ldbc_snb_datagen_spark
arcadedb
ldbc_snb_datagen_spark | arcadedb | |
---|---|---|
5 | 4 | |
165 | 445 | |
1.8% | 2.2% | |
3.7 | 9.7 | |
16 days ago | 7 days ago | |
Java | JavaScript | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ldbc_snb_datagen_spark
-
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.
-
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
-
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.
arcadedb
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.
ldbc_snb_bi - Reference implementations for the LDBC Social Network Benchmark's Business Intelligence (BI) workload
kuzu - Embeddable property graph database management system built for query speed and scalability. Implements Cypher.
benchgraph
simple-graph - This is a simple graph database in SQLite, inspired by "SQLite as a document database"
whippet-db - Fast embedded key-value database engine for Java.
nebula-docker-compose - Docker compose for Nebula Graph
OrientDB - OrientDB is the most versatile DBMS supporting Graph, Document, Reactive, Full-Text and Geospatial models in one Multi-Model product. OrientDB can run distributed (Multi-Master), supports SQL, ACID Transactions, Full-Text indexing and Reactive Queries.
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 🗄️
batch-processing-gateway - The gateway component to make Spark on K8s much easier for Spark users.