Apache AGE
ldbc_snb_datagen_spark
| Apache AGE | ldbc_snb_datagen_spark | |
|---|---|---|
| 132 | 5 | |
| 2,695 | 163 | |
| 3.6% | 0.6% | |
| 8.9 | 3.7 | |
| 5 days ago | 3 days ago | |
| C | Java | |
| Apache License 2.0 | Apache License 2.0 |
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Apache AGE
- Apache AGE: PostgreSQL Extension Graph Data Processing and Analytics
- Apache AGE: PostgreSQL Extension Graph Data Processing and Analytics for RDBMS
- Apache AGE supporting latest PostgreSQL (ver 16)
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Enhancing Fraud Detection with Apache AGE: A Graph Database Approach
For more information and support, visit the Apache AGE website. or github.
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Unlocking the Power of Apache Age: Advanced Techniques for SQL/Cypher Hybrid Queries
These are just a few examples of how you can use Cypher queries in SQL/Cypher Hybrid Queries. By using these advanced techniques, you can perform more complex and powerful queries on your graph data. Apache AGE offer a versatile and powerful platform for working with graph and relational data concurrentlyz. To learn more you can visit age website or github page.
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Mastering Graph Queries with Cypher in Apache Age
Cypher queries in Apache Age empower users to interact with graph data efficiently and intuitively. Whether you're creating nodes, establishing relationships, or performing complex traversals, Cypher provides a robust and expressive language for working with graph databases.As you explore Apache Age and Cypher further, you'll discover additional features and nuances that make graph database management a seamless experience. Embrace the power of graph queries, and unlock the full potential of your interconnected data with Apache Age. Happy graph querying!
- We built An Open-Source platform to process relational and Graph Query simultaneously
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Machine learning and graph databases
Check Apache AGE graph database system here: Website: https://age.apache.org/ GitHub: https://github.com/apache/age
- Is Open Sourcing Technologies Good for Society
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Open Source doesn't win by being cheaper
We are also open Source community at Apache https://github.com/apache/age
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?
surrealdb - A scalable, distributed, collaborative, document-graph database, for the realtime web
ldbc_snb_bi - Reference implementations for the LDBC Social Network Benchmark's Business Intelligence (BI) workload
node-bindgen - Easy way to write Node.js module using Rust
benchgraph
Memgraph - Open-source graph database, tuned for dynamic analytics environments. Easy to adopt, scale and own.
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.
age-viewer - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL.
simple-graph - This is a simple graph database in SQLite, inspired by "SQLite as a document database"
napi-rs - A framework for building compiled Node.js add-ons in Rust via Node-API
nebula-docker-compose - Docker compose for Nebula Graph
neon - Rust bindings for writing safe and fast native Node.js modules.
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 🗄️