silt: a memory-efficient, high-performance key-value store
TRANSCRIPT
SILT: A Memory-Efficient, High-Performance Key-Value Store Hyeontaek Lim, Bin Fan, David G. Andersen, Michael Kaminsky Carnegie Mellon University, Intel Labs
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Outline1- Introduction.2- Problem statement.3. Contributions.
4- Experiments5- Results and conclusion
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1.Introduction
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What is key-value store ?
a data storage paradigm designed for storing, retrieving, and managing associative arrays,
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Introduction
Ecommerce (Amazon) Picture stores (facebook) Web object caching.
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key-value stores used in:
Many projects have examined flash memory-based key-value stores ; Faster than disk, cheaper than DRAM
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DRAM vs Flash
RAM (main memory) a bit more expensive . requires constant power. is much faster.
Flash memory (HD) low-cost . retains data when power is
removed (nonvolatile), but its performance is also
slow.
Memory overhead: Index size per entry, Ideally 0 (no memory overhead)
Read amplification: Flash reads per query, Limits query throughput, Ideally 1 (no wasted flash reads).
Write amplification: Flash writes per entry, Limits insert throughput, Also reduces flash life expectancy
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Three Metrics to Minimize
2.MotivationProblem statement
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MotivationAs key-value stores scale in both size and importance,
index memory efficiency is increasingly becoming one of the most important factors for the system’s scalability and overall cost effectiveness.
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Challenge
Memory efficiency
High performance
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This talk will introduce SILT, which uses drastically less memory than previous systems while retaining high performance.
Related WorkMany studies tried to reduce in-memory index overhead,but:
• there solutions either require more memory.
• or keeping parts of there index on disk (low performance "called read amplification")
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3.Contributions
SILT (Small Index Large Table)
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Contributions1. The design and implementation of three basic key-value
stores (LogStore, HashStore, and SortedStore) .2. Synthesis of these basic stores to build SILT.3. An analytic model that enables an explicit and careful
balance between memory, storage, and computation .
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Contributions1. The design and implementation of three basic key-value
stores (LogStore, HashStore, and SortedStore) .2. Synthesis of these basic stores to build SILT.3. An analytic model that enables an explicit and careful
balance between memory, storage, and computation .
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Basic store designLogStore, HashStore, and SortedStore . ( overall overview).
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Basic store designLogStore, HashStore, and SortedStore . ( overall overview).
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SILT: 1. LogStore1. Write friendly key-value store.2. Use a tag (15 bits) for an index rather than an
entire hash index (160bits) .3. A customized version of Cuckoo hashing is used.4. In-memory hash table to map contents in flash.5. Only one instance.
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SILT: 1. LogStoreHow it works? .
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SILT: 1. LogStore
K2h1(k2)
Tag Offset
DRAM (memory)** store short tag (15b)
FLASH (hard disk)** store the full key (160)
h2(k2)
1 2 3 4
Cuckoo hashing
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SILT: 1. LogStore
K2h1(k2)
Tag Offset
h2(k2)
1
K2
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
h2(k2)
1 2 3 4
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SILT: 1. LogStore
K1h1(k1)
Tag Offset
h2(k2)
1
h1(k1)
2
K2 K1
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
h2(k1)
1 2 3 4
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SILT: 1. LogStore
K4h1(k2)
Tag Offset
h2(k2)
1
h1(k4)
3
h1(k1)
2
K2 K1 K4
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
h2(k2)
1 2 3 4
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SILT: 1. LogStore
K3h1(k3)
Tag Offset
h2(k2)
1
h1(k4)
3
h1(k1)
2
K2 K1 K4 K3
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
h2(k3)
1 2 3 4
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SILT: 1. LogStore
K3h1(k3)
Tag Offset
h1(k4)
3
h2(k2)
1
h1(k1)
2
K2 K1 K4 K3
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
h2(k3)
1 2 3 4
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SILT: 1. LogStore
K3h1(k3)
Tag Offset
h2(k3)
4
h1(k4)
3
h2(k2)
1
h1(k1)
2
K2 K1 K4 K3
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
h2(k3)
1 2 3 4
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SILT: 1. LogStore
K5? h1(k5)
Tag Offset
h2(k3)
4
h1(k4)
3
h2(k2)
1
h1(k1)
2
K2 K1 K4 K3
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
h2(k5)
1 2 3 4
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SILT: 1. LogStore
K5? h1(k5)
Tag Offset
h2(k3)
4
h1(k4)
3
h2(k2)
1
h1(k1)
2
K2 K1 K4 K3
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
h2(k5)
1 2 3 4
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SILT: 1. LogStore
K5? h1(k5)
Tag Offset
h2(k3)
4
h1(k4)
3
h2(k2)
1
h1(k1)
2
K2 K1 K4 K3
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
h2(k5)
1 2 3 4LogStore is full?1. SILT freezes the LogStore 2. initializes a new one without expensive rehashing.3. Convert the old LogStore to a HashStore ( in
background).
