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- Glossary
- Release Notes and Announcements
- User Guide
- Product Introduction
- Purchase Guide
- Getting Started
- Operation Guide
- Resource Management
- Permission Management
- Log Collection
- Collection Overview
- Collecting Logs in Self-Built Kubernetes Cluster
- Collecting Syslog
- Collection by LogListener
- Collecting Text Log
- Uploading Log over Kafka
- Uploading Logs via Anonymous Write
- Uploading Logs via Logback Appender
- Uploading Logs via Log4j Appender
- Uploading Log via SDK
- Uploading Log via API
- Importing Data
- Tencent Cloud Service Log Access
- Metric Collection
- Log Storage
- Metric Storage
- Search and Analysis (Log Topic)
- Syntax and Rules
- Statistical Analysis (SQL)
- Quick Analysis
- SQL Syntax
- SQL Functions
- String Function
- Date and Time Functions
- IP Geographic Function
- URL Function
- Mathematical Calculation Functions
- Mathematical Statistical Function
- General Aggregate Function
- Geospatial Function
- Binary String Function
- Estimation Function
- Type Conversion Function
- Logical Function
- Operators
- Bitwise Operation
- Regular Expression Function
- Lambda Function
- Conditional Expressions
- Array Functions
- Interval-Valued Comparison and Periodicity-Valued Comparison Functions
- JSON Functions
- Window Functions
- Sampling Analysis
- Configuring Indexes
- Reindexing
- Context Search and Analysis
- Downloading Log
- Search and Analysis (Metric Topic)
- Dashboard
- Data Processing documents
- Data Processing
- Creating Processing Task
- Viewing Data Processing Details
- Data Processing Functions
- Function Overview
- Key-Value Extraction Functions
- Enrichment Functions
- Flow Control
- Row Processing Functions
- Field Processing Functions
- Value Structuring Functions
- Regular Expression Processing Functions
- Time Value Processing Functions
- String Processing Functions
- Type Conversion Functions
- Logical and Mathematical Functions
- Encoding and Decoding Functions
- IP Parsing Functions
- Processing Cases
- Scheduled SQL Analysis
- SCF
- Data Processing
- Shipping and Consumption
- Monitoring Alarm
- Historical Documentation
- Practical Tutorial
- Developer Guide
- API Documentation
- History
- Introduction
- API Category
- Making API Requests
- Topic Management APIs
- Log Set Management APIs
- Index APIs
- Topic Partition APIs
- Machine Group APIs
- Collection Configuration APIs
- Log APIs
- Metric APIs
- Alarm Policy APIs
- Data Processing APIs
- Kafka Protocol Consumption APIs
- CKafka Shipping Task APIs
- Kafka Data Subscription APIs
- COS Shipping Task APIs
- SCF Delivery Task APIs
- Scheduled SQL Analysis APIs
- COS Data Import Task APIs
- Data Types
- Error Codes
- FAQs
- CLS Service Level Agreement
- CLS Policy
- Contact Us
- Glossary
Function enrich_table
Function definition
This function uses CSV structure data to match fields in logs and, when matched fields are found, the function adds other fields and values in the CSV data to the source logs.
Syntax description
enrich_table(CSV data string, CSV column name string, output=Target field name or list, mode="overwrite")
Parameter description
Parameter | Description | Parameter Type | Required | Default Value | Value Range |
data | Input CSV data, where the first row is column names and the rest rows are corresponding values. Example: region,count\\nbj, 200\\ngz, 300 | string | Yes | - | - |
fields | Column name to match. If the field name in the CSV data is the same as the field with the same name in the log, the matching is successful. The value can be a single field name or multiple new field names concatenated with commas. | string | Yes | - | - |
output | Output field list. The value can be a single field name or multiple new field names concatenated with commas. | string | Yes | - | - |
mode | Write mode of the new field. Default value: overwrite | string | No | overwrite | - |
Example
Raw log:
{"region": "gz"}
Processing rule:
enrich_table("region,count\\nbj,200\\ngz,300", "region", output="count")
Processing result:
{"count":"300","region":"gz"}
Function enrich_dict
Function definition
This function uses dict structure data to match a field value in a log. If the specified field and value match a key in the dict structure data, the function assigns the value of the key to another field in the log.
Syntax description
enrich_dict(Dict data string, Source field name, output=Target field, mode="overwrite")
Parameter description
Parameter | Description | Parameter Type | Required | Default Value | Value Range |
data | Input dict data, which must be the escaped string of a JSON object. Example: enrich_dict("{"200":"SUCCESS","500":"FAILED"}", "status", output="message") | string | Yes | - | - |
fields | Field name to match. If the value of the key in the dict data is the same as the value of the specified field, the matching is successful. The value can be a single field name or multiple new field names concatenated with commas. | string | Yes | - | - |
output | Target field list. After successful matching, the function writes the corresponding values in the dict data to the target field list. The value can be a single field name or multiple new field names concatenated with commas. | string | Yes | - | - |
mode | Write mode of the new field. | string | No | overwrite | - |
Example
Raw log:
{"status": "500"}
Processing rule:
enrich_dict("{\\"200\\":\\"SUCCESS\\",\\"500\\":\\"FAILED\\"}", "status", output="message")
Processing result:
{"message":"FAILED","status":"500"}
Yes
No
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