106: Greybeards talk Intel’s new HPC file system with Kelsey Prantis, Senior Software Eng. Manager, Intel

We had talked with Intel at Storage Field Day 20 (SFD20), about a month ago. At the virtual event, Intel’s focus was on their Optane PMEM (persistent memory) technology. Kelsey Prantis (@kelseyprantis), Senior Software Engineering Manager, Intel was on the show and gave an introduction into Intel’s DAOS (Distributed Architecture Object Storage, DAOS.io) a new HPC (high performance computing, super computers) file system they developed from scratch to use leading edge, Intel technologies, and Optane PMEM was one of them.

Kelsey has worked on LUSTRE and other HPC file systems for a long time now and came into the company from the acquisition of Whamcloud. Currently, she manages the development team working on DAOS. DAOS is a new HPC object storage file system which is completely open source (available on GitHub).

DAOS was designed from the start to take advantage of NVMe SSDs and Optane PMEM. With PMEM, current servers can support up to 20TB of memory. Besides the large memory sizes, Optane PMEM also offers non-volatile memory and byte addressability (just like DRAM). These two characteristics opens up new functionality that allows DAOS to move beyond legacy, block oriented, storage architectures that have been the only storage solution for HPC (and the enterprise) for decades now.

What’s different about DAOS

DAOS uses PMEM for all metadata and for storing small files. HPC IO has always focused on heavy bandwidth (IO using large blocks) oriented but lately newer applications have emerged, such as AI/ML/DL, data analytics and others, that use smaller files/blocks. Indeed, most new HPC clusters and supercomputers are deploying almost as many GPUs as CPUs in their configurations to support AI activities.

The problem is that these newer applications typically consume much smaller files. Matt mentioned one HPC client he worked with was processing small batches of seismic data, to predict, in real time, earthquakes that were happening around the world.

By using PMEM for metadata and small files, DAOS can be much more responsive to file requests (open, close, delete, status) as well as provide higher performing IO for small files. All this leads to a much better performing system for the new HPC workloads as well as great sustainable performance for the more traditional large file workloads.

DAOS storage

DAOS provides a cluster storage system that can be configured with from 1 (no data protection), but more normally 3 nodes (with data protection) at a minimum to 512 nodes (lab tested). Data protection in DAOS is currently based on mirroring data and can use from 0 to the number of nodes in a cluster as data mirrors.

DAOS system nodes are homogeneous. That is they all come with the same amount of PMEM and NVMe SSDs. Note, DAOS doesn’t support disk drives. Kelsey mentioned DAOS node hardware can be tailored to suit any particular application environment. But they typically require an average of 6% of overall DAOS system capacity in PMEM for metadata and small file activity.

DAOS current supports their own API, POSIX, HDFS5, MPIIO and Apache Spark storage protocols. Kelsey mentioned that standard POSIX uses a pessimistic conflict resolution mode which leads to performance bottlenecks during parallel access. In contrast, DAOS’s versos of POSIX uses optimistic conflict resolution, which means DAOS starts writes assuming there’s no conflict, but if one occurs it handles the conflict in real time. Of course with all the metadata byte addressable and in PMEM this doesn’t take up a lot of (IO) time.

As mentioned earlier, DAOS data protection uses mirror-replicas. However, unlike most other major file systems, DAOS mirroring can be done at the object level. DAOS internally is an object store. Data organization on DAOS starts at the pool level, underneath that is data containers, and then under that are objects. Any object in DAOS can have its own mirroring configuration. DAOS is working towards supporting Erasure Coding as another form of data protection for a future release.

DAOS performance

There’s a new storage benchmark that was developed specifically for HPC, called the IO500. The IO500 benchmark simulates a number of different HPC workloads, measures performance for each of them, and computes an (aggregate) performance score to rank HPC storage systems.

IO500 ranks system performance using two lists: one is for any sized configuration that typically range from 50 to 1000s of nodes and their other list limits the configuration to 10 nodes. The first performance ranking can sometimes be gamed by throwing more hardware into a cluster. The 10 node rankings are much harder to game this way and from our perspective, show a fairer comparison of system performance.

As presented (virtually) at ISC 2020, DAOS took the top spot on the IO500 any size configuration list and performed better than 2X the next best solution. And on the IO500 10 node list, Intel’s DAOS configuration, Texas Advanced Computing (TAC) DAOS configuration, and Argonne Nat Labs DAOS configuration took the top 3 spots and had 3X better performance than the next best, non-DAOS storage system.

