LDBC Full Suite (spec)
Expert reference: for engineers evaluating an engine against benchmark workloads. It assumes familiarity with workload generators, drivers, and benchmark validation. For your first research project, start with choosing data and the basic graph lesson.
Last updated: 2026-08-05
Status: Specification in GraphForge docs. Generators, drivers, and audited runs execute in an external scale harness — not GraphForge core CI. Convenience catalog loaders (
graphforge.datasets) remain a planned extension (Datasets overview); this page is the workload contract, not a shipped loader.Shared evaluation policy (spec vs harness boundary, completeness vs first-fail, evidence schema, pinned identity): Scale Evaluation. Size labeling: GSI. Disk-limited framing: Scale Limits.
The Graph Data Council (GDC) (formerly Linked Data Benchmark Council / LDBC) maintains the LDBC benchmark suite. Workloads remain branded “LDBC benchmarks” after the 2025 rename. This document inventories the full official suite as published on ldbcouncil.org/benchmarks and defines how GraphForge treats it at spec level.
Suite inventory (authoritative portfolio)
Section titled “Suite inventory (authoritative portfolio)”Per GDC’s published benchmarks page (verified 2026-08-05):
| Benchmark | Focus | Workloads (current) | Primary data |
|---|---|---|---|
| SNB — Social Network Benchmark | Property-graph DBMS (OLTP + OLAP-style) | Interactive (v1 / v2) and Business Intelligence (BI) | Synthetic social network (Datagen) |
| Graphalytics | Graph analysis platforms | Six core algorithms + reference outputs | Standard graph datasets (+ optional Graph500-class graphs) |
| FinBench — Financial Benchmark | Distributed transactional graph DBMS | Transaction workload (Analytics workload: future work) | Synthetic financial graph |
| SPB — Semantic Publishing Benchmark | RDF / SPARQL stores | Media-publishing ontology workload | RDF datasets |
GraphForge’s product surface is property-graph + Cypher / analyst verbs. For harness planning:
- In scope for GraphForge-facing runs: SNB Interactive, SNB BI, Graphalytics, FinBench Transaction (as the SUT allows).
- Suite completeness still lists SPB, but SPB is RDF/SPARQL — treat as out of GraphForge product path unless a separate RDF binding exists. Document SPB so “full suite” means the GDC portfolio, not a silent omission.
Historical note: graph-analytics work that once lived near early SNB drafts was delegated to Graphalytics as the official LDBC analytics benchmark.
1. Social Network Benchmark (SNB)
Section titled “1. Social Network Benchmark (SNB)”Home: ldbcouncil.org/benchmarks/snb
Specification: LDBC SNB specification (PDF)
Generator (current): ldbc_snb_datagen_spark
Docs / query repos: ldbc_snb_docs, Interactive / BI impls under github.com/ldbc
Shared dataset
Section titled “Shared dataset”Interactive and BI share a social-network schema (Person, Message/Post/Comment,
Forum, Organisation, Place, Tag, TagClass, and typed relationships such as
knows, likes, hasCreator, …). Exact attribute sets and cardinality rules
are owned by the SNB specification — do not invent alternate schemas for
“LDBC-compatible” claims.
Scale factors
Section titled “Scale factors”SF is defined by serialized CSV size in GiB (not GSI Size Tags). Authoritative SF lists live in the current SNB specification PDF — disclose the spec version used in evidence (pinned identity).
| Run class | Typical SF set | Notes |
|---|---|---|
| Engineering / harness smoke | 0.003, 0.1, 0.3 |
Laptop-feasible; non-audited; preferred developer default |
| Engineering / dedicated harness | 1, 3, 10 |
Common published comparison class (esp. SF1); still not GDC-audited unless commissioned |
| Large harness-only | 30, 100, 300, 1000, 3000, … |
Spec-published production factors; dedicated runners only |
| GDC audited certification | Per current auditing rules / auditor | Member-commissioned; full disclosure report — distinct from engineering green |
| Concern | Rule |
|---|---|
| Reproducibility | Same SF + generator version + serializer → same data |
| Initial vs updates | Interactive splits ~90% bulk load vs update streams (see spec) |
| GSI labeling | Count loaded entities → full GSI; see SF → GSI crosswalk |
| Disclosure | Evidence must record SF, Datagen commit/release, driver commit/release, serializer, and whether the run is engineering vs audited |
Workloads
Section titled “Workloads”| Workload | Intent | Query / op classes (spec-level) |
|---|---|---|
| Interactive | Transactional neighborhood + updates | Complex reads, short reads, inserts/deletes; v1 and v2 driver workflows |
| Business Intelligence (BI) | Aggregation- and join-heavy analytics | Complex analytical queries over large graph fractions; microbatches of insert/delete |
Generators and drivers (external)
Section titled “Generators and drivers (external)”- Datagen (Spark): produce CSV (or other serializers) for a chosen SF.
