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Quickstart

This guide walks you through loading a dataset and running your first Cypher query.

Prerequisites

Build TurboLynx first: see Building TurboLynx.

Step 1 — Import a Dataset

turbolynx import \
    --workspace /path/to/db \
    --nodes Person   data/Person.csv \
    --nodes Comment  data/Comment.csv \
    --relationships KNOWS data/Person_knows_Person.csv
  • --workspace — directory where the database is stored (catalog.bin, store.db, .store_meta)
  • --nodes <Label> <file> — vertex CSV file (repeatable)
  • --relationships <TYPE> <file> — edge CSV file (repeatable)

For CSV format details, see Data Import.

A sample LDBC SNB SF1 dataset (pipe-separated CSV with typed headers, ready for turbolynx import) is hosted on Hugging Face:

# Download via huggingface-cli
pip install huggingface_hub
huggingface-cli download HuggignHajae/TurboLynx-LDBC-SF1 --repo-type dataset --local-dir ./ldbc-sf1

Or browse the files directly: HuggignHajae/TurboLynx-LDBC-SF1.

Step 2 — Open the Shell

turbolynx shell --workspace /path/to/db

TurboLynx shell — type '.help' for commands, ':exit' to quit
TurboLynx >>

Step 3 — Run a Query

TurboLynx >> MATCH (n:Person) RETURN n.firstName, n.lastName LIMIT 10;

Step 4 — Build Statistics (first time only)

Statistics are required for the ORCA cost-based optimizer to generate optimal plans. Run .analyze after loading data:

TurboLynx >> .analyze

Run a Single Query Non-Interactively

turbolynx shell --workspace /path/to/db \
                --query "MATCH (n:Person) RETURN n.firstName LIMIT 5;"

Combine with --mode to control output format:

turbolynx shell --workspace /path/to/db \
                --mode csv \
                --query "MATCH (n:Person) RETURN n.firstName, n.lastName;"

Example: Hop Query

MATCH (a:Person)-[:KNOWS]->(b:Person)
WHERE a.firstName = 'Alice'
RETURN b.firstName, b.lastName
LIMIT 20;

Next Steps