Example Generation for Data Flow Programs

Generating Example Data For Dataflow Programs
Chris Olston Shubham Chopra
Utkarsh Srivastava
Research

Data Processing Renaissance
Lots of data (TBs/day at Yahoo!)
Lots of queries and programs to analyze that data
New data flow languages
Map-Reduce, Pig Latin, Dryad
Other data flow systems
Aurora, Tioga, River

Example Dataflow Program
LOAD
(user, url)
LOAD
(url, pagerank)
JOIN
on url
Find users that tend to visit high-pagerank pages
GROUP
on user
TRANSFORM
user, canonicalize(url)
TRANSFORM
user, AVG(pagerank)
FILTER
avgPR> 0.5

Iterative Process
LOAD
(user, url)
LOAD
(url, pagerank)
Joining on right attribute?
JOIN
on url
GROUP
on user
TRANSFORM
TRANSFORM
user, AVG(pagerank)
Bug in UDF
canonicalize?
Everything being filtered out?
FILTER
avgPR> 0.5
No Output 

How to do test runs?
Run with real data
Too inefficient (TBs of data)
Create smaller data sets (e.g., by sampling)
Empty results due to joins [Chaudhuri et. al. 99], and selective filters
Biased sampling for joins
Indexes not always present

Examples to Illustrate Program
(www.cnn.com, 0.9)
(www.frogs.com, 0.3)
(www.snails.com, 0.4)
LOAD
(user, url)
LOAD
(url, pagerank)
(Amy, cnn.com)
(Amy, http://www.frogs.com)
(Fred, www.snails.com/index.html)
JOIN
on url
(Amy, www.cnn.com, 0.9)
(Amy, www.frogs.com, 0.3)
(Fred, www.snails.com, 0.4)
GROUP
on user
TRANSFORM
)
( Amy,
( Fred,
)
TRANSFORM
user, AVG(pagerank)
(Amy, www.cnn.com)
(Amy, www.frogs.com)
(Fred, www.snails.com)
(Amy, 0.6)
(Fred, 0.4)
FILTER
avgPR> 0.5
(Amy, 0.6)

Value Addition From Examples
Examples can be used for
Debugging
Understanding a program written by someone else
Learning a new operator, or language

Outline
Formalization of good examples
Example Generation Algorithm
Performance Evaluation

Good Examples: Consistency
LOAD
(user, url)
LOAD
(url, pagerank)
(Amy, cnn.com)
JOIN
on url
GROUP
on user
0. Consistency
TRANSFORM
TRANSFORM
user, AVG(pagerank)
output example
=
operator applied on input example
(Amy, www.cnn.com)
FILTER
avgPR> 0.5

Good Examples: Realism
LOAD
(user, url)
LOAD
(url, pagerank)
(Amy, cnn.com)
JOIN
on url
GROUP
on user
1. Realism
TRANSFORM
TRANSFORM
user, AVG(pagerank)
(Amy, www.cnn.com)
Formalization: Fraction of examples that are real or are derived from real records
FILTER
avgPR> 0.5

Good Examples: Completeness
LOAD
(user, url)
LOAD
(url, pagerank)
2. Completeness
JOIN
on url
Demonstrate the salient properties of each operator, e.g., FILTER
GROUP
on user
TRANSFORM
TRANSFORM
user, AVG(pagerank)
(Amy, 0.6)
(Fred, 0.4)
FILTER
avgPR> 0.5
(Amy, 0.6)

Good Examples: Completeness
(www.cnn.com, 0.9)
(www.frogs.com, 0.3)
(www.snails.com, 0.4)
LOAD
(user, url)
LOAD
(url, pagerank)
JOIN
on url
GROUP
on user
TRANSFORM
2. Completeness
TRANSFORM
user, AVG(pagerank)
(Amy, www.cnn.com)
Demonstrate the salient properties of each operator, e.g., JOIN
FILTER
avgPR> 0.5

Formalizing Completeness
For any operator, classify input/output example records into equivalence classes.
Each equivalence class demonstrates one property of the operator.
Try to have at least one example from each class

Equivalence Class Examples
FILTER
E0: All input records that pass the filter
E1: All input records that fail the filter
JOIN
E0: All output records
UNION
E0: All records belonging to first input
E1: All records belonging to second input

Formalizing Completeness
Operator Completeness:
Fraction of equivalence classes that have at least one example record.
Overall Completeness:
Average of per-operator completeness.

Good Examples: Conciseness
LOAD
(user, url)
LOAD
(url, pagerank)
3. Conciseness
(Amy, cnn.com)
JOIN
on url
Operator Conciseness:
# equivalence classes
# example records
GROUP
on user
TRANSFORM
Overall Conciseness:
Average of per-operator conciseness
TRANSFORM
user, AVG(pagerank)
(Amy, www.cnn.com)
FILTER
avgPR> 0.5

Outline
Formalization of good examples
Example Generation Algorithm

Related Work
Related Areas:
Reverse Query Processing
Database Testing
Software and Hardware Verification
Differences
Realism not a concern
Notion of conciseness is different
Intermediate result size is immaterial

Strawman I: Downstream Propagation
Take some portion of input data and run the program over it.
1. Realism
2. Completeness
3. Conciseness

Strawman II: Upstream Propagation
Start from what output is desired, and work backwards
1. Realism
2. Completeness
3. Conciseness

