FlinkForward Berlin 2019 - Scotty: Efficient Window Aggregation with General Stream Slicing

Scotty: Efficient Window Aggregation with
General Stream Slicing
Berlin, October 7-9, 2019
Philipp M. Grulich
Research Associate (TU Berlin)
Jonas Traub
Research Associate (TU Berlin)

Jonas Traub (TU Berlin), Philipp M. Grulich (TU Berlin) - Efficient Window Aggregation with Stream Slicing
Aggregations in Stream Processing Pipelines
A stream processing pipeline is a series of concurrently running operators.
2

Window
Aggregation
2

Window
Aggregation
53
2

Window
Aggregation
8
2

Motivation
3

Research Background
Cutty: Aggregate Sharing for User-Defined Windows
P. Carbone, J. Traub, A. Katsifodimos, S. Haridi, V. Markl
ACM International on Conference on Information and Knowledge Management (CIKM2016)
4

Research Background
Scotty: Efficient Window Aggregation for out-of-order Stream Processing
J. Traub, P. M. Grulich, A. R. Cuéllar, S. Breß, A. Katsifodimos, T. Rabl, V. Markl
IEEE International Conference on Data Engineering (ICDE 2018)
4

Research Background
Efficient Window Aggregation with General Stream Slicing
J. Traub, P. M. Grulich, AR. Cuéllar, S. Breß, A. Katsifodimos, T. Rabl, V. Markl
International Conference on Extending Database Technology (EDBT 2019; Best Paper Award)
4

Research Background
Efficient Window Aggregation with General Stream Slicing
J. Traub, P. M. Grulich, AR. Cuéllar, S. Breß, A. Katsifodimos, T. Rabl, V. Markl
International Conference on Extending Database Technology (EDBT 2019; Best Paper Award)
Scotty Window Processor:
Efficent Window Aggregations for Flink, Beam, and Storm
https://github.com/TU-Berlin-DIMA/scotty-window-processor
4

Stream Slicing Example
5

The number of slices depends on the workload.
6

7

8

9

10
We store partial aggregates instead of all tuples. => Small memory footprint.

11
We assign each tuple to exactly one slice. => O(1) per-tuple complexity.

12

We require just a few computation steps to calculate final aggregates. => Low latency.
13

14

Workload
Characteristics
14

Workload
Characteristics
Aggregation
Functions
distributive
algebraic
holistic
associativity
cummutativity
invertibility
14

Workload
Characteristics
Window
Types
Context Free
Forward Context Free
Forward Context Aware
Aggregation
Functions
distributive
algebraic
holistic
associativity
cummutativity
invertibility
14

Workload
Characteristics
Window
Types
Context Free
Window
Measures
time
tuple count
arbitrary
Aggregation
Functions
distributive
algebraic
holistic
associativity
cummutativity
invertibility
14

Workload
Characteristics
Window
Types
Context Free
Stream
Order
in-order
out-of-order
Window
Measures
time
tuple count
arbitrary
Aggregation
Functions
distributive
algebraic
holistic
associativity
cummutativity
invertibility
14

Workload
Characteristics
Window
Types
Context Free
Stream
Order
in-order
out-of-order
Window
Measures
time
tuple count
arbitrary
Aggregation
Functions
distributive
algebraic
holistic
associativity
cummutativity
invertibility
General Stream Slicing combines generality and efficiency in a single solution.
14

