Compressing Query Results for Mobile Clients

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Compressing Query Results for Mobile Clients

• Zhiyuan Chen and Praveen Seshadri
• Cornell University

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Motivation(1)
Results
Database Server
Slow network!!!
9.6 - 170 kbps, 1 - 10 minutes for 1 MB
Compress results on the server to save network
bandwidth.
Concern compress as much as possible.
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Motivation(2)
Database Server
Results

• I am a PDA, I cant store that much for later
  use!
• Often work offline. Need to store results for
  later use.
• Severe storage constraints.
• Usually only a small portion of results will be
  accessed, random access possible.

Store the result compressed, decompress on demand.
Both space decompression cost matters!
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Why not just GZip or WinZip?

• Small decompression unit for PDA
• Utilize information of the query result
• Choose a combination of compression methods based
  on semantic and statistical information of the
  result.
• Because different attributes have different
  characteristics, there is no unique winner.
• The choice is made by cost-based optimization.

reduce decompression cost
More compression
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An Example

• Query
• Select Year, Month, Day, Ticker, Low, High
• From Quotes where Year is between 1998 and 1999
• Ordered by Year, Month, Day

Semantic compression
Universal compression
Ziv-Lempel on each field
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Outline

• Related work
• An algebraic framework
• To represent the valid combinations.
• Compression optimization
• To choose the best combination.
• Experiments
• Chosen combinations beat universal compression
  tools like WinZip.

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Related Work

• Compression community
• Different compression methods. Run-length
  encoding, Ziv-Lempel, differential, etc.
• Database community compress tables or indices
• Specific compression methods
• Iyer Wilhite - tuple level Ziv-Lempel.
• Goldstein etc.- per-page offset-encoding.
• Ng Ravishankar - tuple differential coding.
• Theodore Johnson - compressing bitmap indices.
• Impact on query processing
• (RothHorn, Graefe Shapiro, Theodore)

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Compression Framework

• Compressed results as a compressed table.
• A compression method as a compression operator
  Input table - output table.
• A combination of compressing methods as a
  compression plan
• Decompression operators and plans.

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Compressed Table

• Field and tuple boundaries may blur. - Compressed
  data blocks
• Extra information enables decompression. -
  Compression schema

A compressed block a value compressed from some
cells in the uncompressed table. Also the unit of
decompression.
A compression schema includes Things
compressed to a compressed block, compression
method, relational schema.
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Compression Operator

• Defines how a compression method is applied on
  what part of the input table.
• The fields the operator is applied on.
• What input blocks will be compressed together.
• The compression method and information used in
  compression.

• Applied on Customer ID
• Same Customer IDs compressed together.
• Method grouping.

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Compression Plan
A sequence of compression operators applied on
the original result table, each takes the output
of the previous operator as the input.
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Optimization - Cost Model

• Formula
• Cost w1 compression cost
• w2 decompression cost
• - w3 saving on network transfer
• - w4 saving on client side storage.
• Adjust weight based on goal of compression.
• Compression cost
• CPU speed, compression plans, results size.
• Decompression cost
• Client processor speed, access pattern, etc.
  Provided by clients!
• Network transfer saving - compressed results
  size.
• Client side storage saving - If decompress on
  demand.

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Searching

• Naïve algorithm has exponential search space.
• Heuristics
• Consider semantic compression first.
• For each field, use the naïve algorithm to find
  the best plan compressing this field only.
• Combine these plans.
• Consider universal compression methods.
• Add Ziv-Lempel applied on each field only if this
  will reduce the overall cost.
• Not complete but polynomial search space
• O(of fields of valid plans on each field)

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Experiments

• Data
• TPCD
• Queries
• Adapted from TPCD queries by deleting aggregates.
• Experiment 1 To save network bandwidth.
• Measure the overall end-to-end time.
• Compression time transfer time decompression
  time.
• Experiment 2 To save PDA clients storage.
• Measure the space and random access time.
• Compare Common tools v.s. chosen combinations.

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• Semantic compression - allow decompress
  individual attribute values. (S)
• WinZip on the whole table. (W)
• WinZip applied on each field. (PW)
• Semantic compression WinZip applied on each
  field. (SPW)

Un/compressed S 3.1 W 3.4 PW 4.4 SPW6.5
Modem
Wireless
Internet
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• Compression plans
• Semantic compression (S) - allow to decompress
  an individual tuple.
• Windows CEs default compressor. (D)
• Ziv-Lempel applied on each page. (Z)
• Result size 3.7 MB. 2 MB Data storage, 2MB
  program storage. 50(1 for semantic compression)
  KB program size.

Storage usage and time to randomly access 1000
tuples

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Summary

• A combination of compression methods based on
  semantic and statistical information of the
  result.
• Choice made by cost-based optimization.
• A framework to model combinations of compression
  methods.
• Future work
• Apply the methodology to compress data tables.
• Joint optimization of result compression query
  processing.