Download - Adaptive Query Processing

Adaptive Query Processing

Adapted from a Tutorial given at SIGMOD 2006 by:

Amol Deshpande, University of Maryland

Joseph M. Hellerstein, University of California, Berkeley

Vijayshankar Raman, IBM Almaden Research Center

Data Independence Redux The taproot of modern database technology Separation of specification (“what”) from implementation (“how”)

Refamiliarizing ourselves: Why do we care about data independence?

d appdt

d env

dt

D. I. Adaptivity Query Optimization: the key to data independence

– bridges specification and implementation– isolates static applications from dynamic environments

How does a DBMS account for dynamics in the environment?

This tutorial is on a 30-year-old topic– With a 21st-Century renaissance

ADAPTIVITYADAPTIVITY

Why the Renaissance? Breakdown of traditional query optimization

– Queries over many tables– Unreliability of traditional cost estimation– Success & maturity make problems more apparent, critical

• c.f. Oracle v6!

Query processing in new environments– E.g. data integration, web services, streams, P2P, sensornets, hosting, etc.– Unknown and dynamic characteristics for data and runtime– Increasingly aggressive sharing of resources and computation– Interactivity in query processing

Note two separate themes? – Unknowns: even static properties often unknown in new environments

• and often unknowable a priori– Dynamics: can be very high -- motivates intra-query adaptivity

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denvdt

20th Century Summary System R’s optimization scheme deemed the winner for 25 years

Nearly all 20thC research varied System R’s individual steps– More efficient measurement (e.g. sampling)– More efficient/effective models (samples, histograms, sketches)– Expanded plan spaces (new operators, bushy trees, richer queries and data

models, materialized views, parallelism, remote data sources, etc)– Alternative planning strategies (heuristic and enumerative)

Speaks to the strength of the scheme– independent innovation on multiple fronts– as compared with tight coupling of INGRES

But… minimal focus on the interrelationship of the steps– Which, as we saw from Ingres, also affects the plan space

21st Century Adaptive Query Processing (well, starts in late 1990’s)

Revisit basic architecture of System R– In effect, change the basic adaptivity loop!

As you examine schemes, keep an eye on:– Rate of change in the environment that is targeted– How radical the scheme is wrt the System R scheme

• ease of evolutionary change– Increase in plan space: are there new, important opportunities?

• even if environment is ostensibly static!– New overheads introduced– How amenable the scheme is to independent innovation at each step

• Measure/Analyze/Plan/Actuate

Tangentially Related Work An incomplete list!!!

Competitive Optimization [Antoshenkov93]– Choose multiple plans, run in parallel for a time, let the most promising finish

• 1x feedback: execution doesn’t affect planning after the competition Parametric Query Optimization [INSS92, CG94, etc.]

– Given partial stats in advance. Do some planning and prune the space. At runtime, given the rest of statistics, quickly finish planning.• Changes interaction of Measure/Model and Planning• No feedback whatsoever, so nothing to adapt to!

“Self-Tuning”/“Autonomic” Optimizers [CR94, CN97, BC02, etc.]– Measure query execution (e.g. cardinalities, etc.)

• Enhances measurement, on its own doesn’t change the loop– Consider building non-existent physical access paths (e.g. indexes, partitions)

• In some senses a separate loop – adaptive database design• Longer timescales

Tangentially Related Work II Robust Query Optimization [CHG02, MRS+04, BC05, etc.]

– Goals: • Pick plans that remain predictable across wide ranges of scenarios• Pick least expected cost plan

– Changes cost function for planning, not necessarily the loop.• If such functions are used in adaptive schemes, less fluctuation [MRS+04]

– Hence fewer adaptations, less adaptation overhead

Adaptive query operators [NKT88, KNT89, PCL93a, PCL93b]– E.g. memory-adaptive sort and hash-join– Doesn’t address whole-query optimization problems– However, if used with AQP, can result in complex feedback loops

• Especially if their actions affect each other’s models!

Extended Topics in Adaptive QP An incomplete list!!