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Basic store designLogStore, HashStore, and SortedStore . ( overall overview).
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SILT: 2. HashStore1. Convert logStore into a more memory-efficient data
structure.2. Sort the LogStore based on ‘HashOrder’3. Saves lots of in-memory by eliminating the index and
reordering the on-flash pairs from insertion order to hash-order
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SILT: 2. HashStore
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SILT: 2. HashStore
Tag Offset
h2(k3)
4
h1(k4)
3
h2(k2)
1
h1(k1)
2
K2 K1 K4 K3
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
1 2 3 4
Convert logStore into a more memory-efficient data structure..
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SILT: 2. HashStore
Tag Offset
h2(k3)
4
h1(k4)
3
h2(k2)
1
h1(k1)
2
K2 K1 K4 K3
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
1 2 3 4
Convert logStore into a more memory-efficient data structure..1. Remove the Offset
column
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SILT: 2. HashStore
Tag
h2(k3)
h1(k4)
h2(k2)
h1(k1)
K2 K1 K4 K3
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
1 2 3 4
Convert logStore into a more memory-efficient data structure..
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SILT: 2. HashStore
Tag
h2(k3)
h1(k4)
h2(k2)
h1(k1)
K2 K1 K4 K3
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
1 2 3 4
Convert logStore into a more memory-efficient data structure..
2. Sort according to
the hash
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SILT: 2. HashStore
Tag
h2(k3)
h1(k4)
h2(k2)
h1(k1)
K3 K4 K2 K1
DRAM (memory)** store short tag
FLASH (hard disk)** store the full key (160)
1 2 3 4
Convert logStore into a more memory-efficient data structure..
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SILT: 2. HashStore
K3 K4 K2 K1
Hashstore store many logstores .
K3 K4 K2 K1
K3 K4 K2 K1
K3 K4 K2 K1
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Basic store designLogStore, HashStore, and SortedStore . ( overall overview).
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SILT: 3. SortedStore Multiple HashStore can be aggregated into one
SortedStore. Focuses on minimize the bit presentation (by Using
Sorted Data on Flash). From the sorted results, indices are re-made ( trie data
structure , uses 0.4 bytes of index memory per key on average).
keeps read amplification low (exactly 1) by directly pointing to the correct location on flash.
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SILT: 3. SortedStoreIndexing Sorted Data with a Trie:leaf = key internal node = common prefix of the keys represented by its descendants
How it works?41
SILT: 3. SortedStoreIndexing Sorted Data with a Trie:
Example
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0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
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0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
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0 1
0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
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0
0 1
1
0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
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0
0 1
1
0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
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0
0
0
1
1
1
0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
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0
0
0
1
1
1
0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
0
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0
0
0
1
1
1
0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
0
Ignored
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0
0
0
1
1
1
0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
0 1
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0
0
0
1
1
1
0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
0 1
2
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10
2
763
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0
0
0
0
0
0
0
1
1
1
1
1 1
1
0 0 0 1 1 1 1 1
0 0 1 0 0 0 1 1
0 1 1 0 1 1 0 1
1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1
0 1 2 3 4 5 6 7
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SortedStore uses a compact recursive representation to eliminate pointers
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SLIT Lookup Process Queries look up stores in sequence (from new to old) Note : Inserts only go to Log
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SLIT Lookup Process Queries look up stores in sequence (from new to old) Note : Inserts only go to Log
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Contributions1. The design and implementation of three basic key-value
stores (LogStore, HashStore, and SortedStore) .2. Synthesis of these basic stores to build SILT.3. An analytic model that enables an explicit and careful
balance between memory, storage, and computation .
57
Contributions1. The design and implementation of three basic key-value
stores (LogStore, HashStore, and SortedStore) .2. Synthesis of these basic stores to build SILT.3. An analytic model that enables an explicit and careful
balance between memory, storage, and computation .
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Analytic model
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Memory overhead (MA)=
Read amplification (RA)=
Write amplification (WA)=data written to flash
data written by application
data read from flash
data read by application
total memory consumed
number of items
4.Evaluation & Experiments
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Experiment Setup
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CPU 2.80 GHz (4 cores)
Flash driveSATA 256 GB
(48 K random 1024-byte reads/sec)
Workload size 20-byte key, 1000-byte value, ≥ 50 M keys
Query pattern Uniformly distributed
Experiment 1LogStore Alone: Too Much Memory Workload: 90% GET (50-100 M keys) + 10% PUT (50 M keys)
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Experiment 2LogStore + SortedStore: Still Much MemoryWorkload: 90% GET (50-100 M keys) + 10% PUT (50 M keys)
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Experiment 3Full SILT: Very Memory EfficientWorkload: 90% GET (50-100 M keys) + 10% PUT (50 M keys)
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“Thanks