Analyst defined storage
Analyst defined storage
Once again Keith and I are talking K8s storage, only this time it was object storage. Anand Babu (AB) Periasamy, Co-founder and CEO of MinIO, has been on our show a couple of times now and its always an insightful discussion. He’s got an uncommon perspective on IT today and what needs to change.
Although MinIO is an open source, uber-compatible, S3 object store, AB more often talks like a revolutionary, touting the benefits of containerization, scale and automation with K8s. Object storage is just one of the vehicles to help get there. Listen to the podcast to learn more.MinIO22-01-transcriptDownload
We started our discussion on the changing role of object storage in applications. Object storage started out as an archive solution. But then, over time, something happened, modern database startups adopted object storage to hold primary data, then analytics moved over to objects in a big way, and finally AI/ML came out with an unquenchable thirst for data and object storage was its only salvation.
Keith questioned the use of objects in analytics. Both AB and I pointed out that Splunk (and Spark) fully supported objects. But Keith said R (and Python) data scientists prefer to use protocols they learned in school, and these were all about (CSV, JPEGs, JSON) files. AB said what usually happens is this data is stored as object storage and then downloaded onto local disk as files to be processed. That’s not to say, that R or Python can’t process objects directly, but when they don’t, the ultimate source of data truth is object storage.
Somehow, we got onto the multi-cloud question. AB said the multi-cloud is really all about containers and K8s. When customers talk multi-cloud, what they really mean is they want applications that can run anywhere, in any cloud, on premise, or anyplace else for that matter.
I thought multi cloud was a DR solution. But AB reiterated it’s more a solution to vendor lock-in. What containerization gives IT is the option (ability) to run applications anywhere, but IT is not obligated to execute that option unless it makes sense
AB said that dev today doesn’t develop apps in the cloud anymore. They develop locally using minikube, once it’s working there they then add CI/CD tool chains and then move it to its final resting place (the cloud or wherever it ultimately needs to run). It turns out, containers, YAML files, scripts etc. are small and trivial to upload, migrate, or move to any internet location. And with ubiquitous K8s support available everywhere, they can move anywhere unchanged.
But where’s the data. AB said anywhere the app executes. It’s never moved, it takes too much time and effort to move this amount of data. But as applications move, any data it generates grows in that location over time.
We next turned to how MinIO was supported in K8s. AB mentioned they have a DirectPV CSI driver that creates a distributed PV to support MinIO services on local disks. In this way, containers needing access to MinIO S3 object storage can directly allocate data to user storage.
Then we asked about opinionated stacks. AB said most customers don’t want these. They may have some value in preserving an infrastructure environment but they’re better off transitioning to containerization and build any stack within those containers and the K8s cluster services.
On the other hand, MinIO object storage is available with the same S3 API, in bare metal, on VMware, OpenShift, K8s, every public cloud and most private clouds, as well. The advantage of the same, single storage interface, available everywhere can’t be beat.
MinIO recently closed a new funding round of $103M. AB mentioned they had new investments from Intel and Softbank, but I was more interested in plans he had for the new cash. And Keith asked where the new funding left MinIO with respect to its competitors in this space.
AB said it was never about the money, it was more about what you did with your team that mattered in the long run. AB’s imperative was to enter an existing market with a better product and succeed with that. Creating a new market plus a new product always cost more, takes longer and is riskier.
As for the new funds, there are really two ways to go: 1) improve the current product or 2) create a new one. My sense is that AB leans towards improving the current product.
For instance, MinIO is often asked to support a different object storage API. But AB’s perspective is that S3 was an early bet that paid off well by becoming the de facto standard for object storage. Supporting another API would divide his resources and probably make their current product worse not better. AB mentioned they are getting 1.1M downloads of their Docker container version so they seem to be succeeding well with the current product
AB Periasamy is the co-founder and CEO of MinIO, an open-source provider of high performance, object storage software. In addition to this role, AB is an active investor and advisor to a wide range of technology companies, from H2O.ai and Manetu where he serves on the board to advisor or investor roles with Humio, Isovalent, Starburst, Yugabyte, Tetrate, Postman, Storj, Procurify, and Helpshift. Successful exits include Gitter.im (Gitlab), Treasure Data (ARM) and Fastor (SMART).
AB co-founded Gluster in 2005 to commoditize scalable storage systems. As CTO, he was the primary architect and strategist for the development of the Gluster file system, a pioneer in software defined storage. After the company was acquired by Red Hat in 2011, AB joined Red Hat’s Office of the CTO. Prior to Gluster, AB was CTO of California Digital Corporation, where his work led to scaling of the commodity cluster computing to supercomputing class performance. His work there resulted in the development of Lawrence Livermore Laboratory’s “Thunder” code, which, at the time was the second fastest in the world.
AB holds a Computer Science Engineering degree from Annamalai University, Tamil Nadu, India.
Jan 27 2022
[Ray’s sorry about his audio, it will be better next time he promises, The Eds] This was supposed to be the year where we killed off COVID for good. Alas, it was not to be and it’s going to be with us for some time to come. However, this didn’t stop that technical juggernaut we call the GreyBeards on Storage podcast.
Once again we got Keith, Matt and Ray together to discuss the past year’s top 3 technology trends that would most likely impact the year(s) ahead. Given our recent podcasts, Kubernetes (K8s) storage was top of the list. To this we add AI-MLops in the enterprise and continued our discussion from last year on how Covid & WFH are remaking the world, including offices, data centers and downtowns around the world. Listen to the podcast to learn more.
