task scheduling for highly concurrent analytical and transactional main-memory workloads iraklis...

Task Scheduling for Highly Concurrent Analytical and Transactional

Main-Memory Workloads

Iraklis Psaroudakis (EPFL), Tobias Scheuer (SAP AG), Norman May (SAP AG), Anastasia Ailamaki (EPFL)

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Scheduling for high concurrency

2

Queries >> H/W contexts

Limited number of H/W contexts

How should the DBMS use available CPU resources?

Scheduling for mixed workloads

3

OLTP OLAP

Short-lived

Reads & updates

Scan-light

Long-running

Read-only

Scan-heavy

How to schedule highly concurrent mixed workloads?

Single thread Parallelism

Contention in highly concurrent situations

12

1

32

12

1

32

Scheduling tactics• OS scheduler

4

1

2

3

Time

12

1

32

TimeTime

Context switchCache thrashing

• Admission control

We need to avoid both underutilization and overutilization

# Threads

Time

# H/W contexts

Overutilization

} underutilization

} overutilization

Coarse granularity of control

Task scheduling• A task can contain any code

5

run() { ...}

• One worker thread per core processing tasksSocket 1 Socket 2Task queues

Provides a solution to efficiently utilize CPU resources

• OLAP queries can parallelize w/o overutilization

• Distributed queues to minimize sync contention• Task stealing to fix imbalance

Task scheduling problems for DBMS

• OLTP tasks can block– Problem: under-utilization of CPU resources– Solution: flexible concurrency level

• OLAP queries can issue an excessive number of tasks in highly concurrent situations– Problem: unnecessary scheduling overhead– Solution: concurrency hint

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Outline• Introduction• Flexible concurrency level• Concurrency hint• Experimental evaluation with SAP HANA• Conclusions

Fixed concurrency level

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A fixed concurrency level is not suitable for DBMS

• Typical task scheduling:

Bypasses the OS scheduler

• OLTP tasks may blockUnderutilization

Fixed

Flexible concurrency level• Issue additional workers when tasks block• Cooperate with the OS scheduler

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Concurrency level = # of worker threads

Active Concurrency level = # of active worker threads

OS

Active concurrency level = # H/W contexts

The OS schedules the threads

Task SchedulerTask Scheduler

Worker states

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Inactive Workers

Watchdog:– Monitoring, statistics, and takes actions– Keeps active concurrency level ≈ # of H/W contexts

Blockedin syscall

Inactiveby user

Waitingfor a task

Parkedworkers

Activeworkers

Watchdog Otherthreads

We dynamically re-adjust the scheduler's concurrency level

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Outline• Introduction• Flexible concurrency level• Concurrency hint• Experimental evaluation with SAP HANA• Conclusions

Partitionable operations• Can be split in a variable number of tasks

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Calculates its task granularity

Σ1 ≤ # tasks

≤ # H/W contexts

• Problem: calculation independent of the system’s concurrency situation- High concurrency: excessive number of tasks

Unnecessary scheduling overheadWe should restrict task granularity under high concurrency

Partition 1

Partition 2

Partition 3

Final result

Restricting task granularityExisting frameworks for data parallelism

– Not straightforward for a commercial DBMS– Simpler way?

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free worker threads = max(0, # of H/W contexts - # active worker threads)

The concurrency hint serves as an upper bound for # tasks

concurrency hint = exponential moving average of free worker threads

High latencyLow scheduling overhead

Higher throughput

Concurrency hint

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Low concurrency situations

Lightweight way to restrict task granularity under high concurrency

High concurrency situations

Concurrency hint # H/W contexts

Concurrency hint 1

ΣΣ Σ

Σ ΣLow latency

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Outline• Introduction• Flexible concurrency level• Concurrency hint• Experimental evaluation with SAP HANA• Conclusions

Experimental evaluation with SAP HANA• TPC-H SF=10 • TPC-H SF=10 + TPC-C WH=200• Configuration:

– 8x10 Intel Xeon E7-8870 2.40 GHz, with hyperthreading, 1 TB RAM, 64-bit SMP Linux (SuSE) 2.6.32 kernel

– Several iterations. No caching. No thinking times.

• We compare:– Fixed (fixed concurrency level)– Flexible (flexible concurrency level)– Hint (flexible concurrency level + concurrency hint)

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TPC-H – Response time

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32160

288416

544672

800928

0

25

50

75

100

125

150 FixedFlexibleHint

Number of concurrent queries

Resp

onse

tim

e (s

ec)

32 128 256 512 1024

11.2%3.5%

Task granularity can affect OLAP performance by 11%

Series10

1000000...

2000000...

3000000...

Fixed Flexible Hint

Inst

ructi

ons

retir

edTPC-H - Measurements

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Series10

200000

400000

600000

800000

1000000

1200000

1400000

# of

task

s (x

1000

0)

0100000200000300000400000500000600000700000800000900000

1000000

Cont

ext S

witc

hes

(x10

000)

Unnecessary overhead by too many tasks under high concurrency

020000

4000060000

80000

100000

120000

1400000

20406080

100120140160

020004000600080001000012000

Active workers Waiting tasks

Time (sec)

# of

H/W

con

text

s

# of waitin

g tasks (x1000)

TPC-H - Timelines

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020000

4000060000

80000

100000

1200000

20406080

100120140160

0100020003000400050006000

Time (sec)#

of H

/W c

onte

xts

# of waitin

g tasks (x1000)

Fixed Hint

16 32 640

50100150200250300350400450500

0

500

1000

1500

2000

2500

3000

3500

FixedFlexibleHintFixedFlexibleHint

TPC-H Concurrent Clients

TPC-

H T

hrou

ghpu

t (q/

min

)

TP

C-C

Th

rough

pu

t (tpm

C)

TP

C-H

TPC-

CTPC-H and TPC-C

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Throughput experiment– Variable TPC-H clients = 16-64. TPC-C clients = 200.

Conclusions• Task scheduling for

– Resources management

• For DBMS– Handle tasks that block

• Solution: flexible concurrency level– Correlate task granularity of analytical queries with

concurrency to avoid unnecessary scheduling overhead• Solution: concurrency hint

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Thank you! Questions?