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152 lines
5.6 KiB
ReStructuredText
===============
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Deduplication
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===============
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Introduction
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============
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Applying data deduplication on an existing software stack is not easy
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due to additional metadata management and original data processing
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procedure.
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In a typical deduplication system, the input source as a data
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object is split into multiple chunks by a chunking algorithm.
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The deduplication system then compares each chunk with
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the existing data chunks, stored in the storage previously.
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To this end, a fingerprint index that stores the hash value
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of each chunk is employed by the deduplication system
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in order to easily find the existing chunks by comparing
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hash value rather than searching all contents that reside in
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the underlying storage.
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There are many challenges in order to implement deduplication on top
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of Ceph. Among them, two issues are essential for deduplication.
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First is managing scalability of fingerprint index; Second is
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it is complex to ensure compatibility between newly applied
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deduplication metadata and existing metadata.
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Key Idea
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========
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1. Content hashing (Double hashing): Each client can find an object data
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for an object ID using CRUSH. With CRUSH, a client knows object's location
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in Base tier.
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By hashing object's content at Base tier, a new OID (chunk ID) is generated.
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Chunk tier stores in the new OID that has a partial content of original object.
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Client 1 -> OID=1 -> HASH(1's content)=K -> OID=K ->
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CRUSH(K) -> chunk's location
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2. Self-contained object: The external metadata design
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makes difficult for integration with storage feature support
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since existing storage features cannot recognize the
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additional external data structures. If we can design data
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deduplication system without any external component, the
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original storage features can be reused.
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More details in https://ieeexplore.ieee.org/document/8416369
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Design
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======
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.. ditaa::
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+-------------+
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| Ceph Client |
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+------+------+
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^
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Tiering is |
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Transparent | Metadata
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to Ceph | +---------------+
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Client Ops | | |
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| +----->+ Base Pool |
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| | | |
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| | +-----+---+-----+
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| | | ^
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v v | | Dedup metadata in Base Pool
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+------+----+--+ | | (Dedup metadata contains chunk offsets
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| Objecter | | | and fingerprints)
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+-----------+--+ | |
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^ | | Data in Chunk Pool
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| v |
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| +-----+---+-----+
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+----->| Chunk Pool |
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+---------------+
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Data
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Pool-based object management:
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We define two pools.
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The metadata pool stores metadata objects and the chunk pool stores
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chunk objects. Since these two pools are divided based on
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the purpose and usage, each pool can be managed more
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efficiently according to its different characteristics. Base
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pool and the chunk pool can separately select a redundancy
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scheme between replication and erasure coding depending on
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its usage and each pool can be placed in a different storage
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location depending on the required performance.
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Regarding how to use, please see osd_internals/manifest.rst
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Usage Patterns
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==============
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The different ceph interface layers present potentially different oportunities
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and costs for deduplication and tiering in general.
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RadosGW
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-------
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S3 big data workloads seem like a good opportunity for deduplication. These
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objects tend to be write once, read mostly objects which don't see partial
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overwrites. As such, it makes sense to fingerprint and dedup up front.
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Unlike cephfs and rbd, radosgw has a system for storing
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explicit metadata in the head object of a logical s3 object for
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locating the remaining pieces. As such, radosgw could use the
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refcounting machinery (osd_internals/refcount.rst) directly without
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needing direct support from rados for manifests.
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RBD/Cephfs
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----------
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RBD and CephFS both use deterministic naming schemes to partition
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block devices/file data over rados objects. As such, the redirection
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metadata would need to be included as part of rados, presumably
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transparently.
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Moreover, unlike radosgw, rbd/cephfs rados objects can see overwrites.
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For those objects, we don't really want to perform dedup, and we don't
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want to pay a write latency penalty in the hot path to do so anyway.
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As such, performing tiering and dedup on cold objects in the background
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is likely to be preferred.
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One important wrinkle, however, is that both rbd and cephfs workloads
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often feature usage of snapshots. This means that the rados manifest
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support needs robust support for snapshots.
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RADOS Machinery
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===============
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For more information on rados redirect/chunk/dedup support, see osd_internals/manifest.rst.
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For more information on rados refcount support, see osd_internals/refcount.rst.
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Status and Future Work
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======================
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At the moment, there exists some preliminary support for manifest
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objects within the osd as well as a dedup tool.
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RadosGW data warehouse workloads probably represent the largest
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opportunity for this feature, so the first priority is probably to add
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direct support for fingerprinting and redirects into the refcount pool
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to radosgw.
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Aside from radosgw, completing work on manifest object support in the
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osd particularly as it relates to snapshots would be the next step for
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rbd and cephfs workloads.
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