Application logs are an age-old indispensable tool for diagnosing and resolving issues. They are still surviving in the every changing world of computing for several reasons:
- Application logs provide a historical record of events, helping developers and system administrators pinpoint the root cause and address the issue quickly.
- Application logs can track access events, user activity, and other security-related data, allowing teams to identify potential security breaches or unusual activity.
- Logs help in monitoring the performance of the application by highlighting slow processes, bottlenecks, or system failures that can affect the user experience.
However Application logs pose following challenges:
- Logs can quickly grow in volume, often containing several gigabytes of data. Sifting through massive log files manually to find relevant entries can be a time-consuming and overwhelming task especially in the event of a production issue.
- Logs often contain a mix of critical errors, informational logs, and redundant messages, making it difficult to distinguish between what’s important and what’s not.
- SRE Engineers who are maintainers of the application in production and new developers in the organization may not always be familiar with the specific errors or exceptions in the logs or their severity. This can lead to missed critical errors and delayed responses.
- Logs may not always be structured, making it harder to search, filter, or analyze data. This makes it hard to navigate the log files at the time of troubleshooting.
yCrash is designed to handle these log analysis challenges. In this post, we’ll explore its unique capabilities. Below is a screenshot of the App Log analysis report, which we will review section by section in detail.
Fig: yCrash’s Application Log Analysis Report
1. Observations
The observation section crisply reports the key findings that came out of analyzing the application logs. It summarizes important errors that surfaced in the application, repeating log patterns and long time gaps that happened between the log statements.
Fig: yCrash Tool Reporting Key Issues and Patterns Identified in Log File
2. Error Analysis
Engineers primarily look for Errors/Exceptions in the application logs to isolate the problems. Typically an application log file runs for several GB. Trying to comb through the entire log and trying to isolate critical errors like OutOfMemoryError is a tedious and time consuming process. Also SRE engineers who are not the developers of the application, don’t know what type of Errors/Exceptions to look for in the application log file. Also they aren’t aware of the severity nature of those Errors/Exceptions. Thus, critical errors often get missed.
yCrash’s Error analysis feature addresses this challenge by identifying and reporting recurring Errors/Exceptions within the application, along with their frequency and severity. This will facilitate engineers to focus on key issues instead of sifting through the entire log file.
Fig: yCrash Reporting Errors & Exception Identified in the Log Data
3. Repeating Statements
Even though a log file contains vital information to debug the production problems, it also contains a lot of unnecessary junk data. During development time, developers add log statements like: “Entering method-A()”, “Exiting method-B()” to debug their programming logic. Oftentimes, developers miss to remove these statements from their code and they make their way to production. These types of log statements don’t add much value.
These kinds of statements, besides adding clutter to the log file, they also add noticeable overhead to the application. Unnecessary logging increases the Disk I/O, CPU consumption and Storage. Also more the unnecessary log contents you ingest into log aggregation tools (like ELK, Splunk, Azure Insights…), your computing cost will go high.
To address this problem, yCrash’s Application Log report uses advanced Machine Learning algorithms to highlight repeating log statements along with their frequency.
Fig: yCrash Reporting Repeating Log Statements and Their Frequency
Repeating Statements also brings application’s unnecessary redundant execution into visibility. In one of the customer’s application log files, the tool reported that “Loading application-threshold.json” statements were happening 50,000+ times. It was a surprise (rather a shock) to the customer, because this configuration file should have been loaded only once during application startup time. It turned out that the developer was accidentally loading this configuration file on every transaction instead of application startup time. Because of the visibility given by the tool, the customer was able to fix this redundant configuration file loading issue.
4. Time Gap Analysis
Whenever there is a bottleneck in the application, such as slow database query, network latency or method taking a long time to complete, then there will be a time gap between the previous log statement and next log statement. Our human eyes are not trained to catch up these time gaps. Even if it’s trained it’s hard to catch it, due to the enormity and verbosity of the log file.
yCrash’s application log analysis addresses this gap. It studies and reports all the log statements which have several seconds time gaps between them. This feature brings application bottlenecks to direct visibility. By studying these time gap report, developers can quickly isolate their performance problems.
Fig: yCrash Reporting Time Gap Between Log Entries
5. Transforming Unstructured Data into a Structured Format
Most Log files are in an unstructured format, making it difficult to read and gather information from it. yCrash advanced algorithms, transforms this unstructured log data into a structure table format. It makes the log file easy to read, search, sort and assimilate information from them. Here are the key highlights of this feature:
- Spotting Critical errors: If any log entry contains an Exception or Error, it is prominently highlighted with a red background and an error icon, making it easy to spot critical issues at a glance.
- Detailed view of stack trace: Users can also expand the entries to view the full stack trace of the Exception or Error or the message, providing in-depth details for troubleshooting.
- Show Errors: The “Show Errors” option allows users to filter and view only the Errors or Exceptions detected within the log, streamlining the analysis process for identifying issues.
- Advance Filters: Users can further refine their view by applying filters to specific columns. Whether searching for a particular class name, log level, timestamp, or looking for specific Errors or Exceptions and so on, the filtering capabilities make it easy to drill down into the exact data points of interest.
- Sorting the Data: Each column in the table offers sorting capabilities, enabling users to organize the log entries in ascending or descending order based on their preferences.
- Customize Row View: To enhance user experience, we also provide a “Show Rows” option, allowing users to customize the number of rows displayed at a time, ensuring flexibility in how the data is viewed and analyzed.
- Advance Search: The Advanced Search functionality enhances your search capabilities, allowing users to perform searches in standard mode, or refine results further by enabling Match Case or Match Whole Word.
Fig: yCrash Structuring Log Data with Advanced Features
6. My Patterns
In the application log that we have acquaintances, we first try to look for certain keywords or key statements in them. To make this look up seamless, My Patterns feature is introduced. “My Patterns” is your personalized feature for fine-tuning log analysis. It allows you to define, refine, and tailor your log pattern recognition. You can create and define custom log patterns, helping you focus your analysis on specific log entries or sequences that matter most to you.
Fig: yCrash Identifying and Displaying Custom Log Patterns
How to use yCrash’s Application Log Analysis Feature?
This documentation explains how to perform log analysis manually or in automated mode using the yCrash application. With yCrash, log analysis becomes more efficient and actionable—helping you identify critical errors and uncover performance bottlenecks with ease.
Conclusion
yCrash’s Application Log Analysis feature transforms the way logs are monitored and analyzed by addressing the common challenges faced during log analysis, such as handling large volumes of unstructured data, identifying critical errors, and detecting performance bottlenecks. By utilizing advanced algorithms and machine learning, yCrash not only simplifies the process of identifying recurring errors and unnecessary log statements but also enhances the ability to spot performance issues through time gap analysis. This results in faster issue resolution, improved application stability, and a more efficient development and operations workflow.
Video
Watch the webinar below for an in-depth walkthrough of how to analyze application logs precisely and easily using yCrash.
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