For some reason, we spent many of this year’s podcasts discussing K8s storage. TK8s was never meant to provide (storage) state AND as a result, any K8s data storage has had to be shoe horned in.
Moreover, why would any IT group even consider containerizing enterprise applications let alone deploy these onto K8s. The most common answers seem to be automatic scalability, cloud like automation and run-anywhere portability.
Keith chimed in with enterprise applications aren’t going anywhere and we were off. Just like the mainframe, client-server and OpenStack applications before them, enterprise apps will likely outlive most developers, continuing to run on their current platforms forever.
But any new apps will likely be born, live a long life and eventually fade away on the latest runtime environment. which is K8s.
Matt mentioned hybrid and multi-cloud as becoming the reason-d’etre for enterprise apps to migrate to containers and K8s. Further, enterprises have pressing need to move their apps to the hybrid- & multi-cloud model. AWS’s recent hiccups, notwithstanding, multi-cloud’s time has come.
Ray and Keith then discussed which is bigger, K8s container apps or enterprise “normal” (meaning virtualized/bare metal) apps. But it all comes down to how you define bigger that matters, Sheer numbers of unique applications – enterprise wins, Compute power devoted to running those apps – it’s a much more difficult race to cal/l. But even Keith had to agree that based on compute power containerized apps are inching ahead.
AI /MLops in the enterprise was up next. For me the most significant indicator for heightened interest in AI-ML was VMware announced native support for NVIDIA management and orchestration AI-MLops technologies.
Just like K8s before it and VMware’s move to Tanzu and it’s predecessors, their move to natively support NVIDIA AI tools signals that the enterprise is starting to seriously consider adding AI to their apps.
We think VMware’s crystal ball is based on
We had some discussion as to where AMD and Intel will end up in this AI trend.. Consensus is that there’s still space for CPU inferencing and “some” specialized training which is unlikely to go away. And of course AMD has their own GPUs and Intel is coming out with their own shortly.
And then there was COVID and WFH. COVID will be here for some time to come. As a result, WFH is not going away, at least not totally any time soon. And is just becoming another way to do business.
WFH works well for some things (like IT office work) and not so well for others (K-12 education). If the GreyBeards were into (non-crypto) investing, we’d be shorting office real estate. What could move into those millions of square feet (meters) of downtime office space is anyones guess. But just like the factories of old, cities and downtowns in particular can take anything and make it useable for other purposes.
That’s about it, 2021 was another “interesteing” year for infrastructure technology. It just goes to show you, “May you live in interesting times” is actually an old (Chinese) curse.
Keith is a IT thought leader who has written articles for many industry publications, interviewed many industry heavyweights, worked with Silicon Valley startups, and engineered cloud infrastructure for large government organizations. Keith is the co-founder of The CTO Advisor, blogs at Virtualized Geek, and can be found on LinkedIN.
Matt Leib has been blogging in the storage space for over 10 years, with work experience both on the engineering and presales/product marketing. His blog is at Virtually Tied to My Desktop and he’s on LinkedIN.
Ray is the host and co-founder of GreyBeardsOnStorage and is President/Founder of Silverton Consulting, and a prominent (AI/storage/systems technology) blogger at RayOnStorage.com. Signup for SCI’s free, monthly industry e-newsletter here, published continuously since 2007. Ray can also be found on LinkedIn
Jan 06 2022
Keith and I had an interesting discussion with Alex Chircop (@chira001), CEO of Ondat, a kubernetes storage provider. They have a high performing system, laser focused on providing storage for k8s stateful container applications. Their storage is entirely containerized and has a number of advanced features for data availability, performance and security that developers need the run stateful container apps. Listen to the podcast to learn more.Ondat-21-12-transcriptDownload
We started by asking Alex how Ondats different from all the other k8s storage solutions out there today (which we’ve been talking with lately). He mentioned three crucial capabilities:
Ondat creates a data mesh (storage pool) out of all storage cluster nodes. Container volumes are carved out of this data mesh and at creation time, data and the apps that use them are co-located on the same cluster nodes.
At volume creation, Dev can specify the number of replicas (mirrors) to be maintained by the system. Alex mentioned that Ondat uses synchronous replication between replica clusters nodes to make sure that all active replica’s are up to date with the last IO that occurred to primary storage.
Ondat compresses all data that goes over the network as well as encrypts data in flight. Dev can easily specify that the data-at-rest also be compressed and/or encrypted. Compressing data in flight helps supply consistent performance where networks are shared.
Alex also mentioned that they support both the 1 reader/writer, k8s block storage volumes as well as multi-reader/multi-writer, k8s file storage volumes for containers.
In Ondat each storage volume includes a mini-brain used to determine primary and replica data placement. Ondat also uses desegregated consensus to decide what happens to primary and replica data after a k8s split cluster occurs. After a split cluster, isolated replica’s are invalidated and replicas are recreate, where possible, in the surviving nodes of the cluster portion that holds the primary copy of the data.
Also replica’s can optionally be located across AZs if available in your k8s cluster. Ondat doesn’t currentlysupport replication across k8s clusters.
Ondat storage works on any hyperscaler k8s solution as well as any onprem k8s system. I asked if Ondat supports VMware TKG and Alex said yes but when pushed mentioned that they have not tested it yet.
