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259 lines
8.8 KiB
ReStructuredText
259 lines
8.8 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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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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Manifest Object:
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Metadata objects are stored in the
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base pool, which contains metadata for data deduplication.
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::
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struct object_manifest_t {
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enum {
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TYPE_NONE = 0,
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TYPE_REDIRECT = 1,
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TYPE_CHUNKED = 2,
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};
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uint8_t type; // redirect, chunked, ...
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hobject_t redirect_target;
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std::map<uint64_t, chunk_info_t> chunk_map;
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}
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A chunk Object:
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Chunk objects are stored in the chunk pool. Chunk object contains chunk data
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and its reference count information.
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Although chunk objects and manifest objects have a different purpose
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from existing objects, they can be handled the same way as
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original objects. Therefore, to support existing features such as replication,
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no additional operations for dedup are needed.
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Usage
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=====
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To set up deduplication pools, you must have two pools. One will act as the
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base pool and the other will act as the chunk pool. The base pool need to be
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configured with fingerprint_algorithm option as follows.
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::
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ceph osd pool set $BASE_POOL fingerprint_algorithm sha1|sha256|sha512
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--yes-i-really-mean-it
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1. Create objects ::
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- rados -p base_pool put foo ./foo
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- rados -p chunk_pool put foo-chunk ./foo-chunk
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2. Make a manifest object ::
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- rados -p base_pool set-chunk foo $START_OFFSET $END_OFFSET --target-pool
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chunk_pool foo-chunk $START_OFFSET --with-reference
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Interface
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=========
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* set-redirect
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set redirection between a base_object in the base_pool and a target_object
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in the target_pool.
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A redirected object will forward all operations from the client to the
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target_object. ::
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rados -p base_pool set-redirect <base_object> --target-pool <target_pool>
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<target_object>
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* set-chunk
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set chunk-offset in a source_object to make a link between it and a
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target_object. ::
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rados -p base_pool set-chunk <source_object> <offset> <length> --target-pool
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<caspool> <target_object> <taget-offset>
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* tier-promote
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promote the object (including chunks). ::
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rados -p base_pool tier-promote <obj-name>
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* unset-manifest
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unset manifest option from the object that has manifest. ::
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rados -p base_pool unset-manifest <obj-name>
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Dedup tool
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==========
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Dedup tool has two features: finding optimal chunk offset for dedup chunking
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and fixing the reference count.
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* find optimal chunk offset
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a. fixed chunk
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To find out a fixed chunk length, you need to run following command many
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times while changing the chunk_size. ::
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ceph-dedup-tool --op estimate --pool $POOL --chunk-size chunk_size
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--chunk-algorithm fixed --fingerprint-algorithm sha1|sha256|sha512
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b. rabin chunk(Rabin-karp algorithm)
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As you know, Rabin-karp algorithm is string-searching algorithm based
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on a rolling-hash. But rolling-hash is not enough to do deduplication because
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we don't know the chunk boundary. So, we need content-based slicing using
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a rolling hash for content-defined chunking.
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The current implementation uses the simplest approach: look for chunk boundaries
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by inspecting the rolling hash for pattern(like the
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lower N bits are all zeroes).
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- Usage
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Users who want to use deduplication need to find an ideal chunk offset.
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To find out ideal chunk offset, Users should discover
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the optimal configuration for their data workload via ceph-dedup-tool.
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And then, this chunking information will be used for object chunking through
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set-chunk api. ::
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ceph-dedup-tool --op estimate --pool $POOL --min-chunk min_size
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--chunk-algorithm rabin --fingerprint-algorithm rabin
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ceph-dedup-tool has many options to utilize rabin chunk.
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These are options for rabin chunk. ::
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--mod-prime <uint64_t>
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--rabin-prime <uint64_t>
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--pow <uint64_t>
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--chunk-mask-bit <uint32_t>
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--window-size <uint32_t>
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--min-chunk <uint32_t>
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--max-chunk <uint64_t>
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Users need to refer following equation to use above options for rabin chunk. ::
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rabin_hash =
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(rabin_hash * rabin_prime + new_byte - old_byte * pow) % (mod_prime)
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c. Fixed chunk vs content-defined chunk
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Content-defined chunking may or not be optimal solution.
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For example,
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Data chunk A : abcdefgabcdefgabcdefg
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Let's think about Data chunk A's deduplication. Ideal chunk offset is
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from 1 to 7 (abcdefg). So, if we use fixed chunk, 7 is optimal chunk length.
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But, in the case of content-based slicing, the optimal chunk length
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could not be found (dedup ratio will not be 100%).
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Because we need to find optimal parameter such
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as boundary bit, window size and prime value. This is as easy as fixed chunk.
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But, content defined chunking is very effective in the following case.
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Data chunk B : abcdefgabcdefgabcdefg
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Data chunk C : Tabcdefgabcdefgabcdefg
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* fix reference count
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The key idea behind of reference counting for dedup is false-positive, which means
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(manifest object (no ref), chunk object(has ref)) happen instead of
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(manifest object (has ref), chunk 1(no ref)).
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To fix such inconsistency, ceph-dedup-tool supports chunk_scrub. ::
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ceph-dedup-tool --op chunk_scrub --pool $POOL --chunk_pool $CHUNK_POOL
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