Extending Yahoo Streaming computation Benchmark to Apache Apex - Application topology - Comparison of results between Storm, Flink and Apex - Variation of the Apex Benchmarking App with event time and 'results query' support

1. Extending the Yahoo
Streaming Benchmark for Apache Apex
San Jose Apache Apex Meetup
May 4th
2016
Sandesh Hegde
sandesh@apache.org

2. Background
• Yahoo created a benchmark to compare Stream processing systems and
compared Storm, Flink and Spark Streaming [1]
• dataArtisans extended the benchmark by comparing Flink and Storm with
different scenarios [2]
• No benchmark comparison about Stream processing is complete without
including Apache Apex.
2

3. Yahoo Streaming Benchmark
Simple Advertisement Application : To see how many times an ad
campaign has been seen in an window.
• Read ads from Kafka
• Deserialize JSON string
• Filter unnecessary ads
• Projection of Fields ( remove non-essential fields )
• Join ad id with campaign id from Redis
• Windowed count per campaign and output to Redis
3

4. Application - with Kafka
4
Kafka Input Deserialize FilterKafka Redis OutputRedis JoinFilter Fields

5. Setup
• Intel(R) Xeon(R) CPU E5-2630 0 @ 2.30GHz
• 10GigE Between compute nodes
• 4 Kafka Brokers ( 2 Partitions each & 1 Replica )
• Kafka Version : 0.8.2
• Apex ( 3.4-SNAPSHOT & 3.3 ) & Flink ( 1.0.2 )
• Yarn-Containers size: 16GB
• 1 ZooKeeper
• Message Size: 218 Bytes
• Sample Message: {"user_id":"e5e0db4b-05ea-4ac5-af7a-4bba5ed27c4c","
page_id":"80f60d0a-b02b-40e2-a667-5548a1120dda","ad_id":"
600589859","ad_type":"banner78","event_type":"purchase","event_time":"
1462374087774","ip_address":"1.2.3.4"}
5

6. Apex Application
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7. Physical Plan
7

8. Quick Primer on Locality
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• CONTAINER_LOCAL
■ Deployed in the same process, different threads
■ No serialization
■ Queue between the operators
• THREAD_LOCAL
■ Same thread
■ No serialization
■ Use it only when operators do light work
Note: [New feature] Anti Affinity is not covered here.

9. Benchmarking Against Previous Releases
9
https://www.datatorrent.com/blog/blog-apex-performance-benchmark/
Part of Release Certification

10. Application : with Kafka
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https://github.com/sandeshh/streaming-benchmarks

11. Application - With Generator
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Kafka Input Deserialize FilterKafka Redis OutputRedis JoinFilter Fields
Generator

12. Application - With Generator
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https://github.com/sandeshh/streaming-benchmarks
Setup: Single Partition

13. State of the Art & Streaming
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Generator Filter Redis OutputRedis JoinFilter Fields
What’s our recommendation to query the State?
In memory Key-Value store in the operators?

14. Application - State Store & Query
14
Generator Filter
Dimensional
Computation
Redis JoinFilter Fields Store (HDHT) QueryResult
1. Durable state ( HDHT is a key value store native to Hadoop ) [4]
2. Single System, scales with your application
3. Easy integration with external Consoles [7]
4. Low operability cost
5. Complex Dimensional Computation [5][6]

15. Demo
15

16. Q&A
16

17. References
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1. https://yahooeng.tumblr.com/post/135321837876/benchmarking-streaming-computation-engines-at
2. http://data-artisans.com/extending-the-yahoo-streaming-benchmark/
3. https://www.datatorrent.com/blog/blog-apex-performance-benchmark/
4. https://www.datatorrent.com/blog/data-store-for-scalable-stream-processing/
5. https://www.datatorrent.com/blog/blog-dimensions-computation-aggregate-navigator-part-1-intro/
6. https://www.datatorrent.com/blog/dimensions-computation-aggregate-navigator-part-2-
implementation/
7. http://docs.datatorrent.com/app_data_framework/

18. © 2016 DataTorrent
Resources
18
• Apache Apex website - http://apex.apache.org/
• Subscribe - http://apex.apache.org/community.html
• Download - http://apex.apache.org/downloads.html
• Twitter - @ApacheApex; Follow - https://twitter.com/apacheapex
• Facebook - https://www.facebook.com/ApacheApex/
• Meetup - http://www.meetup.com/topics/apache-apex
• Free Enterprise License for Startups - https://www.datatorrent.com/product/startup-
accelerator/

19. © 2016 DataTorrent
We Are Hiring
19
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