Keith asked what happens when things go south, i.e., an application starts to suffer worse performance. Alex said that Ondat supplies system telemetry to k8s logging systems which can be used to understand what’s going on. But he also mentioned they are working on a cloud based, Management-aaS offering, to provide multi-cluster operational views of Ondat storage in operation to help understand, isolate and fix problems like this.
Keith mentioned he had attended a talk by Google engineers that developed kubernetes and they said stateful containers don’t belong under kubernetes. So why are stateful containers becoming so ubiquitous now.
Alex said that may have been the case originally but k8s has come a long way from then and nowadays as many enterprises shift left enterprise applications from their old system environment to run as containers they all require state for processing. Having that stateful information or storage volumes accessible directly under k8s makes application re-implementation much easier.
What’s a typical Ondat configuration? Alex said there doesn’t appear to be one. Current Ondat deployments range from a few 100 to 1000s of k8s cluster nodes and 10 to 100s of TB of usable data storage.
Ondat has a simple pricing model, licensing costs are determined by the number of nodes in your k8s cluster. There’s different node pricing depending on deployment options but other than that it’s pretty straightforward.
Alex Chircop is the founder and CEO of Ondat (formerly StorageOS), which makes it possible to easily deploy and manage stateful Kubernetes applications with persistent data volumes. He also serves as co-chair of the CNCF (Cloud Native Computing Foundation) Storage Technical Advisory Group.
Alex comes from a technical background working in IT that includes more than 10 years with Nomura and Goldman Sachs.
Dec 07 2021
We had some technical difficulties with Matt getting on the podcast so, Ray had to fly solo. This month we continue our investigations into K8s storage with a discussion with Tad Lebeck (@TadLebeck) US CTO, ionir, a software defined storage system that only runs under K8s. ionir Kubernetes Data Services platform is an outgrowth of Reduxio a “tin-wrapped” software defined storage system which pivoted to K8s as the environment to target and left the tin behind.
ionir offers a deduplicating, continuous data protection storage system for PVs (persistent volumes) under K8s that uses 3 way mirroring, across data nodes for data protection. Their solution offers a number of unique services that we haven’t seen in other K8s storage systems. Listen to the podcast to learn more.Ionir-21-11-transcriptDownload
Tad opened with a long spiel on what ionir is and we spent the next 40 minutes unpacking that to understand what exactly they were doing.
Let’s start with why stateful containers are all the rage these days. Tad had a slightly different rationale than we’ve heard before. From his perspective, it all comes from current enterprise applications that used database servers/machines. As these apps are re-factored to run as K8s containerized micro services, developers need and want their data be containerized right along with the application.
ionir constructs a block storage system across K8s data nodes or K8s worker nodes with direct attached storage. In the cloud, this storage can be ephemeral (storage that only exists as long as the compute instance operates) or normal block storage (e.g., EBS in AWS). It’s unclear how ephemeral works on-prem. But in any case, they cluster together a set of data nodes into one massive block storage and map PVs onto that. K8s data nodes can be added to the ionir cluster while it’s operating.
As mentioned earlier, they use 3-way mirroring for data protection and ionir insures the 3 copies are stored on different data nodes. As such, when one data node goes down, copies of PV data are available from the other 2 nodes and the data can then be rewritten elsewhere to insure 3-way mirroring continues. We suppose this means a minimum configuration requires at least 3 data nodes.
ionir also provides deduplicating block storage, which should theoretically reduce physical storage footprint for any PV. Data blocks are deduplicated across the cluster. ionir also has a metadata service (also 3 way replicated, to different data space) that records the manifest for all blocks associated with a PV, their hashes and (logical/physical) locations.
There was no mention of data compression or encryption so those are probably not present. We find deduplication very effective for backup storage but less effective for primary storage. Any deduplication ratio for ionir primary storage is likely specific to data being stored, i.e. columnar database, row database, text, office files, etc. Each of these would likely have different dedupe ratios for primary storage.
Furthermore, ionir supplies continuous data protection (CDP) for PV data. PV data written to ionir is immutable, i.e., never modified AND they keep previous versions of PV blocks in storage until they age out. This allows ionir to provide any prior version (well most recent ones) of a PV. ionic uses a timestamp to distinguish different PV versions. So, if ransomware attacked your site, users could ask for a PV version just prior to the time of the attack and you’d have that version of the PV to restart operations. Customer’s can limit how far back ionir saves prior versions of blocks for PVs.
Having CDP for PVs, makes DevOps qualification and testing significantly faster. Normally DevOps would need to copy production data to test environments in order to validate new app code. But ionir can easily instantiate a separate copy of any PV (at any time in their saved set) in a matter of seconds. This can take DevOps deployment testing down from days to minutes or less.
In addition, ionir can teleport PV data to other, remote K8s clusters running ionir. Essentially, this copies PV metadata and it’s “hot” blocks over to any remote ionir cluster. During teleportation, the remote cluster can access PV data as soon as all PV metadata has been copied. The remote site accesses this PV data from the originating cluster (albeit much slower than accesses within the cluster) while “hot” blocks are being copied. Any writes, at the remote site, to PV data would be considered new data, deduplicated at the remote site, and only available at the remote site. Somewhat surprisingly, all of the PV’s data is never copied to the remote system, leaving the PV in a permanent teleported access mode.
