Adaptive Query Processing

Adaptive Query Processing

Amol Deshpande, University of Maryland

Joseph M. Hellerstein, University of California, Berkeley

Vijayshankar Raman, IBM Almaden Research Center

Outline

20th Century Adaptivity: Intuition from the Classical Systems

Adaptive Selection Ordering

Adaptive Join Processing

Research Roundup

Outline

20th Century Adaptivity: Intuition from the Classical Systems– Data Independence and Adaptivity– The Adaptivity Loop– Two case studies

• System R• INGRES

– Tangential topics



Research Roundup

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

?

denvdt

The Adaptivity Loop

ActuateActuatePlanPlan

Measure/ModelMeasure/Model

When?When? When?When? When?When?

Features & Model?Features & Model? Plan Space?Plan Space? What can be changed?What can be changed?

Overhead?Overhead? Overhead?Overhead? Overhead?Overhead?

Need not happen at the same timescales!

An example query SQL: SELECT * FROM Professor P, Course C, Student S WHERE P.pid = C.pid AND S.sid = C.sid

QUEL: range of P is Professor range of C is Course range of S is Student RETRIEVE (P.ALL, C.ALL, S.ALL) WHERE P.pid = C.pid AND S.sid = C.sid

Professor

Course

Student

Dynamic Programming

System R Optimizer

> UPDATE STATISTICS ❚ cardinalities

index lo/hi key

> SELECT * FROM ...

❚

System R Adaptivity



When?When?

Daily/WeeklyDaily/WeeklyWhen?When?

Query CompilationQuery CompilationWhen?When?

Query RuntimeQuery Runtime

Features?Features?

Cardinalities, Indexes, Cardinalities, Indexes, Hi/Lo Keys Hi/Lo KeysModel?Model? (1-bucket) Histograms(1-bucket) Histograms

Plan Space?Plan Space?Trees of Binary OpsTrees of Binary Ops

(TOBO)(TOBO)

What can be changed?What can be changed?Initial Plan ChoiceInitial Plan Choice

Overhead?Overhead? Offline scans of tablesOffline scans of tables

Overhead?Overhead?Exponential Dynamic Exponential Dynamic

ProgrammingProgramming

Overhead?Overhead?

NoneNone

Note different timescales

INGRES “Query Decomposition” 1> RANGE OF P IS ...

❚

> RANGE OF C_T IS … WHERE C_T.pid=44…❚

hashed temps

hashed temps

OVQP

OVQP(selections)

c

P C

S

INGRES “Query Decomposition” 1> RANGE OF P IS ...

❚

c

hashed temps

hashed temps

> RANGE OF T_T IS … WHERE T_T.sid = 273❚ output tuples

OVQP(selections)

OVQP

OVQP

> RANGE OF C_T IS … WHERE C_T.pid=44…❚

P C

S

> RANGE OF CT IS … WHERE CT.pid=26…❚

INGRES “Query Decomposition” 2

hashed temps

hashed temps

output tuples

OVQP

OVQP

> RANGE OF P IS ...

❚

> RANGE OF ST IS … WHERE ST.sid=441❚

OVQP(selections)P C

S

INGRES: Post-Mortem Case 1:

P, S, PC

Case 2:

P, PC, S

INL Hash

INL

INL Hash

INL

Plan choice Plan choice determined by determined by number of C number of C

matches per Pmatches per P

Each P tuple either Each P tuple either Type1 or Type2. Type1 or Type2.

Hash

Hash

Hash

P

C

S

Hash

Hash

Hash

P

C

S

“Post-mortem” behavior– Horizontal partitioning of inputs into different static plans [Ives02]

• “Driving” input relation effectively partitioned by join keys• Each partition participates in a different static plan• Recurses up each different join tree

– End result can be described as a union of static plans over partitions• In general, many such plans!

Note: post-mortem always has a relational description of some sort– But often “unusual”: plans that are simply not considered in System R!

• Often cannot know the partitioning prior to query execution– So: plan-space and adaptivity loop settings have strong interactions!

• A theme we’ll see throughout.

Horizontal Partitioning

INGRES Adaptivity



When?When?

After Each One-After Each One- Variable Query Variable Query

When?When?Before calling OVQPBefore calling OVQP

When?When?Each OVQP callEach OVQP call

Features?Features?

CardinalitiesCardinalities (including temps) (including temps)Model?Model? (1-Bucket) Histograms(1-Bucket) Histograms

Plan Space?Plan Space?Single-table access Single-table access

methodsmethods

What can be changed?What can be changed?Choice of relationChoice of relation

Overhead?Overhead?

Offline tablescans,Offline tablescans, Count temp rows Count temp rows

Overhead?Overhead?Simple FindMin operationSimple FindMin operation

Overhead?Overhead?

