Increasing Concurrency in Databases using Program Analysis

1
Increasing Concurrency in Databases using Program
Analysis

• Roman Vitenberg, Kristian Kvilekval and Ambuj
  Singh
  
• University of California, Santa Barbara

2
Outline

• Motivation
• Shape graphs and prediction
• Predictive scheduler optimizations
• Results
• Conclusions

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Transactional Schedulers

• Most fundamental problem of concurrency control
  Pap86BHG87
  
• Little has been done on predictive schedulers
• Difficult to extract future usage
• Difficult to use it (Optimal Scheduling
  NP-complete)

4
Program analysis for better concurrency

• OODBs increasingly integrated (JDO)
• Shape analysis makes predicting data accesses
  more feasible
  
• Transactional code is known in advance
• Rich type structure available to analysis
• Enhance schedulers with specific optimizations
  based on the extracted info
  
• Deadlock handling, early lock release, adaptive
  preclaiming

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Previous and Related Work

• Scheduling in object bases
• GrahamBarker95
• Shape analysis (pointer analysis)
• Escape analysisBogda99Ruf00
• ParallelizationCorbera99
• Type SafetyGhiya96 Nurit98 Wilhem00
• Others...

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Outline

• Motivation
• Shape graphs and prediction
• Predictive scheduler optimizations
• Results
• Conclusions

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Overview of Prediction

• Predict locking based on program code
• Extract program summary in shape graph
• Provide runtime system with future accesses
• Advantages
• Facilitates integration OODB
• Supports complex pointer-based structures
• Automatically derived from source code

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Example shape
class Connector Part a,b class Part Con
nector left,right,up,down Material m Suppli
er s int volume() weight0 while
(part) weight(part.material.density
part.volume()) partpart.right.b
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Basic Shape Analysis

• Graph
• Abstract locations (heap cells)
• Edges labeled with with field names
• Abstract interpretation
• Extend graph through field references
• Combine graphs when heap location is shared
• Merge shapes bottom up through static call graph

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Predicting with shape graphs

• Compile Time
• Generate shapes for method references
• Self, arguments, and global variables
• Label shape edges with earliest access
• Annotate programs to pass visible references and
  shapes to transaction scheduler
  
• Runtime
• Interpret shape graph on the actual object graph
  generating expected object set
  

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Predicting with Shape Graphs
Object Graph
N5
a1
N
N
o10
o45
o610
J10
F10
F
J
J
o210
o515
a2
a4
K
G5
K10
o320
a5
a3
Shape Graph
(o1,a1) (o4,a1) (o2,a2) (o6,a1) (o5,a4)
(o3,a5)
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Outline

• Motivation
• Shape graphs and prediction
• Predictive scheduler optimizations
• Results
• Conclusions

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Deadlock Handling Background

• Deadlock detection
• Wait-for-graph (WFG)
• Nodes are active transactions
• Edge (ti,tj) indicates that ti waits for tj to
  release a lock
  
• Maintaining throughout execution is expensive
• Deadlock prevention
• Heuristic-based techniques (wound-wait)
• Cheaper but causing unnecessary transaction
  aborts
  
• Resource allocation graph (infeasible w/o future
  knowledge)
  

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Deadlock handling our approach

• Shape analysis helps us prune parts of WFG and
  other graphs in all graph-based algorithms

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Early lock release background

• Strict 2PL all locks are held till the end of
  transaction
  
• Non-strict 2PL read locks are released after the
  last access to the object
  
• Non-strict allows greater concurrency but
• Difficult to determine the last access
• Allows non-serializable execution

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Early lock release non-searializable execution
T1 Acq(o1) T2 read(o1) Rel (o1)
Acq(o1) write(o1) Acq(o2) write(
o2) Rel(o1,o2) Acq(o2) read(o2) Rel
(o2)
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Early lock release our approach

• Estimate the last access to an object in order to
  release early
  
• Causality-aware scheduler
• T1 causally precedes T2 (T1 ?c T2) if either
• T2 is initiated after T1 by the same client, or
• T2 acquires a lock that T1 has released, or
• There is T3 such that T1 ?c T3 and T3 ?c T2
• If T1 ?c T2 the scheduler blocks T2.A(O) if T1
  may access O in the future.

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Early lock release Causality-aware scheduling
T1 Acq(o1) T2 read(o1) Rel (o1)
Acq(o1) write(o1) Acq(o2) locks write(o2) Rel(o1,o2) Acq(o2)
read(o2) Rel(o2)
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Lock preclaiming background

• Standard 2PL acquires locks gradually
• Conservative 2PL preclaims all the locks
• Fewer aborts upon high contention long
  transactions
  
• Shorter wait-for chains
• T1.A(O2)
• T2.A(O1)T2.A(O2)
• T3.A(O1)
• Requires apriori knowledge of locks

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Lock preclaiming our approach

• Predict the set of objects to be preclaimed
  automatically
  
• Adaptive hybrid schemes
• Preclaim when expected contention level is high
  across executing transactions
  
• Preclaim locks only for short transaction

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Outline

• Motivation
• Shape graphs and prediction
• Predictive scheduler optimizations
• Results
• Conclusions

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Overview of performance measurements

• Two applications OO7 Prototype reservation
  system
  
• Mix of transaction types (lock sequence,
  short/long transactions)
  
• Transaction rates, and transaction parameters
• Compare Standard 2PL with our enhanced 2PL based
  on scheduling delay time
  

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Evaluation OO7

• Short Read
• 2ms
• 600/min
• Short Update
• 2ms
• 10/min
• Long Reorg
• 2000ms
• 3/min

Comp Parts
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OO7 Results
Relative delay by scheduler
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Delays in short query
Predictive
Strict 2-Phase
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Conclusions future directions

• Runtime use of shape graph can generate object
  use prediction (even without prior statistics)
  
• Transaction scheduler improved by
• Better deadlock handling (smaller WFG)
• Early read lock release
• Lock preclaiming
• Future lease prediction

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End

• Questions?

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Evaluation Car Reservation
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Evaluation car reservation
Query
Traversal
Starved
Starved
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Construction of Shape Graphs
x
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Construction of Shape Graphs
x.f s
F
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Construction of Shape Graphs
x.f s t x.f.g
F
G
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Construction of Shape Graphs
x.f s x.f.g t x x.n
N
F
?
G
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Construction of Shape Graphs
x.f s x.f.g t if (x ! null) x x.n


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Combining Shape Graphs
x y
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Combining Shape Graphs

• Unify graphs recursively

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Unification of Shape Graphs

• Unify graphs recursively

N
F
J
G
K
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Shape Analysis Algorithm

• ? methods Interpret basic blocks
• Create shapes for basic blocks
• Run until fixed-point is reached
• Propagate in static callgraph

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Static Call Graphs
main
Static representation of calls
m2
m3
m4
m3 a.f s o.m4(a) Class C m4(F f)


Unify(a,f)
f
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Call Graphs
m1

• Propagate bottom up
• Merge polymorphic calls
• Recursive Calls
• Fixed point
• Merge SCCRuf00

m2
B.m4
m3
m4
D1.m4
D2.m4
m1
m2
m1
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Overview of scheduler enhancements

• Better Deadlock handling
• Smaller WFG based on type information
• Conservative Resource Allocation Graph
• Early Lock Release
• Read locks released after last use
• Lock Preclaiming
• Better throughput in high contention