Log parsering transform unstructured data into an easily consumable format, making it possible to quickly identify, understand, and resolve issues using one organized piece of information. Furthermore, log parsers store this data for future reference.
Many logging solutions include built-in parsers for common file formats like Windows event logs and JSON. They use Vector Remap Language (VRL) or regular expressions to recognize these formats before applying their appropriate parsing rules.
Log parsing is the process of converting unstructured data to structured format for easier data analytics and faster problem identification.
Unstructured log files can be time-consuming to analyze, requiring manual inspection. Log management solutions like Loggly help simplify log analysis by collecting, parsing, and parsing logs for universal query access, monitoring, reporting, and visualization.
Advantages of a log parser
Log parsing software enables tech professionals to efficiently organize information into useful categories and obtain different perspectives of it – making it much simpler for them to find and comprehend the data that matters in solving problems or optimizing applications.
Existing solutions typically rely on heuristic rules or regular expressions to automatically extract free-text tokens from raw log messages, construct log templates that represent events from an input dataset, and compare new log messages against these pre-defined templates in order to match and identify incoming log messages. Drain , for instance, is an archetypical heuristic log parser which uses a fixed depth parsing tree to examine raw log messages cluster by cluster before matching these with predefined templates.
Heuristic log parsers tend to have poor accuracy on most real-world log datasets due to their inherent inaccuracy in processing specific log properties such as message length, event distribution and vocabulary size. Furthermore, they require considerable human involvement for parameter tuning and preprocessing to achieve satisfactory performance.
1. Quicker troubleshooting
Troubleshooting performance issues requires the ability to quickly search and filter large volumes of log data quickly. Commercial logging tools also often provide visualization capabilities and reporting functions which come in handy when presenting findings to managers or directors.
An effective log parser helps your log management system understand unstructured logs by translating unorganized entries into categories with predetermined rules for segmenting events into named fields or columns, as well as adding string manipulation functions for additional data processing.
This can be particularly useful when sorting through large volumes of data quickly, such as when trying to identify slow URLs from an error log. Furthermore, some logging software provides a GUI for configuring custom parsing configurations and previewing results so you can ensure all values match those expected in the end result.
Some logging tools offer a grok patterns feature to reduce the need for complex regex syntax, which may be particularly daunting to newcomers.
2. Multiple log types
Log files contain an abundance of data elements, such as dates and timestamps, event types and levels, sources, computer names, user names, task categories and messages. Log parsing transforms these raw elements into structured forms so they’re easier to read, analyze and automate.
Different logging tools use various parsing methods; which one best meets your needs will depend on its capabilities. Some offer parsers for all popular formats while others rely on scripted solutions to support specific log file types.
Log parsing tools should quickly and automatically process raw data so you can spend more time analyzing it. When choosing one, look for solutions with automatic parsers for the formats most often used (and custom rules support for others), real-time search and discovery capabilities, real-time troubleshooting issues in logs in real-time search capabilities – like Loggly’s real-time access to live streams of logs using dynamic field explorer to automatically index raw data with fast semantic searches – for instance.
3. Efficient log analysis
Log analysis tools play an indispensable role in helping engineers efficiently troubleshoot issues, as their success in finding their source is determined by how quickly they can identify its root cause. Log data aggregation and parsing services allow engineers to aggregate log information quickly into easily digestible formats that enable engineers to recognize patterns and troubleshoot more quickly using organized information.
Effective log analysis tools combine the benefits of search engines with data visualization and management capabilities. SolarWinds(r) Loggly and Cribl are powerful log analysis solutions with built-in monitoring, reporting, visualisation features.
An ideal logging tool must support multiple file formats and offer options to transform and display log data based on specific use cases. Vector, an open source observability framework, provides various ‘Transforms’ which make processing key-value data from logs much simpler; some solutions even provide a GUI interface for configuring parsing configurations without complicated code being needed!
What Log Parsing Features to Look For?
Applying appropriate log parser commands to your digital environment can make life much simpler. A reliable logging tool should make creating parsing rules much simpler without needing to write complex SQL queries (which requires expertise). A basic log parser command can organize various unstructured data sources into organized information that’s easier for you to comprehend and troubleshoot.
These tools typically support several formats, including text files, CSV files, the event log and Windows registry data. Some also accommodate advanced data formats like XML and JSON as well as media files such as images or video.
Log parsers should also feature visual aids to help identify patterns in the data that you retrieve, which will aid you in quickly finding areas in need of attention and save you time. Such features could include dashboards and charts which highlight spikes, repeat events or anomalies as well as features to highlight important information within logs or color-code specific fields for easy identification.
Log events are semi-structured data that require expert knowledge and relevant rules for analysis. Manually creating these rules is time consuming; instead using a log parser can rapidly categorize and analyze these events.
Parsing can enable advanced analytics such as searching, filtering, grouping, counting and sophisticated mining. Unfortunately, parsing is usually performed ad-hoc at search time, making it slow and error prone.
