simple-graph
ldbc_snb_datagen_spark
| simple-graph | ldbc_snb_datagen_spark | |
|---|---|---|
| 7 | 5 | |
| 1,329 | 163 | |
| - | 0.6% | |
| 2.5 | 3.7 | |
| about 1 year ago | 2 days ago | |
| Go | Java | |
| MIT License | Apache License 2.0 |
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simple-graph
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PageRank Algorithm for Graph Databases
SimpleGraph might be worth a look. I've found it useful. https://github.com/dpapathanasiou/simple-graph
- Bullshit Graph Database Performance Benchmarks
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is there any graph db like sqlite or nedb that is good for prototyping?
This may fit your needs: https://github.com/dpapathanasiou/simple-graph
- Show HN: Simple-graph – a graph database in SQLite
- Simple-graph: a graph database in SQLite, via “SQLite as a document database”
ldbc_snb_datagen_spark
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Benchgraph Backstory: The Untapped Potential
Because of the size, complexity, and feedback from the community, we decided to add a larger dataset. So the next dataset should be large, more complex, and recognizable. The choice was easy here; the industry-leading benchmark group Linked Data Benchmark Council (LDBC), which Memgraph is a part of, has open-sourced the datasets for benchmarking. The exact dataset is the social network dataset. It is a synthetically generated dataset representing a social network. It is being used in LDBC audited benchmarks, SNB interactive, and SNB Buissines intelligence benchmarks. Keep in mind that this is NOT an official implementation of an LDBC benchmark, the open-source dataset is being used as a basis for benchmarks, and it will be used for our in-house testing process and improving Memgraph.
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Postgres: The Graph Database You Didn't Know You Had
I designed and maintain several graph benchmarks in the Linked Data Benchmark Council, including workloads aimed for databases [1]. We make no restrictions on implementations, they can any query language like Cypher, SQL, etc.
In our last benchmark aimed at analytical systems [2], we found that SQL queries using WITH RECURSIVE can work for expressing reachability and even weighted shortest path queries. However, formulating an efficient algorithm yields very complex SQL queries [3] and their execution requires a system with a sophisticated optimizer such as Umbra developed at TU Munich [4]. Industry SQL systems are not yet at this level but they may attain that sometime in the future.
Another direction to include graph queries in SQL is the upcoming SQL/PGQ (Property Graph Queries) extension. I'm involved in a project at CWI Amsterdam to incorporate this language into DuckDB [5].
[1] https://ldbcouncil.org/benchmarks/snb/
[2] https://www.vldb.org/pvldb/vol16/p877-szarnyas.pdf
[3] https://github.com/ldbc/ldbc_snb_bi/blob/main/umbra/queries/...
[4] https://umbra-db.com/
[5] https://www.cidrdb.org/cidr2023/slides/p66-wolde-slides.pdf
- Bullshit Graph Database Performance Benchmarks
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From Data Preprocessing to Using Graph Database
Pull the source code from https://github.com/ldbc/ldbc_snb_datagen/tree/stable.To generate data for scale factor 1-1000, use the stable branch.
What are some alternatives?
Apache AGE - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL.
kuzu - Embeddable property graph database management system built for query speed and scalability. Implements Cypher.
ldbc_snb_bi - Reference implementations for the LDBC Social Network Benchmark's Business Intelligence (BI) workload
NetworkXum - A NetworkX-like Python wrapper for graphs persisted in a DBMS
benchgraph
ipfs-sqlite-block-store - SQLite based ipfs block store
arcadedb - ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.
nebula-docker-compose - Docker compose for Nebula Graph
hash-db - Experimental distributed pseudomultimodel keyvalue database (it uses python dictionaries) imitating dynamodb querying with join only SQL support, distributed joins and simple Cypher graph support and document storage
ustore - Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️