The RCP/Rakuten Catalog Platform has been growing at a brisk speed over the last couple of years. Our original backbone was Cassandra. However, as they continued their growth, they internally started realizing that it was not suitable for our next stage of growth. As such, they started looking into ScyllaDB as a better ROI solution as well as a much more stable backend. The migration itself was challenging since this has to be done for a production live data processing pipeline with minimal impact on customers. In this talk, Hitesh Shah, Engineering Manager at Rakuten USA will dive deeper into challenges and takeaways. To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.

1. Rakuten’s Catalog
Platform Migration from
Cassandra to ScyllaDB
Hitesh Shah
Engineering Manager

2. Hitesh Shah
■ Passionate Engineer - Predominantly worked with bay area tech
■ Focused on development as well as operations
■ Interests - Distributed systems, Dynamically scalable platforms
■ Foodie, hiking, movie buff, reading
Engineering Manager
YOUR COMPANY
LOGO HERE

3. Agenda
■ Overview of RCP
■ Pipeline Architecture, Data Volumes
■ Our experience with Cassandra
■ Why Scylla?
■ KPI Improvements

4. Rakuten Catalog Platform
rakuten.com rakuten.co.jp

5. Rakuten Catalog Platform

6. Data Processing Volume
■ Total Catalog items: 700M+
■ Number of Items processed daily: 250M+
■ Read QPS upwards of 10k-15k per Node
■ Write QPS in the range of 3k-5k per Node

7. Data Processing Volume

8. Issues with Cassandra
■ Inconsistent Performance; Volatile Latencies
■ JVM Issues; OOM; long GC pauses
■ Single slow node can impact cluster
■ Requires tons of manual operations
■ Horizontal scalability at stiff cost

9. Why Scylla?
■ 5x-6x performance improvement
■ Improved TCO; Better ROI
■ Consistent Latencies
■ Seastar framework; much better parallelism
■ Drop in replacement

10. Why Scylla Enterprise?
■ Enterprise support – private slack channel; SLAs
■ Scylla custom shard-aware driver for Java
■ Incremental Compaction Strategy
■ Workload prioritization
■ Scylla Manager

11. Shard-aware Driver

12. KPI Improvements
■ Average feed ingestion times 30%-35% faster
■ Improved SLAs
■ 2.5x-5x improvement in feed publishing times
■ Much quicker turnaround for merchants/internal customers
■ 21 Nodes in Cassandra to 6 Nodes in Scylla
■ Hardware $$$ savings

13. Thank you!
Stay in touch
Hitesh Shah
linkedin.com/in/hitechshah/
hitesh.shah@rakuten.com