NoneNone

All ‘round the loop each time…

Observations on 20thC Systems Both INGRES & System R used adaptive query processing

– To achieve data independence

They “adapt” at different timescales– Ingres goes ‘round the whole loop many times per query– System R decouples parts of loop, and is coarser-grained

• measurement/modeling: periodic • planning/actuation: once per query

Query Post-Mortem reveals different relational plan spaces– System R is direct: each query mapped to a single relational algebra stmt– Ingres’ decision space generates a union of plans over horizontal partitions

• this “super-plan” not materialized -- recomputed via FindMin

Both have zero-overhead actuation– Never waste query processing work

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

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]

Why Study Selection Ordering Simpler to understand and analyze

– Many fundamental AQP ideas can be demonstrated with these queries– Very good analytical and theoretical results known

• No analogues for general multi-way joins

Big differences to look out for– These queries are stateless; queries involving joins are not stateless

• No burden of routing history– Selections are typically very inexpensive

• The costs of AQP techniques become important

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

Operator-at-a-time execution

Apply predicate R.a = 10 to all tuples of R; materialize result as R1,Apply predicate R.b < 20 to all tuples of R1; materialize result as R2,…

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

R1

R2 MaterializeR2

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

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

Operator-at-a-time execution

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

R1

R2 MaterializeR2

R.a = 10 R.b < 20 R.c like …R result

Fundamentally different from adaptivity perspective

Preferred for selection ordering

Outline 20th Century Adaptivity: Intuition from the Classical Systems

Adaptive Selection Ordering– Setting and motivation– Four Approaches

• Static Selinger-style optimization– KBZ Algorithm for independent selections [KBZ’86]– A 4-approx greedy algorithm for correlated selections [BMMNW’04]

• Mid-query reoptimization [KD’98]– Adapted to handle selection ordering

• A-Greedy [BMMNW’04]• Eddies [AH’00]• Other related work

Adaptive Join Processing Research Roundup

Static Selinger-style Optimization Find a single order of the selections to be used for all tuples

Queryselect *

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

Query plans considered

R.a = 10 R.b < 20R resultR.c like …

R.b < 20 R.c like …R resultR.a = 10 3! = 6 distinctplans possible

Static Selinger-style Optimization Cost metric: CPU instructions Computing the cost of a plan

– Need to know the costs and the selectivities of the predicates

R.a = 10 R.b < 20R resultR.c like …

cost(plan) = |R| * (c1 + s1 * c2 + s1 * s2 * c3)

R1 R2 R3

costs c1 c2 c3selectivities s1 s2 s3

cost per c1 + s1 c2 + s1 s2 c3tuple

Independence assumption

Static Selinger-style Optimization Dynamic programming algorithm

Complexity: O(2n)

Compute optimalorder and cost for

1-subsets of predicates

R.a = 10 R.b < 20 R.c like …


R.a = 10 AND R.b < 20 R.a = 10 AND R.c like ..


R.a = 10 AND R.b < 20 AND R.c like …

Using 1-d histograms or random samples etc

Using 2-d histograms or random samples, or by assuming independence

Static Selinger-style Optimization KBZ algorithm for independent selections [KBZ’86]

– Apply the predicates in the decreasing order of: (1 – s) / c where s = selectivity, c = cost

Correlated selections– NP-hard under several different formulations

• E.g. when given a random sample of the relation– Greedy algorithm:

• Apply the selection with the highest (1 - s)/c • Compute the selectivities of remaining selections over the result

– Conditional selectivities• Repeat

– Can be shown to be 4-approximate [BMMNW’04]• Best possible unless P = NP

Static Selinger-Style



When?When?

Daily/WeeklyDaily/WeeklyWhen?When?

Query CompilationQuery CompilationWhen?When?

Query RuntimeQuery Runtime

Features?Features?

Selectivities Selectivities Model?Model? Histograms, random Histograms, random samples etcsamples etc

Plan Space?Plan Space? Linear orderings of the Linear orderings of the

selectionsselections

What can be changed?What can be changed?Selection ordering usedSelection ordering used

Overhead?Overhead?

Offline tablescansOffline tablescansOverhead?Overhead?

Dynamic programming or Dynamic programming or simple sortingsimple sorting

Overhead?Overhead?

