If the input value is NULL, the function returns NULL.
Function examples
Query and analysis statements
* | select pattern_diff(array[client_ip], array['client_ip'], array[stamp,value], array['stamp','value' ], ReqSize > 2000, 0.1,0.1,0.1) from (select client_ip, stamp,value,ReqSize,RespSize limit 10000)
Last updated: 2026.05.08 14:32:56
All rights reserved © Beijing Volcano Engine Technology Co., Ltd2024
