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Online Aggregation

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Online Aggregation
Joe Hellerstein
UC Berkeley
Online Aggregation: Motivation
Select AVG(grade) from ENROLL;
• A “fancy” interface:
Query Results
A Better Approach
• Don’t process in batch! Online
Isn’t This Just Sampling?
• Yes
– we need values to arrive in random order
– “confidence intervals” similar to sampling work
• … and No!
– stopping condition set on the fly!
– statistical techniques are more sophisticated
– can handle GROUP BY w/o a priori
Select AVG(grade) from ENROLL
GROUP BY major;
• Usability
– Continuous output
• non-blocking query plans
– time/precision control
– fairness/partiality
• Performance
– time to accuracy
– time to completion
– pacing
• Speed controls!
• Large-scale Data Analysis
Online drill-down/roll-up/CUBE
Visualization tools
Data mining
Distributed requests
• Generally:
– Any long-running activity should be visualized
and controllable on line.
– Accuracy rarely critical for non-lookups
A NaГЇve Approach
Select onln_avg(grade) from ENROLL;
• Can do it in Illustra/Informix today!
• But...
– No grouping
– Can’t meet performance & usability needs:
• no guarantee of continuous output
– optimized for completion time!
• no guarantee of fairness (or control over partiality)
• no control over pacing
Random Access to Data
• Heap Scan
– OK if clustering uncorrelated to agg &
grouping attrs
• Index Scan
– can scan an index on attrs uncorrelated to agg
or grouping (or index on random())
• Sampling:
– could introduce new sampling access methods
(e.g. Olken’s work)
Group By & Distinct
• Fair, Non-Blocking Group By/Distinct
– Can’t sort! Must use hash-based techniques
• sorting blocks
• sorting is unfair
– Hybrid hashing!
• especially for duplicate elimination.
– “Hybrid Cache” even better.
Index Striding
• For fair Group By:
– want random tuple from Group 1, random
tuple from Group 2, ...
– Idea: Index gives tuples from a single group.
– Sol’n: one access method opens many cursors
in index, one per group. Fetch round-robin.
– Can control speed by weighting the schedule
– Gives fairness/partiality, info/speed match!
(Next step: “heap stride”)
Join Algorithms
• Non-Blocking Joins
no sorting!
merge join OK, but watch “interesting orders”!
hybrid hash not great
symmetric “pipeline” hash [Wilschut/Apers 91]
nested loops always good, can be too slow
Query Optimization
• 2 components in cost function:
– dead time (td ): time spent doing “invisible”
work -- tax this at a high rate!
– output time (to ): time spent producing output
– this will do a lot automagically!
• User control vs. performance?
– can use competition (e.g., a la Rdb)
• “interesting” (i.e. bad!) ordering
Extended Aggregate Functions
• Aggs need to return tuples in the
middle of processing
– add a 4th “current estimate” function
• open, iterate, estimate, close
– sometimes like close function (AVG),
sometimes not (COUNT)
– aggregation code needs to spit this out to the
• Current API uses built-in methods
– button-press = query invocation
• e.g., select StopGroup(val);
• High overhead, need to know internals.
• Very flexible. Easy to code!
– Estimates returned as tuples
• no distinction between estimates and final answer
• OK?
– A general API for running status?
• Inter-tuple speed is critical!!
Statistical Issues
• Confidence Intervals for SQL aggs
– given an estimate, probability p that we’re
within e of the right answer
• 3 types of estimates
 Conservative (Hoeffding’s inequality)
п‚‚ Large-Sample (Central Limit Theorems)
п‚ѓ Deterministic
Previous work + new results from
Peter Haas
Initial Implementation
• prototype in PostgreSQL
(a/k/a Postgres95, a/k/a PG-Lite)
– aggs with running output
– hash-based grouping, dup elimination
– index striding
– optimizer tweaks
– “API” hacks and a simple UI
Pacing study
Select AVG(grade), Interval(0.99)
Normal plan
Access Methods, Big Group
Select AVG(grade), Interval(0.99)
GROUP BY college;
College L: 925K/1.5 Mtuples
Access Methods, Small Group
Select AVG(grade), Interval(0.99)
GROUP BY college;
Cost of API call?
College S: 15K/1.5 Mtuples
Future Work
• Better UI
– simple example: histograms w/error bars
– online data visualization (Tioga DataSplash)
• data viz = “graphical” aggregate
• sampled points, or sampled wavelet coefficients?
• great for panning, zooming
• Nested Queries
– Tie-ins with AI’s “anytime algorithms”
Future Work II
• Control w/o Indices
– Heap Striding = do multiple scans
• optimization 1: fast scans can pass hints to slow
• optimization 2: “piggyback” scans via buffering
• big payoff for complex queries
• Checkpointing/continuation
– also continuous data streams
Future Work III
• Sample Tracking
– important for financial auditing, statistical
quality control
– cached views, RID-lists, logical views
• More stats:
– non-standard aggs
– simultaneous confidence intervals
– allow for slop in cardinality estimation
• DSS/OLAP users very demanding!
– “speed of thought” performance required
– only choices: precomputation or estimation
– for estimation, need to provide control and
steady, useful feedback
• puts user in the driver’s seat
• takes some performance burden away from system
– Requires significant backend support
• but well worth the effort
• runs the “impossible” queries
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