This document discusses Robinhood's use of Alluxio to improve the performance of their data analytics workflows. It describes Robinhood's data lake architecture and daily traffic patterns, including ad-hoc visualizations queries, data analysis jobs, and report generations. The document notes limitations with their previous approach of reading directly from S3, including slow and unstable reads. It then outlines how Alluxio helps by caching frequently used data to improve read speeds by 30-50% and reduce total data scanned. Technical challenges of reading cold data and handling large schemas and tables are also mentioned. Overall, Alluxio provided a 30% performance improvement for their data-intensive queries.

1. Strictly Confidential 1
Exploring Alluxio for
daily tasks
Our first glance into the Alluxio world Jiawei Zhang
Yichuan Huang
Grace Lu
Wenlong Xiong

2. Strictly Confidential 2
Content
● Background
○ Data warehousing at Robinhood
○ Our Presto use cases
● The limitations we’ve met
● How Alluxio helps
○ Visualization queries
○ Data analyzation
● Our performance test results

3. Strictly Confidential 3
1. How we build our data lake
2. Our daily traffic
3. Perspective
01
Background

4. Strictly Confidential 4
How we build our data lake

5. Strictly Confidential 5
Our daily traffic
● Ad-hoc queries
○ Mainly from visualizations
○ Performing simple filters and aggregations on large range of data
○ Always come with a limit on resultset
○ Featuring data ranging from a day to a month
○ Concentrating on a small number of datasets
○ Repeated many times

6. Strictly Confidential 6
Our daily traffic
● Data Analyzation queries
○ From scheduled jobs, services or manually from data scientists
○ Comprehensive query that contains lots of stages
○ Performing a bunch of queries on same set of data

7. Strictly Confidential 7
Our daily traffic
● Data report generations
○ On-demand from data scientists
○ Doing aggregation on most recent data
○ Running daily or weekly
○ Featuring data from previous day ~ previous week

8. Strictly Confidential 8
Perspective
● We always read data from last several days, from several datasets.
● S3 read speed is slow and unstable
● What if we have a cache for just the most commonly used data?
● Requirements:
○ Able to integrate with Presto/HMS and cache input data based on query
○ Able to flush based on data access frequency
○ Scalable

9. Strictly Confidential 9
1. Our Alluxio Setup
2. Standalone vs. Co-located
3. Performance improvement
02
How Alluxio Helps

10. Strictly Confidential 10
Our Alluxio setup

11. Strictly Confidential 11
Standalone vs. Co-located
Co-located
Pros:
● Lower cost
● Better Utilize VM resource
● Skip the hop between VPCs
Cons:
● Auto-scaling and graceful shutdown for
both services
● Might impact bandwidth between workers
● Extra CPU usage
Standalone
Pros:
● Won’t be affected by Presto cluster
changes
● Can be extended to other usages, e.g.
Spark, Flink, etc
Cons:
● More cost
● Network transfer vs data locality

12. Strictly Confidential 12
Performance Improvements
● Warm data:
○ 30~50% read speed increase
■ Increase read throughput
■ Reduce total rows scanned
○ Overall ~30% improvement over data-intensive queries
○ Eliminated issues caused by unstable connectivity to data source

13. Strictly Confidential 13
Technical Challenges we faced
● Reading Cold data
● Mounting huge schema
● Handling large tables

14. Strictly Confidential 14
Thank you