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FAWN: A Fast Array of Wimpy Nodes

FAWN: A Fast Array of Wimpy Nodes. D. G. Andersen 1 J. Franklin 1 M. Kaminsky 2 A. Phanishayee 1 L. Tan 1 V. Vasudevan 1 1 CMU 2 Intel Labs. Warning. This is not a complete presentation: it just explains some items that were left out of the authors' presentation of FAWN

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FAWN: A Fast Array of Wimpy Nodes

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  1. FAWN:A Fast Array of Wimpy Nodes D. G. Andersen1 J. Franklin1 M. Kaminsky2A. Phanishayee1 L. Tan1 V. Vasudevan1 1CMU 2Intel Labs

  2. Warning • This is not a complete presentation: it just explains some items that were left out of the authors' presentation of FAWN • Topics such as • Consistent hashing • Data Log architecture

  3. Consistent hashing (I) • Technique used in distributed hashing schemes to tolerate the loss of one or more machines • Traditional approach: • Each node corresponds to a single bucket • If a node dies, • We lose a bucket • Must update the hash function forall nodes

  4. Go next! Consistent hashing (II) • Have several buckets per node • Organize all buckets into a ring • Each bucket has a successor • If a bucket is unavailable • Select next bucket

  5. Consistent hashing (III) • Potential problem • Neighboring bucket will become overloaded • Not if we associate with each physical node a set of random disjoint buckets: • AKA virtual nodes • When a physical node fails, its workload get shared by several other physical nodes

  6. Data log architecture • FAWN nodes have two constraints • Small memory • Poor performance of flash drives forrandom writes • FAWN data log architecture • Minimizes its RAM footprint • All writes are append-only

  7. Mapping a key to a value • Through an in-memory hash table • FAWN uses 160-bit keys: • i least significant bits are index bits • next 15 low-order bits are key fragment • Index bits are used to select a bucket • Key fragment is stored in bucket entry • 15 bits + valid bit + 32-bit pointer to address in data log = 48 bits = 6 bytes

  8. The data log • One data log per virtual node • Data log entries consist of • A full 160-bit key • A length field • The actual data

  9. Basic data store functions • Store: • Adds an entry to the log and updates corresponding hash table entry • Lookup: • Locates a data log entry and checks full key • Invalidate: • Marks hash table entry invalid and adds a delete entry to the log (for durability)

  10. Store

  11. Maintenance functions • Split: • Splits a data store between existing virtual node and a new virtual node • Merge • Merge two data stores into one • Compact: • Compacts data log and updates all hash table entries

  12. Split

  13. Split

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