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What Layerbase tested
Layerbase published the benchmark on August 19, 2026. It compared MongoDB 8.0.29 with FerretDB 2.7 on a single 4-vCPU, 8-GB instance running Debian 13 and ZFS. The ZFS ARC cache was capped at 2 GiB. The test used YCSB 0.18.0-SNAPSHOT, 10 million records, one million operations per run and a 600-second run cap. Layerbase estimated the dataset at roughly 10 GiB, intentionally larger than the machine’s RAM.
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- Workload C: 100% reads.
- Workload B: 95% reads and 5% updates.
- Workload A: 50% reads and 50% updates.
- Client-thread settings: 16, 64 and 256.
- Write concern: 1.
These are client-thread settings, not proof of an equal number of simultaneous database connections. Layerbase’s benchmark report describes its configuration and results; the figures below are Layerbase’s reported measurements, not an independent reproduction.
What the reported results say
On this 4-vCPU, 8-GB profile, Layerbase reports that tuned FerretDB achieved roughly five times MongoDB’s read throughput and 3.3 to 3.8 times its throughput on the 50/50 read/update workload. The author also reports that untuned FerretDB was roughly at parity with MongoDB on the mixed workload. The benchmark’s central lesson is therefore not just which number was higher: FerretDB’s performance changed substantially when its storage and memory configuration was adjusted.
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In a separate tuning ablation, Layerbase compared changes against an ablation control box. The results it reports were:
| FerretDB configuration change | Reported result against the ablation control |
|---|---|
| Memory tuning alone, with 128-KiB ZFS recordsize retained | About 1.0× on reads; at 256 threads, performance could be negative, which the author attributed to checkpoint pressure. |
| 16-KiB ZFS recordsize alone | 3.7× to 4.3× on reads. |
| 16-KiB recordsize plus memory tuning | 4.0× to 4.3× on reads and 2.4× to 3.0× on the 50/50 workload. |
Those ablation ratios are not the same comparison as the headline FerretDB-versus-MongoDB multiples. Layerbase says an earlier stock arm used boxes built on different days, so its comparison with that arm differed from the ablation-control comparison.
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Why FerretDB’s settings mattered
ZFS recordsize versus PostgreSQL page size
The FerretDB configuration used PostgreSQL for storage. Layerbase found a 128-KiB ZFS recordsize beneath PostgreSQL’s 8-KiB pages. As the report explains, writing an 8-KiB page into a 128-KiB record can require a read-modify-write of the larger record. Layerbase tested a 16-KiB ZFS recordsize, which it reported as a major contributor to the read-throughput improvement.
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Layerbase also found PostgreSQL’s shared_buffers at its 128-MiB stock default on the 8-GB host, and tested scaling memory settings with the machine. The ablation matters here: memory tuning alone did not reproduce the combined result while the 128-KiB recordsize remained. Layerbase says that at 256 threads it could even reduce performance, attributing that outcome to checkpoint pressure. In its test, changing the recordsize and tuning memory together produced the strongest reported ablation results.
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Why the first benchmark round was discarded
Layerbase says it deleted its initial measurements after finding three harness problems. These errors meant the results could not be treated as valid absolute throughput figures:
- The thread-count property was wrong. YCSB was given
threadsinstead ofthreadcount. It silently ran one thread even when the run was labeled with a higher count. - The connection pool was not set to match the intended concurrency. The MongoDB Java driver’s default connection-pool limit was 100 unless
mongodb.maxconnectionswas set. A 256-thread client setting therefore did not establish 256 simultaneous database connections. - The CSV parser missed throughput output. An unescaped-parentheses regular expression in the extraction script matched no throughput output.
The corrected figures are from the later, corrected harness, according to Layerbase. The connection-pool detail still matters when interpreting the 256-thread label: it denotes client threads, not a demonstrated count of concurrent connections.
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How far the comparison applies
The results are specific to a single-node, 4-vCPU, 8-GB configuration with an estimated 10-GiB dataset, the listed YCSB workloads, and Layerbase’s chosen database and backend settings. Because the working set was larger than RAM, memory pressure and storage behavior were central. Layerbase cautions that the gap may narrow—and the ranking may change—when a working set fits in memory.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches- It is not an Atlas benchmark. The test did not measure Atlas, and its dedicated single-node machine is not architecture-equivalent to Atlas replica-set tiers.
- The ratios do not extend to the other machine sizes. Layerbase also reports 64-thread FerretDB results on 4-GB and 16-GB profiles, but those profiles had no corresponding MongoDB arms.
- It does not settle results at other scales or workloads. Layerbase notes that a third-party billion-record result in which MongoDB wins on writes remains valid for that different scale and setup.
- Performance does not establish application compatibility. FerretDB describes itself as compatible with MongoDB drivers and popular tools, and as a drop-in replacement in many cases. That is not a guarantee that every application feature or command behaves identically; check the target application against FerretDB’s documented supported commands and known differences.
What a useful replication should disclose
A benchmark result is easier to interpret when its workload and operating conditions are visible. MongoDB’s engineering guidance recommends modeling the real phenomenon, measuring, analyzing, repeating to check whether the result recurs, and changing one variable at a time. That methodology is context, not an independent validation of Layerbase’s result. For a comparison like this one, a replication should report:
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- Database versions, hardware, operating system and filesystem.
- Record count, estimated working-set size and its relationship to RAM.
- Backend settings, including ZFS recordsize and PostgreSQL memory configuration.
- Workload read/write mix, write concern, client-thread count and actual connection behavior.
- Harness version, validation of its settings and output extraction, and whether runs were repeated.
- Whether both systems ran on the same single-node or replicated architecture.
Layerbase reports an ablation and says it discarded the flawed initial harness generation. The available report does not establish that an independent party reproduced the corrected setup and figures. For methodology context, see MongoDB’s engineering article on reducing variability in performance tests.
How to read the headline
The fairest takeaway is narrow: in Layerbase’s corrected YCSB run on one memory-constrained 4-vCPU, 8-GB machine, FerretDB with tuned ZFS and PostgreSQL settings outperformed the tested MongoDB configuration on the listed read and mixed workloads. The work also showed how backend defaults and benchmark-harness mistakes can materially affect a database comparison. It does not establish a general winner for different working sets, hardware, deployments, or application requirements.
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