Evaluating Window Joins over Punctuated Streams

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Evaluating Window Joins over Punctuated Streams

• Many slides taken from talk by
• Luping Ding and Elke A. Rundensteiner, CIKM04
• Database Systems Research Group
• Worcester Polytechnic Institute

2
Stream Data Processing

• Online Transaction Management

• Sensor Network Monitoring

• Network Usage Analysis

• Online Auction

Register Continuous Queries
Stream Query Engine
Streaming Data
Streaming Result
3
New Challenges in Stream Context

• Potentially infinite data streams vs. stateful
  operators. e.g., join, distinct,
• Problem potentially unbounded state
• Reason no hint on which data is no longer useful

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Example -Symmetric Hash Join WA93

• Memory overflow resolution state relocation
• Example XJoin UF00,
• Hash-Merge Join MLA04
• Problems
• Join state still grows with no bound
• Delivery of some join results may be highly
  deferred

Memory Overflow
Memory
SA
SB
probe
insert
A
B
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Avoiding Unbounded State

• Solution exploit constraints to detect
  no-longer-useful data
• Sliding window MWA03
• Identify a bounded set of input data based on
  time
• K-constraint BW03
• Models clustered or ordered data arrival pattern
• Punctuation TMSF03
• Dynamically announce termination of certain value

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Sliding Window KNV03
Wa
Wb


Timeline
Stream A
Stream B
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Punctuation

• Meta-knowledge embedded inside data streams
• An ordered set of patterns corresponding to
  attributes of tuples
• Wildcard (), constant (9), list (1,2,3), range
  (1, 20), empty (?)
• Semantics tuples after a punctuation p will NOT
  match p

Bid
180
Marlie
820.00
Nov-13-03 110200
No more tuple will contain Item_id 180.
182
Ultrasale
1000.00
Nov-13-03 110500
180
Jocelyn
850.00
Nov-13-03 111400
180



181
pcfan
50.00
Nov-13-03 113600

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Punctuation-Aware Join DMR04
A
C
A
B
1
200.00
Joinitem_id
2
63.00
SA
SB


175
80.00
175
80.00
175
100.00
175
100.00


No more tuple will have A 175.
175

181
50.00
180
135.00
175
20.00
158
310.00
175
20.00
Stream B
Stream A




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Features of Punctuation

• Purge rule. For any tuple ta from stream A, if
  there exists a punctuation Pb that has already
  been received from stream B such that match (ta,
  ,,Pb), ta will not be joining with any future
  arriving tuples from stream B. ta doesnt need to
  be maintained in the A state after being
  processed.
• Propagation rule. The join operator can also
  propagate punctuations to the output stream in
  order to help downstream operators.

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• Based on punctuation semantics, we derive the
  following theorem as the foundation of our
  punctuation propagation algorithm.
• Theorem 3.1. Let pa and pb be punctuations
  retrieved from streams A and B at time TSa and
  TSb respectively specifying the same punctuated
  value val of join attribute att. Then no output
  tuples with val being the value of attribute att
  will be generated after time max(TSa, TSb).

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Sliding Window Join

• Suppose Ta and Tb are time windows for streams A
  and B respectively. We define the invalidation
  rule from the join state based on the sliding
  window
• Let tuple ta be the latest tuple with timestamp
  TSa from stream A that has been processed.The
  tuple in the B state with timestamp TSb such that
  TSb Tb lt TSa is called a time-expired tuple and
  can be invalidated. The same invalidation rule
  applies to tuples in the A state.

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Basic Window join
TSa-Tb
TSb-Ta
Tb

Ta

TSa
TSb
Stream A
Stream B
timeline
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Optimization Opportunities

• Maintain smaller state than either pure window
  join or pure punctuation-exploiting join
• Bid tuples that have been joined dont need to be
  maintained in state (Punctuation)
• Drop tuples without affecting precision of result
• Bid tuples out of 24-hour window of corresponding
  Auction tuple dont need to be processed
• Aggregate result for some Auction tuples can be
  produced in less than 24 hours

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Features of PWJoin algorithm

• Punctuation-exploiting Window Join is composed of
  three operations
• Probing state to find matching tuples for
  producing join results.
• Purging no-longer-joining tuples by punctuations.
• Invalidating expired tuples by windows. Among
  these operations.

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Window and Punctuation Occur Simultaneously
SELECT A.item_id, Count () FROM
Auction Range 24 Hours A, Bid B
WHERE A.item_id B.item_id GROUP BY
A.item_id
Auction Stream
Group-byitem_id (count())
Joinitem_id
Bid Stream
Out1 (item_id)
Out2 (item_id, count)
Contains punctuations on item_id
Applies a 24-hour window on Auction stream
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PWJoin Basics and Issue
Receive a new tuple ta from stream A
Invalidate tuples from B state
Probe B state
Insert ta into A state
Receive a new punct pa from stream A
Purge tuples from B state
Insert pa into A state

• Issue how to design PWJoin state to facilitate
  all search-based operations?
• Invalidate conducts time-based search
• Probe and Purge needs value-based search

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PWJoin State with Two-dimensional Index
Time List
I-Node Index (Hash Table)
Punctuation Time List
Punctuation Timestamp
p1 T1
p2 T2

Window Begin
8
8
none
10
10
punctuated
8
8
10
tuple
NextValueListTNode
T-Node
4
NextTimeListTNode
8
Key
Head
Tail
PunctFlag
Window End
I-Node
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PWJoin Algorithm

• Invalidate Once a new tuple t is retrieved from
  stream A, its timestamp is used to invalidate
  expired tuples from the head of the time list of
  stream B.
• Probe probe I-Node index and join with tuples in
  value list of matching I-Node.
• After invalidation is done, the join value of t
  is used to probe the I-Node index of the B state.
  If the matching I-Node iNode is found, the
  corresponding value list is located by following
  the Head pointer of iNode. Tuple t then joins
  with all tuples in this value list by following
  the NextValueListTNode pointer of each T-Node.
• Finally, the PunctFlag of iNode is checked. If it
  is punctuated, t is discarded. If it is none,
  t is inserted into the A state.