The Argonne National Labs has already stated that they will be using DAOS in their new HPC system to be deployed in the near future. Early specifications for storage at the new Argonne Lab required support for 230PB of data and 25TB/sec of bandwidth.

The podcast ran ~43 minutes. Kelsey was great to talk with and very knowledgeable about HPC systems and HPC IO in particular. Matt has worked at Argonne in the past so understood these systems better than I. Sadly, we lost Matt’s end of the conversation about 1/2 way into the recording. Both Matt and I thought that DAOS represents the birth of a new generation of HPC storage. Listen to the podcast to learn more.


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Kelsey Prantis, Senior Software Engineering Manager, Intel

 Kelsey Prantis heads the Extreme Storage Architecture and Development division at Intel Corporation. She leads the development of Distributed Asynchronous Object Storage (DAOS), an open-source, low-latency and high IOPS object store designed from the ground up for massively distributed Non-Volatile Memory (NVM).

She joined Intel in 2012 with the acquisition of Whamcloud, where she led the development of the Intel Manager for Lustre* product.

Prior to Whamcloud, she was a software developer at personal genomics and biotechnology company 23andMe.

Prantis holds a Bachelor’s degree in Computer Science from Rochester Institute of Technology

103: Greybeards talk scale-out file and cloud data with Molly Presley & Ben Gitenstein, Qumulo

Sponsored by:

Ray has known Molly Presley (@Molly_J_Presley), Head of Global Product Marketing for just about a decade now and we both just met Ben Gitenstein (@Qumulo_Product), VP of Products & Solutions, Qumulo on this podcast. Both Molly and Ben were very knowledgeable about the problems customers have with massive data troves. 

Molly has been on our podcast before (with another company, (see: GreyBeards talk HPC storage with Molly Rector, CMO & EVP, DDN ). And we have talked with Qumulo before as well (see: GreyBeards talk data-aware, scale-out file systems with Peter Godman, Co-founder & CEO, Qumulo)

Qumulo has a long history of dealing with customer issues with data center application access to data, usually large data repositories, with billions of small or large files, they have accumulated over time.  But recently Qumulo has taken on similar problems in the cloud as well.

Qumulo’s secret has always been to allow researchers to run their applications wherever their data  resides. This has led Qumulo’s software defined storage to offer multiple protocol access as well as a completely native, AWS and GCP cloud version of their solution. 

That way customers can run Qumulo in their data center or in the cloud and have the same great access to data. Molly mentioned one customer that creates and gathers data using SMB protocol on prem and then, after replication, processes it in the cloud. 

Qumulo Shift

Ben mentioned that many competitive storage systems are business model focused. That is they are all about keeping customer data within their solutions so they can charge for capacity. Although Qumulo also charges for capacity, with the new <strong>Qumulo Shift</strong> service, customer can easily move data off Qumulo and into native cloud storage. Using Shift, customers can free up Qumulo storage space (and cost) for any data that only needs to be accessed as objects.

With Shift, customers can replicate or move on prem or in the cloud Qumulo file data to AWS S3 objects. Once in S3, customers can access it with AWS native applications, other applications that make use of AWS S3 data, or can have that data be accessible around the world.

Qumulo customers can select directories to Shift to an AWS S3 bucket. The Qumulo directory name will be mapped to a S3 bucket name and each file in that directory will be copied to an S3 object in that bucket with the same file name.

At the moment, Qumulo Shift only supports AWS S3. Over time, Qumulo plans to offer support for other public cloud storage targets for Shift.

Shift is based on Qumulo replication services. Qumulo has a number of patents on replication technology that provides for sophisticated monitoring, control and high performance for moving vast amounts of data.

How customers use Shift

One large customer uses Qumulo cloud file services to process seismic data but then makes the results of that analysis available to other clients as S3 objects. 

Customers can also take advantage of AWS and other applications that support objects only. For example, AWS SageMaker Machine Learning (ML) processes S3 object data. Qumulo customers could gather training data as files and Shift it to S3 objects for ML training.

Moreover, customers can use Shift to create AWS S3 object  backups, archives and DR repositories of Qumulo file data. Ben mentioned DevOps could also use Qumulo Shift via APIs to move file data to S3 objects as part of new application deployment.

Finally, using Shift to copy or move file data to AWS S3, makes it ideal for collaboration by researchers, analysts and just about other entity that needs access to data. 

