#graphdb — Public Fediverse posts

Let me use this time to write down what I'm building. Not what's in my git repos. What's in my head.

The system is distributed. And distribution is optional. Each node has their own state, their own runtime, their own persistence. All one thing.

Each node in the system is a content-addressed Merkle Tree. Not SHA. Coincidence. A spectral coincidence hash. 5 dimensions. (1 more than spacetime.)

A spectral coincidence hash describes the structure of the data. You can do math on that. Spectral graph analysis. The world becomes navigatable. Because everything has an address.

Each computation. Each inference. Each failure. Each partial success. Each time information was lost. All of it enters the graph. All of it local. All of it distributed if needed. (For the BEAM engineers: it's an mnesia backend.)

A system that doesn't only know what it knows and what it doesn't. A system that knows how it got there. And AI that lives inside that knowledge.

That's what I'm building.

#SovereignTechFellowship grant application open. Release on the horizon. Wish me luck!

#OpenSource #EUTech #AI #Erlang #Fortan #Rust #GraphDB

Let me use this time to write down what I'm building. Not what's in my git repos. What's in my head.

The system is distributed. And distribution is optional. Each node has their own state, their own runtime, their own persistence. All one thing.

Each node in the system is content addressed. Not SHA. Coincidence. A spectral coincidence hash. 5 dimensions. (1 more than spacetime.)

A spectral coincidence hash describes the structure of the data. You can do math on that. Spectral graph analysis. The world becomes navigatable. Because everything has an address.

Each computation. Each inference. Each failure. Each partial success. Each time information was lost. All of it enters the graph. All of it local. All of it distributed if needed. (For the BEAM engineers: it's an mnesia backend.)

A system that doesn't only know what it knows and what it doesn't. A system that knows how it got there. And AI that lives inside that knowledge.

That's what I'm building. #SovereignTechFellowship grant application open. Release on the horizon. Wish me luck!

#OpenSource #EUTech #AI #Erlang #Fortan #Rust #GraphDB

Let me use this time to write down what I'm building. Not what's in my git repos. What's in my head.

The system is distributed. And distribution is optional. Each node has their own state, their own runtime, their own persistence. All one thing.

Each node in the system is a content-addressed Merkle Tree. Not SHA. Coincidence. A spectral coincidence hash. 5 dimensions. (1 more than spacetime.)

A spectral coincidence hash describes the structure of the data. You can do math on that. Spectral graph analysis. The world becomes navigatable. Because everything has an address.

Each computation. Each inference. Each failure. Each partial success. Each time information was lost. All of it enters the graph. All of it local. All of it distributed if needed. (For the BEAM engineers: it's an mnesia backend.)

A system that doesn't only know what it knows and what it doesn't. A system that knows how it got there. And AI that lives inside that knowledge.

That's what I'm building.

#SovereignTechFellowship grant application open. Release on the horizon. Wish me luck!

#OpenSource #EUTech #AI #Erlang #Fortan #Rust #GraphDB

Let me use this time to write down what I'm building. Not what's in my git repos. What's in my head.

The system is distributed. And distribution is optional. Each node has their own state, their own runtime, their own persistence. All one thing.

Each node in the system is a content-addressed Merkle Tree. Not SHA. Coincidence. A spectral coincidence hash. 5 dimensions. (1 more than spacetime.)

A spectral coincidence hash describes the structure of the data. You can do math on that. Spectral graph analysis. The world becomes navigatable. Because everything has an address.

Each computation. Each inference. Each failure. Each partial success. Each time information was lost. All of it enters the graph. All of it local. All of it distributed if needed. (For the BEAM engineers: it's an mnesia backend.)

A system that doesn't only know what it knows and what it doesn't. A system that knows how it got there. And AI that lives inside that knowledge.

That's what I'm building.

#SovereignTechFellowship grant application open. Release on the horizon. Wish me luck!

#OpenSource #EUTech #AI #Erlang #Fortan #Rust #GraphDB

Let me use this time to write down what I'm building. Not what's in my git repos. What's in my head.

The system is distributed. And distribution is optional. Each node has their own state, their own runtime, their own persistence. All one thing.

Each node in the system is a content-addressed Merkle Tree. Not SHA. Coincidence. A spectral coincidence hash. 5 dimensions. (1 more than spacetime.)

A spectral coincidence hash describes the structure of the data. You can do math on that. Spectral graph analysis. The world becomes navigatable. Because everything has an address.

