Textql

TextQL is a command line SQL query engine designed to execute relational queries directly against structured text files, such as CSV and TSV, without requiring a database import. It functions as a relational text file analyzer and a CSV processor that treats plain text files as virtual tables for filtering, joining, and aggregating data.

The tool is built as a pipe-compatible data transformation utility, allowing it to process data from standard input and output formatted datasets. It enables relational joins across multiple files or directories within a single query to analyze relationships between different datasets.

The engine includes automatic data type detection for numeric, date, and time values to ensure accurate calculations and sorting. It also supports the loading of external shared libraries to extend the query language with custom mathematical, string, and aggregate functions. Results can be exported to files using configurable delimiters.

Features

SQL Query Execution Engines - Acts as a SQL query execution engine that processes relational commands directly against structured text files.

Command-Line Data Processors - Provides a high-performance CLI utility for manipulating and analyzing structured datasets via SQL.

Cross-Source Joins - Enables analytical join operations across multiple disparate text files and directories treated as virtual tables.

Flat-File SQL Analysis - Allows running SQL queries on CSV or TSV files to filter and aggregate data without importing it into a database.

Relational Join Engines - Implements a relational engine for combining data from multiple text files based on shared column values.

SQL Engines - Provides a relational query processor that treats CSV files as tables for SQL execution.

In-Memory Execution Engines - Provides an engine that executes SQL operations directly on text data stored in RAM without a persistent database.

Relational Text Analyzers - Performs relational joins and data analysis across multiple plain text files using a SQL interface.

CLI Data Processors - Functions as a CLI tool that transforms input strings or files into structured output based on SQL queries.

Ad Hoc Dataset Querying - Enables quick extraction of information from structured text files without the overhead of setting up a database server.

Automatic Type Detection - Automatically infers column data types during ingestion to ensure accurate sorting and calculations.

Standard Input Ingestion - Reads raw data from standard input to allow the tool to be used within shell command pipelines.

Custom SQL Functions - Provides the ability to extend the query language with custom mathematical, string, and aggregate operations via shared libraries.

Standard Stream Piping - Supports reading from standard input and writing to standard output to integrate with shell pipelines.

Dynamic Library Loading - Allows loading external shared libraries at runtime to extend the SQL dialect with custom mathematical and string functions.

Tabular Data Type Inference - Automatically detects data types such as dates and numbers during the scanning of tabular text files.

Query Engines - Executes SQL queries against structured text files.

dinedaltextql

Features

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