ScaleStore VS NebulaGraph Database

I don't remember exactly why I have any of them saved, but these are some experimental data stores that seems to be fitting what you're looking for somewhat:

- https://github.com/DataManagementLab/ScaleStore - "A Fast and Cost-Efficient Storage Engine using DRAM, NVMe, and RDMA"

- https://github.com/unum-cloud/udisk - "The fastest ACID-transactional persisted Key-Value store designed for NVMe block-devices with GPU-acceleration and SPDK to bypass the Linux kernel."

- https://github.com/capsuleman/ssd-nvme-database - "Columnar database on SSD NVMe"

The linked blog post at the top of this article - https://muratbuffalo.blogspot.com/2023/01/is-scalable-oltp-i... - provides graphics that give extremely useful context. And here's the repo for the paper that discusses: https://github.com/DataManagementLab/ScaleStore

The idea that one of many writer-compute-nodes can literally reach into a memory buffer that is shared across machines, atomically flip some lock bits and propagate some cache-coherence messages, and use that to build a multi-writer distributed database without needing to partition (and where any writer-compute-node can handle any message, so you can just round-robin a firehose of messages at them)... and that there's a chance (though not yet implemented) that one could implement ACID on top of this? It's absolute madness, and wildly exciting.

A NoSQL graph database is a type of non-relational, distributed database which employs a graph model. NoSQL stands for “Not only SQL” and refers to a new breed of databases that differ from traditional relational databases in their data model and performance. Graph databases are especially useful for data associated with relationships—everything from friendships on social netwo#rks to equipment supply chains or business processes. They can quickly traverse vast amounts of linked data points to discover insights and hidden connections between entities, making them ideal for network analysis– such as financial fraud detection, recommendation engines and many other use cases– all while performing at scale.

An open source graph database is always the best place to start as they come with a supportive community that ultimately creates the perfect ecosystem.

When we first came across NebulaGraph, because the ecology was not perfect, and only some businesses migrated to Nebula, we used to import NebulaGraph data, whether full or incremental, by pushing Hive tables to Kafka and consuming Kafka to write NebulaGraph in batch. Later, as more and more data and businesses switched to NebulaGraph, the problem of importing data efficiency became more and more serious. The increase in import time made it unacceptable to still be importing data at full volume during peak business hours.

NebulaGraph NebulaGraph is a popular open-source graph database that can handle large volumes of data with milliseconds of latency, scale up quickly, and have the ability to perform fast graph analytics. Official website https://www.nebula-graph.io/ GitHub projects https://github.com/orgs/vesoft-inc/repositories https://github.com/vesoft-inc/nebula

NebulaGraph, a leading open source graph database, announced it raised tens of millions of US dollars in Series A funding. Investors in the round are led by Jeneration Capital, with participation from the previous investors - Matrix Partner China, Redpoint China Ventures, and Source Code Capital. China Renaissance served as the exclusive financial advisor in this financing round.

Support backup and restore. https://github.com/vesoft-inc/nebula/pull/3469 https://github.com/vesoft-inc/nebula-agent/pull/1 https://github.com/vesoft-inc/nebula-br/pull/22

One option is NebulaGraph if your looking for low latency, scalability, and HA.

After the step-by-step decomposition of the patterns, the expected execution plan for the MATCH clause is finally generated. As you can see, it takes a lot of effort to transform a complicated pattern into the underlying interfaces for a traversal. Of course, the execution plan can be optimized, such as the multi-step traversal can be encapsulated by using the Loop operator and the sub-plan of a one-step traversal can be reused, which will not be detailed in this article. If you are interested, please refer to the source code of Nebula Graph.