Parallelism & Distribution– River [A-D03] – FLuX [SHCF03, SHB04]– Distributed eddies [TD03]

Data Streams– Adaptive load shedding– Shared query processing

Adaptive Selection Ordering

Selection Ordering

Complex predicates on relations common– Eg., on an employee relation:

((salary > 120000) AND (status = 2)) OR ((salary between 90000 and 120000) AND (age < 30) AND (status = 1)) OR …

Selection ordering problem Decide the order in which to evaluate the individual predicates against the tuples

We focus on evaluating conjunctive predicates (containing only AND’s) Example Query

select * from R where R.a = 10 and R.b < 20 and R.c like ‘%name%’;

Why Study Selection Ordering

Many join queries reduce to this problem– Queries posed against a star schema– Queries where only pipelined left-deep plans are considered– Queries involving web indexes

Increasing interest in recent years– Web indexes [CDY’95, EHJKMW’96, GW’00]– Web services [SMWM’06]– Data streams [AH’00, BMMNW’04]– Sensor Networks [DGMH’05]

Similar to many problems in other domains– Sequential testing (e.g. for fault detection) [SF’01, K’01]– Learning with attribute costs [KKM’05]

Execution StrategiesPipelined execution (tuple-at-a-time)

R.a = 10 R.b < 20 R.c like …

For each tuple r Є R Apply predicate R.a = 10 first; If tuple satisfies the selection, apply R.b < 20; If both satisfied, apply R.c like ‘%name%’;

R result

Static optimization ? 1. Using the KBZ algorithm Order by c/(1-p) Assumes predicate independence 2. A greedy algorithm Known to be 4-approximate

Adaptive Greedy [BMMNW’04] Context: Pipelined query plans over streaming data Example:

R.a = 10 R.b < 20 R.c like …

Initial estimated selectivities

0.05 0.1 0.2

Costs 1 unit 1 unit 1 unit

Three independent predicates

R.a = 10 R.b < 20R resultR.c like …R1 R2 R3

Optimal execution plan orders by selectivities (because costs are identical)

Adaptive Greedy [BMMNW’04] Monitor the selectivities Switch order if the predicates not ordered by selectivities


Rsample

Randomly sample R.a = 10

R.b < 20

R.c like …

estimate selectivities of the predicatesover the tuples of the profile

ReoptimizerIF the current plan not optimal w.r.t. these new selectivitiesTHEN reoptimize using the Profile

Profile

Adaptive Greedy [BMMNW’04] Correlated Selections

– Must monitor conditional selectivities

monitor selectivities sel(R.a = 10), sel(R.b < 20), sel(R.c …)

monitor conditional selectivities sel(R.b < 20 | R.a = 10) sel(R.c like … | R.a = 10) sel(R.c like … | R.a = 10 and R.b < 20)


Rsample

Randomly sample R.a = 10

R.b < 20

R.c like …(Profile)

ReoptimizerUses conditional selectivities to detect violationsUses the profile to reoptimize

O(n2) selectivities need to be monitored

Adaptive Greedy [BMMNW’04]

Advantages: – Can adapt very rapidly– Theoretical guarantees on performance

• Not known for any other AQP protocols

Disadvantages:– Limited applicability

• Only applies to selection ordering and specific types of join queries– Possibly high runtime overheads

• Several heuristics described in the paper

Eddies [AH’00] Treat query processing as routing of tuples through operators

Pipelined query execution using an eddy

An eddy operator• Intercepts tuples from sources and output tuples from operators• Executes query by routing source tuples through operators

A traditional pipelined query plan


EddyR

result

R.a = 10

R.c like …

R.b < 20

Eddies [AH’00]

a b c …15 10 AnameA …

An R Tuple: r1

r1

r1

EddyR

result

R.a = 10

R.c like …

R.b < 20

ready bit i : 1 operator i can be applied 0 operator i can’t be applied

Eddies [AH’00]

a b c … ready done15 10 AnameA … 111 000

An R Tuple: r1

r1

Operator 1

Operator 2

Operator 3

EddyR

result

R.a = 10

R.c like …

R.b < 20

done bit i : 1 operator i has been applied 0 operator i hasn’t been applied

Eddies [AH’00]


An R Tuple: r1

r1

Operator 1

Operator 2

Operator 3

EddyR

result

R.a = 10

R.c like …

R.b < 20

Eddies [AH’00]


An R Tuple: r1

r1

Operator 1

Operator 2

Operator 3

Used to decide validity and need of applying operators

EddyR

result

R.a = 10

R.c like …

R.b < 20

Eddies [AH’00]


An R Tuple: r1

r1

Operator 1

Operator 2

Operator 3

satisfiedr1

r1


r1

not satisfied

eddy looks at the next tuple

For a query with only selections, ready = complement(done)

EddyR

result

R.a = 10

R.c like …

R.b < 20

Eddies [AH’00]

a b c …10 15 AnameA …

An R Tuple: r2

Operator 1

Operator 2

Operator 3

r2EddyR

result

R.a = 10

R.c like …

R.b < 20

satisfied

satisfied

satisfied

Eddies [AH’00]


