DeciSym Engine (de)

de is a command-line tool for creating, querying, and inspecting RDF data in HDT (Header, Dictionary, Triples) format.

It is intended for workflows where RDF data needs compact storage and SPARQL querying over both RDF and HDT inputs.

Installation

Run directly from a local clone (no install required):

git clone https://github.com/DeciSym/de.git
cd de
cargo run -- --help

Install the CLI from crates.io:

cargo install de

Install with server command enabled:

cargo install --features server de

Install the CLI from a local clone:

cargo install --path .

Docker image:

docker run --rm decisym/de:latest --help

Example

The example below corresponds to the SPARQL 1.1 Query Recommendation:

• §2.1 Triple Patterns: https://www.w3.org/TR/sparql11-query/#basicpatterns

From the repository root, create the example input data (simple.nt):

<http://example.org/book/book1> <http://purl.org/dc/elements/1.1/title> "SPARQL Tutorial" .

Create the query (simple.rq):

SELECT ?title
WHERE
{
  <http://example.org/book/book1> <http://purl.org/dc/elements/1.1/title> ?title .
}

Run directly against RDF:

cargo run -- query --data simple.nt --sparql simple.rq --output csv

Output:

title
SPARQL Tutorial

Convert to HDT and run the same query:

cargo run -- create --output-name simple.hdt --data simple.nt
cargo run -- query --data simple.hdt --sparql simple.rq --output csv

Output:

title
SPARQL Tutorial

Command Reference

Use CLI help (and installed man pages, if available in your environment) as the canonical command reference:

cargo run -- --help
cargo run -- <command> --help

If you installed the CLI:

de --help
de <command> --help

Examples:

cargo run -- create --help
cargo run -- query --help
cargo run -- view --help

Development

Run core checks:

cargo fmt --check
cargo clippy --all-features --all-targets -- -D warnings
cargo test --all-features

Run W3C RDF/SPARQL integration tests:

cargo test --all-features --test w3c-sparql

Benchmarks

The trainmarks criterion suite covers create and query over the synthetic e-commerce graph from trainmarks, checked out as a submodule at benches/trainmarks. It exists to catch performance regressions at a realistic dataset size, not to compare de against other engines.

It measures four things: building an HDT from N-Triples, building one from Turtle (the only path that runs the RDF parser), the five queries against a prebuilt HDT, and one query given a Turtle file directly, which de converts to a temporary package before evaluating.

make bench first checks out the submodule and generates the fixtures the suite reads:

make bench

The dataset scale is BENCH_SCALE — medium (~100K triples), large (~1M, the default) or xlarge (~10M):

BENCH_SCALE=medium make bench

make bench passes the scale through to the suite as DE_BENCH_SCALE, which is also what to set when driving cargo bench directly. The fixtures and the benchmark must agree on it, since the scale is part of every benchmark id (trainmarks_query/large/q3_join_3_entities) and criterion compares each run against the stored baseline for that id:

make bench-init                          # once, to lay down the fixtures
DE_BENCH_SCALE=large cargo bench --bench trainmarks
DE_BENCH_SCALE=large cargo bench --bench trainmarks -- q3_join_3_entities

Without the fixtures the suite prints how to get them and measures nothing, so cargo bench still works on a fresh clone.

The queries are trainmarks' own q1–q5, shared verbatim with the other engines in that report. q6_delete_insert is omitted: it is a SPARQL Update, and HDT packages are immutable.