Gain insights into diagnostic techniques and anomaly detection with over 200 trace and log analysis patterns, applicable across various software environments, operating systems, and platforms.

General trace and log analysis patterns allow the application of uniform diagnostics and anomaly detection across diverse software environments. It includes troubleshooting, debugging, describing, and explaining problems and incidents on various operating systems and platforms. It is also helpful in recording network traces, malware, general data, narratives, and text and image analysis (space-like narratology). This course, organized as a pattern language dictionary and cross-referenced with classification, explains and teaches more than 200 traces and log analysis patterns.

A Guide to Learning Software Trace and Log Analysis Patterns

# Projective space
When we have a [split trace](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/split-tensor-dimension-extension-homotopy) with hundreds of separate trace files and a smaller split trace with fewer files that were recorded simultaneously with the larger split-trace (for example, a client from a client-server environment), we can “project” the smaller [message space](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/hedges-interspace-layered-transition-poincare) into the larger message space as depicted in the following diagram:


This **projective space** analysis pattern can be used in conjunction with [indexical trace](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/bifurcation-cartesian-correlated-galois-indexical), where time interval can be used as an index for the larger split trace. Such projections may not be completely accurate but, assuming that the target trace [statement current](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/quotient-singleton-sorted-density-current) is uniform on average, it can still be a perfect heuristic instead of a binary search. For example, recently, we had four sequential trace files for the client and 36 sequential files for the server. The software problem interval was specified in [basic facts](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/basic-hidden-indirect-facts-constants-vocabulary). We found it in the second path of the fourth client trace. We, therefore, only inspected the last four traces from the 36 in the server sequential trace set and found that they were contained in the 35th server trace.

This pattern gets its name from a projective space metaphor from mathematics.

This lesson introduces the following analysis patterns: projective space, relative density, shared point, sheaf of activities, and small DA+TA.

Getting Started

Activity

Block and Data

Error and Large Scale

Message 1

Message 2

Trace as a Whole

Trace Set

Vocabulary, Memory, Code and Text

Conclusion

Index

Projective, Relative, Shared, Sheaf, Small DA+TA

Projective space