An R Tuple: r2

Operator 1

Operator 2

Operator 3

r2

if done = 111, send to output

r2

EddyR

result

R.a = 10

R.c like …

R.b < 20

satisfied

satisfied

satisfied

Eddies [AH’00] Adapting order is easy

– Just change the operators to which tuples are sent– Can be done on a per-tuple basis– Can be done in the middle of tuple’s “pipeline”

How are the routing decisions made ?– Using a routing policy

Operator 1

Operator 2

Operator 3

EddyR

result

R.a = 10

R.c like …

R.b < 20

Routing Policy 1: Non-adaptive Simulating a single static order

– E.g. operator 1, then operator 2, then operator 3

Routing policy: if done = 000 route to 1 100 route to 2 110 route to 3

table lookups very efficient

Operator 1

Operator 2

Operator 3

EddyR

result

R.a = 10

R.c like …

R.b < 20

Overhead of Routing PostgreSQL implementation of eddies using bitset lookups [Telegraph Project] Queries with 3 selections, of varying cost

– Routing policy uses a single static order, i.e., no adaptation

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 μsec 10 μsec 100 μsec

Selection cost

Norm

aliz

ed C

ost

No-eddies

Eddies

Routing Policy 2: Deterministic Monitor costs and selectivities continuously Reoptimize periodically using KBZ

Statistics Maintained: Costs of operators Selectivities of operators

Routing policy: Use a single order for a batch of tuples Periodically apply KBZ

Operator 1

Operator 2

Operator 3

EddyR

result

R.a = 10

R.c like …

R.b < 20

Can use the A-Greedy policy for correlated predicates

Overhead of Routing and Reoptimization Adaptation using batching

– Reoptimized every X tuples using monitored selectivities– Identical selectivities throughout experiment measures only the overhead

0

1

2

3

4

5

6

0 μsec 10 μsec 100 μsec

Selection Cost

Norm

aliz

ed C

ost No-eddies

Eddies - No reoptimization

Eddies - Batch Size = 100 tuples

Eddies - Batch Size = 1 tuple

Routing Policy 3: Lottery Scheduling Originally suggested routing policy [AH’00] Applicable when each operator runs in a separate “thread”

– Can also be done single-threaded, via an event-driven query executor Uses two easily obtainable pieces of information for making routing

decisions:– Busy/idle status of operators– Tickets per operator

Operator 1

Operator 2

Operator 3

EddyR

result

R.a = 10

R.c like …

R.b < 20

Routing Policy 3: Lottery Scheduling Routing decisions based on busy/idle status of operators

Rule: IF operator busy, THEN do not route more tuples to it

Rationale: Every thread gets equal time SO IF an operator is busy, THEN its cost is perhaps very high

Operator 1

Operator 2

Operator 3

EddyR

result

R.a = 10

R.c like …

R.b < 20

BUSY

IDLE

IDLE

Routing Policy 3: Lottery Scheduling Routing decisions based on tickets

Rules: 1. Route a new tuple randomly weighted according to the number of tickets

tickets(O1) = 10tickets(O2) = 70tickets(O3) = 20

Will be routed to: O1 w.p. 0.1 O2 w.p. 0.7 O3 w.p. 0.2

Operator 1

Operator 2

Operator 3

Eddy

result

R.a = 10

R.c like …

R.b < 20

r


Rules: 1. Route a new tuple randomly weighted according to the number of tickets


r

Operator 1

Operator 2

Operator 3

Eddy

result

R.a = 10

R.c like …

R.b < 20


Rules: 1. Route a new tuple randomly weighted according to the number of tickets 2. route a tuple to an operator Oi

tickets(Oi) ++; Operator 1

Operator 2

Operator 3

Eddy

result

R.a = 10

R.c like …

R.b < 20



r


tickets(Oi) ++; 3. Oi returns a tuple to eddy tickets(Oi) --;

Operator 1

Operator 2

Operator 3

Eddy

result

R.a = 10

R.c like …

R.b < 20



r



Operator 1

Operator 2

Operator 3

Eddy

result

R.a = 10

R.c like …

R.b < 20


Will be routed to: O2 w.p. 0.777 O3 w.p. 0.222


Rationale: Tickets(Oi) roughly corresponds to (1 - selectivity(Oi)) So more tuples are routed to the more selective operators