Not sure we like the implications of teleporting PVs, from a data integrity perspective. It does make for near-instant access to PV data from other clusters and offers a solution to data gravity (it takes forever to move TB of data across the web), it’s incomplete, as the data is never fully copied to the remote site. Once hot blocks have been copied, remote cluster PV access should run faster. But If there’s 20% of the requested blocks, not in the heat map, those IOs will take 100s mseclonger, depending on wire distance between the sites, to perform. And the write’s at the remote site cause the two copies (one at source site and one at remote site) of the PV to diverge.
Their storage system is priced on a per data node basis which makes it easy to price out their various deployment options. And it works on any K8s standard environment, although Tad admits they haven’t tested VMware Tanzu yet, but they have tested it on GCP, Microsoft Azure, AWS, and Red Hat OpenShift.
They offer a fully functional free trial of ionir storage, only capped at the number of data nodes in use. So, if you only need a small amount of storage (ok 3 data nodes with 24 14TB SSDs each make for large amount of storage) for your K8s environment, you can probably run forever on the free version.
Tad Lebeck is a global technology executive with over two decades of experience in startups and large vendors. Prior to ionir, he founded and led Nuvoloso, an innovator in Kubernetes data services. Earlier, Lebeck served as CTO at Huawei Symantec Technologies, Vice President at Symantec/Veritas, co-founder/CTO at Invio, and CTO at Legato Systems, where he helped create the modern enterprise data-protection market.
Tad was a founding member of the SNIA Technical Council. He earned an MS/CS from the University of Wisconsin, and a combined MBA from the Columbia, London, and HKU Schools of Business.
Nov 08 2021
Stateful containers are becoming a hot topic these days so we thought it a good time to talk to the CNCF (Cloud Native Computing Foundation) Rook team about what they are doing to make storage easier to use for k8s container apps. CNCF put us into contact with Sébastien Han (@leseb_), Ceph Storage Architect and Travis Nielsen (@STravisNielsen), both Principal Software Engineers at Red Hat and active on the Rook project. Rook is a CNCF “graduated” open source project just like Kubernetes, Prometheus, ContainerD, etc., this means it’s mature enough to run production workloads.
Rook is used to configure, deploy and manage a Red Hat Ceph(r) Storage cluster under k8s. Rook creates all the k8s deployment scripts to set up a Ceph Storage cluster as containers, start it and monitor its activities. Rook monitoring of Ceph operations can restart any Ceph service container or scale any Ceph services up/down as needed by container apps using its storage. Rook is not in the Ceph data path, but rather provides a k8s based Ceph control or management plane for running Ceph storage under k8s.
Readers may recall we talked to SoftIron, an appliance provider, for Ceph Storage in the enterprise for our 120th episode. Rook has another take on using Ceph storage, only this time running it under k8s,. Listen to the podcast to learn more.
The main problem Rook is solving is how to easily incorporate storage services and stateful container apps within k8s control. Containerized apps can scale up or down based on activity and storage these apps use needs the same capabilities. The other option is to have storage that stands apart or outside k8s cluster and control. But then tho container apps and their storage have 2 (maybe more) different control environments. Better to have everything under k8s control or nothing at all.
Red Hat Ceph storage has been available as a standalone storage solutions for a long time now and has quite the extensive customer list, many with multiple PB of storage. Rook-Ceph and all of its components run as containers underneath k8s.
Ceph supports replication (mirroring) of data 1 to N ways typically 3 way or erasure coding for data protection and also supports file, block and object protocols or access methods. Ceph normally consumes raw block DAS for it’s backend but Ceph can also support a file gateway to NFS storage behind it. Similarly, Ceph can offers an object storage gateway option. But with either of these approaches, the (NFS or object) storage exists outside k8s scaling and resiliency capabilities and Rook management.
Ceph uses storage pools that can be defined using storage performance levels, storage data protection levels, system affinity, or any combination of the above. Ceph storage pools are mapped to k8s storage classes using the Ceph CSI. Container apps that want to use storage would issue a persistent volume claim (PVC) request specifying a Ceph storage class which would allocate the Ceph storage from the pool to the container.
Besides configuring, deploying and monitoring/managing your Ceph storage cluster, Rook can also automatically upgrade your Ceph cluster for you.
We discussed the difference between running Rook-Ceph within k8s and running Ceph outside k8s. Both approaches depend on Ceph CSI but with Rook, Ceph and all its software is all running under k8s control as containers and Rook manages the Ceph cluster for you. When it’s run outside 1) you manage the Ceph cluster and 2) Ceph storage scaling and resilience are not automatic.
Sebastien Han currently serves as a Senior Principal Software Engineer, Storage Architect for Red Hat. He has been involved with Ceph Storage since 2011 and has built strong expertise around it.
Curious and passionate, he loves working on bleeding edge technologies and identifying opportunities where Ceph can enhance the user experience. He did that with various technology such as OpenStack, Docker.
Now on a daily basis, he rotates between Ceph, Kubernetes, and Rook in an effort to strengthen the integration between all three. He is one of the maintainers of Rook-Ceph.
Travis Nielsen is a Senior Principal Software Engineer at Red Hat with the Ceph distributed storage system team. Travis leads the Rook project and is one of the original maintainers, integrating Ceph storage with Kubernetes.
Prior to Rook, Travis was the storage platform tech lead at Symform, a P2P storage startup, and an engineering lead for the Windows Server group at Microsoft.
Oct 09 2021
The GreyBeards move up the stack this month with a talk on big data and data analytics with Sean Owen (@sean_r_owen), Data Science lead at Databricks and Apache Spark committee and PMC member. The focus of the talk was on Apache Spark.
