This topic lists the best practices of Log Service.
Data collection
Metric storage
Use Prometheus to collect Kubernetes metric dataQuery and analysis
- Query and analyze website logs
- Query and analyze JSON logs
- Associate a Logstore with a MySQL database to perform query and analysis
- Associate a Logstore with an OSS external table to perform query and analysis
- Collect and analyze NGINX monitoring logs
- Collect and analyze NGINX access logs
- Analyze Apache access logs
- Analyze IIS access logs
- Analyze Log4j logs
- Analyze website logs
- Query and analyze application logs
- Analyze layer-7 access logs of SLB
- Paged query
- Analyze vehicle track logs
- Analyze sales system logs
Data transformation
- Cleanse data by using functions
- Check data by using functions
- Convert datetime
- Mask sensitive data
- Parse Syslog messages in standard formats
- Parse NGINX logs
- Parse Java error logs
- Extract dynamic key-value pairs from a string
- Transform logs in specific text formats
- Parse log entries in a CSV-format log file
- Transform complex JSON data
- Convert logs to metrics
- Pull data from one Logstore to enrich log data in another Logstore
- Obtain the IPIP library from OSS and enrich IP address data
- Obtain the IP2Location library from OSS and enrich IP address data
- Pull a CSV file from OSS to enrich data
- Pull data from an ApsaraDB RDS for MySQL database
- Obtain data from an ApsaraDB RDS for MySQL database over the internal network
- Use resource functions to obtain incremental data
- Use the e_dict_map and e_search_dict_map functions to enrich log data
- Pull data from a Hologres database to perform data enrichment
- Build dictionaries and tables for data enrichment
- Use the e_table_map function to enrich HTTP response status codes
- Replicate data from a Logstore
- Transfer data across regions
- Distribute data to multiple destination Logstores
- Aggregate data from multiple source Logstores
- Replicate and distribute data