Our Algorithm
Algorithm Passes
Downstream
Pruning
Upstream
Pruning

Our Algorithm
Algorithm Passes
Downstream
Pruning
Upstream
Pruning
Take a subset of input and propagate through the program.
(Jack, 30)
LOAD
(user, age)
UNION
FILTER
age>18
LOAD
(user, age)
FILTER
udf(user)
(Amy, 20)
(Fred, 25)
(Jack, 30)
(Amy, 20)
(Fred, 25)
(Jack, 30)
(Amy, 20)
(Fred, 25)
(Amy, 20)
(Fred, 25)

Our Algorithm
Algorithm Passes
Downstream
Pruning
Upstream
Pruning
Prune redundant examples, i.e., improve conciseness without hurting completeness.
(Jack, 30)
LOAD
(user, age)
UNION
FILTER
age>18
LOAD
(user, age)
FILTER
udf(user)
(Amy, 20)
(Fred, 25)
(Jack, 30)
(Amy, 20)
(Fred, 25)
(Jack, 30)
(Amy, 20)
(Fred, 25)
(Amy, 20)
(Fred, 25)

Our Algorithm
Algorithm Passes
Downstream
Pruning
Upstream
Pruning
Prune redundant examples, i.e., improve conciseness without hurting completeness.
(Jack, 30)
LOAD
(user, age)
UNION
FILTER
age>18
LOAD
(user, age)
FILTER
udf(user)
(Amy, 20)
(Jack, 30)
(Amy, 20)
(Jack, 30)
(Amy, 20)
(Amy, 20)

Formalization of Pruning
Example Records Elements
Equivalence Classes Sets
Pick minimum #records to cover every equivalence class
Set-Cover Problem
More involved because completeness of other operators must be maintained; details in paper

Our Algorithm
Algorithm Passes
Downstream
Pruning
Upstream
Pruning
Enhance completeness by inserting constraint records (best effort; details in paper)
(Jack, 30)
LOAD
(user, age)
UNION
FILTER
age>18
LOAD
(user, age)
FILTER
udf(user)
(Amy, 20)
(Jack, 30)
(Amy, 20)
(Jack, 30)
(Amy, 20)
(Amy, 20)

Our Algorithm
Algorithm Passes
Downstream
Pruning
Upstream
Pruning
(Jack, 30)
LOAD
(user, age)
UNION
FILTER
age>18
LOAD
(user, age)
FILTER
udf(user)
(Amy, 20)
(Jack, 30)
(Amy, 20)
(Jack, 30)
(--, 17)
(Amy, 20)
(Amy, 20)

Our Algorithm
Algorithm Passes
Downstream
Pruning
Upstream
Pruning
(Jack, 30)
(--, 17)
LOAD
(user, age)
UNION
FILTER
age>18
LOAD
(user, age)
FILTER
udf(user)
(Amy, 20)
(Jack, 30)
(Amy, 20)
(Jack, 30)
(--, 17)
(Amy, 20)
(--, 17)
(Amy, 20)
(Bill, 17)

Our Algorithm
Algorithm Passes
Downstream
Pruning
Upstream
Pruning
(Jack, 30)
(Bob, 17)
LOAD
(user, age)
UNION
FILTER
age>18
LOAD
(user, age)
FILTER
udf(user)
(Amy, 20)
(Jack, 30)
(Amy, 20)
(Jack, 30)
(Bill, 17)
(Bob, 17)
(Amy, 20)
(Bill, 17)
(Amy, 20)
(Bill, 17)

Our Algorithm
Algorithm Passes
Downstream
Pruning
Upstream
Pruning
Prune redundant examples (as in Pass 2). Favor real examples over synthetic ones.
(Jack, 30)
(Bob, 17)
LOAD
(user, age)
UNION
FILTER
age>18
LOAD
(user, age)
FILTER
udf(user)
(Amy, 20)
(Jack, 30)
(Amy, 20)
(Jack, 30)
(Bill, 17)
(Bob, 17)
(Amy, 20)
(Bill, 17)
(Amy, 20)
(Bill, 17)

Our Algorithm
Algorithm Passes
Downstream
Pruning
Upstream
Pruning
Prune redundant examples (as in Pass 2). Favor real examples over synthetic ones.
(Jack, 30)
LOAD
(user, age)
UNION
FILTER
age>18
LOAD
(user, age)
FILTER
udf(user)
(Jack, 30)
(Jack, 30)
(Bill, 17)
(Bill, 17)
(Bill, 17)

Implementation Status
Available as ILLUSTRATE command in open-source release of Pig
Available as Eclipse Plugin (PigPen)

PigPen Snapshot

Program I: (Web Search Result Viewing Statistics)
LOAD
FILTER by compound arithmetic expression
GROUP
TRANSFORM using built-in aggregate function

Performance on Program I

Program II: (Web Advertising Activity)
LOAD table A
FILTER A by compound logical expression
JOIN with table B (highly selective)
TRANSFORM using 4 string manipulation UDFS (non-invertible)

Performance on Program II

Running Time

Conclusions
Research
Writing dataflow programs is an iterative process.
Actual dataset too large for test runs.
Our algorithm can automatically generate examples that illustrate the program through:
Realism
Conciseness
Completeness

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Example Generation for Data Flow Programs

by meetutkarsh on Jun 30, 2009

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Example Generation for Data Flow Programs — Presentation Transcript