ldbc_snb_datagen_spark VS spicedb

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.

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

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.

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