Spark is an Apache Software Foundation open-source data analytics project and has been up and running since 2010. Sean is a long time data scientist and was extremely knowledgeable about data analytics, data science and the role that Spark has played in the analytics ecosystem. Listen to the podcast to learn more.
Spark is not an infrastructure solution as much as an application framework. It’s seems to be a data analytics solution specifically designed to address Hadoops shortcomings. At the moment, it has replaced Hadoop and become the go to solution for data analytics across the world. Essentially, Spark takes data analytic tasks/queries and runs them, very quickly against massive data sets.
Spark takes analytical tasks or queries and splits them up into stages that are run across a cluster of servers. Spark can use many different cluster managers (see below) to schedule stages across worker nodes attempting to parallelize as many as possible.
Spark has replaced Hadoop mainly because it’s faster and has a better, easier to use API. Spark was written in Scala which runs on JVM, but its API supports SQL, Java, R (R on Spark) and Python (PySpark). The latter two have become the defacto standard languages for data science and AI, respectively.
Storage for Spark data can reside on HDFS, Apache HBase, Apache Solr, Apache Kudu and (cloud) object storage. HDFS was the original storage protocol for Hadoop. HBase is the Apache Hadoop database. Apache Solr was designed to support high speed, distributed, indexed search. Apache Kudu is a high speed distributed database solution. Spark, where necessary, can also use local disk storage for interim result storage.
Spark supports three data models: RDD (resilient distributed dataset); DataFrames (column headers and rows of data, like distributed CSVs); and DataSets (distributed typed and untyped data). Spark DataFrame data can be quite large, it seems nothing to have a 100M row dataframe. Spark Datasets are a typed version of dataframes which are only usable in Java API as Python and R have no data typing capabilities.
One thing that helped speed up Spark processing over Hadoop, is its native support for in-memory data. With Hadoop, intermediate data had to be stored on disk. With in-memory data, Spark supports the option to keep it in memory, speeding up subsequent processing of this data. Spark data can be pinned or cached in memory using the API calls. And the availability of bigger servers with Intel Optane or just lots more DRAM, have made this option even more viable.
Another thing that Spark is known for is its support of multiple cluster managers. Spark currently supports Apache Mesos, Kubernetes, Apache Hadoop YARN, and Spark’s own, standalone cluster manager. In any of these, Spark has a main driver program that takes in analytics requests, breaks them into stages and schedules worker nodes to execute them..
Most data analytics work is executed in batch mode, offline, with incoming data stored on disk/flash someplace (see storage options above). But Spark can also run in real-time, streaming mode processing data streams. Indeed, Spark can be combined with Apache Kafka to process Kafka topic streams.
I asked about high availability (HA) characteristics, specifically for data. Sean mentioned that data HA is more of a storage consideration. But Spark does support HA for analytics jobs/tasks as a whole. As stages are essentially state-less tasks, analytics HA can be done by monitoring stage execution to completion and if needed, re-scheduling failed stages to run on other worker nodes.
Regarding Spark usability, it has a CLI and APIs but no GUI. Spark has a number of parameters (I counted over 20 for the driver program alone), that can be used to optimize its execution. So it’s maybe not the easiest solution to configure and optimize by hand, but that’s where other software systems, such as Databricks (see link above) comes in. Databricks supplies a managed Spark solution for customers that don’t want/need to deal with all the configuration complexity of Spark.
Sean is a principal solutions architect focusing on machine learning and data science at Databricks. He is an Apache Spark committee and PMC member, and co-author of Advanced Analytics with Spark.
Previously, Sean was director of Data Science at Cloudera and an engineer at Google.
Sep 14 2021
The GreyBeards had a great discussion with Floyd Christofferson, CEO, StrongBox Data Solutions on their big data/HPC file and archive solution. Floyd’s is very knowledgeable on problems of extremely large data repositories and has been around the HPC and other data intensive industries for decades.
StrongBox’s StrongLink solution offers a global namespace file system that virtualizes NFS, SMB, S3 and Posix file environments and maps this to a software-only, multi-tier, multi-site data repository that can span onsite flash, disk, S3 compatible or Azure object and LTFS tape iibrary storage as well as offsite versions of all the above tiers.
Typical StrongLink customers range in the 10s to 100s of PB, and ingesting or processing PBs a day. 200TB is a minimum StrongLink configuration, but Floyd said any shop with over 500TB has problems with data silos and other issues, but may not understand it yet. StrongLink manages data placement and movement, throughout this hierarchy to better support data access and economical storage. In the process StrongLink eliminates any data silos due to limitations of NAS systems while providing the most economic placement of data to meet user performance requirements.
Floyd said that StrongLink first installs in customer environment and then operates in the background to discover and ingest metadata from the primary customers file storage environment. Some point later the customer reconfigures their end-users share and mount points to StrongLink servers and it’s up and starts running.
The minimal StrongLink, HA environment consists of 3 nodes. They use a NoSQL metadata database which is replicated and sharded across the nodes. It’s shared for performance load balancing and fully replicated (2-way or 3-way) across all the StrongLink server nodes for HA.
The StrongLink nodes create a cluster, called a star in StrongBox vernacular. Multiple clusters onsite can be grouped together to form a StrongLink constellation. And multiple data center sites, can be grouped together to form a StrongLink galaxy. Presumably if you have a constellation or a galaxy, the same metadata is available to all the star clusters across all the sites.