- Interactive / BI drivers: official LDBC driver repos execute the query mix, schedule updates, and collect latency/throughput.
- GraphForge may only expose thin ingest helpers later; the driver remains outside core.
- Identity: every engineering or audited claim must disclose Datagen and driver commit/release plus SNB spec version — see Pinned generator / driver identity and the historical evidence schema.
Pass criteria / validation (spec-level)
Section titled “Pass criteria / validation (spec-level)”Official audited runs follow GDC auditing rules (member-commissioned auditors, full disclosure report, fees — see GDC “Auditing process for LDBC SNB”). For GraphForge harness / engineering runs (non-audited), require at minimum:
- Correctness: driver validation / substitution parameters match expected results for the SF (or documented golden outputs from the LDBC tooling).
- Workload completeness: the declared query set for Interactive and/or BI is executed (no silent subset presented as “full SNB”).
- Disclosure: SF, generator commit/version, serializer, hardware, resource policy, and whether updates were enabled.
- Honest partial runs: if only Interactive SF0.1 is green, say so — do not imply BI or audited certification.
Retrospective GDC reviews called out partial SNB compliance; GraphForge docs must not encourage “SNB-like” marketing without naming the exact workload + SF.
2. Graphalytics
Section titled “2. Graphalytics”Home: ldbcouncil.org/benchmarks/graphalytics
Driver: ldbc_graphalytics
Industrial-grade benchmark for graph analysis platforms (Giraph, GraphX, GraphBLAS, …). Consists of:
- Six core algorithms (BFS, PageRank, weakly connected components, local clustering coefficient, community detection via label propagation, SSSP — exact names/parameters per the Graphalytics specification PDF).
- Standard datasets with reference outputs for validation (SURF/CWI repository).
- Optional large synthetic graphs (including Graph500-class sizes) where the Graphalytics rules allow.
GraphForge-facing dataset shortlist (ordered)
Section titled “GraphForge-facing dataset shortlist (ordered)”Harnesses targeting GraphForge should prefer this ordered shortlist unless a full Graphalytics standard-benchmark job composition is explicitly claimed. Sizes are approximate from the Graphalytics specification; re-profile → GSI after load. Full catalog remains authoritative at the GDC/Graphalytics homes.
| Order | Dataset id | Class | ≈ n | ≈ m | Typical GSI band | Role |
|---|---|---|---|---|---|---|
| 1 | wiki-Talk |
Real R1 (2XS) | 2.39M | 5.02M | GU-06-MD-… |
First engineering dataset |
| 2 | kgs |
Real R2 (XS) | 0.83M | 17.9M | GU-05-SM-… |
Gaming / denser |
| 3 | cit-Patents |
Real R3 (XS) | 3.77M | 16.5M | GU-06-MD-… |
Knowledge / citation |
| 4 | dota-league |
Real R4 (S) | 0.06M | 50.9M | GU-04-XS-… (n) / high E |
Dense gaming |
| 5 | Graph500-22 |
Synthetic G22 (S) | 2.4M | 64.2M | GU-06-MD-D00 |
Bridge to Official Graph500 SCALE 22 |
| 6 | Datagen-7.9-fb (or nearest Datagen-S) |
Synthetic D7.9 (S) | ~1.4M | ~85.7M | GU-06-MD-… |
Datagen family smoke |
| 7+ | com-Friendster, Graph500-24+, larger Datagen |
XL+ | — | — | 07+ |
Harness-only; not default laptop |
For a “full Graphalytics at dataset X” claim: run all six core algorithms on X with reference-output validation. For engineering green on the shortlist: complete algorithms the SUT implements via the official Graphalytics driver path and label omissions (do not imply six-algorithm coverage if only a subset ran).