Each computation. Each inference. Each failure. Each partial success. Each time information was lost. All of it enters the graph. All of it local. All of it distributed if needed. (For the BEAM engineers: it's an mnesia backend.)

A system that doesn't only know what it knows and what it doesn't. A system that knows how it got there. And AI that lives inside that knowledge.

That's what I'm building.

#SovereignTechFellowship grant application open. Release on the horizon. Wish me luck!

#OpenSource #EUTech #AI #Erlang #Fortan #Rust #GraphDB

I just published a blog post that tells of my early experience with Gastown, a multi-agent workspace manager, in trying to build pure javascript Gremlator implementation: https://stephen.genoprime.com/snippet/2026/04/03/snippet-23.html #graphdb

Knowledge graphs are useful representations for knowledge bases, #pkm, #AImemory systems, #GraphRAG, intelligent tutoring systems, etc., and usually implemented in graph databases. LadybugDB, a fork of the discontinued Kuzu, is a lightweight embedded (like SQLite) graph database: https://github.com/LadybugDB/ladybug
Sample applications in development: https://github.com/inventivepotter/dotmd & https://github.com/tejzpr/Smriti-MCP
See also Grafeo: https://github.com/GrafeoDB/grafeo
#AIEd #AIEngineering #KnowledgeGraph #GraphDB #graphdatabase

Knowledge graphs are useful representations for knowledge bases, #pkm, #AImemory systems, #GraphRAG, intelligent tutoring systems, etc., and usually implemented in graph databases. LadybugDB, a fork of the discontinued Kuzu, is a lightweight embedded (like SQLite) graph database: https://github.com/LadybugDB/ladybug
Sample applications in development: https://github.com/inventivepotter/dotmd & https://github.com/tejzpr/Smriti-MCP
See also Grafeo: https://github.com/GrafeoDB/grafeo
#AIEd #AIEngineering #KnowledgeGraph #GraphDB #graphdatabase

Knowledge graphs are useful representations for knowledge bases, #pkm, #AImemory systems, #GraphRAG, intelligent tutoring systems, etc., and usually implemented in graph databases. LadybugDB, a fork of the discontinued Kuzu, is a lightweight embedded (like SQLite) graph database: https://github.com/LadybugDB/ladybug
Sample applications in development: https://github.com/inventivepotter/dotmd & https://github.com/tejzpr/Smriti-MCP
See also Grafeo: https://github.com/GrafeoDB/grafeo
#AIEd #AIEngineering #KnowledgeGraph #GraphDB #graphdatabase

Knowledge graphs are useful representations for knowledge bases, #pkm, #AImemory systems, #GraphRAG, intelligent tutoring systems, etc., and usually implemented in graph databases. LadybugDB, a fork of the discontinued Kuzu, is a lightweight embedded (like SQLite) graph database: https://github.com/LadybugDB/ladybug
Sample applications in development: https://github.com/inventivepotter/dotmd & https://github.com/tejzpr/Smriti-MCP
See also Grafeo: https://github.com/GrafeoDB/grafeo
#AIEd #AIEngineering #KnowledgeGraph #GraphDB #graphdatabase

Knowledge graphs are useful representations for knowledge bases, #pkm, #AImemory systems, #GraphRAG, intelligent tutoring systems, etc., and usually implemented in graph databases. LadybugDB, a fork of the discontinued Kuzu, is a lightweight embedded (like SQLite) graph database: https://github.com/LadybugDB/ladybug
Sample applications in development: https://github.com/inventivepotter/dotmd & https://github.com/tejzpr/Smriti-MCP
See also Grafeo: https://github.com/GrafeoDB/grafeo
#AIEd #AIEngineering #KnowledgeGraph #GraphDB #graphdatabase

Also if you are into #rdf and #knowledgegraph you have heard of tinker pop gremlin 👹 then the following pipeline might remind you some good souvenir:

´´´
(let ((g (make-graph gremlin-n gremlin-e 10 12345)))
(display "Graph: ")
(display gremlin-n)
(display " vertices, ")
(display gremlin-e)
(display " edges/vertex, 10 groups")
(newline)
(let ((tri (time
(traverse g
(V)
(as a)
(out)
(as b)
(where (same-group? g a b))
(out)
(as c)
(where (same-group? g a c))
(where (edge? g c a))
(count)))))
(display tri)
(display " triangles")
(newline)))
´´´

ref: https://github.com/amirouche/seed/blob/seed3/src/src/seed3/benchmarks/gremlin-pipeline.seed3

#scheme #graphdb

ArcadeDB v26.2.1 is full of surprises. Did you know we introduced a new SQL parser, built with ANTLR (instead of JavaCC) that is 75.9% faster? blog.arcadedb.com/new-sql-parser #multimodel #graphdb #sql

With ArcadeDB 26.2.1 you can schedule automatic backups. Check out this new feature: blog.arcadedb.com/introducing-... #graphdb #multimodel

blog.arcadedb.com/introducing-au...

