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- 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
This document describes the basic syntax and examples of aggregate functions.
An aggregate function calculates a set of values and returns the calculation result. CLS supports the following aggregate functions.
Note:
In CLS analysis statements, strings must be included in single quotes (''), and field names and column names are unsigned or included in double quotes (""). For example, 'status' indicates the string 'status', and status or "status" indicates the log field
status.Function | Description | Example |
arbitrary(KEY) | Returns an arbitrary non-null value of the KEY column. | * | SELECT arbitrary(request_method) AS request_method |
avg(KEY) | Returns the average (arithmetic mean) of the KEY column. | * | SELECT AVG(request_time) |
bitwise_and_agg(KEY) | Returns the bitwise AND result of all input values of the KEY column. | * | SELECT bitwise_and_agg(status) |
bitwise_or_agg(KEY) | Returns the bitwise OR result of all input values of the KEY column. | * | SELECT bitwise_or_agg(request_length) |
checksum(KEY) | Returns the checksum of the KEY column. The return result is of Base64 encoding type. | * | SELECT checksum(request_method) AS request_method |
count(*) | Returns the number of input rows. | * | SELECT COUNT(*) WHERE http_status >200 |
count(1) | Returns the number of input rows. This function is equivalent to count(*). | * | SELECT COUNT(1) |
count(KEY) | Returns the number of non-null input values of the KEY column. | * | SELECT COUNT(request_time) WHERE request_time >5.0 |
count_if(boolean) | Returns the number of logs that meet specified conditions. | * | select count_if(returnCode>=400) as errorCounts |
geometric_mean(KEY) | Returns the geometric mean of KEY, which cannot contain negative numbers; otherwise, the result will be NaN. | * | SELECT geometric_mean(request_time) AS request_time |
max(KEY) | Returns the maximum value of KEY. | * | SELECT MAX(request_time) AS max_request_time |
max_by(x,y) | Returns the value of x associated with the maximum value of y over all input values. | * | SELECT MAX_BY(request_method, request_time) AS method |
max_by(x,y,n) | Returns n values of x associated with the n largest of all input values of y in descending order of y. | * | SELECT max_by(request_method, request_time, 3) AS method |
min(KEY) | Returns the minimum value of KEY. | * | SELECT MIN(request_time) AS min_request_time |
min_by(x,y) | Returns the value of x associated with the minimum value of y over all input values. | * | SELECT min_by(request_method, request_time) AS method |
min_by(x,y,n) | Returns n values of x associated with the n smallest of all input values of y in descending order of y. | * | SELECT min_by(request_method, request_time, 3) AS method |
sum(KEY) | Returns the sum of the KEY column. | * | SELECT SUM(body_bytes_sent) AS sum_bytes |
bool_and(boolean) | Returns TRUE if all logs meet the specified condition or FALSE otherwise. | * | select bool_and(returnCode>=400) |
bool_or(boolean) | Returns TRUE if any log meets the specified condition or FALSE otherwise. | * | select bool_or(returnCode>=400) |
every(boolean) | Equivalent to bool_and(boolean). | * | select every(returnCode>=400) |
Field description
Parameter | Description |
KEY | Name of the log field. |
x | The parameter value can be of any data type. |
y | The parameter value can be of any data type. |
n | An integer greater than 0. |
Yes
No
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