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Grid Asia2008 Low Latency Data Grid

Grid Asia2008 Low Latency Data Grid



Investment banks rely extensively on grids to dramatically increase throughput for their calculations for analytics (especially risk). The traditional design pattern involves executing compute ...

Investment banks rely extensively on grids to dramatically increase throughput for their calculations for analytics (especially risk). The traditional design pattern involves executing compute intensive workflows where jobs require movement of large data files to the compute nodes, calculation results creating files which then are again consumed by the next job in the flow. Increasingly, the pattern is shifting to running short lived tasks where the bottleneck is data i.e. the time spent to move data back and forth between compute nodes can be overwhelming - turning a compute bound job to be a IO bound one. For instance, real time pricing for financial derivative instruments could just take a few milliseconds, but, the time required for the data transfer could be hundreds of milliseconds.

The talk focuses on one architectural pattern gaining popularity - move the compute to the data. The data is partitioned in grid memory across many nodes and the compute task is routed to the node with the right data set provisioned based on the data hints it provides during launch.
We discuss the features of the main-memory based data grid solution that uses different data partitioning policies such as hashing or data relationship based to manage data across a large cluster of nodes. We also discuss techniques for rebalancing data and behavior across the Grid nodes to achieve the best throughput and lowest latency.



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    Grid Asia2008 Low Latency Data Grid Grid Asia2008 Low Latency Data Grid Presentation Transcript

    • Low Latency Data Grids in Finance Jags Ramnarayan Chief Architect GemStone Systems [email_address]
    • Background on GemStone Systems
      • Known for its Object Database technology since 1982
      • Now specializes in memory-oriented distributed data management
      • Over 200 installed customers in global 2000
      • Grid focus driven by:
        • Very high performance with predictable throughput, latency and availability
          • Capital markets
          • Large e-commerce portals – real time fraud
          • Federal intelligence
    • Use of Grid computing in finance
      • Two primary areas in tier 1 investment banks
        • Risk Analytics
        • Pricing
    • State of affairs – Risk Analytics
      • Deluge of data (market data, trade data, etc)
      • Overnight batch job doesn’t cut it
        • Want intra-day risk metrics
        • In some cases, real-time risk
      • Explosion in simulation scenarios
        • More accurate risk exposure
        • Compliance
      • Increasing number of smaller calculations
    • State of affairs – Pricing (derivatives)
      • Too many products
      • Increasing complexity in products
        • Too many underliers
        • Many relationships
      • Hunger for latency reduction
        • Calculating the new price with lowest possible latency
        • Pushing the prices to distributed applications
    • Where is the problem? Compute farm Data warehouses Rational databases
      • Database/file access contention
        • Too many concurrent connections
        • Large database server bottlenecks on network
        • Queries results are large causing CPU bottlenecks
        • Even a parallel file system throttled by disk speeds
      • Too much data transfer
        • Between tasks, Jobs
        • Between Grid and file systems, databases
        • Data consistency issues
      File system CPU bound job turns into a IO bound Job Grid Scheduler
    • Data Fabric for Risk Analytics When data is stored, it is transparently replicated and/or partitioned; Redundant storage can be in memory and/or on disk— ensures continuous availability Keep reference data replicated on many; partition trade data Machine nodes can be added dynamically to expand storage capacity or to handle increased client load Pool memory (and disk) across cluster ; parallelize data access and computation to achieve very high aggregate throughput
    • Data Fabric for Risk Analytics TaskFlow - As results are generated push events to compute nodes to initiate subsequent computation Avoid bulk data transfer across tasks or Jobs Thousands of compute nodes can maintain local cache of most frequently used data; Optionally use local disk for overflow Move reference data to local cache Synchronous read through, write through or Asynchronous write-behind to other data sources and sinks
    • Move business logic to data f 1 , f 2 , … f n FIFO Queue Data fabric Resources Exec functions Sept Trades Submit (f1) -> AggregateHighValueTrades(<input data>, “ where trades.month=‘Sept ’) Function (f1) Function (f2)
        • Principle: Move task to computational resource with most of the relevant data before considering other nodes where data transfer becomes necessary
        • Parallel function execution service (“Map Reduce”)
        • Data dependency hints
          • Routing key, collection of keys, “where clause(s)”
        • Serial or parallel execution
    • Key lessons
      • Apps should think about capitalizing memory across Grid (it is abundant)
      • Keep IO cycles to minimum through main memory caching of operational data sets
        • Scavange Grid memory and avoid data source access
      • Achieve linear scaling for your Grid apps by horizontally partitioning your data and behavior
        • Read “Pat helland’s – Life beyond Distributed transactions” ( http://www-db.cs.wisc.edu/cidr/cidr2007/papers/cidr07p15.pdf )
      • Get more info on the GemFire data fabric
        • http:// www.gemstone.com/gemfire