Impact of Workload Characteristics
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
Count-based tumbling
window with a length of 5
tuples.
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
tuples.
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
tuples.
11 13 12
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
11 13 12
What if the stream is out-of-order?
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
Event Time
5 12 13 20 35 37 42 46 48 51 52 57 63 64 65
11 13 12
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
Event Time
5 12 13 20 35 37 42 46 48 51 52 57 63 64 65
11 13 12
5
49
Out-of-order Tuple
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
Event Time
5 12 13 20 35 37 42 46 48 51 52 57 63 64 65
11 13 12
5
49
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
Event Time
5 12 13 20 35 37 42 46 48 51 52 57 63 64 65
11 13 12
5
49
13 12
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
Event Time
5 12 13 20 35 37 42 46 48 51 52 57 63 64 65
11 13 12
1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
5
49
13 12
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
Event Time
5 12 13 20 35 37 42 46 48 51 52 57 63 64 65
11 13 12
1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
5
49
13 12
5
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
Event Time
5 12 13 20 35 37 42 46 48 51 52 57 63 64 65
11 13 12
1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
5
49
13 125 + - 3
5
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
Event Time
5 12 13 20 35 37 42 46 48 51 52 57 63 64 65
11 13 12
1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
5
49
13 123 1+ -5 + - 3
5
15

1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tuple Count
15
Event Time
5 12 13 20 35 37 42 46 48 51 52 57 63 64 65
11 13 12
1 2 1 4 3 1 5 2 2 3 6 1 2 2 1
5
49
13 123 1+ -5 + - 3
5
What if the aggregation function is not invertible?
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Scotty Window Processor:
Efficent Window Aggregations
for Flink, Beam, and Storm
https://github.com/TU-Berlin-DIMA/scotty-window-processor
16

Key-Facts
Features:
17

Key-Facts
Features:
● One window operator for many systems.
17

Key-Facts
Features:
● High performance window aggregations with stream slicing.
17

Key-Facts
Features:
● Scales to thousands of concurrent windows.
17

Key-Facts
Features:
● Aggregate sharing among multiple window queries.
17

Key-Facts
Features:
● Adapts to workload characteristics:
○ Window Types
○ Aggregation Functions
○ Window Measures
○ Stream Order
17

Key-Facts
Features:
● Adapts to workload characteristics:
○ Window Types
○ Aggregation Functions
○ Window Measures
○ Stream Order
Connectors:
…more coming soon…
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Scotty Core
18

Scotty Core
Scotty adapts to work load characteristics
and combines generality and efficiency in a single solution.
18

Benchmark
Concurrent Windows with Built-in Window Operator:
● Flink performs well
with a single window
(no overlap; one
bucket at a time)
0
500.000
1.000.000
1.500.000
2.000.000
2.500.000
1 10 20 50 100 500 1000
Flink Storm Flink on Beam
Throughput(Tuples/sec.)
Number of Councurrent Windows
19

Benchmark
Concurrent Windows with Built-in Window Operator:
● Flink performs well
with a single window
(no overlap; one
bucket at a time)
0
500.000
1.000.000
1.500.000
2.000.000
2.500.000
1 10 20 50 100 500 1000
Flink Storm Flink on Beam
● With overlapping
concurrent windows,
the throughput drops
drastically.
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0
500.000
1.000.000
1.500.000
2.000.000
2.500.000
1 10 20 50 100 500 1000
Flink+Scotty Storm+Scotty Beam+Flink+Scotty
Benchmark
Concurrent Windows with Scotty:
● With Scotty, the throughput
is independent of the
number of concurrent
windows.
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Using Scotty on Flink
1. Clone Scotty and install to maven
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1. Clone Scotty and install to maven
2. Add Scotty to your Flink Project:
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1. Initialize Scotty Window Operator
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2. Add Window Definitions
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3. Add Scotty to your Flink Job
2. Add Window Definitions
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Acknowledgements: This talk is supported by the Berlin Big Data Center (01IS14013A), the Berlin Center for Machine Learning (01IS18037A), and Software Campus (1-3000473-18TP).
Scotty Window Processor
Scotty Features:
● High performance with stream slicing.
● Adapts to workload characteristics
tu-berlin-dima.github.io/
scotty-window-processor
Open Source Repository:
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FlinkForward Berlin 2019 - Scotty: Efficient Window Aggregation with General Stream Slicing

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