ArcadeDB 26.2.1 is out! Biggest highlights: - @Neo4j Bolt protocol support - use standard Neo4j drivers - Full @openCypher TCK compliance - New SQL parser - SQL Triggers (SQL, JS, Java) - Scheduled Backups 200+ issues closed github.com/ArcadeData/a... #graphdb #multimodel

Release 26.2.1 · ArcadeData/ar...

ArcadeDB v26.1.1 is out! New Native OpenCypher Engine, huge LSM Vector updates (Quantization, PQ and much more), 92 total issues resolved (!) github.com/ArcadeData/a... #ArcadeDB #GraphDB #OpenCypher #VectorSearch #Database #OpenSource

Release 26.1.1 · ArcadeData/ar...

ArcadeDB v26.1.1 is out! New Native OpenCypher Engine, huge LSM Vector updates (Quantization, PQ and much more), 92 total issues resolved (!) github.com/ArcadeData/a... #ArcadeDB #GraphDB #OpenCypher #VectorSearch #Database #OpenSource

Release 26.1.1 · ArcadeData/ar...

ArcadeDB v26.1.1 is out! New Native OpenCypher Engine, huge LSM Vector updates (Quantization, PQ and much more), 92 total issues resolved (!) github.com/ArcadeData/a... #ArcadeDB #GraphDB #OpenCypher #VectorSearch #Database #OpenSource

Release 26.1.1 · ArcadeData/ar...

ArcadeDB v26.1.1 is out! New Native OpenCypher Engine, huge LSM Vector updates (Quantization, PQ and much more), 92 total issues resolved (!) github.com/ArcadeData/a... #ArcadeDB #GraphDB #OpenCypher #VectorSearch #Database #OpenSource

Release 26.1.1 · ArcadeData/ar...

olu 0.9.0 RELEASED

olu is a JSON document store with automatic graph relationships.

Repo:
https://github.com/ha1tch/olu

Manual
https://github.com/ha1tch/olu/blob/main/MANUAL.md

Release binaries:
https://github.com/ha1tch/olu/releases/tag/v0.9.0

Use cases:

• Rapid prototyping
• Small to medium CRUD APIs
• IoT asset/device management
• Multi-tenant SaaS backends
• Content management systems
• Configuration stores with relationships
• Graph analysis and traversal

Features

• REST API: Full CRUD, filtering, pagination, field projection, REF embedding
• OQL: (sql-like subset) — SELECT, INSERT, UPDATE, DELETE, WHERE, ORDER BY, TOP, GROUP BY, aggregates (COUNT, SUM, AVG, MIN, MAX)
• Sulpher: Cypher-like graph queries — MATCH patterns, shortest path, common neighbours, incoming/outgoing traversal, cycle detection, node degree

New in 0.9.0:

• SQLite + FTS5 full-text search
• REF field embedding with configurable depth
• Graph cycle detection (warn/error/ignore)
• JWT/API key authentication
• Rate limiting with per-key quotas
• Prometheus metrics
• Multi-tenant isolation (path or strict mode)
• Docker Compose profiles for dev/test/prod

250+ unit tests, stress tests with race detector, 17 Docker integration tests. Redis cache at 36K ops/sec.

#olu #database #json #golang #foss #datastore #graph #graphdb #REST

olu 0.9.0 RELEASED

olu is a JSON document store with automatic graph relationships.

Repo:
https://github.com/ha1tch/olu

Manual
https://github.com/ha1tch/olu/blob/main/MANUAL.md

Release binaries:
https://github.com/ha1tch/olu/releases/tag/v0.9.0

Use cases:

• Rapid prototyping
• Small to medium CRUD APIs
• IoT asset/device management
• Multi-tenant SaaS backends
• Content management systems
• Configuration stores with relationships
• Graph analysis and traversal

Features

• REST API: Full CRUD, filtering, pagination, field projection, REF embedding
• OQL: (sql-like subset) — SELECT, INSERT, UPDATE, DELETE, WHERE, ORDER BY, TOP, GROUP BY, aggregates (COUNT, SUM, AVG, MIN, MAX)
• Sulpher: Cypher-like graph queries — MATCH patterns, shortest path, common neighbours, incoming/outgoing traversal, cycle detection, node degree

New in 0.9.0:

• SQLite + FTS5 full-text search
• REF field embedding with configurable depth
• Graph cycle detection (warn/error/ignore)
• JWT/API key authentication
• Rate limiting with per-key quotas
• Prometheus metrics
• Multi-tenant isolation (path or strict mode)
• Docker Compose profiles for dev/test/prod

250+ unit tests, stress tests with race detector, 17 Docker integration tests. Redis cache at 36K ops/sec.