NoneNone

Mid-query Reoptimization At materialization points, re-evaluate the rest of the query plan Example:

R.a = 10 R.b < 20R resultR.c like …R1 R2 R3Materialize

R1

Initial query plan chosen

Estimated selectivities

0.05 0.1 0.2

A free opportunity to re-evaluate the rest of the query plan - Exploit by gathering information about the materialized result


R.a = 10 R.b < 20R resultR.c like …R1 R2 R3Materialize

R1; build1-d hists



0.05 0.1 0.2

A free opportunity to re-evaluate the rest of the query plan - Exploit by gathering information about the materialized result


R.b < 20 R.c like …R2 R3

R.a = 10RR1 Materialize

R1



0.05 0.1 0.2

Re-estimatedselectivities

0.5 0.01

Significantly different original plan probably sub-optimalReoptimize the remaining part of the query

MaterializeR1; build1-d hists

Mid-query Reoptimization Explored plan space identical to static

– The operators are applied to the tuples in the same order• The order is determined lazily

– The specific approach equivalent to the 4-Approx Greedy algorithm

Cost of adaptivity:– Materialization cost

• Many (join) query plans typically have materialization points • May want to introduce materialization points if there is high uncertainty

– Constructing statistics on intermediate results• Depends on the statistics maintained

– Re-optimization cost• Optimizer should be re-invoked only if the estimates are significantly wrong

Mid-query Reoptimization Advantages:

– Easy to implement in a traditional query processing system– Familiar plan space; easy to optimize and understand what's going on

• Operator-at-a-time query processing

Disadvantages:– Granularity of adaptivity is coarse

• Once an operator starts executing, can’t change that decision– Explored plan space identical to static optimization

• Can’t apply different orders to different sets of tuples– Requires materialization

• Cost of materialization can be high• Ill-suited for data streams and similar environments

Mid-query Adaptivity



When?When?

At each materialization At each materialization pointpoint

When?When? At each materialization At each materialization

pointpoint

When?When?After planningAfter planning

Features?Features?

Selectivities of Selectivities of remaining predicatesremaining predicatesModel?Model? Histograms etcHistograms etc


selections and selections and materialization pointsmaterialization points


Overhead?Overhead?

Statistics collection, Statistics collection, materialization costmaterialization cost

Overhead?Overhead?Dynamic programming or Dynamic programming or

simple sortingsimple sorting

Overhead?Overhead?

MinimalMinimal

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]

Cost of adaptivity:– Profile maintenance

• Must evaluate a (random) fraction of tuples against all operators – Detecting violations

• Periodic checks for detecting if the current order is optimal• Doing this per tuple too expensive

– Reoptimization cost• Can require multiple passes over the profile

Adaptive Greedy: Post-Mortem Plan Space explored

– “Horizontal partitioning” by order of arrival

If the selectivities correlated with tuple arrival order, this can lead to huge savings

R.a = 10 R.b < 20 R.c like …

..

Plan switch point

R.b < 20 R.a= 10 R.c like …

orderof arrival

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

A-Greedy Adaptivity



When?When?

Periodically during Periodically during executionexecution

When?When? Current plan suboptimal Current plan suboptimal

on last data windowon last data window

When?When?After planningAfter planning

Features?Features?

Conditional selectivities Conditional selectivities Model?Model? Random sample, a Random sample, a specific sufficient statisticspecific sufficient statistic



What can be changed?What can be changed?Selection ordering used Selection ordering used

for remaining tuplesfor remaining tuples

Overhead?Overhead?

Statistics maintenance, Statistics maintenance, suboptimality detectionsuboptimality detection

Overhead?Overhead?Running greedy algorithm Running greedy algorithm

over profile tuplesover profile tuples

Overhead?Overhead?

MinimalMinimal

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

Encapsulates the full adaptivity loop in a “standard” dataflow operator: measure, model, plan and actuate.

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 Plan Space explored

– Allows arbitrary “horizontal partitioning”– Not necessarily correlated with order of arrival (unlike A-greedy)

• E.g. with lottery scheduling policy, content-based routing.

..

R.a = 10 R.b < 20 R.c like …

R.b < 20 R.a= 10 R.c like …

.

.

orderof arrival

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

Lottery Scheduling



When?When?

Continuously during Continuously during executionexecution

When?When? Continuously during Continuously during

executionexecution

When?When?Continuously during Continuously during

executionexecution

Features?Features?

Operator status (ready/ Operator status (ready/ idle), tickets per operatoridle), tickets per operatorModel?Model? Random samples (in Random samples (in essence)essence)




Overhead?Overhead?

Statistics collection, Statistics collection, cost of explorationcost of exploration

Overhead?Overhead?NoneNone

Overhead?Overhead?

Routing overheadRouting overhead

Related Problems Multi-way join queries

– Some classes of queries reduce to selection ordering with minor differences– Many classes of execution plans are equivalent to selection ordering for

adaptivity purposes• E.g. plans with only index joins, pipelined left-deep plans

– Will study next

Queries with parallel or distributed selections– E.g. parallel multi-processor systems [CDHW’06], queries over web data

sources [EHJ+’96, SMWM’06]– Main differences

• The optimization metric is throughput– Requires use of aggressive horizontal partitioning

• Precedence constraints between the selections

Many open problems

Recap Studied four query processing techniques for selection ordering

Static Selinger-StyleOptimization

Mid-queryReoptimization

A-Greedy Eddies

increasing adaptivity

Exploit horizontal partitioning

increasing explored plan space

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]

Outline

20th Century Adaptivity: Intuition from the Classical Systems


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

Differences from Adaptive Selections



When?When?

