This document is for a development version of Ceph.



Applying data deduplication on an existing software stack is not easy due to additional metadata management and original data processing procedure.

In a typical deduplication system, the input source as a data object is split into multiple chunks by a chunking algorithm. The deduplication system then compares each chunk with the existing data chunks, stored in the storage previously. To this end, a fingerprint index that stores the hash value of each chunk is employed by the deduplication system in order to easily find the existing chunks by comparing hash value rather than searching all contents that reside in the underlying storage.

There are many challenges in order to implement deduplication on top of Ceph. Among them, two issues are essential for deduplication. First is managing scalability of fingerprint index; Second is it is complex to ensure compatibility between newly applied deduplication metadata and existing metadata.

Key Idea

1. Content hashing (Double hashing): Each client can find an object data for an object ID using CRUSH. With CRUSH, a client knows object’s location in Base tier. By hashing object’s content at Base tier, a new OID (chunk ID) is generated. Chunk tier stores in the new OID that has a partial content of original object.

Client 1 -> OID=1 -> HASH(1’s content)=K -> OID=K -> CRUSH(K) -> chunk’s location

2. Self-contained object: The external metadata design makes difficult for integration with storage feature support since existing storage features cannot recognize the additional external data structures. If we can design data deduplication system without any external component, the original storage features can be reused.

More details in


Pool-based object management: We define two pools. The metadata pool stores metadata objects and the chunk pool stores chunk objects. Since these two pools are divided based on the purpose and usage, each pool can be managed more efficiently according to its different characteristics. Base pool and the chunk pool can separately select a redundancy scheme between replication and erasure coding depending on its usage and each pool can be placed in a different storage location depending on the required performance.

Regarding how to use, please see osd_internals/manifest.rst

Usage Patterns

Each Ceph interface layer presents unique opportunities and costs for deduplication and tiering in general.


S3 big data workloads seem like a good opportunity for deduplication. These objects tend to be write once, read mostly objects which don’t see partial overwrites. As such, it makes sense to fingerprint and dedup up front.

Unlike cephfs and rbd, radosgw has a system for storing explicit metadata in the head object of a logical s3 object for locating the remaining pieces. As such, radosgw could use the refcounting machinery (osd_internals/refcount.rst) directly without needing direct support from rados for manifests.


RBD and CephFS both use deterministic naming schemes to partition block devices/file data over rados objects. As such, the redirection metadata would need to be included as part of rados, presumably transparently.

Moreover, unlike radosgw, rbd/cephfs rados objects can see overwrites. For those objects, we don’t really want to perform dedup, and we don’t want to pay a write latency penalty in the hot path to do so anyway. As such, performing tiering and dedup on cold objects in the background is likely to be preferred.

One important wrinkle, however, is that both rbd and cephfs workloads often feature usage of snapshots. This means that the rados manifest support needs robust support for snapshots.

RADOS Machinery

For more information on rados redirect/chunk/dedup support, see osd_internals/manifest.rst. For more information on rados refcount support, see osd_internals/refcount.rst.

Status and Future Work

At the moment, there exists some preliminary support for manifest objects within the OSD as well as a dedup tool.

RadosGW data warehouse workloads probably represent the largest opportunity for this feature, so the first priority is probably to add direct support for fingerprinting and redirects into the refcount pool to radosgw.

Aside from radosgw, completing work on manifest object support in the OSD particularly as it relates to snapshots would be the next step for rbd and cephfs workloads.

How to use deduplication

  • This feature is highly experimental and is subject to change or removal.

Ceph provides deduplication using RADOS machinery. Below we explain how to perform deduplication.

  1. Estimate space saving ratio of a target pool using ceph-dedup-tool.

ceph-dedup-tool --op estimate --pool $POOL --chunk-size chunk_size
  --chunk-algorithm fixed|fastcdc --fingerprint-algorithm sha1|sha256|sha512
  --max-thread THREAD_COUNT

This CLI command will show how much storage space can be saved when deduplication is applied on the pool. If the amount of the saved space is higher than user’s expectation, the pool probably is worth performing deduplication. Users should specify $POOL where the object---the users want to perform deduplication---is stored. The users also need to run ceph-dedup-tool multiple time with varying chunk_size to find the optimal chunk size. Note that the optimal value probably differs in the content of each object in case of fastcdc chunk algorithm (not fixed). Example output:

  "chunk_algo": "fastcdc",
  "chunk_sizes": [
      "target_chunk_size": 8192,
      "dedup_bytes_ratio": 0.4897049
      "dedup_object_ratio": 34.567315
      "chunk_size_average": 64439,
      "chunk_size_stddev": 33620
  "summary": {
    "examined_objects": 95,
    "examined_bytes": 214968649

The above is an example output when executing estimate. target_chunk_size is the same as chunk_size given by the user. dedup_bytes_ratio shows how many bytes are redundant from examined bytes. For instance, 1 - dedup_bytes_ratio means the percentage of saved storage space. dedup_object_ratio is the generated chunk objects / examined_objects. chunk_size_average means that the divided chunk size on average when performing CDC---this may differnet from target_chunk_size because CDC genarates different chunk-boundary depending on the content. chunk_size_stddev represents the standard deviation of the chunk size.

  1. Create chunk pool.

ceph osd pool create CHUNK_POOL
  1. Run dedup command (there are two ways).

ceph-dedup-tool --op sample-dedup --pool POOL --chunk-pool CHUNK_POOL --chunk-size
CHUNK_SIZE --chunk-algorithm fastcdc --fingerprint-algorithm sha1|sha256|sha512
--chunk-dedup-threshold THRESHOLD --max-thread THREAD_COUNT ----sampling-ratio SAMPLE_RATIO
--wakeup-period WAKEUP_PERIOD --loop --snap

The sample-dedup comamnd spawns threads specified by THREAD_COUNT to deduplicate objects on the POOL. According to sampling-ratio---do a full search if SAMPLE_RATIO is 100, the threads selectively perform deduplication if the chunk is redundant over THRESHOLD times during iteration. If --loop is set, the theads will wakeup after WAKEUP_PERIOD. If not, the threads will exit after one iteration.

ceph-dedup-tool --op object-dedup --pool POOL --object OID --chunk-pool CHUNK_POOL
  --fingerprint-algorithm sha1|sha256|sha512 --dedup-cdc-chunk-size CHUNK_SIZE

The object-dedup command triggers deduplication on the RADOS object specified by OID. All parameters shown above must be specified. CHUNK_SIZE should be taken from the results of step 1 above. Note that when this command is executed, fastcdc will be set by default and other parameters such as FP and CHUNK_SIZE will be set as defaults for the pool. Deduplicated objects will appear in the chunk pool. If the object is mutated over time, user needs to re-run object-dedup because chunk-boundary should be recalculated based on updated contents. The user needs to specify snap if the target object is snapshotted. After deduplication is done, the target object size in POOL is zero (evicted) and chunks objects are genereated---these appear in CHUNK_POOL.

  1. Read/write I/Os

After step 3, the users don’t need to consider anything about I/Os. Deduplicated objects are completely compatible with existing RAODS operations.

  1. Run scrub to fix reference count

Reference mismatches can on rare occasions occur to false positives when handling reference counts for deduplicated RADOS objects. These mismatches will be fixed by periodically scrubbing the pool:

ceph-dedup-tool --op chunk-scrub --op chunk-scrub --chunk-pool CHUNK_POOL --pool POOL --max-thread THREAD_COUNT