Pass criteria / validation (spec-level)
Section titled “Pass criteria / validation (spec-level)”- Algorithm outputs validate against reference outputs within the specification’s tolerances.
- All six core algorithms run on each declared dataset for a “full Graphalytics” claim at that dataset.
- Report platform, parallelism, dataset ids, driver commit/release, and Graphalytics spec version exactly.
Relation to GraphForge: analyst verbs (PageRank, components, paths, …) may implement kernels that overlap Graphalytics algorithms, but a Graphalytics result claim requires the official driver/validation path in the external harness — not a one-off Python script.
Relation to GSI / Graph500: Graphalytics dataset sizes and optional Graph500-class graphs should be labeled with GSI after load. The Official Graph500 size ladder and Derived density matrix are separate tracks — see Scale Evaluation. Graphalytics is the algorithm workload track.
3. FinBench (Financial Benchmark)
Section titled “3. FinBench (Financial Benchmark)”Home: ldbcouncil.org/benchmarks/finbench
Specification: FinBench specification (PDF)
Targets financial scenarios (anti-fraud, risk control): high-degree hubs, edge multiplicity, asymmetric directed graphs, time-window and recursive path patterns.
| Workload | Status (per GDC / spec) |
|---|---|
| Transaction | Defined — OLTP-style complex reads + continuous insert/delete |
| Analytics | Future work (not yet a required GraphForge harness lane) |
Scale factors (Transaction)
Section titled “Scale factors (Transaction)”Per FinBench specification (v0.2.0-alpha class; disclose the exact spec version used). SF ≈ serialized CSV GiB; default temporal window three years from 2020; default split 97% initial bulk / 3% incremental. Published SF set:
| SF | ≈ CSV size (published datasets page) | Engineering default? | Notes |
|---|---|---|---|
0.01 |
~6 MB | Yes — smoke | Smallest published factor |
0.1 |
~66 MB | Yes — laptop/harness | |
0.3 |
~202 MB | Yes — harness | |
1 |
~679 MB | Dedicated harness | Spec validation-class scale |
3 |
~2 GB | Dedicated harness | |
10 |
~6 GB | Large harness | Spec notes audited Transaction runs at SF10 |
Entity counts per SF (accounts, companies, transfers, …) are in FinBench
Appendix B — re-count after load and emit GD-… GSI. Pre-built dataset tarballs:
FinBench datasets.
Pass criteria / validation (spec-level)
Section titled “Pass criteria / validation (spec-level)”- Use FinBench datagen + Transaction driver at a declared SF from the table.
- Meet driver validation for the Transaction query set.
- Disclose SF, datagen/driver commit or release, FinBench spec version, hardware, and consistency/write mode.
- Do not claim “full FinBench” if only a read subset ran, or if Analytics is implied before GDC publishes it.
Profile loaded graphs with GD-… GSI (directed).
4. Semantic Publishing Benchmark (SPB)
Section titled “4. Semantic Publishing Benchmark (SPB)”Home: listed under GDC Benchmarks
RDF/SPARQL workload based on a media-publishing ontology.
| GraphForge posture | Detail |
|---|---|
| Suite inventory | Included — part of the official LDBC portfolio (name it; do not omit silently) |
| Product path | Out of scope for Cypher/property-graph GraphForge — RDF/SPARQL only |
| Harness / SUT | Inventory-only — omit SPB from GraphForge SUT execution; no pass/fail lane |
| Evidence | When listing suite coverage, record spb: "inventory_only" (or equivalent) rather than green/red |
Loading and qualification
Section titled “Loading and qualification”GraphForge v0.6.0 does not ship a dataset catalog or LDBC convenience loader. An integration must map the generator’s actual output through supported construction APIs and verify that mapping against the workload specification. A small loaded sample is not an LDBC-compliant run. Official compliance requires the external harness and LDBC drivers.
References
Section titled “References”- Graph Data Council
- Benchmarks portfolio
- SNB · Graphalytics · FinBench
- LDBC GitHub org
- Graph Scale Index — size axis + SF crosswalk
- Scale Evaluation — harness contract, evidence schema, pinned identity, completeness vs first-fail
- Scale limits — product envelopes