What is log parsing?
Log parsing is a process that converts structured or unstructured log file data into a common format so a computer can analyze it.
A log file is a record of events, activities, and errors that occur within an IT system. Logs include timestamps, IP addresses, error codes, and usernames—typically in a plain text format, either structured or unstructured. The goal of log parsing is to identify and group log entries into relevant fields and relational data sets using a structured format, such as a database, JSON, or CSV. This allows the information to be easily searched and analyzed.
Log parsing consists of the following activities:
- Collecting log files. Log files generated by web servers, application servers, databases, and network devices are stored in various formats within predefined directories or locations.
- Ingesting log files. Log parsing tools ingest log files from their specified locations.
- Pattern recognition. Log parsing tools use predefined patterns or regular expressions to recognize and extract relevant data from the log entries.
- Tokenizing. Log parsing tokenizes log entries into key-value pairs or structured data to make it easier to analyze. With tokenizing, analysts can search and filter logs based on specific criteria.
- Normalizing. The log parsing process normalizes data to ensure consistency. Normalizing might involve converting timestamps to a standardized format or resolving abbreviations.
- Storage. Extracted and parsed log data is stored in a central repository or database for analysis and retention.
- Query and analysis. Once the log data is parsed and stored, it can be queried and analyzed to gain insights into system performance, security incidents, or other events of interest. This can involve creating dashboards, generating reports, setting up alerts, or performing ad-hoc queries.
