6 Repos
Engines that execute SQL queries natively using in-memory data structures like dictionaries.
Distinct from SQL Query Execution Engines: Distinct from general SQL Query Execution Engines by targeting in-memory Python objects for testing purposes.
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sqlglot is a SQL parser and transpiler that represents queries as abstract syntax trees to enable structural analysis, modification, and semantic transformation. It functions as a dialect translator and query optimizer, converting SQL code between different database engines and simplifying syntax trees through rule-based normalization. The project provides a framework for defining custom SQL dialects by overriding tokenizers, parsers, and generators. It includes a lineage analyzer to track data flow from source tables through complex queries to identify the origin of specific columns. Additi
Interprets and executes SQL queries natively using Python dictionaries as data sources.
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
Provides an engine that executes SQL operations directly on text data stored in RAM without a persistent database.
Osmedeus is a security workflow orchestration engine that coordinates AI agents, shell commands, and scanning tools through declarative YAML pipelines. It functions as a distributed security scanner, a declarative workflow automator, and an AI agent framework for security, enabling automated multi-step security analysis with conditional branching, parallel execution, and distributed workers. The engine distinguishes itself through a hybrid runner model that executes workflow steps on the local host, inside Docker containers, or over SSH to remote machines, selected per step or module. It supp
Provides functions to extract data from JSON strings or files using jq query syntax.
csvkit is a composable Unix-style command-line toolkit for converting, filtering, and analyzing CSV files directly from the terminal. It provides a suite of focused single-purpose commands that can be combined via pipes to build complex data processing workflows, with a modular architecture that includes a column-type inference engine for automatically detecting data types and a streaming-pipeline design for efficient handling of tabular data. The toolkit distinguishes itself through its SQL-engine abstraction layer, which allows users to run SQL queries directly against CSV files without req
Computes summary statistics and aggregations entirely in memory using Python data structures.
jnv is an interactive terminal application for querying and filtering JSON data using jq expressions. It combines a keyboard-driven JSON browser with a real-time jq filter editor, allowing users to navigate, expand, and collapse JSON structures while simultaneously writing and previewing filter results. The tool reads JSON from files or standard input, including JSON Lines format, and provides immediate visual feedback as filters are typed. The application distinguishes itself by integrating jq filter development with live preview and auto-completion, suggesting completions for identifiers, o
Embeds the jq query language runtime to parse and execute filter expressions against JSON data.
gojq is a JSON query engine and transformation tool implemented in Go. It serves as a Go language port of the jq command, providing a library for embedding JSON and YAML manipulation capabilities directly into Go applications. The tool functions as an arbitrary-precision JSON processor, performing mathematical operations on large integers to prevent precision loss or numeric overflow during data transformations. Its broader capabilities include filtering and reshaping structured data using a specialized query language, converting between YAML and JSON formats, and formatting date and time st
Implements a Go-based runtime to parse and execute jq filter expressions against JSON data.