#olu #database #json #golang #foss #datastore #graph #graphdb #REST

olu 0.9.0 RELEASED

olu is a JSON document store with automatic graph relationships.

Repo:
https://github.com/ha1tch/olu

Manual
https://github.com/ha1tch/olu/blob/main/MANUAL.md

Release binaries:
https://github.com/ha1tch/olu/releases/tag/v0.9.0

Use cases:

• Rapid prototyping
• Small to medium CRUD APIs
• IoT asset/device management
• Multi-tenant SaaS backends
• Content management systems
• Configuration stores with relationships
• Graph analysis and traversal

Features

• REST API: Full CRUD, filtering, pagination, field projection, REF embedding
• OQL: (sql-like subset) — SELECT, INSERT, UPDATE, DELETE, WHERE, ORDER BY, TOP, GROUP BY, aggregates (COUNT, SUM, AVG, MIN, MAX)
• Sulpher: Cypher-like graph queries — MATCH patterns, shortest path, common neighbours, incoming/outgoing traversal, cycle detection, node degree

New in 0.9.0:

• SQLite + FTS5 full-text search
• REF field embedding with configurable depth
• Graph cycle detection (warn/error/ignore)
• JWT/API key authentication
• Rate limiting with per-key quotas
• Prometheus metrics
• Multi-tenant isolation (path or strict mode)
• Docker Compose profiles for dev/test/prod

250+ unit tests, stress tests with race detector, 17 Docker integration tests. Redis cache at 36K ops/sec.

#olu #database #json #golang #foss #datastore #graph #graphdb #REST

olu 0.9.0 RELEASED

olu is a JSON document store with automatic graph relationships.

Repo:
https://github.com/ha1tch/olu

Manual
https://github.com/ha1tch/olu/blob/main/MANUAL.md

Release binaries:
https://github.com/ha1tch/olu/releases/tag/v0.9.0

Use cases:

• Rapid prototyping
• Small to medium CRUD APIs
• IoT asset/device management
• Multi-tenant SaaS backends
• Content management systems
• Configuration stores with relationships
• Graph analysis and traversal

Features

• REST API: Full CRUD, filtering, pagination, field projection, REF embedding
• OQL: (sql-like subset) — SELECT, INSERT, UPDATE, DELETE, WHERE, ORDER BY, TOP, GROUP BY, aggregates (COUNT, SUM, AVG, MIN, MAX)
• Sulpher: Cypher-like graph queries — MATCH patterns, shortest path, common neighbours, incoming/outgoing traversal, cycle detection, node degree

New in 0.9.0:

• SQLite + FTS5 full-text search
• REF field embedding with configurable depth
• Graph cycle detection (warn/error/ignore)
• JWT/API key authentication
• Rate limiting with per-key quotas
• Prometheus metrics
• Multi-tenant isolation (path or strict mode)
• Docker Compose profiles for dev/test/prod

250+ unit tests, stress tests with race detector, 17 Docker integration tests. Redis cache at 36K ops/sec.

#olu #database #json #golang #foss #datastore #graph #graphdb #REST

olu 0.9.0 RELEASED

olu is a JSON document store with automatic graph relationships.

Repo:
https://github.com/ha1tch/olu

Manual
https://github.com/ha1tch/olu/blob/main/MANUAL.md

Release binaries:
https://github.com/ha1tch/olu/releases/tag/v0.9.0

Use cases:

• Rapid prototyping
• Small to medium CRUD APIs
• IoT asset/device management
• Multi-tenant SaaS backends
• Content management systems
• Configuration stores with relationships
• Graph analysis and traversal

Features

• REST API: Full CRUD, filtering, pagination, field projection, REF embedding
• OQL: (sql-like subset) — SELECT, INSERT, UPDATE, DELETE, WHERE, ORDER BY, TOP, GROUP BY, aggregates (COUNT, SUM, AVG, MIN, MAX)
• Sulpher: Cypher-like graph queries — MATCH patterns, shortest path, common neighbours, incoming/outgoing traversal, cycle detection, node degree

New in 0.9.0:

• SQLite + FTS5 full-text search
• REF field embedding with configurable depth
• Graph cycle detection (warn/error/ignore)
• JWT/API key authentication
• Rate limiting with per-key quotas
• Prometheus metrics
• Multi-tenant isolation (path or strict mode)
• Docker Compose profiles for dev/test/prod

250+ unit tests, stress tests with race detector, 17 Docker integration tests. Redis cache at 36K ops/sec.

