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Scalable Distributed Stream
        Processing
         Matt Garren
Aurora

● Tuple Processor
● Uses "Boxes and Arrows" paradigm for
  dataflow
● Queries are continuous
Aurora: Operators

● Filter
● Union
● WSort
● Tumble
● Map
● XSection
● Slide
● Join
● Resample
Aurora: Runtime

● Scheduler
● Storage Manager
● Load Shedder
● QoS
Distributed Stream Processing

● Benefits of a Node Federation
  ○ Sharing/Multiplexing
  ○ Resiliency
  ○ Programmatically the same
Aurora*: Distributed Aurora

● Federation of nodes running Aurora
● Single Admin domain
● Queries allowed on all nodes
● Boxes partitioned to nodes
● Overloaded nodes offload tasks
● Decentralized reconfiguration
Medusa: Distributed Aurora

● Multiple Administrative domains
● "Participants"
● Agoric system
● "Contracts" with senders/receivers
● Receiver "pays" sender
● Goal: Stable "Economy"
Infrastructure Requirements

● Naming and Discovery
  ○ Global namespace
  ○ Catalogs (Inter/Intra participant)
    ■ Operators
    ■ Schemas
    ■ Queries
    ■ Streams
    ■ Contacts
    ■ Event locations
Infrastructure Requirements

● Routing
  ○ Before producing: register schema
  ○ Label events w/ stream name
  ○ Intermediary nodes forward (Intra)
  ○ Explicit connections to cross boundaries
Infrastructure Requirements

● Message Transport
  ○ Individual TCP connections bad idea
  ○ Multiplex messages
     ■ Single TCP connection
     ■ Message scheduler
● Remote Definition
     ■ Users only see what they care about
Load Sharing

● Load Share Daemon per node
● Box Sliding
● Box Splitting
Load Sharing

● Considerations
  ○ How often to load share?
  ○ When is it not ok to offload?
  ○ What should be split?
Availability

● Primary/Back up servers
● Heartbeats to neighbors
● Additional work at failover
QoS

● Intermediate/Output
● Precision
Questions?

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