- 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)
- Overview of Search and Analysis
- 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
- Multi-Topic Search
- Context Search and Analysis
- Custom Redirect
- Downloading Log
- Search and Analysis (Metric Topic)
- Dashboard
- Data Processing documents
- Data Processing
- Data Processing Overview
- 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
- 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)
- Overview of Search and Analysis
- 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
- Multi-Topic Search
- Context Search and Analysis
- Custom Redirect
- Downloading Log
- Search and Analysis (Metric Topic)
- Dashboard
- Data Processing documents
- Data Processing
- Data Processing Overview
- 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
Cloud Load Balancer (CLB) is a product of hundreds of billions of QPS and relies heavily on refined operation. Access logs provided by Cloud Log Service (CLS) are key to refined operation. You can go to CLB > Access Logs to mine the value of massive amounts of access log data. By analyzing access logs, you can monitor client requests, troubleshoot issues, and analyze user behaviors to provide data support for refined operation. This document introduces how to use CLS to analyze CLB access logs.
Example: OPS Monitoring Scenario
Jack is in charge of the OPS of the advertising platform of an internet business. He received feedback from an advertising partner complaining that, when users click on an advertisement on the platform, the advertisement opens very slowly. The advertising partner has high requirements for timeliness and stability and proposed that if the service experiences an exception, an alarm should be generated within 1 minute and solved within 5 minutes. To meet this requirement, CLB logs can be used to implement the following capabilities:
Monitor client access latency and exceptional requests, and report alarms when the specified threshold is exceeded.
When there are alarms, the following additional information is provided to help you determine fault causes:
Websites, LB instances, and real server (RS) instances that the requests whose latency exceeds the specified threshold access
Access latency statistics of the LB and RS instances
OPS Monitoring Solution
Based on the 1-minute real-time alarms and multidimensional analysis capabilities of CLS, you can quickly monitor CLB access logs to locate and rectify faults.
Step 1. Enable the feature of shipping CLB access logs to CLS
1. Log in to the CLB console.
2. On the left sidebar, choose Instance Management to go to the instance management page.
3. Click a CLB ID/name to go to the CLB management page.
4. Locate and enable Cloud Log Service (CLS), as shown in the following figure.
For more information, please see Configuring Access Logs.
Step 2. Configure the client access latency statistics and alarm policy
Client access latency statistics
* | select time_series(__TIMESTAMP__, '1m', '%Y-%m-%d %H:%i:%s', '0') as time, round(avg(request_time)*1000,2) as "Average Access Latency" group by time order by time limit 1000
Error request statistics
status:>200 | select time_series(__TIMESTAMP__, '1m', '%Y-%m-%d %H:%i:%s', '0') as time, status, count(1) group by time,status order by time limit 1000
Configure an alarm policy to detect alarms whose average latency per minute is higher than a specified threshold
Configure multidimensional analysis in the alarm policy to obtain additional information when an alarm occurs
Access latency statistics of the LB and RS instances
Websites, LB instances, and real server (RS) instances that the requests whose latency exceeds the specified threshold access
Configure notification channels. The following channels are supported:
Email
SMS
WeChat
WeCom
Phone
Custom API callback
Step 3. Receive alarms and perform quick fault locating
Once an alarm is triggered, you can receive alarm information and details through channels such as WeChat, WeCom, SMS, and phone.
Alarm details include information such as affected RS and LB instances.
It can be seen that the average latency of LB instances is relatively high and the LB instance most affected is
9.*****.1. In this way, Jack can quickly locate the exceptional LB instance and restore it. The entire process takes only 1 minute.Example: Operations Statistics Scenario
Jane, responsible for operating a technology content app, needs to plan an offline salon next month to increase the stickiness of existing users and take the opportunity to promote products and attract new users. Due to the short preparation time and limited funds, in order to achieve the KPI target, Jane lists the following information that needs to be obtained:
Where to hold the offline salon: it is necessary to understand the geographical sources of visiting customers and the geographical locations of key customer groups.
What is the theme of salon: collect statistics on the top ranking of hot websites to understand which content users pay more attention to
Which clients are currently used by users to access CLS: design the landing page according to the current client distribution.
Which channels should the landing page be placed: collect statistics on the request sources of the current website to find out the diversion entries with high traffic and use them as the key areas for advertising.
Operations Statistics Solution
Analyzing CLB access logs helps Jane solve operations statistics problems with ease.
Step 1. Find out the geographical sources of visiting customers
Use the IP function provided by CLS to convert client IPs to the corresponding provinces or countries.
Collect statistics on customer distribution by province in China
* | select count(1) as c, ip_to_province(remote_addr) as address group by address limit 100
Collect statistics on customer distribution by country
* | select count(1) as c, ip_to_country(remote_addr) as address group by address limit 100
Step 2. Sort hot websites
The
http_host field records request domain names. By calculating the PV and UV of request domain names, you can sort out top hosts.* | select http_host, count(*) as pv, approx_distinct(remote_addr) as uv group by http_host order by pv desc limit 100
Step 3. Collect statistics on client distribution
* | select http_user_agent, count(*) group by http_user_agent
Step 4. Collect statistics on the request sources of the current website
The
http_referer records the request sources of the website.* | select http_referer, count(*) as count group by http_referer order by count desc limit 100
Summary
By analyzing CLB access logs, you can obtain various valuable results, such as PV and UV trends, client packet traffic statistics, status code distribution, and p99 and p95 access latency statistics. To help you quickly analyze CLB access logs, CLS and CLB have jointly created a visual analysis scheme out of the box. You can enjoy it immediately by enabling the feature of shipping CLB access logs to CLS.
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