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Short answer: ZFS can compress a record only as far as its remaining redundancy allows. JPEG images, H.264/HEVC video, ZIP archives, compressed game assets and encrypted data have already discarded most predictable patterns, so compression=lz4 or compression=zstd may produce almost no reduction. Even a modestly smaller result may consume the same number of whole disk sectors, and ZFS applies a default 12.5% minimum-saving threshold. A reported compressratio of 1.00x can therefore be normal rather than evidence that compression is broken.
What ZFS compression does—and what it cannot do
ZFS compresses data as it writes records to the pool. It attempts to encode each record with the dataset’s active compression algorithm, then compares the compressed representation with the original. If the result is not worthwhile after allocation rules are considered, ZFS keeps the data uncompressed. Decompression happens transparently when applications read the record; no application changes are required.
Compression algorithms exploit repetition and predictable patterns. Plain-text logs, database pages, virtual-machine disk images with free space, sparse data and many zero-filled regions often contain substantial redundancy. A format such as JPEG, MP4, PNG, a ZIP archive or a compressed game package has already used a codec designed to remove much of that redundancy. A second general-purpose compressor sees data that looks comparatively random and has little left to eliminate.
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Why a smaller compressed record may use the same disk space
Allocation is measured in sectors, not arbitrary byte counts
A compressed record is allocated in whole sectors. If compression saves a few bytes but the result still occupies the same number of sectors, the pool’s allocated space does not fall. This is why a file can have a nontrivial byte-level reduction while used and the apparent free space on the pool barely change.
The exact sector size and record layout vary by pool and platform. You should therefore treat any ratio as an accounting result for your pool, not as a universal property of the file format.
The 12.5% minimum-saving threshold
Rounding is not the only hurdle. The OpenZFS zfsprops(7) manual documents a default minimum-saving threshold of 12.5%: “There is also a 12.5% minimum-saving threshold on top of the rounding.” A result that is only slightly smaller can be rejected and stored in its original form. The rule prevents ZFS from spending CPU and metadata effort on reductions that do not produce a meaningful allocation benefit. See the zfsprops(7) manual.
The documented 16K/4K example
OpenZFS gives a concrete illustration. With a 16 KiB record and 4 KiB sectors, an uncompressed record spans four sectors. To drop to three sectors, compression must save at least 25%; saving 10% or 20% still leaves the record needing four sectors. The 12.5% threshold is applied in addition to this sector-rounding effect. The example appears in the OpenZFS compression guide.
| Situation | What happens | Why the pool may show no reduction |
|---|---|---|
| Already-compressed input | The codec finds little additional redundancy | The compressed record is nearly as large as the original |
| Small byte-level saving | The result still rounds to the same sector count | Allocated blocks occupy the same whole sectors |
| Saving below the default threshold | ZFS may retain the uncompressed record | The 12.5% minimum-saving rule rejects marginal compression |
| Saving large enough to cross a sector boundary | Fewer sectors are allocated | used can fall relative to logical data |
How to verify what your dataset is actually doing
Start with the effective property, not the setting you remember applying. Dataset properties inherit from parents, and a child dataset can override them. Run these commands with the pool and dataset names changed to match your system:
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Check compression recursively:
zfs get -r compression poolLook for the effective value on the dataset containing the files. Values such as
lz4,zstd,gziporoffmay appear at different levels of the hierarchy. -
Compare logical and allocated usage, including descendants:
Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.zfs list -o name,used,logicalused,compressratio -r poollogicalusedrepresents the logical amount of data, whileusedreflects allocated space according to ZFS accounting.compressratioincludes the dataset’s descendants, so a parent’s ratio can differ substantially from a child’s ratio. -
When you need only the dataset’s referenced space, inspect
refcompressratio:zfs get compression,compressratio,refcompressratio pool/datarefcompressratiocovers referenced space for that dataset rather than descendant usage. Use it when snapshots, clones or child datasets would otherwise make the parent-level number misleading.Rank #3
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Check the timestamp of the data you are measuring. A ratio calculated over old blocks will not change merely because you changed a property today.
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Ratios are aggregate values. A mixed dataset can show useful overall compression even when its video and photo directories are effectively 1.00x, because text, metadata, sparse regions or other files compress well. Conversely, a dataset dominated by media can remain close to 1.00x even with compression enabled correctly.
Changing the setting does not recompress existing files
Compression properties are forward-looking. OpenZFS documents that “Changing this property affects only newly-written data.” Existing blocks keep the representation they received when they were written. Switching from lz4 to zstd, or from off to lz4, therefore does not immediately reclaim space.
The same timing rule matters for recordsize. The workload-tuning documentation describes a 128 KiB default dataset recordsize (defaults and maximums can depend on the OpenZFS release and platform) and notes that a recordsize change affects files created afterward. A property change is not an in-place conversion of every existing file.