They support any tape library and any NFS, SMB, S3 orAzure compatible object or file storage. Stronglink can move or copy data from one tier/cluster to another based on policies AND the end-users never sees any difference in their workflow or mount/share points.
One challenge with typical tape archives is that they can make use of proprietary tape data formats which are not accessible outside those systems. StrongLink has gone with a completely open-source, LTFS file format on tape, which is well documented and is available to anyone.
Floyd also made it a point of saying they don’t use any stubs, or soft links to provide their data placement magic. They only use standard file metadata.
File data moves across the hierarchy based on policies or by request. One of the secrets to StrongLink success is all the work they have done to ensure that any data movement can occur at line rate speeds. They heavily parallelize any data movement that’s required to support data placement across as many servers as the customer wants to throw at it. StrongBox services will help right-size the customer deployment to support any data movement performance that is required.
StrongLink supports up to 3-way replication of a customer’s data archives. This supports a primary archive and 3 more replicas of data.
Floyd mentioned a couple of big customers:
NASA, another StrongLink customer, operates slightly differently than the above, in that they have integrated StrongLink functionality directly into their applications by making use of StrongBox’s API.
StrongLink can work in three ways.
StrongBox customers can of course, use all three modes of operation, at the same time for their StrongLink data galaxy. StrongLink is billed by CPU/vCPU level and not for the amount of data customers throw into the archive. This has the effect of Customers gaining a flat expense cost, once StrongLink is deployed, at least until they decide to modify their server configuration.
As a professional involved in content management and storage workflows for over 25 years, Floyd has focused on methods and technologies needed to manage massive volumes of data across many different storage types and use cases.
Prior to joining SBDS, Floyd worked with software and hardware companies in this space, including over 10 years at SGI, where he managed storage and data management products. In that role, he was part of the team that provided solutions used in some of the largest data environments around the world.
Floyd’s background includes work at CBS Television Distribution, where he helped implement file-based content management and syndicated content distribution strategies, and Pathfire (now ExtremeReach), where he led the team that developed and implemented a satellite-based IP-multicast content distribution platform that manages delivery of syndicated content to nearly 1,000 TV stations throughout the US.
Earlier in his career, he ran Potomac Television, a news syndication and production service in Washington DC, and Manhattan Center Studios, an audio, video, graphics, and performance facility in New York.
Aug 20 2021
GreyBeards had an amazing discussion with Peter Thompson (@Lucid_Link), CEO & co-founder and George Dochev (@GDochev), CTO & co-founder of LucidLink. Both Peter and George were very knowledgeable and easy to talk with.
LucidLink’s Cloud NAS creates a NAS storage system out of cloud (any S3 compatible AND Azure Blob) object storage. LucidLink is made up of client software, LucidLink SaaS (metadata service) and data on object storage. Their client software runs on any Linux, MacOS, or Windows desktop/laptop. LucidLink provides streaming, collaborative access to remote users for (file) data on object storage.
Just when 90% of the workforce was sent home for the pandemic, LucidLink emerged to provide all those users secure file access to any and all corporate data in the cloud. Peter mentioned one M&E customer who had just sent 300 video editors home with laptops and a disk drive which would last them all of 2 weeks. But they needed an ongoing solution for after that. The customer started with 300 users and ~100TB of file storage on LucidLink and a few months later, they had 1000 users with a PB+ of LucidLink data and was getting rid of all their NAS boxes. Listen to the podcast to learn more.
They are finding a lot of success in M&E, engineering design, Oil&Gas exploration, geo-spatial design firms and just about anywhere user collaboration on file data is required outside al data center.
LucidLink constructs a FileSpace for customer file (object) data, which represents a drive letter or mount point that remote users can use to access files from the cloud. LucidLink supports a POSIX compliant file service for that data.
LucidLink data and user generated metadata is encrypted, using client owned/stored keys. So, data-at-rest (and -in-flight) can always be secure. They also support LDAP security and other standard SSO solutions to secure user access to data.
The LucidLink SaaS (metadata) service runs in a hyperscaler and links clients to file data on object storage. It also supports user distributed, byte range locking of file data.
One interesting nuance is that when a client locks a file, the system changes from an eventual to strongly consistent POSIX compliant file system. This ensures that the object storage is always the single source of truth.
The key that differentiates LucidLink from cloud gateways or file synch & share systems is that they 1) are not intended to operate in a data center, (yes, object storage can be located on prem but users are remote) and 2) don’t copy files from one user/access point to another.
George said latency is enemy number one. LucidLink’s secret is prefetching. Each client uses a customer configured local persistent cache which can range from 5GB to a TB or more. LucidLink maintains a data and (in the next version) metadata working set for the user in their local cache.
Customer file data is split across multiple objects, that way LucidLink can stream data from all of them, in parallel, if needed. And doing so can supply extreme throughput when needed.
As for GDPR and data compliance, the customer controls who has access to the LucidLink SaaS as well as encryption keys.
LucidLink considers their solution “fault tolerant” or DR ready, because customers can load client software on any device and access any LucidLink file data. They also consider themselves “highly available” because their metadata/LucidLink SaaS service runs in a hyper scaler and object backing storage can be configured as highly available.
As mentioned earlier, LucidLink customers can use any S3 compatible or Azure Blob object storage, on prem or in the cloud. But when using cloud object storage, one pays egress charges. LucidLink’s local caching can minimize but cannot eliminate egress charges.