Operator 1

Operator 2

Operator 3

Eddy

result

R.a = 10

R.c like …

R.b < 20


Routing Policy 3: Lottery Scheduling

Effect of the combined lottery scheduling policy:– Low cost operators get more tuples– Highly selective operators get more tuples– Some tuples are randomly, knowingly routed according to sub-optimal

orders• To explore• Necessary to detect selectivity changes over time

Routing Policy 4: Content-based Routing Routing decisions made based on the values of the attributes [BBDW’05] Also called “conditional planning” in a static setting [DGHM’05] Less useful unless the predicates are expensive

– At the least, more expensive than r.d > 100

Example Eddy notices: R.d > 100 sel(op1) > sel(op2) & R.d < 100 sel(op1) < sel(op2)

Routing decisions for new tuple “r”: IF (r.d > 100): Route to op1 first w.h.p ELSE Route to op2 first w.h.p

Operator 1

Operator 2

Eddy result

Expensive predicates

Eddies: Post-Mortem

Cost of adaptivity– Routing overheads

• Minimal with careful engineering– E.g. using bitset-indexed routing arrays

• “Batching” helps tremendously– Statistics Maintenance– Executing the routing policy logic

Discussion

Benefits for AQP techniques come from two places– Increased explored plan space

• Can use different plans for different parts of data – Adaptation

• Can change the plan according to changing data characteristics

Selection ordering is STATELESS– No inherent “cost of switching plans”

• Can switch the plan without worrying about operator states– Key to the simplicity of the techniques

Discussion

Adaptation is not free– Costs of monitoring and maintaining statistics can be very high

• A selection operation may take only 1 instruction to execute• Comparable to updating a count

– “Sufficient statistics”• May need to maintain only a small set of statistics to detect

violations • E.g. The O(n2) matrix in Adaptive-Greedy [BBMNW’04]

Adaptive Join Processing

Outline

20th Century Adaptivity: Intuition from the Classical Systems

Adaptive Selection Ordering

Adaptive Join Processing– Additional complexities beyond selection ordering– Four plan spaces

• Simplest: pipelines of Nested Loop Joins• Traditional: Trees of Binary Operators (TOBO)• Multi-TOBO: horizontal partitioning• Dataflows of unary operators

– Handling asynchrony Research Roundup

Select-Project-Join Processing Query: select count(*) from R, S, T

where R.a=S.a and S.b=T.b and S.c like ‘%name%’ and T.d = 10

An execution plan

Cost metric: CPU + I/O Plan Space:

– Traditionally, tree of binary join operators (TOBO):• access methods• Join algorithms• Join order

– Adaptive systems: • Some use the same plan space, but switch between plans during execution• Others use much larger plan spaces

– different adaptation techniques adapt within different plan spaces

S

T

R

SMJ

NLJ

Approach Pipelined nested-loops plans

– Static Rank Ordering– Dynamic Rank Ordering – Eddies, Competition

Trees of Binary Join Operators (TOBO)– Static: System R– Dynamic: Switching plans during execution

Multiple Trees of Binary Join Operators– Convergent Query Processing– Eddies with Binary Join Operators– STAIRs: Moving state across joins

Dataflows of Unary Operators– N-way joins

switching join algorithms during execution

Asynchrony in Query Processing

BNLJ

CNLJ

ANLJ

Pipelined Nested Loops Join

Simplest method of joining tables– Pick a driver table (R). Call the rest driven tables– Pick access methods (AMs) on the driven tables– Order the driven tables– Flow R tuples through the driven tables

For each r R do:look for matches for r in A;for each match a do:

look for matches for <r,a> in B;…

RB

NLJ

C

NLJ

A

NLJ

Adapting a Pipelined Nested Loops Join

Simplest method of joining tables– Pick a driver table (R). Call the rest driven tables– Pick access methods (AMs) on the driven tables– Order the driven tables– Flow R tuples through the driven tables

For each r R do:look for matches for r in A;for each match a do:

look for matches for <r,a> in B;…

RB

NLJ

C

NLJ

A

NLJ

Keep this fixed for now

Almost identical to selection

ordering

Tree Of Binary Join Operators (TOBO) recap

Standard plan space considered by most DBMSs today– Pick access methods, join order, join algorithms– search in this plan space done by dynamic programming

A

B

R

MJ

NLJ

NLJ

DC

HJ

Switching plans during query execution

Monitor cardinalities at well-defined checkpoints

If actual cardinality is too different from estimated,

re-optimize to switch to a new plan

Most widely studied technique:-- Federated systems (InterViso 90, MOOD 96), Red Brick,

Query scrambling (96), Mid-query re-optimization (98), Progressive Optimization (04), Proactive Reoptimization (05), …

A

C

B

R

MJ

NLJ

MJ

B

C

HJ

MJ

sort

sort

Questions

Where to place checkpoints ?– Typically at materialization points

When to reoptimize ?– State reuse a key – The cost of switching shouldn’t be more than cost of continuing

