Querying the Internet with PIER

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Querying the Internet with PIER

• CS294-4
• Paul Burstein
• 11/10/2003

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Outline

• Motivation
• Architecture
• Join Algorithms
• Evaluation
• Discussion

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Motivation

• Inject a degree of distribution into databases
• Internet scale systems vs. hundred node systems
• Large scale applications requiring database
  functionaity

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Applications

• P2P Databases
• Highly distributed and available data
• Network Monitoring
• Intrusion detection
• Fingerprint queries

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Design Principles

• Relaxed Consistency
• Sacrifice Consistency in face of Availability and
  Partition tolerance
• Organic Scaling
• Growth with deployment
• Natural Habitats for Data
• Data remains in original format with a DB
  interface
• Standard Schemas
• Achieved though common software

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Outline

• Motivation
• Architecture
• Join Algorithms
• Evaluation
• Discussion

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PIER Architecture
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DHT Design

• Implemented with CAN and Chord
• Routing Layer
• Mapping for keys
• Storage Manager
• Node data storage

• Provider
• Storage access interface for higher levels

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Routing Storage

• Routing Layer
• DHT-based API
• locationMapChange local key set change
• Storage Manager
• Easy to realize API
• Efficient performance relative to network
• Main-memory storage manager used

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Provider

• Couples the routing and storage layers
• namespace relation
• resourceId primary key

• namespace resourceId ? key
• instanceId distinguishes objects with same
  namespace and resourceID
• lifetime item storage duration
• multicast contacts namespaces nodes
• lscan iterates over a nodes local data
• newData application callback on data arrival

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PIER Query Processor

• Query dataflow engine
• Operators
• Selection, projection, joins, grouping,
  aggregation
• Operators push and pull data
• Current data modification is though the DHT
  interface

• Relaxed consistency and reachable snapshot
• Working only with nodes reachable at the time a
  query is issued

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Outline

• Motivation
• Architecture
• Join Algorithms
• Evaluation
• Discussion

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Join Algorithms

• Symmetric Hash Join
• Rehashes the relations
• Scan and copy
• Fetch Matches
• One relation already hashed on join attribute
• R, S relations
• Nr, Ns relation namespaces
• Nq - DHT-based temporary table

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Join Rewriting

• Aimed at lowering the bandwidth utilization
• Symmetric semi-join
• Local projections to join keys
• Global fetch matches join
• Bloom joins
• Local bloom filters are published into temporary
  namespaces
• Filters multicast to opposite relations nodes

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• How does this scale?

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Outline

• Motivation
• Architecture
• Join Algorithms
• Evaluation
• Discussion

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Workload Parameters

• CAN configuration d 4
• R 10 times larger than S
• Constants provide 50 selectivity
• f(x,y) evaluated after the join
• 90 of R tuples match a tuple in S
• Result tuples are 1KB each
• Symmetric hash join used

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Simulation Setup

• Up to 10,000 nodes
• Network cross-traffic, CPU and memory
  utilizations ignored
• 1. 100ms and 10Mbps fully connected links
• 2. GT-ITM transit-stub topology

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Scalability

• 1MB data per node
• Fully-connected topology
• Variable number of computation nodes
• Network congestion is an issue with few
  computation nodes
• How is the computation workload distributed?

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Join Algorithms (1/2)

• Infinite Bandwidth
• 1024 data and computation nodes
• Core join Algorithms
• Perform faster
• Rewrites
• Bloom Filter two multicasts
• Semi-join two CAN lookups

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Join Algorithms (2/2)

• Limited Bandwidth
• 10Mbps inbound capacity
• 25GB relations, 1024 nodes
• Symmetric Hash Join
• Rehashes both tables
• Semi-join
• Transfers only matching tuples
• At 40 selectivity, bottleneck switches from
  computation nodes to query sites

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Soft State

• Failure detection and recovery
• 15 second failure detection
• 4096 nodes
• Refresh period
• Time to reinsert lost tuples

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Transit Stub Topology

• GT-ITM
• 4 Domains, 10 nodes per domain, 3 stubs per node
• 50ms, 10ms, 2ms latency
• 10Mbps inbound links
• Similar trends as fully connected topology
• A bit longer end-to-end delays

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Experimental Results

• 64 PCs on 1Gbps network
• All nodes are computation nodes

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Outline

• Motivation
• Architecture
• Join Algorithms
• Evaluation
• Discussion

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Discussion

• PIER presents a distributed query engine
• What remains to be done?
• DB issues
• Networking issues