What Features?What Features?

Join Join fanoutsfanouts

When?When?More sparinglyMore sparingly

When?When?

At points where it is easy to At points where it is easy to switch plans. switch plans. -- complicated by -- complicated by stateful operators stateful operators

Model?Model?

Plan Space?Plan Space?

Graphs of Graphs of statefulstateful, , multi-ary multi-ary operatorsoperators

What can be changed?What can be changed?

Join order, access Join order, access methods, join algorithms, methods, join algorithms, ……

Overhead?Overhead?

same as beforesame as beforeOverhead?Overhead?

Optimization costOptimization costOverhead?Overhead?

Switching costsSwitching costs

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

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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

“competition”

Almost identical to selection

ordering

Ordering the driven tables

Let ci = cost/lookup into i’th driven table, si = fanout of the lookup

As with selection, cost = |R| x (c1 + s1c2 + s1s2c3) Only difference from selection ordering

– Fanouts s1,s2,… can be > 1– So the cost formula, and rank ordering, don’t quite work (why?)

Otherwise, both static optimization and adaptive techniques are exactly as for selection ordering– rank ordering, eddies, etc. are all applicable

RB

NLJ

C

NLJ

A

NLJ

RC

NLJ

B

NLJ

A

NLJ

(c1, s1) (c2, s2) (c3, s3)

ASIDE: Cache effects in Join Processing

cached lookups are much faster than others– especially important when fanout>1:

for each tuple of R, all but 1 of the s1 rows in RxA will hit in the cache of B

– Say c1 = c2 = 1 (non-cached), and = 0 (cached)– Say s2 = 2– Say s1 = 0 for 50% of the tuples; = 7 for 50% of the tuples

=> on average s1 = 3.5 Rank ordering suggests (RBA) (same cost, B has lower fanout) BUT, cost(RBA) = ( 1 + 1*1) = 2 |R|

cost(RAB) = ( 1 + 0.5*1) = 1.5 |R|– fanout and cost are both random variables– fanout and cost are correlated– so E(cs) <> E(c)E(s)

RB

NLJ

A

NLJ

(c1, s1) (c2, s2)

Cache is an ugly detail in join processing. Not well studied.

Picking access methods (AM) on each table

Multiple AMs / table => Combinatorial explosion of possible plans– choice depends on selectivity, clustering, memory availability, …– correlations

Static optimization: explore all possibilities and pick best Adaptive: Run multiple plans in parallel for a while,

and then pick one and discard the rest (DEC RDB 96)– Cannot easily explore combinatorial options– Problem of duplicates: will return to this later

RB

NLJ

C

NLJ

A

NLJ

RA

NLJ

C

NLJNLJ

B

Pipelined NLJN Adaptivity



When?When?

On each tupleOn each tupleWhen?When?

On each tupleOn each tupleWhen?When?

On each tupleOn each tuple


Fanouts for each table; Fanouts for each table; costs for each AMcosts for each AM

Plan Space?Plan Space?ordering of driven tablesordering of driven tables

choice of AMschoice of AMs


ordering of driven tablesordering of driven tables choice of AMschoice of AMs

Overhead?Overhead?Statistics collection,Statistics collection,Cost of explorationCost of exploration

Overhead?Overhead?NoneNone

Overhead?Overhead?

Switching AM Switching AM can lead to duplicatescan lead to duplicates








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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– Provide plentiful opportunities for switching plans

If actual cardinality is too different from estimated,– Avoid unnecessary plan switching (where the plan doesn’t change)

re-optimize to switch to a new plan– Avoid loss of work during plan switching

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), …

Where?

How?

Threshold?

A

C

B

R

MJ

NLJ

MJ

B

C

HJ

MJ

sort

sort

Challenges

Where to place Checkpoints? (1)

Lazy checkpoints: placed above materialization points – No work need be wasted if we switch plans here

Eager checkpoints: can be placed anywhere– May have to discard some partially computed results– useful where optimizer estimates have high uncertainty

A

C

B

R

MJ

NLJ

MJ

sort

Lots of checkpoints => lots of opportunities for switching plans– Overhead of monitoring is small [LEO 01]

BUT, it is easier to switch plans at some checkpoints than others

sortLazy

Eager

Where to place checkpoints? (2) Lazy checkpoints depend on materialization

Materializations are quite common – Sorts– Inner of hash joins– Explicit materializations operators (e.g., for common sub-expressions, …)

Forced materialization– E.g., outer of nested loops join (NLJ)– Outer cardinality must be low for NLJ to be good

(if it isn’t low, the plan is likely suboptimal anyway)

“Rescheduling” plan operators so that checkpoints are reached early– This allows mis-estimations to be caught early– Especially useful in federated queries

When to re-optimize? Say at a checkpoint actual cardinality is different from estimates:

how high a difference should trigger a re-optimization?