LucidLink offers two licensing models: 1) a BYO (bring your own) object storage and LucidLink provides the software to support your Cloud NAS or 2) LucidLink supplies both the object storage as well as the LucidLink service that glues it all together. The later is a combination of IBM COS and LucidLink that offers less expensive egress charges.
The LucidLink service is billed on capacity under management and user count basis. Capacity is billed on a GB/day, summed over a month. Their minimum solution is 5TB/5 users but they have customers with 1000s of users and PB+ of data. They offer a free 2-week trial period where customers can try LucidLink out.
Peter Thompson, co-founder, and CEO of LucidLink is a passionate and experienced leader and business builder. Thompson has over 30 years of experience in driving business expansion, key programs, and partnerships across regions such as APAC and the Americas mostly in the storage and file system market.
With over 14 years at DataCore Software, most recently as VP of Emerging and Developing Markets, Thompson drove DataCore’s expansion into China working with key industry partners, technology alliances and global teams to develop programs and business focused on emerging markets. Thompson also held the role of Managing Director, APAC responsible for the bottom-line of all Asia operations. He also was President and Representative Director of DataCore Japan, acquiring the majority of ownership and running it as a standalone entity as a beachhead of marquis customers in Japan.
Thompson studied Japanese, history, and economics at Kansai Gaidai and has a BA in International Management, Psychology, Japanese, from Gustavus Adolphus College, is a graduate of Stanford University Business School’s MSx program, with a focus on entrepreneurial finance, design thinking, and the soft skills required to build and lead world-class, high performing teams.
George Dochev, co-founder and CTO of LucidLink, is a storage and file system expert with extensive experience in bringing emerging technologies to market. Dochev has over 20 years of success leading the development of complex virtualization products for the storage industry. He specializes in research and development in the fields of high-performance distributed systems, storage infrastructure software, and cloud technologies.
Dochev was co-founder and principal member of the engineering team at DataCore Software for nearly 17 years. While at Datacore, Dochev helped transform that company from a start-up into a global leader in software-defined storage. Underscoring Dochev’s impact as an entrepreneur is the fact that DataCore Software now powers the data centers of 10,000+ large enterprises around the world.
Dochev holds a degree in Mathematics from Sofia University St. Kliment Ohridski in Bulgaria, and an MS in Computer Science from the University of National and World Economy, in Sofia, Bulgaria.
Jul 15 2021
GreyBeards talk universal CEPH storage solutions with Phil Straw (@SoftIronCEO), CEO of SoftIron. Phil’s been around IT and electronics technology for a long time and has gone from scuba diving electronics, to DARPA/DOD researcher, to networking, and is now doing storage. He’s also their former CTO and co-founder of the company. SoftIron make hardware storage appliances for CEPH, an open source, software defined storage system.
CEPH storage includes file (CEPHFS, POSIX), object (S3) and block (RBD, RADOS block device, Kernel/librbd) services and has been out since 2006. CEPH storage also offers redundancy, mirroring, encryption, thin provisioning, snapshots, and a host of other storage options. CEPH is available as an open source solution, downloadable at CEPH.io, but it’s also offered as a licensed option from RedHat, SUSE and others. For SoftIron, it’s bundled into their HyperDrive storage appliances. Listen to the podcast to learn more.
SoftIron uses the open source version of CEPH and incorporates this into their own, HyperDrive storage appliances, purpose built to support CEPH storage.
There are two challenges to using open source solutions:
SoftIron has taken both of these on to offer a CEPH commercial product offering.
Take support, SoftIron offers enterprise level support that customers can contract for on their own, even if they don’t use SoftIron hardware. Phil said the would often get kudos for their expert support of CEPH and have often been requested to offer this as a standalone CEPH service. Needless to say their support of SoftIron appliances is also excellent.
As for ease of operations, SoftIron makes the HyperDrive Storage Manager appliance, which offers a standalone GUI, that takes the PHD out of managing CEPH. Anything one can do with the CEPH CLI can be done with SoftIron’s Storage Manager. It’s also a very popular offering with SoftIron customers. Similar to SoftIron’s CEPH support above, customers are requesting that their Storage Manager be offered as a standalone solution for CEPH users as well.
HyperDrive hardware appliances are storage media boxes that offer extremely low-power storage for CEPH. Their appliances range from high density (120TB/1U) to high performance NVMe SSDs (26TB/1U) to just about everything in between. On their website, I count 8 different storage appliance offerings with various spinning disk, hybrid (disk-SSD), SATA and NVMe SSDs (SSD only) systems.
SoftIron designs, develops and manufacturers all their own appliance hardware. Manufacturing is entirely in the US and design and development takes place in the US and Europe only. This provides a secure provenance for HyperDrive appliances that other storage companies can only dream about. Defense, intelligence and other security conscious organizations/industries are increasingly concerned about where electronic systems come from and want assurances that there are no security compromises inside them. SoftIron puts this concern to rest.
Yes they use CPUs, DRAMs and other standardized chips as well as storage media manufactured by others, but SoftIron has have gone out of their way to source all of these other parts and media from secure, trusted suppliers.
All other major storage companies use storage servers, shelves and media that come from anywhere, usually sourced from manufacturers anywhere in the world.
Moreover, such off the shelf hardware usually comes with added hardware that increases cost and complexity, such as graphics memory/interfaces, Cables, over configured power supplies, etc., but aren’t required for storage. Phil mentioned that each HyperDrive appliance has been reduced to just what’s required to support their CEPH storage appliance.
