The document discusses streaming computing, focusing on technologies such as GigaSpaces, Storm, and Akka, and their use in event-driven processing. It highlights the importance of parallelism, the distinction between streaming and batch processing, and factors affecting performance such as throughput and latency. The author suggests that both streaming and batch approaches are valid depending on the application requirements and emphasizes opportunities like lambda architecture.

1. Streaming
Computing
Some thoughts and technology choices for event-driven processing
Natalino Busa - 29 Aug. 2013

2. Outline
● Concurrency
● Streaming computing
● Technologies
○ Gigaspaces
○ Storm
○ Akka
● Comparison matrix
● Opportunities

3. Algorithms: a tribute
Numbers and Algorithms:
9th century Persian Muslim
mathematician Abu Abdullah
Muhammad ibn Musa Al-Khwarizmi,
whose work built upon that of the 7th
century Indian mathematician
Brahmagupta.
We own a lot to these guys !!!

4. Why do we need parallelism?
It gets bigger,
It doesn’t get much faster
BUT
We get more cores in a chip.
More cores = more parallelism
We are happy now, right?

5. Moore’s law
Every 18 months, the number of CPU
core’s double
Another
interpretation:
Every 18 months, the number of idle
CPU core’s double

6. More parallelism
We trade:
Time vs ( CPU, Memory, I/O)

7. Modern applications
Scalability:
Vertical: concurrency
(use all the cores, memory and I/O of a given machine)
Horizontal: distribution
(use all the machines in the cluster)
High availability:
Fault tolerance: all levels (local, distributed)
(the terminator effect: you can stop it but can’t kill it )

8. Streaming applications
Performance:
Efficient use of resources:
CPU and memory, but also OS threads and sockets
Asynchronous:
event driven, reacts on new data
Distributed:
more machines = more performance
the algorithm is partitioned and/or replicated on the cluster

9. What to increase?
More CPU: It helps when there is
computation involved
More MEMORY: It helps when there is
more state to keep
More I/O: It helps when there are
more messages to transfer

10. Streaming or batch?
ProcessingData
Natalino Busa - 12 Feb. 2013
Data
source system target systemour system
What differentiate Streaming from Batch?
● Granularity of Data
● Granularity of Processing
Granularity impacts:
Throughput, Latency, and the Cost of the system!

11. The choice is yours
1000 events/sec (1 KB/event)
running on 100 cores all day long
“Wait a day, then process”
860 M events = 86 GB of data
Latency: 24 hours
Throughput: 1 update/day
BATCH: Hadoop
Latency 1ms
Throughput: 1000 updates/sec
STREAMING: Akka
“Do not wait”
Process the 1KB of data each msec.
“Both are valid options. It depends on the application domain and the
requirements/specs of the target and source systems”

12. Mapping it to existing applications
Granularity of
Data
256 GB 256 GB
Granularity of
Processing
1 CPU 100 CPU’s
Traditional DB systems Big Data (Hadoop)
Granularity of
Data
1 KB 1 KB
Granularity of
Processing
1 CPU 100 CPU’s
Traditional mail server Web application server

13. Technologies: Gigaspaces

14. Technologies: Storm
Topology
Supervising
Scaling

15. Technologies: Akka
Supervising:
tree of actors
Topology (statics and dynamic actors)
Scaling and
distributed processing

16. Technology matrix
GranularityofData Granularity of Processing
Small Big
Small Akka Akka
Gigaspaces
Big ? Storm
System end-to-end throughput
High ~ 10’000 events/sec Medium ~100 events/sec Low ~10 events/sec
Akka Storm/ Gigaspaces Scripting languages

17. Big Data in motion
Both are:
Distributed, fault-tolerant, streaming
- Storm
++ multi-language
-- not user/admin friendly
-- slow supervising
processing elements are jvm’s
ideal when data is coarse grained
- Akka
++ high throughput, fine grained actors
++ dynamic topologies
-- low-level, but high performance
processing elements are small and lightweight
ideal for millions of transactions per second
- Gigaspaces
++ combines memory + application distribution
-- framework api is not very flexible
processing elements are jvms
ideal for all-in-one solution, with little customization

18. Opportunity: Lambda Architecture
Logic layer
Software as a Service
e.g realt-time predictor
Natalino Busa - 12 Feb. 2013
from http://www.manning.com/marz/

19. Opportunity: Batch + Streaming
Batch
Computing
Front End Services
In-Memory
Distributed Database
In-memory
Distributed DB’s
Batch
Streaming
HTML5 Client / Responsive Applow-latency
HTTP API services FETCH
(refresh)
Streaming
Computing
Data Warehouses Messaging Busses
PUSH
(SSE, notifications)

20. Thanks
linkedin:
www.linkedin.com/in/natalinobusa
blog:
www.natalinobusa.com
twitter:
@natalinobusa