badger
Fast key-value DB in Go.
Runssource: GitHubhomepageGoApache-2.0commit 5688f1406ced
Go, Apache-2.0 licensed. The project labels itself: database, document database, go, golang, key value, library and ssd.
badger runs. An Argusic agent installed it in 0.5 minutes and hit 4 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
BadgerDB compiles, all core package tests pass, CLI binary builds and successfully writes 10M entries and reads them back at full scan, and the Go library API works for disk-based and in-memory Open/Set/Get/Backup/Load operations.
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 100 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.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- Go not installed in container0.5 minutes
- go test -race OOM on TestSyncForRace
- pb.TestProtosRegenerate fails: protoc not installed
- jemalloc tag build fails: jemalloc/jemalloc.h missing
- Install time
- under a minute
- Cold machine to finish
- 11 minutes
- Errors hit and fixed
- 4 hit, 4 fixed with no human help
- How the result was proved
- go test -count=1 -timeout=15m . passed (root); go test ./badger/cmd/... ./trie/... ./skl/... ./y/... ./table/... all passed; custom Go e2e program (Open, Set, Get, Backup, Load, InMemory) printed 'ALL CHECKS PASSED'; CLI benchmark wrote/read 10M entries at ~480k entries/sec
- Model tokens used
- 219,679
- Exact commit tested
- 5688f1406ced
- 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.
Error: Received unexpected error:
exit status 127
Test: TestProtosRegenerate
Messages: Got error while regenerating protos: exit status 127
--- FAIL: TestProtosRegenerate (6.01s)
FAIL
FAIL github.com/dgraph-io/badger/v4/pb 6.014s
FAIL
All checks passed. Here's the final summary:
tokens used
219,679
All checks passed. Here's the final summary: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 0.5 minutes, faster than the median of the 18 comparable projects Argusic has measured.
- Recovered from all 4 errors without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed Apache-2.0, as reported by its host.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 19 database projects Argusic has installed and timed, badger was the 4th fastest to reach a running state, and 16 of 19 reached one at all.
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 0.5 minutes after the clone.
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
Topics (from GitHub)
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/badger)Questions
- Does badger run?
- Yes. badger runs. Argusic installed and launched it on a clean machine in 1 minutes, hitting 4 errors on the way, and recorded the session.
- How did Argusic test badger?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 5688f1406ced. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test badger?
- The run that produced this verdict cost $0.05: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does badger take to install?
- 0.5 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 badger need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing badger?
- 4 things broke in the recorded run, and 4 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does badger compare with the alternatives?
- Of the 19 database projects Argusic has installed and timed, badger was the 4th fastest to reach a running state, and 16 of 19 reached one at all.
- Where is the evidence for badger?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.