Sort

1
Parallel Sorting
2
Sort

• Sorting operation is frequently used for database
  processing.
• For example sorting may be requested by users
  through the use of Distinct, Order By and Group
  By clauses in SQL.
• In general, parallel database sorting consists of
  multiple rounds of local sort, performed in each
  PN in parallel, followed by movement of tuples
  among PNs.
• A dynamic load balancing approach has been
  proposed, recently.

3
Parallel Sorting

• Traditional Technologies
• Parallel Merge-All Sort
• Parallel Binary-Merge Sort
• Parallel Partitioned Sort
• Dynamic Load Balancing Approaches
• Partitioned with Redistribution Sort (PRS)
• Load Balancing Optimization Sort (LBOS)
• Load Balancing Optimization Sort with Fragment
  Feature (LBOS-F)

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Parallel Merge-All Sort
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Parallel Binary-Merge Sort
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Parallel Partitioned Sort
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Partitioned with Redistribution Sort (PRS)

• Partition Phase each PN first scans its local
  tuples and distributes them as buckets according
  to range partitioning.
• Optimization Phase a coordinator PN is used to
  monitor the load for every PN. If every PN has
  the similar amount of data to process, Go to
  Sorting Phase. Otherwise, the imbalanced work
  load is detected, thus, a redistributed
  partitioning is needed.
• Redistribution Phase data redistribution.
• Sorting Phase local sort is carried out in each
  PN.

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PRS
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Load Balancing Optimization Sort (LBOS)

• Phase 1. Bucket Sizes Counting Each PN reads
  its local data and computes each bucket size
  according to range distribution. Thereafter, each
  PN transfers the bucket information to a
  coordinator.
• Phase 2. Load Balancing Optimization
  coordinator decides the distribution strategy for
  assigning each bucket to PNs according to their
  bucket IDs sequentially. Whether coordinator
  assigns one more next bucket (size B) to current
  PNi will be decided on if (PiB)-Pavg lt d, it
  will assign next bucket to PNi. Otherwise, the
  bucket will be assigned to next PNi1. This phase
  only decides the distribution strategy without
  any data transferred.

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• Phase 3. Data Partitioning Each PN transfers
  and receives data by using the distribution
  strategy decided in Phase 2.
• Phase 4. Sorting Each PN performs local sort.

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LBOS
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Effect of Bucket Skew
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Effect of No. of Processing Nodes
PNs16
PNs256
PRS PPS ? 21.8
PRS PPS ? 21.8
LOBS PPS ? 59.9
LOBS PPS ? 39.7
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Load Balancing Optimization Sort with Fragment
Feature (LBOS-F)

• When the skew condition is severe, LBOS performs
  not very well.
• LBSO-F is used to address this drawback and it
  also consists of four phases.
• Its Phase 1, 3, and 4 are exactly the same as in
  LBOS except the Phase 2 in which LBOS-F can
  fragment oversize buckets and distribute them to
  different PNs.

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Phase 2 for LBOS-F

• Phase 2. Load Balancing Optimization with
  Fragment Feature coordinator decides the
  distribution strategy for assigning each bucket
  to PNs according to their bucket IDs
  sequentially. Suppose that the next bucket (size
  B and PiBgtPavg), with the interval of data
  values from rs to re ( rs lt re ), is being
  considered for the assignment. The coordinator
  will fragment this bucket into two portions.
  Suppose that PNi already has total size Pi of
  tuples which is d tuples less than Pavg
  (PidPavg). The first portion which consists of
  those data values within the range of rs to
  rs(re-rs)d/B of the bucket will be assigned to
  current PNi. The second portion which consists of
  the remaining data of the bucket will be assigned
  to next PNi1. Again, this phase is without any
  data transferred.

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Effect of Bucket Skew
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Effect of Number of PNs