The document details a distributed query service by Walmart Labs that leverages Presto and Alluxio to optimize data access across various cloud environments. It highlights the motivations for adopting Presto, such as scalability and federated query capability, alongside architectural components that enhance query performance. Additionally, the service supports multi-tenancy with custom authentication, authorization, and auto-scaling features to provide efficient data analysis and resource utilization.

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Enterprise Distributed Query Service powered by Presto
& Alluxio across clouds @ WalmartLabs
Ashish Tadose
Principal Engineer

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Agenda
• Data stores @ Walmart Labs
• Motivation for Presto as Distributed Query service
• Multi-tenant managed Distributed Query service
• Alluxio caching to optimize the performance
• Architectural components
• Alluxio to support query federation in hybrid
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Data stores @ Walmart Labs
Access needs are varied from team to team – one solution does not fit all….

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Motivation for Presto..
• DataLake cluster - powered by on-prem Hadoop/HDFS
• Compute storage colocation – GOOD
• Need to ingest data from all diverse sources – CHALLENGING
• Scaling out compute with growing needs – CHALLENGING
• Need to separate storage & compute / support federated query capability – PRESTO..
• Isolated clusters in private cloud powering dedicated data-marts
Datajourney

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• Simplified query access layer
• Leverage cloud elastic compute
• Better scalability & Effective cluster utilization by auto-scaling
• Performant query response times
• Security
– Authentication – LDAP
– Authorization – work with existing policies
• Handle sensitive data – encryption at rest & over the wire
• Efficient Monitoring & alerting
• Dedicated quotas – SLA guarantees
• Flexibility to configure query configuration per tenant
Multi-tenant Query service - requirements

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• Authentication
– Presto LDAP
– Custom authentication service
• Authorization
– Custom Presto ranger plugin
– Hadoop impersonation support
• Quota
– Presto resource groups
• Query configuration tuning
– Session property managers
– Customizations to make it for Unix groups
• Query audit
– Presto’s event listener framework
• Auto-scaling in GCP
– GCP instance group auto-scaling
– Auto scaling based on CPU load and queued
queries
Architectural components

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Presto & Alluxio
Works well together…
Small range query response time
Lower is better
Large scan query response time
Lower is better
Concurrency
Higher is better
Presto Presto + Alluxio
• Query performance bottlenecks
• Un-predictable network IO
• Query pattern - Datasets modelled in star
schema could benefit by dimension table
caching
• Presto + Alluxio
• Avoids unpredictable network
• Consistent query latency
• Higher throughput and better concurrency

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• Presto + Alluxio collocated
cluster
• Meta synch components to
automatically crate alluxio
backed tables and create
alluxio mount points
• Tweak auto scaling to keep
the min number of alluxio
workers
• Pin frequently used dimension
tables to avoid cache
evictions
Presto + Alluxio – architectural components

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• Ability to query datasets couldn’t make it
to public clouds
• Alluxio greatly improved query
performance to avoid network hops
recurrent queries
• Avoids creating data copies in clouds of
datasets – alluxio mounts file meta
changes
• Enabled query guards in Presto to avoid
abuse of this connectors
Presto + Alluxio – hybrid cloud

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Query service backed by Presto + Alluxio
We provide the analyst with a query tool for interactive ad-hoc analysis over different source system through a unified SQL query interface.
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Query service hosted in GCP & On-
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and is offered as a managed
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We also help business in optimizing
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Using the platform’s
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No fancy technology
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Performance boost
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Queries can be executed
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regardless of where the
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With auto-scaling in place,
queries always get enough
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Choose BI tool
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THANKS!
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