Logica: language of Big Data

Logica is an open source declarative logic programming language for data manipulation. Logica is a successor to Yedalog, a language created at Google earlier.

Why?

Logica is for engineers, data scientists and other specialists who want to use logic programming syntax when writing queries and pipelines to run on BigQuery.

Logica compiles to StandardSQL and gives you access to the power of BigQuery engine with the convenience of logic programming syntax. This is useful because BigQuery is magnitudes more powerful than state of the art native logic programming engines.

We encourage you to try Logica, especially if

you already use logic programming and need more computational power, or
you use SQL, but feel unsatisfied about its readability, or
you want to learn logic programming and apply it to processing of Big Data.

In the future we plan to support more SQL dialects and engines.

I have not heard of logic programming. What is it?

Logic programming is a declarative programming paradigm where the program is written as a set of logical statements.

Logic programming was developed in academia from the late 60s. Prolog and Datalog are the most prominent examples of logic programming languages. Logica is a language of the Datalog family.

Datalog and relational databases start from the same idea: think of data as relations and think of data manipulation as a sequence of operations over these relations. But Datalog and SQL differ in how these operations are described. Datalog is inspired by the mathematical syntax of the first order propositional logic and SQL follows the syntax of natural language.

SQL was based on the natural language to give access to databases to the people without formal training in computer programming or mathematics. This convenience may become costly when the logic that you want to express is non trivial. There are many examples of hard-to-read SQL queries that correspond to simple logic programs.

How does Logica work?

Logica compiles the logic program into a SQL expression, so it can be executed on BigQuery, the state of the art SQL engine.

Among database theoreticians Datalog and SQL are known to be equivalent. And indeed the conversion from Datalog to SQL and back is often straightforward. However there are a few nuances, for example how to treat disjunction and negation. In Logica we tried to make choices that make understanding of the resulting SQL structure as easy as possible, thus empowering user to write programs that are executed efficiently.

Why is it called Logica?

Logica stands for Logic with aggregation.

How to learn?

Learn basics of Logica with the CoLab tutorial located at tutorial folder. See examples of using Logica in examples folder.

Tutorial and examples show how to access Logica from CoLab. You can also install Logica command line tool.

Prerequisites

To run Logica programs on BigQuery you will need a Google Cloud Project. Once you have a project you can run Logica programs in CoLab providing your project id.

To run Logica locally you need Python3.

To initiate Logica predicates execution from the command line you will need bq, a BigQuery command line tool. For that you need to install Google Cloud SDK.

Installation

Google Cloud Project is the only thing you need to run Logica in Colab, see Hello World example.

You can install Logica command with pip as follows.

# Install.
python3 -m pip install logica
# Run:
# To see usage message.
python3 -m logica
# To print SQL for HelloWorld program.
python3 -m logica - print Greet <<<'Greet(greeting: "Hello world!")'

If your PATH includes Python's bin folder then you will also be able to run it simply as

logica - print Greet <<<'Greet(greeting: "Hello world!")'

Alternatively, you can clone GitHub repository:

git clone https://github.com/evgskv/logica
cd logica
./logica - print Greet <<<'Greet(greeting: "Hello world!")'

Code samples

Here a couple examples of how Logica code looks like.

Prime numbers

Find prime numbers less than 30.

Program primes.l:

# Define natural numbers from 1 to 29.
N(x) :- x in Range(30);
# Define primes.
Prime(prime: x) :-
  N(x),
  x > 1,
  ~(
    N(y),
    y > 1,
    y != x,
    x % y == 0
  );

Running primes.l

$ logica primes.l run Prime
+-------+
| prime |
+-------+
|     2 |
|     3 |
|     5 |
|     7 |
|    11 |
|    13 |
|    17 |
|    19 |
|    23 |
|    29 |
+-------+

News mentions

Who was mentioned in the news in 2020 the most? Let's query GDELT Project dataset.

Program mentions.l

@OrderBy(Mentions, "mentions desc");
@Limit(Mentions, 10);
Mentions(person:, mentions? += 1) distinct :-
  gdelt-bq.gdeltv2.gkg(persons:, date:),
  Substr(ToString(date), 0, 4) == "2020",
  the_persons == Split(persons, ";"),
  person in the_persons;

Running mentions.l

$ logica mentions.l run Mentions
+----------------+----------+
|     person     | mentions |
+----------------+----------+
| donald trump   |  3624228 |
| joe biden      |  1591320 |
| los angeles    |  1221998 |
| george floyd   |   923472 |
| boris johnson  |   845955 |
| barack obama   |   541672 |
| vladimir putin |   486428 |
| bernie sanders |   409224 |
| andrew cuomo   |   375594 |
| nancy pelosi   |   375373 |
+----------------+----------+

Note that cities of Los Angeles and Las Vegas are mentioned in this table due to known missclasification issue in the GDELT data analysis.

Feedback

Feel free to create github issues for bugs and feature requests.

You questions and comments are welcome at our github discussions!

Unless otherwise noted, the Logica source files are distributed under the Apache 2.0 license found in the LICENSE file.

This is not an officially supported Google product.

EvgSkv/logica

EvgSkv

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