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ZFS compression can be enabled and working correctly while saving almost no space on JPEGs, H.264/H.265 video, ZIP archives, compressed game assets, encrypted files, and similar data. Those formats have already removed most of their statistical redundancy. ZFS then has little left to compress, and sector rounding can erase even a modest reduction.
A reported compressratio of 1.00x is therefore not proof that compression is broken. It can mean the input is incompressible, the compressed record still occupies the same number of sectors, the files were written before the property changed, or the figure includes other datasets. The sections below show how to distinguish those cases and what action is worthwhile.
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What ZFS compression actually does
ZFS compresses individual data records before writing them to the pool. The filesystem keeps the logical contents unchanged while storing a smaller representation when the result meets its savings rules. Reads transparently decompress the record, so applications do not need to know whether a block is compressed.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThis is different from compressing a whole directory as one stream. Each record is considered separately, and a directory containing text, databases, photographs and videos can have very different results for each file. A dataset-level ratio is an aggregate of those records, not a promise that every file will shrink by the same amount.
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The current OpenZFS compression documentation describes the practical choice as lz4 when latency and CPU matter, zstd at levels 3–7 when capacity matters, and off only for data that is already compressed or encrypted. Leaving compression enabled is still useful on a mixed dataset because uncompressed text, logs, database pages, sparse data and runs of zeroes may compress well even when media files do not.
Why JPEG, video, ZIP and game assets barely shrink
The first compressor already removed the easy redundancy
JPEG, PNG, modern video codecs, ZIP, many installer packages and game archives are designed to reduce repeated patterns before they reach ZFS. Their bytes often look close to random to a general-purpose compressor. LZ4 or Zstandard cannot recreate patterns that the original codec discarded, so the second compression pass may produce a result that is the same size or slightly larger.
OpenZFS states this explicitly for games: “Games whose assets are already compressed will see little to no benefit.” The statement applies to the underlying principle, not only to games. A ZIP file can contain compressible filenames or metadata, but its payload is generally already compressed; a video container may have a few compressible headers while its encoded frames remain effectively incompressible.
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Well-designed encryption intentionally makes ciphertext appear random. Compressing encrypted backups, encrypted archive files or an encrypted dataset’s output normally cannot save meaningful space. Compression must occur before encryption if both operations are under your control.
Changing algorithms cannot restore discarded redundancy
Zstandard can beat LZ4 on data that still contains redundancy, usually at a higher CPU cost. It cannot make a JPEG or H.265 stream substantially smaller merely because the setting changed. A different algorithm can expose a small gain on headers or lightly compressed material, but there is no universal percentage for already-compressed files.
Why a small reduction may produce no allocated-space reduction
ZFS allocates whole sectors, not arbitrary byte counts. A compressed record is rounded up to the pool’s sector size. If the compressed byte count fits within the same number of sectors as the original record, the pool may report no reduction even though the compressor produced fewer bytes.
The OpenZFS zfsprops(7) manual also documents a default 12.5% minimum-saving threshold in addition to sector rounding. Very small records can therefore need a larger percentage improvement before their allocated size falls.
The documented 16K/4K example
OpenZFS gives a concrete example in its compression guide. With a 16 KB record and 4 KB sectors, the uncompressed record occupies four sectors. A result of 13 KB still needs four sectors, so allocated space does not change. A result of 12 KB fits in three sectors: that is a 25% reduction, the hurdle required to drop one sector in this example. The minimum-saving rule can impose an additional practical hurdle, especially for small records.
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This explains why a compressor can report a mathematically smaller output while used remains nearly identical. The effect is accounting granularity, not a failed compression operation.
How to check what your dataset is really reporting
Run these commands with the pool and dataset names that apply to your system:
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Check the effective compression property and inheritance across the pool:
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Compare logical and allocated usage, plus the ratio for every descendant:
zfs list -o name,used,logicalused,compressratio -r poollogicalusedrepresents the logical amount of file data, whileusedreflects space allocated in the pool. The ratio is an aggregate and can be affected by children beneath the dataset.Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy. -
For one dataset, inspect both aggregate and referenced-space ratios:
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zfs get compression,compressratio,refcompressratio pool/datacompressratioincludes descendant usage.refcompressratiodescribes the space referenced directly by that dataset, which is the more useful figure when snapshots or child datasets would otherwise obscure the result. -
Identify the files that make up the measurement. A directory-level estimate that mixes a large video library with a small text tree can hide useful savings in the text while showing almost none on the videos.
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A ratio of 1.00x can result from incompressible input, sector rounding, or a population dominated by old blocks. Measure a newly written sample of representative data if you need to isolate the algorithm’s behavior.
Compression properties affect new writes, not old blocks
Changing a dataset property does not walk through every existing block. The manual states: “Changing this property affects only newly-written data.” The same forward-looking behavior applies to record size. A file written while compression was disabled keeps its existing on-disk representation until its data blocks are rewritten or the file is recreated.