The podcast ran ~26 minutes. Molly has always been easy to talk with and Ben turned out also to be easy to talk with and knew an awful lot  about the product and how customers can use it. Keith and I enjoyed our time with Molly and Ben discussing Qumulo and their new Shift service. Listen to the podcast to learn more.

Ben Gitenstein, VP of Products and Solutions, Qumulo

Ben Gitenstein runs Product at Qumulo. He and his team of product managers and data scientists have conducted nearly 1,000 interviews with storage users and analyzed millions of data points to understand customer needs and the direction of the storage market.

Prior to working at Qumulo, Ben spent five years at Microsoft, where he split his time between Corporate Strategy and Product Planning.

Molly Presley, Head of Global Product Marketing, Qumulo

Molly Presley joined Qumulo in 2018 and leads worldwide product marketing. Molly brings over 15 years of file system and archive technology leadership experience to the role. 

Prior to Qumulo, Molly held executive product and marketing leadership roles at Quantum, DataDirect Networks (DDN) and Spectra Logic.

Presley also created the term “Active Archive”, founded the Active Archive Alliance and has served on the Board of the Storage Networking Industry Association (SNIA).

(Updated due to formatting problem, The Eds.)

0102 GreyBeards talk big memory data with Charles Fan, CEO & Co-founder, MemVerge

It’s been a couple of months since we last talked with a startup, so the GreyBeards thought it was time. We reached out to Charles Fan (@CharlesFan14), CEO and Co-Founder of MemVerge to find out about their big memory solution or as Charles likes to call it, “software defined (big) memory”. Although neither Matt or I had ever talked with Charles before, he’s been just about everywhere in the storage industry throughout his career.

If you have been following my RayOnStorage blog you will have seen a post (Need memory, Intel’s Optane DC PM to the rescue) last year on Intel’s new Persistent Memory solutions using 3D XPoint, called Optane DC PM (data center, persistent memory) . At the announcement Intel made available a couple of ways customers could use Optane DC PM (PMem).

Optane DC PM primer

Native Optane DC PM access modes include:

  • A Memory Mode, which has Pmem emulating a large volatile memory space and uses a defined ratio of DRAM to PMem as a cache to access the Optane DC PM memory behind it.
  • An Application Direct (AppDirect) Mode which supports two sub-modes: a storage device mode that uses Pmem to emulate a persistent, 4KB block storage device; and a byte addressable, persistent memory address space mode that uses Pmem to emulate a large, non-volatile memory space . AppDirect memory content persists across boots, power failures and other system crashes.

Native PMem modes are selectected in the BIOS and are deployed at Boot time. Optane DC PM on a server can be split up into any of the three modes. And currently with Optane DC PM (Gen 1), a single server can have up to 6TB of DC PM which will go up to 8TB with Optane DC PM Gen 2 coming out later this year.

MemVerge Memory Machine

MemVerge has written a “software defined memory” service called the Memory Machine, that sits above the Intel Optane DC PM in server(s) and provides application access AND data services for PMem. .

Charles likens their Memory Machine to what VMware did for CPU cores, ie. they provide memory virtualization. This, Charles believes will bring on the age of Big Memory applications. He feels that PMem, with Memory Machine on top of it, will eliminate the need for high performance, tier 0 storage. Tier 0 storage is ~$10B market today, which he sees shifting from networked storage to PMem solutions. 

Memory Machine Data Services

One of the data services that the Memory Machine offers is a Pmem snapshot service. PMem thick or thin snapshots can be taken any (infinite) number of times (for thick snapshots storage space availability may limit their number) and can be taken up to once per minute. PMem thin snapshots take little time to accomplish and are very PMem space efficient but thick snapshots are a PMem to PMem copy of data, which will take longer to accomplish and will take double the memory of the original PMem being snapshot.

One significant use case for Pmem snapshots is for checkpoint crash recovery. Charles mentioned many securities and financial analysis firms use KDB as streaming data base service to monitor/analyze market activity and provide automated trading and other market services. These firms are always trying to gain an advantage through speed and reduced latency and as a result have moved their time sensitive processing to use in memory data structures/databases.

However, because checkpointing for crash recovery takes time, they usually checkpoint in memory databases only once a day (after market close) and maintain a log of database transactions on SSD. If there’s a system crash, they reload the last checkpoint and re-play all the transaction logs since that checkpoint to bring their in memory database back to the point of crash. Due to the number of transactions these firms do, this sort of crash recoverys can take hours.

