Sharding Social Networks
Quang Duong, Sharad Goel, Jake Hofman, Sergei Vassilvitskii
Presented by Andreas Adolfsson and David Perez
Social Network Data Growing in Complexity
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Extra Terms before we continue
Shard - a subset of nodes in a graph stored on an individual machine.
Sharding - Distributing data across a collection of shards.
Neighborhood Queries - Query that retrieves data from a node and all of its
neighbors.
Replication Ratio ρ - Characterizes the level of node duplication in a system.
Query Plan - a set of indices that indicate where to access a node when executing a
neighborhood query.
Hotspots - shards with much higher-than-average loads.
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NetworkSharding Problem
Given a graph G, the total number of shards, T, and a per shard capacity constraint
M, find a valid query plan Q with minimal cost.
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Stochastic Block Model
Random networks with community structure.
Nodes belong to K communities(blocks). Probability of an edge (relationship) existing
between two nodes depends on their block assignments.
For each node i, independently roll a K-sided die with bias π to determine the node's
block assignment zi in {1, …, K}.
For each ordered pair of nodes ( i, j ), flip a coin with bias Θ+ (resp. Θ- ) for nodes
in the same (resp. different) blocks to determine if an edge exists from i to j.
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Sharding Techniques
Random Sharding - randomly assign nodes to separate shards.
Geographic Sharding - take geographic distribution of nodes into account.
Network Sharding - consider network structure that may exist in the data.
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Random Sharding
Random Sharding refers to distributing nodes to shards at random.
This is a very common strategy used in industry due to its simplicity.
Degrades into worst-cost neighborhood querying, where each accessed neighbor is
stored on different shards.
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Geographic sharding
Geographic sharding refers to distributing users across shards based on available
geographical location information.
Caching a small number of “local celebrity” users and creating hotspot shards reduces
average load time.
Unfortunately, geographic information is not always available, and random sharding is
often used when Geo-data not found.
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Network Sharding
Attempts to partition blocks of densely connected communities together.
Maps blocks to shards via taking advantage of between-block structure to minimize
sharding costs.
Replication of frequently accessed nodes across multiple shards reduces sharding cost,
hotspots, and average load across the system.
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The Solution
Two pronged approach for scalable network sharding, as well as an extensible
replication algorithm:
VBLabelProp - Identify densely connected regions in the graph.
BlockShard - Greedily partition blocks to suitable shards, minimizing sharding cost.
NodeRep - Allocate copies of very densely connected region into non-full shards.
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BlockShard
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NodeRep
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Empirical Evaluation Strategy
Evaluate Random, Geographic,and Network-Aware sharding strategies.
Two Datasets
LiveJournal - 5.1 million nodes, 150 million directed edges, 1.6 million user profiles.
Twitter - 1.4 billion directed edges, 41 million user profiles.
Roughly 1MB of in-memory space per user, 40GB of RAM available of shard storage.
Evaluate methods with and without replication.
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Results Overview
Average Load
Load Balance
Replication
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Sharding Cost across
methods
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Sharding Cost with
Replication
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Sharding Cost with
Replication cont.
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Load Dispersion
across methods.
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Per-Shard Load
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Effects of Replication on Load Balance
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Conclusion
Network Sharding considers the structure of a dataset, and provides significant
improvements over Random and Geographic Sharding strategies.
Two pronged approach with an optional data replication algorithm to minimize
sharding cost.
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Questions?
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References
Quang Duong, Sharad Goel, Jake Hofman, Sergei Vassilvitskii, Sharding Social Networks, Proceedings of the sixth ACM international
conference on web search and data mining, New York, New York, USA.
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