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Changing the Face of Database Cloud Services with Personalized Service Level Agreements Jennifer Ortiz, Victor Teixeira de Almeida, Magdalena Balazinska University of Washington, Computer Science and Engineering PETROBRAS S.A., Rio de Janerio,

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Changing the Face of Database Cloud Services with Personalized Service Level Agreements

Jennifer Ortiz, Victor Teixeira de Almeida, Magdalena Balazinska

University of Washington, Computer Science and Engineering PETROBRAS S.A., Rio de Janerio, RJ, Brazil

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CIDR 2015

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Many Data Management & Analytics Systems Available

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Many Systems are Available as Cloud Services

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Cloud Services Today

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How many instances of the service? Which Hadoop Version? Pig or Hive?

Amazon EMR

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Cloud Services Today

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How many instances of the service? Which Hadoop Version? Pig or Hive?

Amazon EMR

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Cloud Services Today

How long will my query take?

BigQuery

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Cloud Services Can Do Better!

System Internals

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Cloud Services Can Do Better!

System Internals

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Cloud Services Can Do Better!

SELECT … FROM … WHERE …

Query:

  • Query Capabilities
  • Time
  • Money
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A new proposal

Time to Re-think the interface…

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  • Hide details of cluster deployment and resources
  • Show users monetary costs and performance estimates on their data
  • Let users pick the desired trade-off between options shown

Personalized Service Level Agreements

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A PSLA Example

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Tier 1: $0.10/hour

Within 20 seconds: SELECT <up to 10 attributes> FROM <Fact | Dimension> WHERE <up to 100% of data> Within 1 minute: SELECT <up to 5 attributes> FROM <JOIN Fact + 4 Dimensions> WHERE <up to 10% of data> Within 10 minutes: SELECT <up to 10 attributes> FROM <JOIN Fact + 8 Dimensions> WHERE <up to 100% of data>

Fixed, hourly price Expected performance Templates capture capabilities

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A PSLA Example

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Tier 1: $0.10/hour

Within 20 seconds: SELECT <up to 10 attributes> FROM <Fact | Dimension> WHERE <up to 100% of data> Within 1 minute: SELECT <up to 5 attributes> FROM <JOIN Fact + 4 Dimensions> WHERE <up to 10% of data> Within 10 minutes: SELECT <up to 10 attributes> FROM <JOIN Fact + 8 Dimensions> WHERE <up to 100% of data>

Different tiers

  • f service

Within 1 second: SELECT <up to 10 attributes> FROM <Fact | Dimension> WHERE <up to 100% of data>

Tier 2: $0.50/hour

Fixed, hourly price Expected performance Templates capture capabilities

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Goals

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Database D Cloud C

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Goals

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Database D Cloud C PSLA P

Money, Time, Capabilities

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Goals

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Database D Cloud C PSLA P PSLAManager

Money, Time, Capabilities

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Goals

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Database D Cloud C PSLA P PSLAManager

Money, Time, Capabilities

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Example of a Real PSLA

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TPC-H Star Schema Benchmark

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  • Based on TPC-H
  • 10GB
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Myria is a data management service in the cloud that we built at UW. It has a parallel, shared-nothing back-end query execution engine called MyriaX

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PSLA for Myria

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PSLA for Myria

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PSLA for Myria

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PSLA for Myria

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PSLA for Myria

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PSLAManager

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Database D Cloud C PSLA P PSLAManager

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Query Workload Generation

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  • Which queries to generate?

– Joins drive performance

  • Think about possible combinations of joins

– Only consider most expensive queries – Build toward more complex queries, include selections and projections

Tables in Order by Size: Lineitem, Part, Customer, Supplier, Date

(Lineitem Part) (Customer Date), (Lineitem Supplier), etc.

