Angle Grinder

Angle Grinder is a command line log processor and analytics tool used for parsing, filtering, and aggregating logs through a pipeline of text transformations. It functions as a text transformation pipeline that converts unstructured logs, as well as JSON and logfmt serialized data, into structured fields for analysis.

The tool enables the computation of summary statistics, including running totals, counts, averages, and percentiles. It specifically supports time series log processing by partitioning data into discrete time windows to analyze event frequency and system behavior.

The processing surface covers data extraction using regular expressions and wildcards, conditional filtering, and field transformation via mathematical and logical operators. It also provides capabilities for sorting aggregate results and formatting output into JSON, logfmt, or plain text.

Features

Log Processing Pipelines - Functions as a sequential pipeline for parsing, filtering, and transforming unstructured log data into structured formats.
Log Analytics Tools - A utility for computing summary statistics, running totals, and time-based aggregates from structured and unstructured log data.
Log Analysis - Parses and interprets raw log files from the terminal to extract insights and identify patterns.
Structured Log Parsing - Parses standardized formats like JSON and logfmt within log streams for analytical use.
Regex Extraction Utilities - Uses named capture groups and regular expressions to extract structured fields from unstructured log text.
Conditional Data Filters - Filters log entries by dropping rows that do not meet specific boolean expressions or comparison criteria.
Statistical Aggregators - Computes summary statistics including counts, sums, averages, and percentiles grouped by specific fields.
Logfmt and JSON Parsers - Converts JSON and logfmt serialized rows into structured fields for downstream analysis.
Data Aggregators - Computes summary statistics such as counts, sums, and averages across log datasets.
Field Mapping Expressions - Uses custom expressions to transform input fields into new values for downstream analysis.
Field Transformations - Modifies field values using mathematical operators, string functions, and conditional logic.
JSON Data Extraction - Parses JSON-serialized logs into structured fields with support for nested structures.
Log Statistics Aggregations - Computes summaries such as counts, averages, and percentiles from large datasets to identify trends.
Logfmt Data Extraction - Parses logfmt-serialized rows into structured fields for downstream analysis.
Pattern-Based Data Extraction - Extracts variables from text using greedy wildcards and named regular expression captures.
Time Bucketing - Aggregates data into fixed time windows by slicing the timeline into discrete buckets.
Time Series Slicing - Slices temporal log data into discrete windows for time-series analysis.
Time-Windowed Log Analysis - Groups log entries into discrete time windows to analyze event frequency and system behavior.
Command-Line Data Processors - Functions as a CLI tool for parsing and transforming structured log data streams.
Data Transformation Pipelines - Implements a customizable sequence of processing steps to transform raw logs into structured data.
Temporal Bucket Partitioning - Groups log entries into discrete temporal buckets to analyze event frequency and system behavior.
Data Transformation Expressions - Implements a custom logic engine for computing new field values using mathematical operators and string functions.
Log Entry Filters - Provides predicate-based logic to determine which log entries should be emitted based on matching patterns.
Dynamic Field Mapping - Tracks structured data as dynamic key-value pairs that evolve as records pass through pipeline stages.
Pipeline Aliases - Allows storing frequently used processing pipelines as named aliases in configuration files.
Aggregate Result Sorting - Orders aggregated analysis results based on computed values or expressions.
Output Format Rendering - Renders processed logs and aggregate results into various formats like JSON and logfmt.
Stream Result Limits - Allows restricting the number of processed log rows from the start or end of the stream.
Multi-Format Serializers - Converts internal data structures into multiple output formats including JSON, logfmt, and plain text.
Running Total Calculators - Calculates a cumulative sum of a specific field across the entire processed dataset.
Pipeline Alias Definitions - Enables the definition of reusable named pipelines in configuration files to simplify parsing tasks.
Configuration-Driven Pipelines - Provides the ability to define named sequences of processing steps in configuration files for reusable log analysis.
Custom Log Formatting - Transforms raw log data into custom layouts and specific formats like JSON or plain text.
Output Field Filters - Includes filters to control which fields are retained or dropped in the final output to reduce noise.
Command Line Tools - Listed in the “Command Line Tools” section of the The Book Of Secret Knowledge awesome list.