Traditional tools require engineers to write regular expressions to match specific event patterns, which can become unmanageable when there are too many types of events to match and it becomes hard to stay up-to-date with system logging format changes. Luckily, the latest generation of log management platforms like SolarWinds Loggly perform automatic log parsing as soon as data arrives; eliminating manual work and improving accuracy. Other online log parsing methods such as SHISO, LenMa and MoLFI use heuristic algorithms like longest common subsequence to compare new messages against representative clusters of existing events.
An effective log parser enables users to customize the data they extract through a graphical user interface, regular expressions or the solution’s own language. No matter the method employed by this tool, its aim should always be the same – to allow for custom configuration creation in an iterative fashion before saving for optimal results.
Papertrail provides support for multiple log formats so its automatic parser can parse them easily, giving teams more freedom when troubleshooting issues and discovering actionable insights.
If the field in retrieved logs doesn’t match an expected value, this can easily be resolved by creating a variable section in your parsing rule. Doing this will ensure that only logs containing matching data will be processed by the parser – helping prevent data overload and bloat. To define one, highlight desired text within log and select “Define Variable Section“, this will open a window allowing you to assign matching values directly into log fields.
Log parsing tools should provide users with an easily understandable visual display of the results of their queries, making this feature invaluable to teams of all sizes as they monitor application health and craft solutions.
Log viewers that allow for the creation of charts and dashboards can help your team identify patterns in data that might otherwise be hard to decipher from lines of text alone. Search for tools with filtering features, color coding options and dynamic field explorer capabilities so they can focus on information most important to them more easily.
Tech professionals’ primary job is to keep applications healthy and operational, which requires understanding why an application is experiencing issues so appropriate solutions can be developed. Unfortunately, sorting through vast amounts of data is time consuming; an efficient log parser like Loggly can speed up this process so you can find information faster than ever. Give it a try now to experience its difference on your IT infrastructure!
How Does a Log Parser Work?
Log parsing involves breaking up large chunks of information stored as plain text into manageable bits that are easier to comprehend, understand, organize, and store. By employing specific commands and queries IT staff can quickly structure available information in ways most suitable to their needs.
IT professionals can use the grok pattern parser command to quickly search through logs for specific data points that indicate an issue, helping identify its source and isolate its cause.
Filtering and searching are among the many log parsing features provided by most logging solutions, which help ensure only relevant and accurate information is gleaned from incoming data. Many solutions offer built-in grok parsers to match and pull from multiple log formats; alternatively, solutions like Crible allow users to build their own using either graphical interfaces or regular expression syntax syntax.
Centralize your logs for deep analysis
Logging tools that cannot centralize all events across various layers make it hard to correlate events and identify trends, making correlation difficult or impossible. For example, when your infrastructure and application logs both show sudden spikes in errors or performance slowdowns that appear similar, it can be challenging to tell whether they represent isolated behaviour or part of an underlying trend.
An effective centralized log management initiative begins by selecting a tool with automated parsers for common log formats, so that less time will be spent processing log ingest and more can be dedicated to analyzing the data.
Some logging solutions utilize clustering algorithms to further investigate logs, sorting them into distinct clusters based on similarity. These clusters then serve as templates for parsing new log entries more accurately and recognize specific event types more quickly compared with traditional parsing tools that utilize regular expressions and grammar rules for parsing log entries. This approach may provide more efficient results.
Log Parser Input Formats
Your digital environment contains various kinds of data, some of which is unstructured. Unorganized information makes it more difficult to locate and interpret what’s contained therein and can consume significant processing power.
Log parsers typically use text, XML and JSON as input formats for their analysis, extracting free-text tokens automatically from raw log messages before building log templates representing each log message in an input dataset. These templates can then be reused to parse new log entries by replacing tokens with predetermined placeholders.
An effective log management solution should include a visual parser configuration editor that enables you to write custom parsing rules, and preview key-value pairs from sample log data before saving your parser. This feature is important because an incorrect parsing configuration may lead to inaccurate log analysis results; additionally, performing dry runs on sample log data before applying it live will ensure its successful deployment on real-time logs.
Log parsing is an integral component of data analytics tools, but can be an intricate process. Current solutions often rely on heuristic algorithms and optimization techniques with limited generalizability; additionally, they do not take into account that modern ICT systems constantly evolve.
Gao et al.  provide a search-based algorithm to navigate raw logs and extract event templates. Delog  uses token-integer hash mapping to search logs while LCS-based matching classifies them; Slop  utilizes sequence alignment technology for improved parsing accuracy.
NuLog  employs self-supervised learning by masking input tokens, vectorizing them and positionally encoding them for feeding into a two-layer neural network model. Once fed, this neural network model processes new log messages by masking variable parts as being associated with event types before creating a log template accordingly. This approach has proven highly successful when dealing with complex format strings as well as multiple variations of identical log messages.