#olu #database #json #golang #foss #datastore #graph #graphdb #REST

https://blog.martinscloud.com/graphdb-uise-docker-compose-for-graphdb/ #docker #compose #docker_compose #docker_container #graphdb

🚀 ArcadeDB v25.11.1 is live! We've integrated the JVector engine for high-performance vector search, critical SQL fixes, smarter indexing for embedded lists, and improved gRPC serialization. github.com/ArcadeData/a... #ArcadeDB #OpenSource #GraphDB #VectorDatabase #NoSQL

Release 25.11.1 · ArcadeData/a...

As @apachetinkerpop reaches it's 75th release on the 3.x line, with 3.7.5 and 3.8.0 officially announced and 4.0.0 on the horizon, I thought I'd write this blog post reflecting a bit on all this: https://stephen.genoprime.com/snippet/2025/11/14/snippet-22.html #graphdb

It is a good feeling when you can unlock performance gains with some simple changes. Memgraph performance was already great, but keeps getting better.

#programming #memgraph #graphdb #database #db

I don't often post about work, but its worth sharing and celebrating from time to time.

A small team, including myself, have worked for a while to improve replication and introduce high availability to #memgraph

It is now at a stage which is developer ready. There maybe some future small changes to be done around UX but the majority of the work is done.

🥳 A big milestone 🚀

https://memgraph.com/blog/announcing-memgraph-high-availability-automatic-failover-developer-ready

#database #databases #graphdb

rserv and olu

This paints a clearer picture of the evolution of the two projects and API coverage over the last 12 months. The goal is to bring Olu to api parity with rserv 0.5.3 targeting completion sometime between March and June 2026.

rserv 0.5.3

• https://github.com/ha1tch/rserv

olu 0.7.0

• https://github.com/ha1tch/olu

#olu #graphdb #documentdb #foss #golang

Olu

Olu is a JSON document store with automatic graph relationship tracking. When documents reference each other, the graph layer maintains edges automatically. RESTful API, dual storage backends, basic graph queries. Written in Go.

https://github.com/ha1tch/olu?tab=readme-ov-file

Apart from REST queries, it features an implementation of Sulpher, a small subset of Neo4js Cypher. Comes batteries included with an in-memory LRU cache, or configurable with an external Redis cache (Docker Compose-ready, just make docker-run)

Core CRUD and graph operations work. About half the planned API is implemented. Still early but functional for prototyping.

#olu #graph #graphdb #documentdb #foss #go

Just received news that today, Semantic Web Company (SWC) and Ontotext have joined forces and merged into Graphwise! Definitely the right step to bring platform, tools, methodologies, and expertise all together.

Graphwise website: https://graphwise.ai/

#semanticweb #ontologies #SPARQL #triplestore #taxonomies #knowledgegraphs #biz #ontotext #graphdb #poolparty #llms #explainableAI #generativeAI #AI

New tutorial: Setting up a #graph database using #GTFS data & #Neo4J

http://anitagraser.com/2023/11/27/setting-up-a-graph-db-using-gtfs-data-neo4j/

#PublicTransport #GraphDB #MobilityDataAnalytics #Mobility #DataScience #Cypher #GISChat #SustainableMobility

Automate expiry of graph components in your Amazon Neptune database using Time to Live (TTL) and the techniques discussed in this blog post series.

https://aws.amazon.com/blogs/database/implement-time-to-live-in-amazon-neptune-part-1-property-graph/

#GraphDatabases #graphdb #AmazonNeptune

So I decided part of the reason development on #AetherMUD is going so slow is me repeating the same code in my DAO layer in multiple spots. A bit of a shortcoming of #Ferma

Ferma is a database extraction layer for graph databases

Luckily as the founder of Ferma I can always just add new features to the code base there (its also open-source). So im going to try to toss in some new Ferma features for handling sets and object copying.

