Open-Source Databases · EvgSkv

logica

Logica is an open-source declarative logic programming language that compiles to SQL, enabling engineers and data scientists to write queries for BigQuery, PostgreSQL, SQLite, and DuckDB using logic programming syntax instead of SQL. It bridges the gap between the mathematical clarity of Datalog and the computational power of modern SQL engines.

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Source: GitHub — github.com/EvgSkv/logica

2.1k

GitHub stars

109

Forks

Jupyter Notebook

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	EvgSkv/logica
Owner	EvgSkv
Primary language	Jupyter Notebook
License	Apache-2.0 — OSI-approved
Stars	2.1k
Forks	109
Open issues	41
Latest release	Unknown
Last updated	2026-06-13
Source	https://github.com/EvgSkv/logica

What logica is

Logica is a Datalog-family language that transpiles logic programs into SQL for execution on multiple backends (BigQuery, PostgreSQL, SQLite, DuckDB). It handles aggregation, negation, and disjunction with explicit translation semantics, allowing developers to express relational data operations declaratively while leveraging the optimization and parallelization capabilities of the underlying SQL engine.

Quickstart

Get the logica source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/EvgSkv/logica.git2cd logica3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Complex relational queries on BigQuery

Teams already using BigQuery can express intricate multi-step queries (joins, aggregations, nested logic) more concisely and readably in Logica than in SQL, with automatic compilation to optimized BigQuery SQL.

Data pipeline development with logic programming background

Engineers or data scientists with Datalog or Prolog experience can apply formal logic programming techniques to big data problems without learning SQL syntax or sacrificing computational scale.

Exploratory data analysis on local databases

Analysts can use SQLite or DuckDB backends to prototype and run logic programs locally during development, then port to cloud engines (BigQuery) for production without rewriting logic.

Implementation considerations

Requires Python 3 and pip; optional Google Cloud SDK for BigQuery command-line integration. Local development possible with SQLite or DuckDB without cloud credentials.
Learning curve depends on team familiarity with Datalog/logic programming. Budget time for syntax training and experimentation with tutorial and example datasets.
Compilation and debugging workflow differs from SQL; developers must understand the SQL output that Logica generates to optimize performance and troubleshoot logic errors.
Integration with existing data pipelines (dbt, Airflow, etc.) requires wrapper scripts or custom operators; Logica output is SQL, so downstream tools expecting direct database queries need adapter logic.
Version and release cycle: no tagged release in repo; last commit June 2026. Evaluate stability and maintenance cadence before adopting in production systems.

When to avoid it — and what to weigh

Team has no logic programming expertise — Logica introduces a new language and paradigm. Teams unfamiliar with Datalog or logic programming face a steeper adoption curve than using SQL or ORM tools they may already know.
Vendor lock-in concerns with BigQuery primary use — Logica's main value proposition centers on BigQuery; while it supports PostgreSQL, SQLite, and DuckDB, the ecosystem and examples favor Google Cloud, creating potential lock-in for organizations evaluating portability.
Heavy reliance on database-specific features or dialects — Logica abstracts to a common SQL subset. Projects requiring vendor-specific extensions (JSON operators, window functions with custom syntax) may lose expressiveness or require fallback to raw SQL.
Real-time or streaming data pipelines — Logica targets batch query execution on SQL engines. Projects requiring sub-second latency, stream processing, or complex event processing are better served by Kafka, Flink, or dedicated streaming languages.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license permitting commercial use, modification, and distribution with standard liability disclaimers and attribution requirements.

Apache-2.0 is a permissive open-source license that explicitly permits commercial use and derivative works. No restrictive terms prevent building commercial products or services with Logica. However, confirm that any customizations or vendor-specific extensions comply with the license. No SLA, support contract, or warranty is implied; support depends on community forums, issues, and the original maintainers' availability.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Good
Assessment confidence	Medium

Security considerations

Logica is a compiler; security depends on the underlying SQL engine (BigQuery, PostgreSQL, SQLite, DuckDB) and network/credential configuration. Input validation: Logica parses logic programs; untrusted program text should be treated carefully. SQL injection: Logica compiles to parameterized SQL, reducing injection risk, but developers must validate data sources and query logic. No cryptographic operations or sensitive data handling specific to Logica is mentioned. Audit compiled SQL output before production deployment. Credential management for cloud backends (BigQuery authentication) is the responsibility of the deployment environment.

Alternatives to consider

Presto/Trino

Distributed SQL query engine supporting multiple backends. Better for teams already invested in SQL; offers strong data federation but lacks Logica's logic programming syntax and aggregation-friendly semantics.

dbt (data build tool)

SQL-based data transformation framework with templating, testing, and DAG orchestration. Widely adopted; integrates with Logica (Logica generates SQL dbt can consume), but does not offer logic programming paradigm.

SWI-Prolog or GNU Prolog with external DB libraries

Native logic programming engines with mature ecosystems. Do not scale to big data without custom extensions; much less suitable for cloud data warehouses, but useful for on-machine logic problems.

Software development agency

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logica FAQ

Can I use Logica for real-time queries?

No. Logica compiles to SQL and executes batch queries on SQL engines. Real-time or streaming use cases are outside its scope; consider Kafka, Flink, or stream processing languages.

Do I need to know SQL to use Logica?

Not required, but helpful. Logica assumes familiarity with relational data concepts. Knowledge of Datalog or logic programming is an asset; SQL knowledge aids in understanding and debugging compiled output.

How is performance compared to hand-written SQL?

Unknown without benchmarks in provided data. Logica outputs SQL that engines optimize; performance depends on compilation quality and the underlying engine's query optimizer. Review compiled SQL for critical queries.

What is the status of support for Presto/Trino mentioned in topics?

Not clearly stated in README or examples. Requires review of GitHub issues and code to confirm actual support scope and maturity.

Custom software development services

Need help beyond evaluating logica? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source databases integrations — and maintain them long-term.

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