Consider: All possible 2-way joins

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Tier Selection

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50 100 150 200 250 300 350 2 4 6 8 10 12 14 16 18

Seconds Workers

Runtime Distributions of Query Workload Per Configuration in Myria

Workers (Configurations) Seconds

EMD 17.43 EMD 7.07 EMD 13.74

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Tier Selection

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Workload Compression

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2 4 6 8 10 12 14 16 18

Workers

  • Threshold-based
  • Density-based

STEP 1: Query Clustering

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2 4 6 8 10 12 14 16 18

Workers 42

th

Workload Compression

STEP 1: Query Clustering

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2 4 6 8 10 12 14 16 18

Workers

Workload Compression

STEP 1: Query Clustering

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Workload Compression

STEP 1: Query Clustering

Tier 1: $0.XX/hour

Within Cluster Max Threshold: Query Templates in Cluster… Within Cluster Max Threshold: Query Templates in Cluster…

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Workload Compression

STEP 2: Template Generation

Time(s) Configuration SELECT (5 ATT) FROM (5 TABLES) WHERE 10% SELECT (4 ATT) FROM (4 TABLES) WHERE 1%

Query Dominance

Queries  Query Templates

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Workload Compression

STEP 2: Template Generation

Time(s) Configuration SELECT (5 ATT) FROM (5 TABLES) WHERE 10%

Query Dominance

SELECT (4 ATT) FROM (4 TABLES) WHERE 1% Attributes Projected Tables Selectivity

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Workload Compression

STEP 2: Template Generation

Time(s) Configuration

Given:

SELECT (5 ATT) FROM (5 TABLES) WHERE 10%

Query Dominance

SELECT (4 ATT) FROM (4 TABLES) WHERE 1% Attributes Projected Tables Selectivity

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Workload Compression

STEP 2: Template Generation

Time(s) Configuration

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Workload Compression

Time(s) Configuration

STEP 2: Template Generation

Root Query Template: We call a query template a root query template if no

  • ther query template in the same cluster dominates

it.

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Configuration 1

Time(s)

Workload Compression

STEP 3: Dropping Queries with Similar Times

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Configuration 1

Time(s)

Configuration 2

Workload Compression

Root Query Templates Queries  Query Templates

STEP 3: Dropping Queries with Similar Times

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Configuration 1

Time(s)

Configuration 2

Workload Compression

STEP 3: Dropping Queries with Similar Times

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Configuration 1

Time(s)

Configuration 2

Workload Compression

STEP 3: Dropping Queries with Similar Times

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Configuration 1

Time(s)

Configuration 2

Workload Compression

STEP 3: Dropping Queries with Similar Times

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PSLA Quality Assessment

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PSLA Quality Metrics

  • PSLA Query Capabilities
  • PSLA Complexity
  • PSLA Performance Error Metric

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Time(s)

High Complexity Low Error

Time(s)

Low Complexity High Error

Quality Metric Trade-offs

Found that a log interval is best without tuning

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PSLA Evaluation on Predicted Runtimes

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Query Feature Vector

Query workload

Est. Rows

  • Est. IO

Avg. Row

Query Feature Vector

Est. Rows

  • Est. IO

Avg. Row

Cloud Configuration Cloud Configuration

runtime runtime

Based on Predicting Multiple Metrics for Queries: Better Decisions enabled by Machine Learning [Ganapathi et. al. 2009]

Train model

  • ffline on
  • ther data

and queries Predict runtime from query features

Performance Model (Offline)

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Training Dataset

  • Synthetic Dataset

– 10GB – 6 Tables – 61 Attributes

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Workload Compression into PSLA (repeat for each tier) Workload Generation Query Clustering Template Generation

Dropping Queries with Similar Times

PSLA

  • Perf. Modeling

Tier Selection

Data Service

PSLAManager Workflow

Perform Offline Perform Online

Runtime Prediction

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Predicted Myria PSLA (Predicted Runtimes)

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Looking Forward

  • Direct extensions to the approach

– Add support for indexes – Improve time predictions

  • Longer-term future work

– Can we guarantee the runtimes? – Can we update the PSLA as user queries data

  • Goal is to show increasingly more complex queries
  • Usability testing

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Conclusion

  • Many cloud DBMSs exist
  • Require users to reason about resources
  • We propose to re-think that interface
  • Personalized Service Level Agreements

– Service Tiers – Price/Capabilities/Performance

  • Important direction for cloud DBMSs

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