With Memory Machine, these customers can take in memory checkpoints every minute and in the event of a crash, only have to re-play a minutes worth of transaction logs which could be done in no time to get back up

Other environments do similar checkpoint crash recoveries all of which could also take advantage of PMem snapshots to take more frequent checkpoints. Charles mentioned Rendering farms on the podcast but long scientific simulations (HPC) and others use checkpoints for crash recovery.

Another data (or application) service offered by Memory Machine is application cloning. Most in memory applications are single threaded. meaning they can only take advantage of a single CPU core (thread). In order to speed up processing, customers must shard (split up) or copy their database and application onto other servers/CPU/cores to provide more processing power. Memory Machine can use its thick or thin snapshots to clone applications in seconds.

Charles also mentioned that Memory Machine offers PMem dynamic reconfiguration. That is instead of having to make BIOS changes and re-boot server(s) to re-allocate PMem across different applications, Memory Machine is allocated 100% of the PMem at boot time but then, on demand, anytime its operating, operators using MemVerge’s GUI/CLI can carve Pmem up into any number of application memory spaces. That is as application demand for in memory data changes, operations can use the Memory Machine to re-allocate PMem to keep up.

Memory Machine also supports PMem clustering or scaling across servers. With the current 6TB (and soon 8TB) per server PMem limit, some customer applications still run out of memory. Memory Machine is able to cluster or aggregate PMem across up to 32 servers to support a single larger, PMem address space of 192TB (Gen 1) or 256TB (Gen 2) DC PM. The Memory Machine uses an RDMA (RoCE Ethernet or InfiniBand) cluster interconnect which adds ~1 microsecond of overhead to access PMem in another server. This comes with PMem automatic data tiering using DRAM, local (on the server) PMem and remote (across cluster interconnect) PMem.

Charles mentioned another data service provided by Memory Machine is (Synch or Asynch) replication. One use case for replication is to create a Pub-Sub service for market data.

Charles believes that in memory databases and data processing workloads are just starting to become popular these days. Besides KDB and rendering, other data processing such as AI training/inferencing, Reddis applications, and other database systems are able to take advantage of in memory, large data structures to speed up their data processing

MemVerge’s EAP (early access program) opened up recently (5/19/2020). Charles suggested anyone using large, in memory data processing, take a look at what the Memory Machine can do and contact them to sign up.

The podcast runs ~45 minutes. Charles was very articulate as well as knowledgeable about the technology and its applications. He was great to talk tech with. Matt and I had a fun time talking Optane DC PM and Memory Machine functionality/applications with him. Listen to the podcast to learn more.

Charles Fan, CEO & Co-founder, MemVerge

Charles Fan is co-founder and CEO of MemVerge. Prior to MemVerge, Charles was a SVP/GM at VMware, founding the storage business unit that developed the Virtual SAN product.

Charles also worked at EMC and was the founder of the EMC China R&D Center. Charles joined EMC via the acquisition of Rainfinity, where he was a co-founder and CTO.

Charles received his Ph.D. and M.S. in Electrical Engineering from the California Institute of Technology, and his B.E. in Electrical Engineering from the Cooper Union.

098: GreyBeards talk data protection & visualization for massive unstructured data repositories with Christian Smith, VP Product at Igneous

Sponsored By:

Even before COVID-19 there was a lot of file data being created and mined, but with the advent of the pandemic, this has accelerated considerably. As such, it seemed an appropriate time to talk with Christian Smith, VP of Product at Igneous, (@IgneousIO) a company that targets the protection and visibility of massive quantities of unstructured data, on premise, in the cloud, or just about anywhere else it may live.

Let me state at the outset, that my belief had always been, that you don’t backup 10PB of data, rather you bite the (big expense) bullet to replicate it and hope for the best. After talking with Christian and Igneous I am going to have to modify that belief by a couple of more orders of magnitude.

All this data is coming from: LIDAR, RADAR, audio, video, pictures, medical film, MRI/CAT Scans, etc., and as noted above, it’s exploding. Christian talked about one customer of theirs that supplies aerial photography/LIDAR/RADAR scans of areas on request. This can used to better understand crop, forest, wildlife, land health and use. One surprise Igneous found with this customer is that the data is typically archived after first use, but within a month or so it’s moved back online for some other purpose.

Igneous heritage

Many of the people who started up and currently work at Igneous have been around file storage for some time having, primarily coming from (Dell EMC) Isilon, NetApp, Qumulo and other industry heavyweights. When they started Igneous, they realized the world didn’t need another NAS box or file system. Rather, with the advent of 10-100PB unstructured data farms, what was needed was an effective way to protect and understand that data.

