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.

094: GreyBeards talk shedding light on data with Scott Baker, Dir. Content & Data Intelligence at Hitachi Vantara

Sponsored By:

At Hitachi NEXT 2019 Conference, last month, there was a lot of talk about new data services from Hitachi. Keith and I thought it would be a good time to sit down and talk with Scott Baker (@Kraken-Scuba), Director of Content and Data Intelligence, at Hitachi Vantara about what’s going on with data operations these days and how customers are shedding more light on their data.

Information supply chain

Something Scott said in his opening remarks caught my attention when he mentioned customer information supply chains. The information supply chain is similar to manufacturing supply chains, but it’s all about data. Just like manufacturing supply chains where parts and services come from anywhere and are used to create products/services for customers,

information supply chains are about the data used in their organization operations. Information supply chain data is A) being sourced from many places (or applications); B) being added to by supply chain processing (or other applications); and C) ultimately used by the organization to supply a product/service to customers.

But after the product/service is supplied the similarity between manufacturing and information supply chains breaks down. With the information supply chain, data is effectively indestructible, is infinitely re-useable and can live forever. Who throws data away anymore?

The problem most organizations have with information supply chains is once the product/service is supplied, data is often put away never to be seen again or as Scott puts it, goes dark.

This is where Hitachi Content intelligence (HCI) comes in. HCI is designed to take (unstructured or structured) data and analyze it (using natural language and other processing tools) to surround it with information and other metadata, so that it can become more visible and useful to the organization for the life of its existence.

Customers can also use HCI to extract and blend data streams together, automating the creation of an information rich, data repository. The data repository can readily be searched to re-discover or uncover attributes about the data not visible before.

Scott also mentioned the Hitachi Pentaho Platform which can be used to make real time decision from structured data. Pentaho information can also be fed into HCI to provide more intelligence for your structured data.

But HCI can also be used to analyze other database data as well. For instance, database blob and text elements can be fed to and analyzed by HCI. HCI analysis can include natural language processing and other functionality to tag the data by adding key:value information, all of which can be supplied back to the database or Pentaho to add further value to structured data.

Customers can also use HCI to read and transform database tables into XML files. XML files can be stored in object stores as objects or in file systems. XML data could easily be textually indexed and be searched by various tools to better understand the structured data information

We also talked about Hadoop data that can be offloaded to Hitachi Content Platform (HCP) object storage with a stub left behind. Once data is in HCP, HCI can be triggered to index and add more metadata, which can then later be used to decide when to move data back to Hadoop for further analysis.

Finally, Keith mentioned that he just got back from KubeCon and there was an increasing cry for data being used with containerized applications. Scott mentioned HCP for Cloud Scale, the newest member of the HCP object store family, focused on scale out capabilities to provide highly consistent, object storage performance for customers that need it. Customers running containerized workloads use scale-out capabilities to respond to user demand and now they have on premises object storage that can scale with them, as needs change.

The podcast ran ~24 minutes. Scott was very knowledgeable about data workflows, pipelines and the need for better discovery tools. We had a great time discussing information supply chains and how Hitachi can help customers optimize their data pipelines. Listen to the podcast to learn more.

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Scott Baker, Director of Content and Data Intelligence at Hitachi Vantara

Scott Baker is, and has been, an active member of the information technology, data analytics, data management, and data protection disciplines for longer than he is willing to admit.

In his present role at Hitachi, Scott is the Senior Director of the Content and Data Intelligence organization focused on Hitachi’s Digital Transformation, Data Management, Data Governance, Data Mobility, Data Protection and Data Analytics solutions which includes Hitachi Content Platform (HCP), HCP Anywhere, HCP Gateway, Hitachi Content Intelligence, and Hitachi Data Protection Solutions.

Scott is a VMware Certified Professional, recognized as a subject matter expert, industry speaker, and author. Scott has been a panelist on topics related to storage, cloud, information governance, data security, infrastructure standardization, and social media topics. His educational background includes an MBA, Master’s & Bachelor’s in Computer Science.

When he’s not working, Scott is an avid scuba diver, underwater photographer, and PADI Scuba Instructor. He has a passion for public speaking, whiteboarding, teaching, and traveling the world.

80: Greybeards talk composable infrastructure with Tom Lyon, Co-Founder/Chief Scientist and Brian Pawlowski, CTO, DriveScale

We haven’t talked with Tom Lyon (@aka_pugs) or Brian Pawlowski before on our show but both Howard and I know Brian from his prior employers. Tom and Brian work for DriveScale, a composable infrastructure software supplier.