This is the most common explanation for an unchanged ratio after an administrator switches from lz4 to zstd. The setting is active for future writes, but the files being inspected still reference blocks created under the previous setting. Snapshots can also continue referencing the old blocks, so deleting or replacing a live file may not immediately return all of its apparent space to the pool.
How to recompress existing files safely
If the data is worth recompressing, plan it as a rewrite operation rather than a property change. Preserve backups and snapshots before removing any original copy.
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Record the current state and make sure the destination has free space for a second copy. Check
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Set the desired property on the dataset that will receive new blocks, for example
zfs set compression=zstd pool/data. Confirm inheritance withzfs get compression pool/data. -
Copy files to new files or to a freshly created destination dataset while the new property is active. A cross-dataset copy or a send/receive workflow makes the new write boundary explicit. A simple rename within one dataset changes directory metadata, not the file’s data blocks.
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Verify the copied data with checksums or application-level validation before deleting originals. For databases and virtual-machine images, use the application’s consistent-backup procedure rather than copying a live, changing file blindly.
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After old snapshots and references are no longer needed, remove the old copies and run the normal snapshot-retention process. Space held by a snapshot is not reclaimable until that snapshot is destroyed.
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Measure the result with
logicalused,usedand the appropriate ratio property. Compare equivalent datasets; do not compare a live dataset with one that still has a different snapshot history.
Rewriting an already-compressed media library often consumes time and temporary capacity for little or no gain. Test a representative subset first.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing LZ4, Zstandard or off
| Setting | Best fit | What to expect | Important caveat |
|---|---|---|---|
lz4 |
General-purpose datasets where latency and CPU overhead matter | Fast compression and decompression; useful for text, logs, databases and sparse data | Already-compressed files may remain the same allocated size |
zstd levels 3–7 |
Capacity-focused workloads with substantially compressible input | Can achieve higher ratios than LZ4 on suitable data | Uses more CPU, and cannot recover redundancy removed by a prior codec |
off |
A dataset known to contain only compressed or encrypted data | Avoids compression work and possible expansion on incompressible records | New writes only; disabling it on a mixed dataset can sacrifice savings from text and other files |
OpenZFS’s guidance is a workload recommendation, not a guaranteed ratio. If a dataset contains photographs, video, archives and documents together, keep compression enabled unless measurements show that the CPU cost or occasional expansion is a problem. If the dataset is a dedicated repository of encrypted backups or precompressed archives, off can be reasonable.
Record size: when it helps and when it does not
OpenZFS documents a default dataset recordsize of 128 KB, although defaults and maximums can depend on the release and platform. Larger records give a compressor more context and can reduce the impact of sector rounding. They can also increase read-modify-write work or be a poor fit for databases and random I/O.
Changing recordsize affects files created afterward; it does not repartition existing files into new records. It is therefore a workload-tuning decision, not a guaranteed cure for JPEGs or video. Consider the application’s access pattern, synchronous-write behavior, database page size and snapshot workload before changing it. The OpenZFS workload-tuning guide discusses these trade-offs.
A practical troubleshooting decision tree
If the property is not what you expected
Use zfs get -r compression pool and inspect the child dataset containing the files. Correct the property at the appropriate dataset level, then write a test file. Do not infer the effective value from a parent dataset alone.
If only old files show no savings
Check their write dates against the compression change. Existing blocks were not recompressed. Rewrite a small sample to a new destination and compare it with the original before scheduling a larger migration.
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If new text compresses but media does not
That is normal. Keep the mixed dataset compressed if the aggregate CPU and space results are acceptable. Separate datasets are useful when you need different policies for encrypted archives, databases and ordinary documents.
If a new sample is slightly smaller but used is unchanged
Check record and sector sizes. The result may not cross a whole-sector boundary or the 12.5% minimum-saving threshold. Larger records can change the economics, but changing them only affects newly created files and may alter I/O behavior.
If the ratio looks worse after deleting files
Inspect snapshots, clones and descendants. Referenced blocks can remain allocated even after the live pathname is removed. Compare refcompressratio with compressratio and review retention before concluding that compression regressed.
What published benchmark numbers do—and do not—tell you
The OpenZFS workload-tuning page cites LZ4 project testing on the Silesia corpus averaging 2.1:1 for LZ4 and 2.7:1 for gzip-1. Those are corpus benchmark figures, not measurements of your pool and not predictions for already-compressed media. A ratio from a general benchmark should never be turned into a promise for a particular file type.
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Bottom line for a compressratio of 1.00x
First verify the dataset scope and distinguish logical from allocated usage. Then confirm that the measured files were written after compression was enabled. If a fresh sample of JPEGs, video, ZIP files or encrypted data still reports roughly 1.00x, ZFS is behaving as designed: the input has little redundancy left, and block allocation rules may hide small byte-level reductions.
Use LZ4 for low overhead, Zstandard levels 3–7 when compressible data justifies more CPU, and off only after evaluating the entire dataset. Rewrite existing files only when a measured benefit outweighs the temporary space, I/O and snapshot-management cost.
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