Istoric stele

rcohangle-grinder

Name: rcoh/angle-grinder
Author: rcoh

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3,740 stele74 fork-uriRustMIT13 vizualizări

Angle Grinder

Features

Log Processing Pipelines - Functions as a sequential pipeline for parsing, filtering, and transforming unstructured log data into structured formats.
Log Analytics Tools - A utility for computing summary statistics, running totals, and time-based aggregates from structured and unstructured log data.
Log Analysis - Parses and interprets raw log files from the terminal to extract insights and identify patterns.
Structured Log Parsing - Parses standardized formats like JSON and logfmt within log streams for analytical use.
Regex Extraction Utilities - Uses named capture groups and regular expressions to extract structured fields from unstructured log text.
Conditional Data Filters - Filters log entries by dropping rows that do not meet specific boolean expressions or comparison criteria.
Statistical Aggregators - Computes summary statistics including counts, sums, averages, and percentiles grouped by specific fields.
Logfmt and JSON Parsers - Converts JSON and logfmt serialized rows into structured fields for downstream analysis.
Data Aggregators - Computes summary statistics such as counts, sums, and averages across log datasets.
Field Mapping Expressions - Uses custom expressions to transform input fields into new values for downstream analysis.
Field Transformations - Modifies field values using mathematical operators, string functions, and conditional logic.
JSON Data Extraction - Parses JSON-serialized logs into structured fields with support for nested structures.
Log Statistics Aggregations - Computes summaries such as counts, averages, and percentiles from large datasets to identify trends.
Logfmt Data Extraction - Parses logfmt-serialized rows into structured fields for downstream analysis.
Pattern-Based Data Extraction - Extracts variables from text using greedy wildcards and named regular expression captures.
Time Bucketing - Aggregates data into fixed time windows by slicing the timeline into discrete buckets.
Time Series Slicing - Slices temporal log data into discrete windows for time-series analysis.
Time-Windowed Log Analysis - Groups log entries into discrete time windows to analyze event frequency and system behavior.
Command-Line Data Processors - Functions as a CLI tool for parsing and transforming structured log data streams.
Data Transformation Pipelines - Implements a customizable sequence of processing steps to transform raw logs into structured data.
Temporal Bucket Partitioning - Groups log entries into discrete temporal buckets to analyze event frequency and system behavior.
Data Transformation Expressions - Implements a custom logic engine for computing new field values using mathematical operators and string functions.
Log Entry Filters - Provides predicate-based logic to determine which log entries should be emitted based on matching patterns.
Dynamic Field Mapping - Tracks structured data as dynamic key-value pairs that evolve as records pass through pipeline stages.
Pipeline Aliases - Allows storing frequently used processing pipelines as named aliases in configuration files.
Aggregate Result Sorting - Orders aggregated analysis results based on computed values or expressions.
Output Format Rendering - Renders processed logs and aggregate results into various formats like JSON and logfmt.
Stream Result Limits - Allows restricting the number of processed log rows from the start or end of the stream.
Multi-Format Serializers - Converts internal data structures into multiple output formats including JSON, logfmt, and plain text.
Running Total Calculators - Calculates a cumulative sum of a specific field across the entire processed dataset.
Pipeline Alias Definitions - Enables the definition of reusable named pipelines in configuration files to simplify parsing tasks.
Configuration-Driven Pipelines - Provides the ability to define named sequences of processing steps in configuration files for reusable log analysis.
Custom Log Formatting - Transforms raw log data into custom layouts and specific formats like JSON or plain text.
Output Field Filters - Includes filters to control which fields are retained or dropped in the final output to reduce noise.
Command Line Tools - Listed in the “Command Line Tools” section of the The Book Of Secret Knowledge awesome list.

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Întrebări frecvente

Ce face rcoh/angle-grinder?

Care sunt principalele funcționalități ale rcoh/angle-grinder?

Principalele funcționalități ale rcoh/angle-grinder sunt: Log Processing Pipelines, Log Analytics Tools, Log Analysis, Structured Log Parsing, Regex Extraction Utilities, Conditional Data Filters, Statistical Aggregators, Logfmt and JSON Parsers.

Care sunt câteva alternative open-source pentru rcoh/angle-grinder?

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