September 23, 2019

Open Source Object Storage For All Of Your Data

1 hour 8 minutes

Summary

Object storage is quickly becoming the unifying layer for data intensive applications and analytics. Modern, cloud oriented data warehouses and data lakes both rely on the durability and ease of use that it provides. S3 from Amazon has quickly become the de-facto API for interacting with this service, so the team at MinIO have built a production grade, easy to manage storage engine that replicates that interface. In this episode Anand Babu Periasamy shares the origin story for the MinIO platform, the myriad use cases that it supports, and the challenges that they have faced in replicating the functionality of S3. He also explains the technical implementation, innovative design, and broad vision for the project.

Announcements

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Your host is Tobias Macey and today I’m interviewing Anand Babu Periasamy about MinIO, the neutral, open source, enterprise grade object storage system.

Interview

Introduction

How did you get involved in the area of data management?

Can you explain what MinIO is and its origin story?

What are some of the main use cases that MinIO enables?

How does MinIO compare to other object storage options and what benefits does it provide over other open source platforms?

Your marketing focuses on the utility of MinIO for ML and AI workloads. What benefits does object storage provide as compared to distributed file systems? (e.g. HDFS, GlusterFS, Ceph)

What are some of the challenges that you face in terms of maintaining compatibility with the S3 interface?

What are the constraints and opportunities that are provided by adhering to that API?

Can you describe how MinIO is implemented and the overall system design?

How has that design evolved since you first began working on it?

What assumptions did you have at the outset and how have they been challenged or updated?

What are the axes for scaling that MinIO provides and how does it handle clustering?

Where does it fall on the axes of availability and consistency in the CAP theorem?

One of the useful features that you provide is efficient erasure coding, as well as protection against data corruption. How much overhead do those capabilties incur, in terms of computational efficiency and, in a clustered scenario, storage volume?

For someone who is interested in running MinIO, what is involved in deploying and maintaining an installation of it?

What are the cases where it makes sense to use MinIO in place of a cloud-native object store such as S3 or Google Cloud Storage?

How do you approach project governance and sustainability?

What are some of the most interesting/innovative/unexpected ways that you have seen MinIO used?

What do you have planned for the future of MinIO?

Contact Info

@abperiasamy on Twitter

abperiasamy on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.

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Links

MinIO

GlusterFS

Object Storage

RedHat

Bionics

AWS S3

Ceph

Swift Stack

POSIX

HDFS

Google BigQuery

AzureML

AWS SageMaker

AWS Athena

S3 Select

Azure Blob Store

BackBlaze

Round Robin DNS

Service Mesh

Istio

Envoy

SmartStack

Free Software

RocksDB

TanTan Blog Post

Presto

SparkML

MCAdmin Trace

DTrace

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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By Tobias Macey

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