When they considered how to protect and visualize 100PB of unstructured data, the only they found to do this was to build a scale-out solution that used on premise and cloud infrastructure and was offered as a service.

Igneous DataProtect solution

With 10PB or 100PB of files, located across a gaggle of heterogeneous file servers, with billions of files across ~100s of servers, each of with has ~1K or more file shares, just scanning all the file servers would take weeks, if not longer and then you need to move the data someplace to protect it. Seems like an impossible task.

Igneous immediately figured out the first thing they needed was a radically new, scale out architecture to rapidly scan of the file servers. Thus was born ActiveScan. Christian said it was designed to scan a trillion files and they have customers with a billion files using their service today. ActiveScan doesn’t use NFS/SMB/Object (S3) access protocols to talk with file servers rather it uses internal APIs to access file metadata. DataProtect currently supports APIs for NetApp, Dell EMC Isilon, Pure FlashBlade, Qumulo, Gluster, Lustre, & GPFS (IBM Spectrum Scale) file systems. They use ActiveScan to build a file index database.

Their other major concern was hot to move PBs of data rapidly across to the cloud and other locations. Again they created a scale out, multi-threaded service to do this and also made use of internal APIs rather than standard file or object protocols. This became IntelliMove. That same customer above with billions of files, has 6PB of file data to protect.

Normal data movement is fine for largish, files but bogs down with lots of small files or extremely large files to back up. DataProtect gathers together small files into a large chunks and splits up extremely large files into smaller chunks and moves these chunks to secondary storage.

Data expiration is another problem, especially when you chunk files together. Here they came up with an intelligent garbage collection algorithm which only collects free space when it makes the most sense but deletes data access at the time of expiration.

DataProtect uses a cloud based, SaaS control plane that manages and coordinates its activities across data centers, sites and cloud instances. It also has a client VM (OVA, with 8 core CPU, 32GB DRAM, ~100MB) that runs in the customers infrastructure, on site, in CoLo’s or in the cloud that is used to scan-move-protect customer unstructured data. If more scan and data movement performance is needed, the VM can spawn additional threads automatically and more VMs can be added to provide even more throughput.

DataDiscover solution

The other service that Igneous offers is DataDiscover a data visualization tool. DataDiscover uses ActiveScan and its database to provide customers a way to understand the file data that resides in their massive unstructured data farms across the data center, cloud or wherever else it resides.

We didn’t discuss this solution as much but having a way to better understand the files in a 10-100PB unstructured data farm could be very useful and a great way to keep that 100PB from growing to 1EB faster than it has too.

As part of their outreach to the world, Igneous is giving away free DataProtect services to organizations that are focused on COVID-19 research. Check out their offer here

The podcast ran ~24 minutes. Christian was extremely knowledgeable about the problems that happen with very large unstructured data farms and how Igneous solutions can provide a better way to protect and visualize that data. Matt and I had a fun time discussing Igneous’s approach with Christian. Listen to the podcast to learn more.

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Christian Smith, VP Product at Igneous

Christian is VP of Product, responsible for product management, solutions, and customer success. Prior to Igneous, Christian spent 15 years running field engineering organizations at EMC, Isilon Systems, NetApp and Silicon Graphics.

Christian has been working with organizations that work with file data since working at Silicon Graphics. Before that Christian was co-founder of a small management consulting company associated with Y2K and deregulation.

Christian received dual bachelor’s degrees in Chemistry and Computer Science from the University of Missouri-Columbia. Christian is an avid camper, skier and traveler and has long since traveled through all of the continental 48 states.

097: GreyBeards talk open source S3 object store with AB Periasamy, CEO MinIO

Ray was at SFD19 a few weeks ago and the last session of the week (usually dead) was with MinIO and they just blew us away (see videos of MinIO’s session here). Ray thought Anand Babu (AB) Periasamy (@ABPeriasamy), CEO MinIO, who was the main presenter at the session, would be a great invite for our GreyBeards podcast. Keith and I had a ball talking with AB.

Why object store

There’s something afoot in object storage space over the last year or so. It seems everybody is looking to deploy object store whether that be on prem, in CoLo facilities and in the cloud. It could be just the mass of data coming online but that trend has remained the same for years no. No it’s something else.

It all starts with AWS and S3. Over the last couple of years AWS has been rolling out new functionality that only works with S3 and this has been driving even more adoption of S3 as well as other object storage solutions.

S3 compatible object stores are available in just about every cloud service, available from major (and minor) storage vendors and in open source from MinIO.

