lehrstuhl informatik iii: datenbanksysteme community training: partitioning schemes in good shape...
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Lehrstuhl Informatik III: Datenbanksysteme
Community Training: Partitioning Schemes in Good Shape for Federated Data Grids
Tobias Scholl, Richard Kuntschke, Angelika Reiser, Alfons Kemper
3rd IEEE International Conference on e-Science and Grid ComputingBangalore, IndiaDecember 10th – 13th 2007
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Lehrstuhl Informatik III: Datenbanksysteme
The AstroGrid-D Project
German Astronomy Community Grid http://www.gac-grid.org/
funded by the German Ministry of Education and Research
part of the D-Grid
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Lehrstuhl Informatik III: Datenbanksysteme
The Multiwavelength Milky Way
http://adc.gsfc.nasa.gov/mw/
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Data-intensive e-Science Applications
Many e-science application areas astrophysics geosciences climatology
Combination of various, globally distributed data sources
Increasing popularity (within community and public domain): more users
Scalability issues with current approaches
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Up-coming Data-intensive Applications
LOFAR
LHC
Data rates Terabytes a day/night Petabytes a year
LOFAR LSST Pan-STARRS LHC
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Current Sharing in Data Grids
Images from: “Data-Intensive Grid Computing” by Srikumar Venugopal
Data autonomy Policies allow partners to access data Each institution ensures
Availability (replication) Scalability
Various organizational structures: Centralized Hierarchical Federated Hybrid
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Community-Driven Data Distribution
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The Training Phase
Extract data from the archives Compute partitioning schemes Compare different partitioning schemes
Standard Quadtrees Median-based Quadtrees Zones
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Quadtrees
Well-known index structure
Recursive decomposition
Adaptive to data resolution
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Quadtree-based Schemes: Splitting Variants
Center splitting Always bisects each
dimension congruent subareas Splitting points stored
or computed
Median heuristics Compute median for
each dimension independently
O(n) median algorithm
Splitting points stored
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The Zones Index(J. Gray, M. Nieto-Santisteban, A. Szalay) Index structure for databases Specific spatial clustering in zones Optimized for
points-in-region queries self-match, cross-match queries
Equi-distant partitioning Declination coordinate Zone(ra, dec) = floor((dec + 90.0) / h)
Implemented directly in SQLImage by J. Gray et al.
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Evaluation Setup
2 data sets: skewed and uniform Size of data sample: 0.01%, 0.1%, 1%, 10% Number of partitions:
4, 16, 64, 256, 1024, 4096, 8192, 16384, 32768, 65536, 131072 (24 – 217)
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Skewed Training Data (Dskew
)
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Comparing Partitioning Schemes
Duration Average data population Variance in partition population Empty partitions Size of the training set Baseline comparison
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Average Data Population
0
10
20
30
40
50
60
70
80
90
100
1310726553632768163848192409610242566416
aver
age
data
pop
ulat
ion
(%)
# partitions
center splitting (skewed data)median splitting (skewed data)center splitting (uniform data)
median splitting (uniform data)
10% training sample, Dskew
avg(# objects in partitions)
# objects in biggest
partition
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Evolution of the Partitioning Scheme
4096 partitions 8192 partitions 16384 partitions
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Empty Leaves
0
1
2
3
4
5
6
7
8
1310726553632768163848192409610242566416
em
pty
pa
rtiti
on
s (%
)
# partitions
center splittingmedian splittingzones
10% training sample, Dskew
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Size of the Training Set
1
4
16
64
256
1024
4096
16384
1310726553632768163848192409610242566416 0
5
10
15
20
25
30
35
sam
ple
ra
tio
em
pty
pa
rtiti
ons
(%)
# partitions
sample ratiocenter splitting
median splitting
0.1% training sample, Dskew
# objects in training set
# partitions to be created
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Standard Quadtree vs. Median Heuristics
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Evaluation – Summary
Quadtrees: good adaption to data distribution Quadtrees: Trade-off between data load
balancing and uniformly shaped regions Median-based heuristics: best data load
balancing even for skewed data sets Zone Index: good for uniform data sets Training set needs to be sufficiently large in
order not to artificially create empty partitions
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HiSbase
Peer-to-Peer layer assigns data partitions to peers
Higher flexibility New peers are
integrated seamlessly
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Query Submission (at Peer d)
Determine relevant regions: [1,3]
Select coordinator:Region 1
Send CoordinateQuery-message to id1:{[1,3], SQL}
Message gets routed to Peer a.
01 2
3 4
5 6
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Query Coordination (at Peer a)
Peer a is coordinator Contact relevant
regions Collect intermediate
results Send complete result
to client
select …
select …
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Prototype Implementation
Java-based prototype FreePastry library (Pastry implementation)
Rice University MPI-SWS
Presentations at BTW 2007, Aachen, Germany VLDB 2007, Vienna, Austria
Deployed in various settings LAN WAN (AstroGrid-D, PlanetLab)
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Summary
Training phase Community-driven Data Grids
Domain-specific partitioning scheme Partitioning scheme supports
Data skew Region-based queries
Framework for comparing partitioning schemes
Various measures with regard to data load balancing
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Ongoing Work
Database-driven comparison 0.1% of a Petabyte still is 1 Terabyte Feasibility of median-based techniques
Workload-aware data partitioning Heterogeneous data nodes
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Get in Touch
Database systems group, TU München Web site: http://www-db.in.tum.de E-mail: [email protected]
HiSbase http://www-db.in.tum.de/research/projects/hisbase/
Data stream management “Grid-based Data Stream Processing in e-Science”
(e-Science '06) http://www.gac-grid.de/project-products/Software/
DataStreamManagement.html
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AstroGrid-D Research Demo
Finding Galaxy Clusters using Grid Computing Technology
Room:Banquet
Wednesday3:30 pm to6:00 pm