Idea: do not re-optimize if current plan is still the best

Validity range: range of a parameter within which plan is optimal– During execution, re-optimize if value at a checkpoint falls outside this range– Place eager checkpoints where ever the validity range is narrow

ASIDEthis is related to parametric and robust optimization also. Quite a few recent papers.E.g., bounding boxes [BBD05] model uncertainty of estimates

Validity Range Determination

when plans P1 and P2 are considered during optimizer pruning– costP1(est_cardouter) < costP2(est_cardouter)

– numerically find lower and upper bounds (lb, ub) on cardouter s.t. P1 beats P2

– validity_rangeP1 = validity_rangeP1 (lb,ub) This finds the correct validity range w.r.t plans with the same join order

P2

L2

P Q

P3

validityrange (P1)

P1P2cost

cardinality

P1

L1

P Q

ASIDE • Cost functions are rarely well-behaved (see [Haritsa 05])

• still, numerical techniques work okay on 1-dimension• Open problems

• multi-dimensional cost functions• validity ranges w.r.t. plans with different join orders

How to switch to a new plan? (1)One Approach: Re-invoke the optimizer: Getting a better plan:

– Plug in actual cardinality information learned during this query,and re-run the optimizer

Reusing work when switching to the better plan:– Treat fully computed intermediate results as materialized views

• Everything that is under a materialization point

– Note: It is optional for the optimizer to use these in the new plan

ASIDEit is not always easy to exploit cardinalities over intermediate results. E.g, given actual |ab(R)| and |bc (R)|, what is best estimate for |abc(R)|?Recent work using maximum entropy [MMKTHS 05]

How to switch to a new plan? (2) Other approaches also possible

Query Scrambling [UFA’98]– Reacts to delayed data sources by rescheduling operators in the plan

Proactive reoptimization [BBD’05]– Chooses plans that are easy to switch from, during optimization

Post-Mortem

Switching plans during execution has been the most popular adaptation method till now– Has proven to be fairly easy to add to existing query processors

Works well for plans with lots of dams Where it doesn’t work well

– Pipelined plans– Databases with endemic correlation

Open issues– Switching between parallel query plans; need global barriers

Adaptivity in Switching Plans during Execution



When?When?

At checkpointsAt checkpointsWhen?When?

At failed checkpointsAt failed checkpointsWhen?When?

At failed checkpointsAt failed checkpoints


Actual cardinalitiesActual cardinalitiesModel?Model?

Usual optimizer statisticsUsual optimizer statistics

Plan Space?Plan Space?TOBOTOBO


entire planentire plan

Overhead?Overhead?

Monitoring overheadMonitoring overheadOverhead?Overhead?

Re-optimization costRe-optimization costOverhead?Overhead?

Lose all the work that is Lose all the work that is not captured in mat. views not captured in mat. views

or is not reused.or is not reused.








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Convergent Query Processing Ives02 Switch plans, but use

pipelining query operators

Measure cardinalities at runtime and compare to optimizer estimates

Replan when different– Can be done mid-pipeline

Execute new plan starting on new data inputs

End of query: cleanup phase computes cross-phase results

CQP Discussion Direct connection to horizontal partitioning

– Clean algebraic interpretation Easy to extend to more complex queries

– Aggregation, grouping, subqueries, etc.

Cross-phase results postponed to final phase– Despite all the data having arrived

Adaptivity in CQP



When?When?

After a tuple is consumed After a tuple is consumed in pipelinein pipeline

When?When?Upon drift of execution Upon drift of execution

from estimatesfrom estimates

When?When?Upon generation of new Upon generation of new

planplan


Actual cardinalitiesActual cardinalitiesModel?Model?

Usual optimizer statisticsUsual optimizer statistics

Plan Space?Plan Space?Optimizer’s native space Optimizer’s native space

(TOBO)(TOBO)


Plan for next phase, Plan for next phase, eventual state of cleanup eventual state of cleanup

phasephase

Overhead?Overhead?

Monitoring overheadMonitoring overheadOverhead?Overhead?

Re-optimization costRe-optimization costOverhead?Overhead?

Postponement of results to Postponement of results to cleanup.cleanup.