Why S3 is so popular

Because object store is accessed via RestFUL interfaces, traditionally most implementations used their own API to access it. But when AWS created S3 (simple storage service) with their own API/SDK to access it, it somehow became the de-facto standard interface for all other object stores. S3 compatibility became a significant feature that all object stores had to support.

Sometime after that MinIO came into existence. MinIO provides a 100% open source, fully AWS S3 compatible object store that you can run anywhere on prem, in CoLo facilities and indeed in the cloud. In fact, there exist customers that run MinIO in AWS AB says this is probably just customers using a packaged software solution which happens to include MinIO but it’s nonetheless more expensive than AWS S3 as it uses EC2 instances and EBS storage to create an object store

Customers can access MinIO object stores with the AWS S3 SDK or the MinIO SDK. and you can access AWS S3 storage with AWS S3 SDK or use MinIO SDK. Occosionally, AWS S3 updates have broken MinIO’s SDK but these have been later fixed by AWS. It seems AWS and MinIO are on good terms.

AB mentioned that as customers get up to a few PBs of AWS S3 storage they often find the costs to be too high. It’s at this point that they start looking at other object storage solutions. But because MinIO is 100% S3 compatible and it’s open source many of these customers deploy it in their own data center facilities or in colo environments.

For those customers that want it, MinIO also offers an S3 gateway. With the gateway on prem customers can use S3 or standard file services to access S3 object storage located in the cloud. The gateway also works in the public cloud and can support both AWS s3 as well as Microsoft Blob storage as a backend.

MinIO matches AWS S3 features

AWS S3 has a number of great features and MinIO has matched or exceeded them all, step by step. AWS S3 has cross region replication options where customers can replicate S3 data from one region to another. MinIO supports both asynchronous replication of S3 data and synchronous replication (using RADIO).

But MinIO adds support for erasure coding within a fault domain. Default is Nx2 erasure coding which duplicates all your data so as long as 1/2 of your servers and storage are available you continue to have access to all your data. But this can be configured down like 12+4 where data is split accross 16 servers any four of which can fail and you can still access data.

AWS customers can use a Snowball (standalone storage device) to transfer data to or from S3 storage. AWS Snowball implements a subset of S3 API and requires a NAS staging area of equivalent size to migrate data out of S3. MinIO has support for Snowball’s limited S3 API and as such, Snowball’s can be used to migrate data into or out of MinIO. MinIO has a blog post which describes their support for AWS Snowball.

AWS also offers S3 Lambda services or server less computing services where compute services can be invoked when data is loaded in a bucket and then turned off when no longer needed. AWS Lambda depends on AWS messaging and other services to work properly. But MinIO supports Lambda like functionality using other open source services. AB mentions MQTT and Kafka services. MinIO has another blog post discussing their Lambda like services based on Kafka.

AWS recently implemented Snowflake a SQL database server for unstructured data that uses S3 storage to hold data. Ray and Keith almost choked on that statement as unstructured data and databases never used to be uttered in the same breath. But what AWS has shown was that you can use object store for database data as long as you are willing to load the table into memory and process it there and then unload any modified table data back into the object store. Indexing of the object data seems to be done as the data is being loaded and is also being done in a (random IO) cache or in memory and once done can also be unloaded into the object store.

Now Snowflake uses S3 but it’s not available on prem. MinIO has a number of data base partners that make use of their object store as a backend to host a Snowflake like service onprem. AB mentioned Spark and Splunk but there are others as well.

We ended up the discussion with what does it mean to have 20K stars on GitHub. AB said if you did a java script getting 20K stars would be easy but you just don’t see this sort of open source popularity for storage systems. He said the number is interesting but the growth rate is even more interesting.

The podcast runs ~47 minutes. AB was a great to talk tech with. Keith and I could have talked all afternoon with AB. It was very hard to stop the recording as we could have talked with him for another hour or more. AB said he doesn’t like to do podcasts or videos but he had no problem with us firing away questions. Listen to the podcast to learn more.

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Anand Babu Periasamy, CEO MinIO

AB Periasamy is the CEO and co-founder of MinIO. One of the leading thinkers and technologists in the open source software movement, AB was a co-founder and CTO of GlusterFS which was acquired by RedHat in 2011. Following the acquisition, he served in the office of the CTO at RedHat prior to founding MinIO in late 2015. AB is an active angel investor and serves on the board of H2O.ai and the Free Software Foundation of India.

He earned his BE in Computer Science and Engineering from Annamalai University.