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

# Activity disruption
Sometimes a few [error messages](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/value-message-distribution-powerset-thread) or [periodic errors](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/exception-stack-false-positive-hidden-periodic)
with low [statement density](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/quotient-singleton-sorted-density-current) for specific [activity regions or adjoint threads of activity](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/region-theatre-adjoint-thread-blackout-braid-group) (for a specific component, file, or function) may constitute **activity disruption**. If a particular functionality was no longer available at logging time, then its unavailability may not be explained by such disruptions, and such messages may be considered
[false positive errors](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/exception-stack-false-positive-hidden-periodic) in relation to the reported problem.

But if we have [periodic message blocks](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/periodic-message-renormalisation-significant) containing only [periodic errors](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/exception-stack-false-positive-hidden-periodic), [activity region](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/region-theatre-adjoint-thread-blackout-braid-group) or [adjoint thread](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/region-theatre-adjoint-thread-blackout-braid-group), 
[discontinuity](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/discontinuity-fiber-flag-glued-last), or simply [no activity](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/no-phantom-piecewise-punctuated-resume), then we may have a complete cessation of activity that may correlate with the unavailable functionality.

# Activity divergence
Sometimes we have several [threads of activity](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/ruptured-strand-thread-time-delta-timeout-weave)—for example, from the same process—visible for a certain period. Then, suddenly, we see only one such thread till the end of a trace (or even none). This may be an indication of an application hang or, if several active threads doing logging are normal, some other abnormal behavior. If we consider such activities (including [adjoint threads](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/region-theatre-adjoint-thread-blackout-braid-group)) as vectors running through some temporal “surface,” we can use the analogy of a **divergence**.

# Activity overlap
Sometimes, specific parts of simultaneous [use case trails](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/repeat-scale-flux-frames-viewpoints-use-case), blocks of [significant events](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/significant-silent-singleton-split-surveyor), or [message sets](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/flow-interleave-invariant-pattern-set) in general may overlap. This may be due to possible synchronization problems, such as race conditions (prognostics), or be due to visible root causes that are reported (diagnostics). We call this pattern **activity overlap**.

For example, a first request may start a new session, and we would expect that the second request would be processed by the same session.

However, users report that a second session started upon the second request. If we filter the execution log by session ID, we find that session initialization prologs are overlapped. The new session started because the first session initialization was not yet completed.

# Activity packet
Sometimes, we are interested in a [message set](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/flow-interleave-invariant-pattern-set) that has the same time attribute value (or rounded to a digit). We call this analysis pattern **activity packet** from analogy with wave packets. It may allow the identification of related threads and activities.

It is different from the activity quantum analysis pattern below where time attribute value may change for a continuous [message set](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/flow-interleave-invariant-pattern-set) with the same thread ID.

# Activity quantum
Once we have chosen a trace message, we are interested in its [message context](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/annotations-change-context-cover-directory), which can span all “continuous” messages before and after from the same [thread of activity](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/ruptured-strand-thread-time-delta-timeout-weave). We call this **activity quantum**, which is variable and independent from CPU quantum. Different messages from activity quantum may be executed on different CPUs. The following diagram depicts this analysis pattern:

You start tracing your service app and notice a few errors from dependencies, but otherwise, your app successfully continues without further errors. What analysis pattern describes the situation best?

You start tracing your service app and notice a few errors from dependencies, but otherwise, your app successfully continues without further errors. What analysis pattern describes the situation best?

You have an interesting log message you have never seen before. You want to investigate what happened before and after that message before any other thread interrupts the activity. What analysis pattern describes this scenario?

You have an interesting log message you have never seen before. You want to investigate what happened before and after that message before any other thread interrupts the activity. What analysis pattern describes this scenario?

You are interested in messages from a specific time. What analysis pattern is the most appropriate for this scenario?

You are interested in messages from a specific time. What analysis pattern is the most appropriate for this scenario?

This lesson introduces the following analysis patterns: activity disruption, activity divergence, activity overlap, activity packet, and activity quantum.

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

Disruption, Divergence, Overlap, Packet, Quantum