How to switch ?– Try to reuse the work

Next Eddies…

Example Database

Name Level

Joe Junior

Jen Senior

Name Course

Joe CS1

Jen CS2

Course Instructor

CS2 Smith

select *from students, enrolled, courseswhere students.name = enrolled.name and enrolled.course = courses.course

Students Enrolled

Name Level Course

Joe Junior CS1

Jen Senior CS2

Enrolled Courses

Students Enrolled

Courses

Name Level Course Instructor

Jen Senior CS2 Smith

Symmetric Hash Join

Name Level

Jen Senior

Joe Junior

Name Course

Joe CS1

Jen CS2

Joe CS2

select * from students, enrolled where students.name = enrolled.name

Name Level CourseJen Senior CS2

Joe Junior CS1

Joe Senior CS2

Students Enrolled

Pipelined hash join [WA’91] Simultaneously builds and probes hash tables on

both sides

Widely used: adaptive qp, stream joins, online aggregation, …

Naïve version degrades to NLJ once memory runs out– Quadratic time complexity– memory needed = sum of inputs

Improved by XJoins [UF 00] Needs more investigation

Query Execution using Eddies

EddySEC

Probe to find matches

S EHashTable

S.NameHashTable

E.Name

E C

HashTableE.Course

HashTableC.Course

Joe Junior

Joe JuniorJoe Junior

No matches; Eddy processesthe next tuple

Output

Insert with key hash(joe)


EddySEC

InsertProbe

S EHashTable

S.NameHashTable

E.Name

E C

HashTableE.Course

HashTableC.Course

Joe Jr

Jen Sr

Joe CS1

Joe CS1Joe CS1

Joe Jr CS1

Joe Jr CS1Joe Jr CS1

Output

CS2 Smith


EddySEC

Output

Probe

S EHashTable

S.NameHashTable

E.Name

E C

HashTableE.Course

HashTableC.Course

Joe Jr

Jen Sr

CS2 Smith

Jen CS2

Joe CS1

Joe Jr CS1Jen CS2

Jen CS2Jen CS2 Smith

Probe

Jen CS2 SmithJen CS2 SmithJen Sr. CS2 Smith

Jen Sr. CS2 Smith

Eddies: Postmortem (1)

• Eddy executes different TOBO plans for different parts of data

Students Enrolled

Output

Courses

E C

S E

Courses Enrolled

Output

Students

E S

C E

Course InstructorCS2 Smith

Course InstructorCS2 Smith

Name CourseJoe CS1

Name LevelJoe Junior

Jen Senior

Name LevelJoe Junior

Jen Senior

Name CourseJen CS2

Routing Policies routing of tuples determines join order

same policies as described for selections– can simulate static order– lottery scheduling– can tune policy for interactivity metric [RH02]

much more work remains to be done

Summary

Eddies dynamically reorder pipelined operators, on a per-tuple basis– selections, nested loop joins, symmetric hash joins– extends naturally to combinations of the above

This can also be extended to non-pipelined operators (e.g. hybrid hash)

But, eddy can adapt only when it gets control (e.g., after hash table build)– just like with mid-query reoptimization

Next we study two extensions that widen the plan space still further– STAIRs: moving state across join operators– SteMs: unary operators for adapting access methods,

join algorithms dynamically

N-way Symmetric Hash Join

Name Level

Jen Senior

Joe Junior

Name Course

Joe CS1

Jen CS2

Joe CS2


Name Level Course InstructorJen Senior CS2 Prof. B

Joe Junior CS1 Prof. A

Joe Senior CS2 Prof. B

Students Enrolled

Pipelined join– XJoin-like disk spilling possible

[VNB’03] Simplest version atomically does

one build + (n-1) probes Breaking this atomicity is key to

adaptation

Cour Inst

CS1 P. A

CS2 P. B

Levels

N-way Joins State Modules (SteMs)

Name Level

Jen Senior

Joe Junior

Name Course

Joe CS1

Jen CS2

Joe CS2


Name Level Course InstructorJen Senior CS2 Prof. B

Joe Junior CS1 Prof. A

Joe Senior CS2 Prof. B

Students Enrolled

Cour Inst

CS1 P. A

CS2 P. B

Levels

References [A-D03] R. Arpaci-Dusseau. Runtime Adaptation in River. ACM TOCS 2003. [AH’00] R. Avnur, J. M. Hellerstein: Eddies: Continuously Adaptive Query Processing SIGMOD

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