For those interested the source code :opensource: for both are below, they are written in Java :java:

https://github.com/Syncleus/AetherMUD

https://github.com/Syncleus/Ferma

#Java #OSS #FOSS #GameDev #Gaming #NoSQL #GraphDB #OpenSource

Olu

I'm considering adding this query language syntax that looks like SQL but operates on Olu's graphs.

#olu #graphdb #golang #foss

GenAI has uncovered a number of new and unique use cases for Large Language Models (LLMs). One of those is the use of LLMs for code generation. More specifically, using LLMs to generate database queries from natural language questions. In the following video, see how you can use the open source LangChain library with various LLMs to generate queries for graph data stored in Amazon Neptune.

https://youtu.be/B7GtC1IeIUA

#graphdb #aws #amazonneptune

I really REALLY love the look of the new site I just launched for my Goblin project.

http://goblin-ogm.com/

Its an Open-source :opensource: OGM (Object-Graph Mapper) for Python.

#Python #OGM #ORM #GraphDB #Tinkerpop #TP3 #TP #OSS #FLOSS

Arcade 25.10.1 is out! Python Bindings for embedded use, significant query and indexing enhancements, bug fixes, SQL/Cypher compatibility, and much more #multimodel #graphdb github.com/ArcadeData/a...

Release 25.10.1 · ArcadeData/a...

We're excited to announce the release of ArcadeDB version 25.9.1! This is a significant update that introduces powerful new features for developers, including experimental gRPC protocol support and multiple issues fixed. #graphdb #multimodel github.com/ArcadeData/a...

Release 25.9.1 · ArcadeData/ar...

We're excited to announce the release of ArcadeDB version 25.9.1! This is a significant update that introduces powerful new features for developers, including experimental gRPC protocol support and multiple issues fixed. #graphdb #multimodel https://github.com/ArcadeData/arcadedb/releases/tag/25.9.1

We're excited to announce the release of ArcadeDB v25.8.1! This version introduces a powerful new schema feature, enhances Kubernetes support, and resolves several important bugs to improve stability and performance. #graphdb #multimodel github.com/ArcadeData/a...

Release 25.8.1 · ArcadeData/ar...

We are excited to announce the general availability of ArcadeDB version 25.7.1! Check all the improvements on: github.com/ArcadeData/a... #multimodel #graphdb

Release 25.7.1 · ArcadeData/ar...

We're excited to announce ArcadeDB v25.6.1! This release brings significant improvements in stability, performance, and developer experience. We've focused on explicit locking and enhancing remote transaction handling. github.com/ArcadeData/a... #multimodel #graphdb #nosql

Release 25.6.1 · ArcadeData/ar...

ArcadeDB v25.4.1 introduces is now based on Java 21, it has improved connection strategy for HA setups, define unidirectional edges in the schema, faster HTTP protocol and much more! github.com/ArcadeData/a... #multimodel #graphdb #nosql

Release 25.4.1 · ArcadeData/ar...

follow-up question on datascience.meta.stackexchange.com after original Stack Overflow question closed "off-topic":

https://datascience.meta.stackexchange.com/q/2658/153859

#stackoverflow #tinkerpop #gremlin #graphtheory #graphdatabase #graphdb #vertexdb #janusdb #semanticweb #knowledgeweb #knowledgegraph #epistemology #datascience

updated in response to comments with motivating example and illustrative diagram:

https://stackoverflow.com/revisions/76947672/2

#stackoverflow #tinkerpop #gremlin #graphtheory #graphdatabase #graphdb #vertexdb #janusdb #semanticweb #knowledgeweb #knowledgegraph #epistemology #datascience

#stackoverflow: how to represent competing or concurrent "realities" in TinkerPop (or other graph/vertex databases)

https://stackoverflow.com/q/76947672/155090

#tinkerpop #gremlin #graphtheory #graphdatabase #graphdb #vertexdb #janusdb #semanticweb #knowledgeweb #knowledgegraph #epistemology #datascience

Automate expiry of graph components in your Amazon Neptune database using Time to Live (TTL) and the techniques discussed in this blog post series.

https://aws.amazon.com/blogs/database/implement-time-to-live-in-amazon-neptune-part-1-property-graph/

#GraphDatabases #graphdb #AmazonNeptune

Three years ago I was learning Gremilin for TinkerPop, since I really want to use a graph database for this project and I hate Cypher.

Now I'm looking at this code and going "how the fuck does this work, again?”

#gremlin #tinkerpop #nodejs #graphdb