Kolibrie Database Tutorial
Table of Contents
System Architecture
Kolibrie Database handles large-scale RDF data using a modular architecture:
- Triple Store: Stores RDF triples.
- Dictionary: Encodes and decodes RDF terms efficiently.
- Index Manager: Accelerates query processing.
- Query Parser: Parses SPARQL queries into executable plans.
- Query Executor: Executes queries with parallelism and SIMD optimizations.
- User-Defined Functions (UDFs): Enables custom functions.
- Stream Manager: Manages real-time data streams and windowing.
Getting Started
Ensure KolibrieDB is installed and configured. Import required modules in Rust:
use kolibrie::parser::*;
use kolibrie::sparql_database::*;
Introduction to SPARQL
SPARQL (SPARQL Protocol and RDF Query Language) is a powerful query language for retrieving and manipulating data stored in RDF (Resource Description Framework) format. RDF data is represented as triples, consisting of subject, predicate, and object. SPARQL queries match patterns in RDF triples to extract relevant data.
SPARQL Syntax Basics
- PREFIX: Defines namespaces to simplify URIs.
- SELECT: Retrieves data matching query patterns.
- WHERE: Specifies triple patterns for data retrieval.
- FILTER: Restricts query results based on conditions.
- BIND: Assigns a value to a variable within a query.
- GROUP BY: Aggregates results based on specified variables.
- INSERT: Adds new data triples into the RDF dataset.
Example query structure:
PREFIX ex: <http://example.org/>
SELECT ?subject ?predicate ?object
WHERE {
?subject ?predicate ?object .
FILTER(?predicate = ex:someProperty)
}
Writing Queries
Basic Queries
Retrieve individuals by occupation:
PREFIX ex: <http://example.org/>
SELECT ?person WHERE {
?person ex:hasOccupation "Engineer"
}
Aggregations
Calculate average salary:
PREFIX ds: <https://data.cityofchicago.org/resource/xzkq-xp2w/>
SELECT AVG(?salary) AS ?average_salary WHERE {
?employee ds:annual_salary ?salary
}
GROUP BY ?average_salary
Filtering and Bindings
Filter by author:
PREFIX dc: <http://purl.org/dc/elements/1.1/>
SELECT ?title ?author WHERE {
?book dc:title ?title .
?book dc:creator ?author .
FILTER (?author = "Jane Austen")
}
Concatenate names:
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name WHERE {
?P foaf:givenName ?G .
?P foaf:surname ?S .
BIND(CONCAT(?G, " ", ?S) AS ?name)
}
Nested Queries
Find names of friends connected to Alice:
PREFIX ex: <http://example.org/>
SELECT ?friendName WHERE {
?person ex:name "Alice" .
?person ex:knows ?friend {
SELECT ?friend ?friendName WHERE {
?friend ex:name ?friendName
}
}
}
Data Insertion
Insert a new triple:
PREFIX ex: <http://example.org/>
INSERT {
<http://example.org/JohnDoe> ex:occupation "Software Developer"
} WHERE {
<http://example.org/JohnDoe> ex:age "30"
}
Advanced Examples
Join multiple RDF descriptions:
PREFIX ex: <http://example.org/>
SELECT ?person ?location ?city ?zipcode WHERE {
?person ex:worksAt ?location .
?location ex:located ?city .
?location ex:zipcode ?zipcode
}
Integrate ML predictions:
RULE :DetectCongestion() :-
CONSTRUCT {?road ex:congestionLevel ?level.}
WHERE {
?d ex:road ?road;
ex:avgVehicleSpeed ?speed;
ex:vehicleCount ?count.
}
ML.PREDICT(MODEL "congestion_model",
INPUT {
SELECT ?road (AVG(?speed) AS ?avgSpeed) (MAX(?count) AS ?maxCount)
WHERE {
?d ex:road ?road;
ex:avgVehicleSpeed ?speed;
ex:vehicleCount ?count.
}
}, OUTPUT ?level)
Advanced Processing Modes
Single-Threaded Processing
Process RDF data in single-threaded mode:
database.parse_rdf(&file);
Multi-Threaded Processing
Leverage multi-threading:
database.parse_rdf_from_file(file_path);
CUDA-Enabled Processing
GPU-accelerated query processing:
Setup:
For Unix:
export LD_LIBRARY_PATH=<path>:$LD_LIBRARY_PATH
cmake .
cmake --build .
For Windows:
cmake -G "NMake Makefiles" -DCMAKE_BUILD_TYPE=Release .
cmake --build .
Rust example with CUDA:
#[gpu::main]
fn main() {
database.parse_rdf_from_file(file_path);
// Execute queries with GPU acceleration
}