NebulaGraph Database VS spicedb

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.

Have you taken a look at SpiceDB? The Authzed blog has a few posts that are useful to improving your understanding -- I can think of two: New Enemies and Writing relationships to SpiceDB.

Things I can't live without in a new Go project in no particular order:

- https://github.com/golangci/golangci-lint - meta-linter

- https://goreleaser.com - automate release workflows

- https://magefile.org - build tool that can version your tools

- https://github.com/ory/dockertest/v3 - run containers for e2e testing

- https://github.com/ecordell/optgen - generate functional options

- https://golang.org/x/tools/cmd/stringer - generate String()

- https://mvdan.cc/gofumpt - stricter gofmt

- https://github.com/stretchr/testify - test assertion library

- https://github.com/rs/zerolog - logging

- https://github.com/spf13/cobra - CLI framework

FWIW, I just lifted all the tools we use for https://github.com/authzed/spicedb

We've also written some custom linters that might be useful for other folks: https://github.com/authzed/spicedb/tree/main/tools/analyzers

At AuthZed, we think about this topic regularly while developing SpiceDB[0], except we believe feature flags are a subset of authorization. I'd disagree with the author that permissions are always long-lived -- authorization can also be ephemeral (and often that's how it's most secure) or dependent on run-time context[1]. What's more, using SpiceDB, we can often collapse checking for authorization and feature-flags into a single round-trip by defining a permission that can additionally require a feature flag (e.g. permission = admin & has_feature_flag).

It's a little silly, but lots of folks ask for the moon when it comes to performance for authorization because it's critical to every request, but then go on and sprinkle a dozen feature flag RPCs each adding more and more latency. We think you should be able to have both.

What we're excited about is use cases beyond feature flags and authorization: we've also seen some folks use SpiceDB as an update graph or others as a dependency graph.

[0]: https://github.com/authzed/spicedb

[1]: https://authzed.com/blog/caveats/

It scaled well compared to a naive graph abstraction implemented outside the database, but when performance wasn't great, it REALLY wasn't great. We ended up throwing it out in later versions to try and get more consistent performance.

I've since worked on SpiceDB[1] which takes the traditional design approach for graph databases and simply treating Postgres as triple-store and that scales far better. IME, if you need a graph, you probably want to use a database optimized for graph access patterns. Most general-purpose graph databases are just bags of optimizations for common traversals.

[0]: https://github.com/quay/clair

[1]: https://github.com/authzed/spicedb

I get the sentiment. We held off on building an operator until we felt there was actually value in doing so (for the most part, Deployments cover the operational needs pretty well).

Migrations can be run in containers (and they are, even with the operator), but it's actually a lot of work to run them at the right time, only once, with the right flags, in the right order, waiting for SpiceDB to reach a specific spot in a phased migrations, etc.

Moving from v1.13.0 to v1.14.0 of SpiceDB requires a multi-phase migration to avoid downtime[0], as could any phased migration for any stateful workload. The operator will walk you through them correctly, without intervention. Users who aren't running on Kubernetes or aren't using the operator often have problems running these steps correctly.

The value is in this automation, but also in the API interface itself. RDS is just some automation and an API on top of EC2, and I think RDS has value over running postgres on EC2 myself directly.

As for helm charts, this is just my opinion, but I don't think they're a good way to distribute software to end users. The interface for a helm chart becomes polluted over time in the same way that most operator APIs become polluted over time, as more and more configuration is pulled up to the top. I think helm is better suited to managing configuration you write yourself to deploy on your own clusters (I realize I'm in the minority here).

[0]: https://github.com/authzed/spicedb/releases/tag/v1.14.0

To my understanding, the only ReBAC system that supports dynamic attributes is SpiceDB.

If you're curious to see a Postgres-based implementation, SpiceDB has a Postgres driver: https://github.com/authzed/spicedb/tree/main/internal/datast...

Interesting, for SpiceDB[0], one place we've struggled with MySQL is preemptively establishing connections in the pool so that it's always full. PGX[1] has been fantastic for Postgres and CockroachDB, but I haven't found something with enough control for MySQL.

[0]: https://github.com/authzed/spicedb