There’s been a lot of press lately on NVMeoF and the GreyBeards thought it would be good time to hear from another way to supply DAS like performance and functionality. Tom and Brian have been around long enough to qualify as greybeards in their own right.

The GreyBeards have heard of composable infrastructure before but this was based on PCIe switching hardware and limited to a rack or less of hardware. DriveScale is working with large enterprises and their data center’s full of hardware.

Composable infrastructure has many definitions but the one DriveScale probably prefers is that it manages resource pools of servers and storage, that can be combined, per request, to create any mix of servers and DAS storage needed by an application running in a data center. DriveScale is targeting organizations that have from 1K to 10K servers with from 10K to 100K disk drives/SSDs.

Composable infrastructure for large enterprises

DriveScale provides large data centers the flexibility to better support workloads and applications that change over time. That is, these customers may, at one moment, be doing big data analytics on PBs of data using Hadoop, and the next, MongoDB or other advanced solution to further process the data generated by Hadoop.

In these environments, having standard servers with embedded DAS infrastructure may be overkill and will cost too much. For example., because one has no way to reconfigure (1000) server’s storage for each application that comes along, without exerting lots of person-power, enterprises typically over provision storage for those servers, which leads to higher expense.

But if one had some software that could configure 1 logical server or a 10,000 logical servers, with the computational resources, DAS disk/SSDs, or NVMe SSDs needed to support a specific application, then enterprises could reduce their server and storage expense while at the same time provide applications with all the necessary hardware resources.

When that application completes, all those hardware resources could be returned back to their respective pools and used to support the next application to be run. It’s probably not that useful when an enterprise only runs one application at a time, but when you have 3 or more running at any instant, then composable infrastructure can reduce hardware expenses considerably.

DriveScale composable infrastructure

DriveScale is a software solution that manages three types of resources: servers, disk drives, and SSDs over high speed Ethernet networking. SAS disk drives and SAS SSDs are managed in an EBoD/EBoF (Ethernet (iSCSI to SAS) bridge box) and NVMe SSDs are managed using JBoFs and NVMeoF/RoCE.

DriveScale uses standard (RDMA enabled) Ethernet networking to compose servers and storage to provide DAS like/NVMe like levels of response times.

DriveScale’s composer orchestrator self-discovers all hardware resources in a data center that it can manage. It uses an API to compose logical servers from server, disk and SSD resources under its control available, throughout the data center.

Using Ethernet switching any storage resource (SAS disk, SAS SSD or NVMe SSD) can be connected to any server operating in the data center and be used to run any application.

There’s a lot more to DriveScale software. They don’t sell hardware. but have a number of system integrators (like Dell) that sell their own hardware and supply DriveScale software to run a data center.

The podcast runs ~44 minutes. The GreyBeards could have talked with Tom and Brian for hours and Brian’s very funny. They were extremely knowledgeable and have been around the IT industry almost since the beginning of time. They certainly changed the definition of composable infrastructure for both of us, which is hard to do. Listen to the podcast to learn more. .

Tom Lyon, Co-Founder and Chief Scientist

Tom Lyon is a computing systems architect, a serial entrepreneur and a kernel hacker.

Prior to founding DriveScale, Tom was founder and Chief Scientist of Nuova Systems, a start-up that led a new architectural approach to systems and networking. Nuova was acquired in 2008 by Cisco, whose highly successful UCS servers and Nexus switches are based on Nuova’s technology.

He was also founder and CTO of two other technology companies. Netillion, Inc. was an early promoter of memory-over-network technology. At Ipsilon Networks, Tom invented IP Switching. Ipsilon was acquired by Nokia and provided the IP routing technology for many mobile network backbones.

As employee #8 at Sun Microsystems, Tom was there from the beginning, where he contributed to the UNIX kernel, created the SunLink product family, and was one of the NFS and SPARC architects. He started his Silicon Valley career at Amdahl Corp., where he was a software architect responsible for creating Amdahl’s UNIX for mainframes technology.

Brian Pawlowski, CTO

Brian Pawlowski is a distinguished technologist, with more than 35 years of experience in building technologies and leading teams in high-growth environments at global technology companies such as Sun Microsystems, NetApp and Pure Storage.

Before joining DriveScale as CTO, Brian served as vice president and chief architect at Pure Storage, where he focused on improving the user experience for the all-flash storage platform provider’s rapidly growing customer base. He also was CTO at storage pioneer NetApp, which he joined as employee #18.

Brian began his career as a software engineer for a number of well-known technology companies. Early in his days as a technologist, he worked at Sun, where he drove the technical analysis and discussion on alternate file systems technologies. Brian has also served on the board of trustees for the Anita Borg Institute for Women and Technology as well as a member of the board at the Linux Foundation.