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Transition to Eddies with Joins We have just seen one method that adapts, within TOBO

– Switch plans at checkpoints – primarily at materialization points

Now we return to eddies, but this time using them to do joins. – Eddy is a router that sits between operators,

intercepts tuples from sources and output tuples from operators, and – Uses feedback from the operators to route

In plans with join operators, Eddy can change the plan in a more fine grained fashion, provided:– Operators give it control in a fine-grained fashion

• Needs pipelined join operator

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


Name CourseJoe CS1

Name LevelJoe Junior

Jen Senior


Jen Senior

Name CourseJen CS2

Eddies: Postmortem (2)

• By changing the routing of tuples, Eddy adapts the join order during query execution• Access methods and join algorithms still chosen up front

Students Enrolled

Output

S E

Courses

C E


Name CourseJoe CS1

Jen CS2


Jen Senior

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

Adaptivity in Eddies with binary join operators



When?When?

Continuously during query Continuously during query executionexecution

When?When? Continuously during query Continuously during query

executionexecution

When?When?

Continuously duringContinuously duringquery executionquery execution


Cardinalities, costsCardinalities, costsModel?Model?

Plan Space?Plan Space?Multiple Trees of Binary Multiple Trees of Binary

Join OperatorsJoin Operators


operator orderoperator order

Overhead?Overhead?

Monitoring cost, Monitoring cost, exploration costexploration cost

Overhead?Overhead?

nonenoneOverhead?Overhead?

Routing costRouting cost








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Burden of Routing History

EddySEC

S EHashTable

S.NameHashTable

E.Name

E C

HashTableE.Course

HashTableC.Course

Joe Jr

Jen Sr

Joe CS1

Joe Jr CS1

Output

CS2 Smith

As a result of routing decisions, state got embedded inside the operators

What if these turn out to be wrong ?

Burden of Routing History

EddySEC

S EHashTable

S.NameHashTable

E.Name

E C

HashTableE.Course

HashTableC.Course

Output

Hypothetical scenario 1. Tuples from E arrive before either S or C. 2. Eddy routes E tuples to

3. Turns out to be a bad idea; not selective 4. NO WAY TO RECOVER !!

E

E E S E

S E

S

SSE

S

Embedded state

STAIRs [DH’04] Observation:

– Changing the operator ordering not sufficient– Must allow manipulation of state

New operator: STAIR– Expose join state to the eddy

• By splitting a join into two halves– Provide state management primitives

• That guarantee correctness of execution• Able to lift the burden of history

– Enable many other adaptation opportunities• E.g. adapting spanning trees, selective caching, pre-computation

STAIRs [DH’04]

EddySEC

S EHashTable

S.NameHashTable

E.Name

E C

HashTableE.Course

HashTableC.Course

Output

ES

STAIRs [DH’04]

EddySEC

Output

HashTable

SS.Name STAIR

HashTable

EE.Name STAIR

HashTable

E.course STAIR

HashTable

C.Course STAIR

E C

Provide state manipulation primitives - For moving state from one STAIR to another - Guaranteed to be correct

Can be used to move E from from E.name STAIR to E.course STAIRPrevents S JOIN E








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Granularity of Query Operators

Mostly, we have seen plans that use binary join operatorsBut A Join operator encapsulates multiple physical operations– Tuple access– Matching tuples on join predicate– Composing matching input tuples – Caching– ...– Access methods and join algorithms embedded inside join

This encapsulation is in itself a hindrance to adaptation

Will now see another style of join processing– Unary operators and N-ary joins– Varying the tuple routing to the unary operators changes:

• Join algorithm• Access methods used

Index Join

EddyP

cacheP

index

http://en.wikipedia.org/wiki/Image:US_long_grain_rice.jpg

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

SteM is an abstraction of a unary operator By adapting the routing between SteMs, we can

– Adapt the join ordering (as before)– Adapt access method choices– Adapt Join Algorithms

• Hybridized Join Algos (e.g. hash join index join on memory ov.flow)• Much larger space of join algorithms

– Adapt join spanning trees

Also useful for Sharing State across Joins– Useful for continuous queries [MSHR’02, VNB’03]

State Modules (SteMs)

EddyRS

T

JOINRS

JOINRT

EddyRS

TT

SteMS

SteMT

SteMR

Query Processing with Unary operators (1)

SELECT FROM R,S,TWHERE <conditions>

Pick one access method / tablePick one spanning treePick join algorithms

EddyRS

T

JOINRS

JOINRT

SELECT FROM R,S,TWHERE <conditions>

Pick all access methods (AMs)Create a SteM on each base table(optionally on intermediate tables also)

EddyRS

T

SteMS

SteMT

SteMR

T

Example

Benefit: Exposes internal join operations to Eddy– For monitoring/measurement– For control

Query Processing with Unary operators (2)

R

Eddy

ind.jn

R

Eddy

P PR

Eddy

P

hash jn

+P

State Modules (SteMs): data structures– Dictionary of homogeneous tuples– (insert) build and (search) probe operations– Evictions possible, see paper

Access modules (AMs)– probe with tuples and get matches– scan: probe with empty “seed” tuple

