nebula
A distributed, fast open-source graph database featuring horizontal scalability and high availability
Runssource: GitHubhomepageC++Apache-2.0commit 426266b5c3df
C++, Apache-2.0 licensed. The project labels itself: big data, cpp, database, distributed, distributed systems, graph, graph database and graphdb.
nebula runs. An Argusic agent installed it in 3.5 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
NebulaGraph v3.8.0 metad, storaged, and graphd run and respond to queries: graphd HTTP at 127.0.0.1:19669 returns 200, graph thrift port 9669 accepts connections, space creation and CRUD operations execute successfully.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 60 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- 4 minutes
- Cold machine to finish
- 9 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- python nebula3 client connected to graphd (127.0.0.1:9669), authenticated as root, ADD HOSTS registered storage, CREATE SPACE/TAG/EDGE succeeded, INSERT VERTEX/EDGE succeeded, FETCH PROP returned 'Alice' and 30, GO FROM returned weight=5 and Bob, MATCH queries returned vertex IDs. All three HTTP endpoints (/status) returned HTTP 200 with status running. Build from source was not attempted due to missing third-party deps.
- Model tokens used
- 362,072
- Exact commit tested
- 426266b5c3df
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
Here is the final result: tokens used 362,072 Now I have all the data I need. Let me compile the final report. The full NebulaGraph stack has been installed and verified: - **v3.8.0** prebuilt binaries from GitHub Releases - **metad** (meta service) running on port 9559, HTTP endpoint responded 200 - **graphd** (graph service) running on port 9669, HTTP endpoint responded 200 - **storaged** (storage service) running on port 9779, shown as ONLINE - **nebula3-python** client installed, connected, authenticated - Created a test space, defined a tag (`person`) and edge type (`know`), inserted vertices and edge… Here is the final result:
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 3.5 minutes.
- Nothing broke on the way: zero errors between clone and running.
- Ran without a GPU, so it does not need one to start.
- Licensed Apache-2.0, as reported by its host.
- Measured 2 times, so the result is not a one-off.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 3.5 minutes after the clone.
- Following its own README: the documented path worked with nothing to fix.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Runs | 100.00 | 0.06 | |
| Argusic Runner | 1/3 | Did not run | 20.00 | 0.04 |
Topics (from GitHub)
big-datacppdatabasedistributeddistributed-systemsgraphgraph-databasegraphdbgraphraghacktoberfestknowledge-baseknowledge-graphnebulanebula-graphnebulagraphraftscalability
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/nebula)Questions
- Does nebula run?
- Yes. nebula runs. Argusic installed and launched it on a clean machine in 4 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test nebula?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 426266b5c3df. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test nebula?
- The run that produced this verdict cost $0.06: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does nebula take to install?
- 3.5 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does nebula need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing nebula?
- Nothing did, in the recorded run: zero errors between clone and running.
- Where is the evidence for nebula?
- All 2 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.