Brian studied computer science at Arizona State University, physics at the University of Texas at Austin, as well as physics at MIT.

77: GreyBeards talk high performance databases with Brian Bulkowski, Founder & CTO, Aerospike

In this episode we discuss high performance databases and the storage needed to get there, with Brian Bulkowski, Founder and CTO of Aerospike. Howard met Brian at an Intel Optane event last summer and thought he’d be a good person to talk with. I couldn’t agree more.

Howard and I both thought Aerospike was an in memory database but we were wrong. Aerospike supports in memory, DAS resident and SAN resident distributed databases.

Database performance is all about the storage (or memory)

When Brian first started Aerospike, they discovered that other enterprise database vendors were using fast path SAS SSDs for backend storage and so that’s where Aerospike started with on storage.

As NVMe SSDs came out, Brian expected higher performance but wasn’t too impressed with what he found out with NVMe SSD’s real performance as compared to SAS SSDs. However lately, the SSD industry has bifurcated into fast, low-capacity (NVMe) SSDs and slow, large capacity (SAS) SSDs. And over time the Linux Kernel (4.4 and above) has sped up NVMe IO stack. So now he has become more of a proponent of NVMe SSDs for high performing database storage.

In addition to SAS and NVMe SSDs, Aerospike supports SAN storage. One recent large customer uses SAN shared storage and loves the performance. Moreover, Aerospike also offers an in memory database option for the ultimate in high performance (low capacity) databases.

Write IO performance

One thing that Aerospike is known for is their high performance under mixed R:W workloads. Brian says just about any database can perform well with an 80:20 R:W IO mix, but at 50:50 R:W, most databases fall over.

Aerospike did detailed studies of SSD performance with high write IO and used SSD native APIs to understand what exactly was going on with SAS SSDs. Today, they understand when SSDs go into garbage collection and and can quiesce IO activity to them during these slowdowns. Similar APIs are available for NVMe SSDs.

Optane memory

The talk eventually turned to Optane DIMMs (3D Crosspoint Memory). With Optane DIMMs, server memory address space will increase from 1TB to 6TB. From Brian’s perspective this is still not enough to host a copy of a typical database but it would suffice to hold cache a  database index. Which is exactly how they are going to use Optane DIMMs.

Optane DIMMs are accessed via PMEM (an Intel open source memory access API) and can specify  caching (L1-L2-L3) characteristics, so that the processor(s) data and instruction caching tiers don’t get flooded with database information. Aerospike has done for in-memory databases in the past, it’s just requires a different API.

As a distributed database, they support data protection for DAS and in memory databases through mirroring, dual redundancy.  But Aerospike was developed as a  distributed database, so data can be sharded, across multiple servers to support higher, parallelized performance.

With Optane DIMMs being 1000X faster than NVMe SSD, the performance bottleneck has now moved back to the network. Given the dual redundancy data protection scheme, any data written on one server would need to be also written (across the network) to another server.

Data consistency in databases

This brought us around to the subject of database consistency.  Brian said Aerospike database consistency for reads was completely parameterized, e.g. one can specify linear (database wide) consistency to session level consistency, with some steps in between. Aerospike is always 100% write consistent but read consistency can be relaxed for better performance.

Howard and I took a deep breath and said data has to be a 100% consistent. Brian disagreed, and in fact, historically relational databases were not fully read consistent. Somehow this felt like a religious discussion and in the end, we determined that database consistency is just another knob to turn if you want high performance.

Brian mentioned that  Aerospike is available in an open source edition which anyone can access and download. He suggested we tell our DBA friends about it, maybe, if we have any…

The podcast runs ~44 minutes. Brian’s been around databases for a long time and seemingly, most of that time has been figuring out the best ways to use storage to gain better performance. He has a great perspective on  NVMe vs. SAS SSD performance as well as (real) memory vs SCM performance, which we all need to understand better as SCM rolls out. Possibly, barring the consistency discussion, Brian was also easy to talk with.  Listen to our podcast to learn more.

Brian Bulkowski, Founder and CTO, Aerospike

Brian is a Founder and the CTO of Aerospike. With almost 30 years in Silicon Valley, his motivation for starting Aerospike was the confluence of what he saw as the rapidly advancing flash storage technology with lower costs that weren’t being fully leveraged by database systems as well as the scaling limitations of sharded MySQL systems and the need for a new distributed database.

He was able to see these needs as both a Lead Engineer at Novell and Chief Architect at Cable Solutions at Liberate – where he built a high-performance, embedded networking stack and high scale broadcast server infrastructure.