This document compares the streaming frameworks Spark Streaming and Storm. Spark Streaming processes data in micro-batches of around 500ms, uses batch processing semantics, and guarantees exactly-once processing. Storm processes data record-by-record with sub-second latency but only guarantees at least once processing. Benchmarking showed that as the number of data producers increased, Storm's throughput and spout latency also increased, while Spark Streaming's throughput increased with larger batch sizes and higher data loads. The document proposes further testing different use cases and adding monitoring dashboards.

1. Testing Processing Frameworks
Streaming
and
Gabriela Choy

2. Spark / Storm
Spark Storm
Implemented in Scala Clojure, Java
Delivery Semantics
Exactly once At least once. Exactly
once with Trident
API
Python, Java, Scala Java, Scala, Clojure,
Python, etc. Trident:
Java, Scala, Clojure.
Processing Model
Batch. Micro-batches
with Spark Streaming.
~ 500ms
Record at a time/
Trident allows for
micro-batches.
Latency 1 - 2 seconds sub-seconds

3. Pipeline
Streaming
MySQL
MySQL
Producer/s
Producer/s
Each pipeline is run
independently

4. Node Specifications
Spark Streaming Storm
4 AWS nodes m3.
medium
Zookeeper 3.4.6
Kafka 0.8.2.1
Spark (streaming) 1.3
4 AWS nodes m3.
medium
Zookeeper 3.4.6
Kafka 0.8.2.1
Storm 0.9.5

5. Spark Streaming: 1 master node, 3 workers
Cluster Configuration
Master node
Worker 1
Worker 2
Worker 3

6. Storm : 1 nimbus, 3 Supervisors
Cluster Configuration
Nimbus
Supervisor 1
Supervisor 2
Supervisor 3

7. Metric
Throughput: amount of data that is being
processed.
● By changing batch size
● By changing load (i.e. Scaling up)
● Programs used for benchmarking will be wordcount.

8. # Producers Batch Interval
1 1s, 2s, 3s, 4s, 6s
4
1s, 2s, 3s, 4s, 6s
8
1s, 2s, 3s, 4s, 6s
Tests for Spark Streaming

9. Throughput for 1 producer with 95% CI

10. Throughput for 1 producer

11. Throughput for 4 producers with 95% CI

12. Throughput for 4 producers

13. Throughput for 8 producers

14. Throughput for 8 producers

15. # Producers Tuples Emitted-Acked
1 10 min
4 10 min
8 10 min
Tests for Storm
Preliminary results for storm

16. Tuples Emitted per Second

17. Tuples Acked per Second

18. Spout Latency

19. Takeaways
● Setting the batch interval in spark streaming
should be done by monitoring processing times
and load size
● For Storm as numbers of producers increase so
does throughput and spout latency.

20. Would like to add:
● Increase number of producers. Use real data.
● Add a graph as a second use case.
● Dashboard to monitor live streaming.

21. Gabriela Choy
Bsc. in Chem. Engineering. ULA, Vnzla.
Msc in Statistics. UT Dallas
Previously: Worked in Device Reliability Engineering at View, Inc.
About Me