Ways to rewrite data deliberately
If you have a reason to recompress, activate the desired property first and then recreate the data using a method appropriate to your backup and snapshot plan:
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- Copy and replace: copy files to a separate location or temporary dataset, verify them, then replace the originals. The new writes use the active compression and recordsize properties.
- Move between datasets: moving across datasets generally entails new writes, but confirm the tool’s behavior and preserve permissions, ownership, ACLs, extended attributes and sparse-file handling.
- Send and receive: recreate a dataset through a ZFS send/receive workflow when that fits your replication and snapshot design. Plan destination properties and verify the received data before deleting the source.
Do not delete the original until you have a verified copy and have considered snapshots, clones, replication jobs and rollback requirements. Rewriting can also require temporary capacity equal to part of the dataset.
Choosing LZ4, Zstandard or compression off
No compressor can recover redundancy removed by an earlier codec. Zstandard may produce better ratios than LZ4 on data that remains compressible, but changing algorithms is not a reliable way to shrink JPEG, MP4, ZIP or encrypted content.
| Setting | Best fit | Important limitation |
|---|---|---|
lz4 |
Latency and CPU efficiency are priorities; the dataset is mixed or its contents are uncertain | Fast and often worthwhile for ordinary compressible data, but usually adds little to already-compressed files |
zstd levels 3–7 |
Capacity is more important and the workload contains compressible data | Can use more CPU; it cannot recreate redundancy removed by another codec |
compression=off |
A dataset is known to contain already-compressed or encrypted data | Affects only new writes; disabling it on a mixed dataset can forfeit savings on text, sparse data and other compressible records |
OpenZFS summarizes this trade-off as: “In practice: lz4 when latency and CPU matter, zstd (level 3–7) when capacity matters, off only for data that is already compressed or encrypted.” The recommendation is workload-based, not a promise of a particular ratio.
For context, the workload-tuning page reports LZ4 averaging 2.1:1 and gzip-1 2.7:1 on the Silesia corpus, with those figures attributed to LZ4 project testing. They are corpus benchmark results, not predictions for your pool and especially not for already-compressed media.
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How recordsize fits into the diagnosis
Larger records can give a compressor more bytes in which to find patterns and can reduce the relative impact of per-record overhead. That does not make a compressed video stream suddenly compressible, and it does not override sector rounding or the minimum-saving rule. Recordsize is also a workload choice: databases, virtual machines and random-update files may have different latency and write-amplification needs than sequential media.
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Before changing it, identify which files will be newly created under the new setting and whether the access pattern benefits. If you change recordsize only to chase a better ratio on already-compressed files, you may add operational complexity without reducing allocated space.
A practical troubleshooting sequence
- Confirm scope: run
zfs get -r compression pooland verify the child dataset that owns the files. - Measure both views: compare
used,logicalused,compressratioand, where appropriate,refcompressratio. - Classify the data: separate media, archives, encrypted containers and game assets from logs, source code, databases, sparse images and other potentially compressible files.
- Check write history: determine whether the measured blocks were written after compression was enabled or changed.
- Account for allocation: consider sector size, recordsize and the 12.5% threshold before interpreting a small byte-level difference as a failed configuration.
- Test a representative rewrite: if policy allows, copy a sample into a test dataset with the proposed setting and compare logical and allocated usage rather than relying on file-size intuition.
- Rewrite only when justified: recreate files after selecting the property, while preserving backups and snapshots.
Common conclusions that are wrong
“A 1.00x ratio means ZFS compression is disabled.”
Not necessarily. It can mean the data is already compressed, the savings do not cross a sector boundary, or the result fails the minimum-saving test. Verify the effective property and the age of the blocks before changing anything.
“Zstandard will fix my media library.”
Zstandard can improve ratios on compressible input, but a prior media codec has already removed much of the structure a general-purpose compressor needs. Expect little change for typical JPEG, MP4 and archive files unless testing your own data demonstrates otherwise.
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“Turning compression on should immediately free space.”
Property changes affect new writes. Existing blocks must be rewritten or recreated before the new setting can influence their on-disk representation.
“The parent dataset’s ratio describes this directory.”
A directory is not a ZFS accounting boundary. Dataset-level ratios can include descendants, snapshots and other content. Measure the dataset that owns the files and use referenced-space metrics when descendant usage would confuse the result.
Bottom line for a mixed dataset
Leave compression enabled when a dataset contains a meaningful mix of compressible and pre-compressed data; the latter does not prevent the former from benefiting. Choose LZ4 when low latency and CPU cost matter, or Zstandard levels 3–7 when capacity is the priority and your workload remains compressible. Consider compression=off only for a dataset you have established is overwhelmingly pre-compressed or encrypted. In every case, judge the result with ZFS’s logical and allocated usage counters, and remember that changing the property is not the same as rewriting old blocks.
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