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PWJoin Algorithm

• Purge probe I-Node index and delete tuples in
  value list of matching I-Node.
• When a new punctuation p is retrieved from stream
  A, p is used to probe the I-Node index of the B
  state. If the matching I-Node iNode is found, all
  tuples in the corresponding value list are
  deleted. iNode is removed from the I-Node index
  as well. If the PunctFlag of iNode is
  punctuated, p is discarded. If iNode is not
  found or iNodes PunctFlag is none, p is used
  to probe the I-Node index of the A state and set
  the PunctFlag of the matching I-Node iNodea as
  punctuated.
• If iNodea does not exist, a new I-Node is created
  with its PunctFlag marked as true and inserted
  into the I-Node index of the A state.

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Punctuation Propagation CIKM04

• An operator may propagate punctuations to benefit
  downstream operators

Auction Stream
Group-byitem_id (count())
Joinitem_id
Bid Stream
Item_id
Bidder_id
Bid_price
be unblocked by punctuations propagated by join
operator
propagate punctuations on item_id
180


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Optimizations Enabled by Combined Constraints
Early Punctuation Propagation
Tuple Dropping
a1
a1
a6
a6
a1
a1
a2
a3
a2
a3
a3
a3
a3
a3
a7
a7
a4
a4
a3
a3
a2
a2
a1
a1
a8
a8
a3
a3
propagation point 2
a2
a2
a6
a6
a3
a3
a10
a10
a3
propagation point 1
a3
Stream S1
Stream S2
Stream S1
Stream S2
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Achieving Optimizations by Combined Constraints

• Early propagation
• Invalidate punctuations in punctuation time list
  as invalidating tuples
• Expired punctuations can be propagated
• Tuple dropping
• When early propagation happens, set PunctFlag of
  matching I-Node as propagated
• Drop new tuples that matches an I-Node whose
  PunctFlag is propagated

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Memory Cost Analysis

• SbT SbTinsert - SbTpurge SbTarrive -
  SbTpurge
• ?bTb - ? bTb(? paT/NKb,T)
• ?b tuple input rate of stream B
• ?pa punctuation input rate of stream A
• NKb,T - of distinct join values occurred in
  stream B up to Tth time unit
• Tb time window on stream B

Saving by Punctuation
Window Join
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PWJoin vs. WJoin Memory and Tuple Output Rate
Stream A, B punct-asc-100-40
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PWJoin vs. PJoin Punctuation Output Rate
Stream A punct-asc-100-40, Stream B
punct-random-30-40 Window 1 second
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Conclusion

• PWJoin algorithm
• Designed storage structure for PWJoin state
• Memory cost analysis of PWJoin

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Thanks

• WPI Database Research Group

many slides are from davis.wpi.edu/dsrg/CAPE/sl
ides
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References

• CIKM04, L. Ding and E.A. Rundensteiner.
  Evaluating Window Joins over Punctuated Streams.
  CIKM04.
• KNV03 J. Kang, J. F. Naughton and S. D. Viglas.
  Evaluating Window Joins over Unbounded Streams.
  ICDE03.
• UF00 T. Urhan and M. Franklin, XJoin A
  Reactively Scheduled Pipelined Join Operator.
  IEEE Data Engineering Bulletin, 23(2), 2000.
• HH99 P. Haas and J. Hellerstein, Ripple Joins
  for Online Aggregation. SIGMOD99.
• GO03 L. Golab and M. T. Ozsu, Processing
  Sliding Window Multi-Joins in Continuous Queries
  over Data Streams. VLDB03.
• GGO04 L. Golab, S. Garg and M. T. Ozsu, On
  Indexing Sliding Windows over On-line Data
  Streams, EDBT04.
• RDS04 E. A. Rundensteiner, L. Ding, T.
  Sutherland, Y. Zhu, B. Pielech and N. Mehta,
  CAPE Continuous Query Engine with
  Heterogeneous-Grained Adaptivity. VLDB Demo,
  2004.
• BW04 S. Babu and J. Widom. Exploiting
  k-Constraints to Reduce Memory Overhead in
  Continuous Queries over Data Streams
• TMS03 P. A. Tucker, D. Maier, T. Sheard and L.
  Fegaras. Exploiting Punctuation Semantics in
  Continuous Data Streams. TKDE, 15(3), 2003.
• DMR04 L. Ding, N. Mehta, E. A. Rundensteiner
  and G. T. Heineman, Joining Punctuated Streams.
  EDBT04.
• MWA03 R. Motwani, J. Widom, A. Arasu et al.
  Query Processing, Resource Management, and
  Approximation in a Data Stream Management System.
  CIDR03.

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Thanks!