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Transcript Babu_netezza

Data-centric computing with
Netezza Architecture
DISC reading group
September 24, 2007
High Level Points
• Supercomputer use model today:
– Compile, submit, wait
– Does a poor job of taking advantage of
human insight available in interactive models
• Large datasets can be interactively
processed using Netezza
What is Netezza?
• Essentially: A big, fast SQL database
What is Netezza?
• Frontend provides SQL interface
• Backend is a large rack of specialized blades
Custom Backend Blades
• Commodity CPU, NIC, disk
• Custom FPGA replaces disk interface
– Can do basic filtering in hardware,
i.e., stream processing before data hits main memory
Division of Data
• Database distributed across multiple (100+)
• Each SPU controls, manages its slice of DB
• No info on data management, replciation, etc.
Division of Labor
• SPU FPGA handles basic filtering tasks
• SPU CPU handles record level processing:
filtering, parsing, projecting, logging, etc.
• SPU CPU handles most operations on
intermediate results: sorts, joins, aggregates
• Frontend CPU handles remaining operation
>>> Processing close to disk
What can this be used for?
• Paper gives 3 examples:
– Citation graph processing
– Search for particular structure in electrical
– Word meaning disambiguation through search
of ontology
Citation graph example
• Look through large, sparse graph (16 million nodes, 388
million edges)
• Find both strong (direct edge) and weak couplings (e.g.,
two papers cite the same work)
• Essentially same code for workstation and Netezza – no
need to expose parallel architecture
• Workstation DNF; 80-100x speedup on smaller tests
IC netlist example
• Flattened netlist of 3.5 million transistors, 10
million wires
• Search for AND structure
IC example results
• Combinatorial explosion makes directly joining all
possibilities for each element impossible
• Can constrain better using fanouts of signals internal to
the circuit
• Individual SQL queries for finding possible matches for
the individual transistors took under 10 seconds
• Found all uses of the AND macro, as well as many other
(1300+) identical structures generated through other
Ontology example
• Expand out all possible interpretations of a
• Ontology specifies lexical elements, IS-A
relations, concepts, and constraints on concepts
• Goal is to search the space, expand concepts to
find all matches to given phrase
Ontology results
• Partially unfolded ontology
– Greatly expands database size, but reduces
iterations / recursions
• Recoded ontology triples as integers
• 5.58 sec. vs. 262 sec.
• can pipeline multiple queries
• Works if you can reduce your problem to SQL queries
• All of the problems were based on graph expansion /
exploration – how about other domains?
• Issues of database partitioning? How does arbitrary
slicing across 108 blades affect performance / scalability,
esp. for non-sparse problems?
• Strawman comparison to workstation class machine:
how does a traditional DB server / storage cluster