All operators can return results asynchronously will bounce back incoming tuples, in some circumstances

– Returning the tuple to the Eddy (because it still needs to probe other tables to generate output tuples)

Two Unary operators

build R

probe ST

RST matches

SteMR

SRP probe

S matches

AMS

Query Execution

EddySEC

Insert

Probe

S SteM

Joe Jr

Jen Sr

Joe CS1

CS2 Smith

E SteM

C SteM

Jen CS2

Jen CS2 Smith

Jen Sr. CS2 SmithJen CS2Jen CS2

Jen CS2

Jen CS2

Jen CS2 Smith

Jen Sr. CS2 Smith

Probe

Join algorithm determined

by how we route tuples

Symmetric Hash Join– Build SteMR

– Probe SteMS with R tuple– Build SteMS

– Probe SteMR with R tuple– Repeat

(BLDRPROBESTEM_S | BLDSPROBESTEM_R)*

Symmetric Hash join

R

Eddy

S

S probe

S bl

dR bld

S probeR probe

Symmetric Hash Join(BLDRPROBESTEM_S | BLDSPROBESTEM_R)*

– Alternate builds and probes Synchronous Index Join

(PROBEIDX_S)*

Synchronous Index join

R

Eddy

Synchronous index join S

with Caching:– S matches are cached in SteMS

– R tuples can probe SteMS first before AMS

Asynchronous Index join

R

EddyS matches

S probe

S bl

d

R

R bldRS RSR probe

S

cacherendezvousbuffer

First introduced for deep-web (WSQ/DSQ [GW’00])

With SteMs– build R into SteMR (rendezvous buffer)

– probe AMS with R

– S matches async. probe SteMR

– (BLDRPROBEIDX_S)* | (PROBESTEM_R)*

ASIDE: Asynchrony is a very useful technique in several contexts – disk, parallel cpus, distributed data, …

Understudied in research literature

Symmetric Hash Join– (BLDRPROBESTEM_SBLDSPROBESTEM_R)*

– Alternate builds and probes Synchronous Index Join

(PROBEIDX_S)*

Grace/Hybrid Hash Join– Build all R into SteMR

– Build all S into SteMS

– probe SteMR with all S– (BLDR)* (BLDS)* (PROBESTEM_R)*– For i/o efficiency, we must cluster probes by partitions

• Importance of asynchronous operations

Grace Hash join

R

Eddy

S

S probe

S bl

dR bld

S probeR probe

Eddy can adapt between both joins within the same query

Hybridized Join Algorithmse.g. – Start with index join

(for pipelining first few rows)– Later switch to hash join for performance– When memory runs out, switch back to index join

Alternate AMs (access modules) run competitively– But lookup cache (SteMS) shared by both AMs

• eliminates duplicates• Avoids cache fragmentation => switch join algorithms w/o losing work

Hybridized Joins

R

Eddy

S matches

S probe

S bl

d

R

R bldRS RSR probe

S

S

Hybridized Hash-Index Join

0 50 100 150 200 Time(seconds)N

umbe

r of r

esul

t tup

les

outp

ut200

150

100

50

0

Num

ber o

f res

ult t

uple

s ou

tput

indexjoin

hybridhybrid

hashjoin

idx. join

1000

800

600

400

200

00 10 20 30

Time(seconds)

R

EddyRS

S probe

S bl

d

R

R bld RSR probe

S

cacherendezvousbuffer

S

S scan

Starts off as async index join

Ends like symm hash join

But, routing constraints needed to make this work correctly!

Routing Constraints Flexibility given by SteMs can allow incorrect algorithms E.g. BLDRBLDSPROBESTEM_RPROBESTEM_S

– Causes False Duplicates– Arises whenever we decouple build & probe

Cyclic queries, Competitive AMs, Asynchrony also cause errors

Need Routing constraints on order of builds and probes– define space of permissible query executions– Subject to these constraint, Eddy routes to maximize performance

Paper [R01, R+03] give routing constraints that characterize the space of correct routing policies

– Essentially, a space of correct join algorithms• Much broader than TOBO

R

Eddy

S

S probe

S bl

dR bldS probe

R probe








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Need for Asynchrony Many events in query processing are inherently asynchronous

– I/O– Remote data sources– Parallel computation

Forcing these to be synchronous hinders flexibility in doing joins

Fascinating topic with plenty of possibilities– Will only touch on this briefly here– See papers for more info: WSQ/DSQ, SteMs, Query scrambling

Symmetric Hash Join(BLDRPROBESTEM_SBLDSPROBESTEM_R)*

– Alternate builds and probes Asynchronous Index Join

– More general & flexible than sync. join– build R into SteMR (rendezvous buffer)– probe AMS with R– S matches async. probe SteMR