Each appliance has 6Tbps network that connects all the components, which means no cabling in the box. Also, each storage appliance has CPUs matched to its performance requirements, for low performance appliances – ARM cores, for high performance appliances – AMD EPYC CPUs. All HyperDrive appliances support wire speed IO, i.e, if a box is configured to support 1GbE or 100GbE, it transfers data at that speed, across all ports connected to it.
Because of their minimalist hardware design approach, HyperDrive appliances run much cooler and use less power than other storage appliances. They only consume 100W or 200W for high performance storage per appliance, where most other storage systems come in at around 1500W or more.
In fact, SoftIron HyperDrive boxes run so cold, that they don’t need fans for CPUs, they just redirect air flom from storage media over CPUs. And running colder, improves reliability of disk and SSD drives. Phil said they are seeing field results that are 2X better reliability than the drives normally see in the field.
They also offer a HyperDrive Storage Router that provides a NFS/SMB/iSCSI gateway to CEPH. With their Storage Router, customers using VMware, HyperV and other systems that depend on NFS/SMB/iSCSI for storage can just plug and play with SoftIron CEPH storage. With the Storage Router, the only storage interface HyperDrive appliances can’t support is FC.
Although we didn’t discuss this on the podcast, in addition to HyperDrive CEPH storage appliances, SoftIron also provides HyperCast, transcoding hardware designed for real time transcoding of one or more video streams and HyperSwitch networking hardware, which supplies a secure provenance, SONiC (Software for Open Networking in [the Azure] Cloud) SDN switch for 1GbE up to 100GbE networks.
Standing up PB of (CEPH) storage should always be this easy.
The technical visionary co-founder behind SoftIron, Phil Straw initially served as the company’s CTO before stepping into the role as CEO.
Previously Phil served as CEO of Heliox Technologies, co-founder and CTO of dotFX, VP of Engineering at Securify and worked in both technical and product roles at both Cisco and 3Com.
Phil holds a degree in Computer Science from UMIST.
Jun 15 2021
This month we turn to distributed (cloud) filesystems as we talk with Glen Shok (@gshok), VP of Alliances for Panzura. Panzura uses backend (cloud or onprem, S3 compatible) object store with a ring of software (VMs) or hardware (appliance) gateways that provides caching for local files as well as managing and maintaining metadata which creates a global NFS and SMB file system with near local access times.
Glen is an industry (without the grey beard) veteran with the knowledge to back that up. He’s been in the industry so long that we could probably have spent an hour just talking about where people are that we both know. Listen to the podcast to learn more
The interesting part about Panzura is their gateway ring. It not only manages local file caching and metadata maintenance/access, but it provides an out-of-(data path)-band file (byte range) lock coordination service, cache coherency (via delta block changes) and other services. All the metadata (and data) is backed up on backend object storage, but it’s the direct access to the metadata and its out of band control path as well as its caching service that supplies the near local access times for data.
Panzura supports any public (AWS, Azure, GCP & IBM) cloud object storage for backend data storage as well as a few, on prem, solutions (I think Glen mentioned IBM COS & Cloudian and their website mentions Wasabi, Scality and NetApp StorageGrid). Glen said they are on each of the public cloud’s marketplaces and with virtual gateways, its very easy to spin up and try.
Their system provides global (local, at the gateway) dedupe to reduce backend storage footprint and (both out of band and from backend storage) delta block changes for local cache updates. So in the event that an old version of the file happens to be present in their local cache gateway, it only needs retrieve the changed data from the object storage backend (or another gateway). All this local caching, dedupe and changed block tracking, helps to reduce cloud egress charges.
Data written to backend storage is immutable and versioned. So customers can retrieve any version of any file that was ever destaged to their backend. Glen said they write huge objects, presumably to help reduce storage footprint, IO overhead and API calls.
Glen claimed what with 3-way replication within a cloud region and 1-way replication outside the cloud region, customers no longer have to backup data. I respectively disagreed. He believes over time, customers will come to realize their use of backups for restores, becomes so rare that they can reduce backup frequency, if not eliminate it altogether. Some follow on discussion ensued, but in the end we seemed to agree to disagree on this topic.
Panzura also supports cross cloud mirroring. So, one could have their data mirrored from one cloud to another. One of these clouds will be used as a primary and only in the event that a majority of the gateway rings agree that the primary is DOWN and the secondary is UP, will they all automatically cut over to using the secondary storage cloud. While failover is automated, fail back requires operator intervention.
Panzura is charged for on managed data capacity. But cloud or on prem object storage is in addition to this and is charged for separately by the object storage provider.
As far what size file systems they support, Glen mentioned that they are ZFS internally, so any size imaginable. But he did concede, that at some point, metadata management becomes a problem and that they often suggest splitting apart 20PB file systems into 2 10PB (gateway rings) file systems to deal with this issue.
As for other solutions offered by Panzura, they have a K8s container block storage for persistent volumes that scales in capacity/performance using K8s services/resources.
Glen Shok has been in the data center and storage industry for over 20 years.
Starting his career at Cisco in the late 90s. Moving to a few startups which were acquired by Brocade and Oracle. Glen has held positions in sales, sales leadership, product management and marketing, and Office of the CTO at Zones, prior to coming to Panzura.
He can’t decide what he likes to do, but at Panzura, he’s the VP of Strategic Alliances.
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