First introduced for web data sources – WSQ/DSQ [GW’00]

Asynchronous Index Join

R

Eddy

S matches

S probe

R

R bldRS

S

Symmetric Hash Join(BLDRPROBESTEM_SBLDSPROBESTEM_R)*

– Alternate builds and probes Asynchronous Index Join

– More general & flexible than sync. join– build R into SteMR (rendezvous buffer)– probe AMS with R– S matches async. probe SteMR


– with Caching:• S matches are cached in SteMR

• R tuples can probe SteMS first before AMS

Asynchronous Index Join

R probe

R

Eddy

S matches

S probe

S bl

d

R

R bldRS RS

S

Research Roundup

Metrics What are the metrics of choice for database users? How do you get them?

– Completion time– Query throughput– Interactivity (& ability to change query on the fly)– Business process based

Digging deeper, parameterize these metrics:– Expected value of metric (and for what distributions)– Variance in value of metric -- i.e. predictability– Quality relative to worst-case value of metric (“competitive analysis”)

• Under what kind of adversarial model for manipulating environment?

Cost and Benefit of adaptivity in these settings:– Benefit: for which is a static plan likely to fail badly? – Cost: for which is it low-overhead to change plans?– Relative costs of the different adaptivity schemes for various metrics?

As yet, not systematically

explored

Measurement & Models What is the right mix of model granularity and measurement timescales?

– Rich models periodically built on data (a la standard practice)– Lighter-weight models built during query processing

• For use in that query (as discussed today)• For use in future queries -- i.e. query feedback

– Some mixture:• To support both intra- and inter-query measurement• How to synchronize multiple models?

Can we deal with correlation without combinatorial explosions in stats?– Or “fleeing from knowledge to ignorance”


explored

Execution Space Identify the “complete” space of post-mortem executions:

– Partitioning– Caching– State migration– Competition & redundant work

What aspects of this space are important? When?– A buried lesson of AQP work : “non-Selingerian” plans can win big!

• But characterize the circumstances? Can low-overhead “switchpoints” be well characterized?

– Generalize “moments of symmetry” argument from Eddy paper [AH00] Given this (much!) larger plan space, navigate it efficiently

– Especially on-the-fly


explored

Robustness How do users get to understand and control the system

– What is the equivalent of EXPLAIN in an adaptive system?– Is this important? Will it be important in 10 years?

Synergy between “robust optimization” and adaptivity? Robust metrics may be important to “gate” feedback overheads Adaptivity may help system stay within predictable parameters– Track these metrics on the fly?– Do they significantly prune the plan space?– What sacrifice is made in terms of ideal performance


explored

Formalisms and Tie-ins There are clear connections here to:

– Online algorithms– Machine Learning and Control Theory

• Bandit Problems• Reinforcement Learning

– Operations Research scheduling

Connections in their infancy– Mostly for selection ordering, and even there, mostly static– Can we capture adaptivity and correlation together?– Can we capture join processing

• Access method selection• The “burden” of state, and state migration• Cache effects

– For many metrics and computational models• Including parallelism and asynchrony!


explored

Engineering: Evolutionary & Revolutionary What can/should be exploited by the Big Three

– Based on an understanding of the expanded plan space– How “hard” is it to graft techniques into a traditional engine?

• Iterator-based dataflows• Encapsulated join algorithms• Carefully tuned but simple cost models, measurement and models

– Note: this is the subject of some controversy• E.g. how “fundamental” a shift is it to add eddies?

What is the right “from-scratch” architecture for new systems– What is the execution model?

• Pull-oriented iterators wrong for anything that accesses a network• Eddy-centric architecture: overkill? Eddy as operator, not architecture.• Middle ground -- inspiration from NW routers or search engines?

– What is the role of an explicit optimizer?• Is it used for apriori planning?• Is it called during execution?

– Connection to auto-tuning database design component?


explored

In Sum Adaptivity is the future (and past!) of query execution

– No new system can afford to ignore it– Old systems probably can’t either

Lots of innovation over the last few years– Basic reassessment of 25 years of conventional wisdom– Exploration of architectures, algorithms and optimizations

Lessons and structure emerging– The adaptivity “loop” and its separable components– Selection ordering as a clean “kernel”, and its limitations– Horizontal partitioning as a logical framework for understanding/explaining

adaptive execution• Expanding the plan space• The corresponding challenge of learning/exploiting correlations

– The critical and tricky role of state in join processing– Asynchrony and its challenges

In Sum But given all this innovation…

There is a lot of science and engineering to be done– Much new material has been exposed, but little of it is fully understood

• Either theoretically or empirically– As with most of query processing there are many opportunities for various

modes of research• Architectural• Algorithmic • Experimental

Success depends on future applications of DB query processing ideas– Challenge: identify (or create!) the new environments that matter

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