Why MCS Means Rapid In-Memory Computing Adoption

What does the relatively new acronym MCS have to do with the accelerated adoption of in-memory computing? I’d say everything.

MCS stands for Memory Channel Storage and it essentially allows you to put NAND flash storage into a DIMM form factor and enable it to interface with a CPU via a standard memory controller. Put another way, MCS provides a drop-in replacement for DDR3 RDIMMs with 10x the memory capacity and a 10x reduction in price.

Historically, one of the major inhibitors behind in-memory computing adoption was the high cost of DRAM relative to disks and flash storage. While advantages such as 100x performance, lower power consumption and higher reliability were clearly known for years, the price delta was and is still relatively high:

Storage ~ Performance ~ Price
1TB MCS 20-200x TBD ~$5,000
1TB DDR3 RDIMM (32 DIMMs) 1000-10,000x $20,000
1TB PCI-E 10-100x $5,000
1TB SSD 10-100x $1,000
1TB HDD 1x $100

While spinning HDDs are essentially cost-free for enterprise consumption, and flash storage is enjoying mass adoption, DRAM storage still lags behind simply due to higher cost.

MCS-based storage is about to change this once and for all as it aims to bring the price of flash-based DRAM to the same level as today’s SSD and PCI-E flash storage.

MCI vs. PCI-E Flash

If prices are relatively similar between MCS and PCI-E storage, what makes MCS so much more important? The answer is direct memory access vs. block-based device.

All of the PCI-E flash storage today (FusionIO, Violin, basic SSDs, etc.) are recognized by the OS as block devices, i.e. essentially fast hard drives. Applications access these devices via typical file interface involving all typical marshaling, buffering, OS context switching, networking and IO overhead.

MCS provides an option to view its flash storage simply as main system memory, eliminating all the OS/IO/network overhead, while working directly via a highly optimized memory controller – the same controller that handles massive CPU-DDR3 data exchange – and enabling software like GridGain’s to access the flash storage as normal memory. This is a game changer and potentially a final frontier in the storage placement technology. In fact, you can’t place application data any closer to the CPU than the main memory and that is precisely what MCI enables us to do on terabyte and petabyte scale.

Moreover, MCS provides direct improvements over PCI-E storage. Diablo Technology, the pioneer behind MCS technology, claims that MCS is more performant (lower latencies and higher bandwidth) than typical PCI-E and SATA SSDs while providing ever elusive constant latency that is unachievable with standard PCE-E or SSD technologies.

Plug-n-Play

Another important characteristic of MCS storage is the plug-n-play fashion in which it can be used – no custom hardware, no custom software required. Imagine, for example, an array of 100 micro-servers (ARM-based servers in micro form factor), each with 256GB of MCI-based system memory, drawing less than 10 watts of power, costing less than $1000 each.

You now have a cluster with 25TB in-memory storage, 200 cores of processing power, running standard Linux, drawing around 1000 watts for about the same cost as a fully loaded Tesla Model S. Put GridGain’s In-Memory Computing Stack on it and you have an eco-friendly, cost effective, powerful real-time big data cluster ready for any task.

Welcome to the future.

Columnar vs. Key-Value Storage Models

What are the performance differences between in-memory columnar databases like SAP HANA and GridGain’s In-Memory Database (IMDB) utilizing distributed key-value storage? This questions comes up regularly in conversations with our customers and the answer is not very obvious.

Storage Models

First off, let’s clearly state that we are talking about storage model only and its implications on performance for various use cases. It’s important to note that:

  • Storage model doesn’t dictate of preclude a particular transactionality or consistency guarantees; there are columnar databases that support ACID (HANA) and those that don’t (HBase); there are distributed key-value databases that support ACID (GridGain) and those that don’t (for example, Riak and memcached).
  • Storage model doesn’t dictate specific query language; using above examples – GridGain and HANA support SQL – HBase, for example, doesn’t.

Unlike transactionality and query language – performance considerations, however, are not that straightforward.

Note also: SAP HANA has pluggable storage model and experimental row-based storage implementation. We’ll concentrate on columnar storage that apparently accounts for all HANA usage at this point.

HANA’s Columnar Storage Model

Let’s recall what columnar storage model entails in general and note its HANA specifics.

Some of its stand out characteristics include:

  • Data in columnar model is kept in column (vs. rows as in row storage models).
  • Since data in a single column is almost always homogeneous it’s frequently compressed for storage (especially in in-memory systems like HANA).
  • Aggregate functions (i.e. column functions) are very fast on columnar data model since the entire column can be fetched very quickly and effectively indexed.
  • Inserts, updates and row functions, however, are significantly slower than their row-based counterparts as a trade-off of columnar approach (inserting a row leads to multiple columns inserts). Because of this characteristic – columnar databased typically used in R/OLAP scenario (where data doesn’t change) and very rarely in OLTP use cases (where data changes frequently).
  • Since columnar storage is fairly compact it doesn’t generally require distribution (i.e. data partitioning) to store large datasets – the entire database can often be logically stored in memory of a single server. HANA, however, provides comprehensive support for data partitioning.

It is important to emphasize that columnar storage model is ideally suited for very compact memory utilization for the two main reasons:

  • Columnar model is a naturally fit for compression which often provides for dramatic reduction in memory consumption.
  • Since column-based functions are very fast – there is no need for materialized views for aggregated values in exchange for simply computing necessary values on the fly; this leads to significantly reduced memory footprint as well.

GridGain’s IMDB Key-Value Storage Model

Key-value (KV) storage model is less defined than its columnar counterpart and usually involves a fair amount of vendor specifics.

Historically, there are two schools of KV storage models:

  • Traditional (examples include Riak, memcached, Redis). The common characteristic of these systems is a raw, language independent storage format for the keys and values.
  • Data Grid (examples include GridGain IMDB, GigaSpaces, Coherence). The common trait of these systems is the reliance on JVM as underlying runtime platform, and treating keys and values as user-defined JVM objects.

GridGain’s IMDB belongs to Data Grid branch of KV storage models. Some of its key characteristics are:

  • Data is stored in a set of distributed maps (a.k.a. dictionaries or caches); in a simple approximation you can think of a value as a row in row-based model, and a key as that row’s primary key. Following this analogy a single KV map can be approximated as row-based table with automatic primary key index.
  • Keys and values are represented as user-defined JVM objects and therefore no automatic compression can be performed.
  • Data distribution is designed from the ground up. Data is partitioned across the cluster mitigating, in part, lack of compression. Unlike HANA – data partitioning is mandatory.
  • MapReduce is the main API for data processing (SQL is supported as well).
  • Strong affinity and co-location semantics provided by default.
  • No bias towards aggregate or row-based processing performance and therefore no bias towards either OLAP or OLTP applicability.

Performance Considerations

It is somewhat expected that for heavy transactional processing GridGain will provide overall better performance in most cases:

  • Columnar model is rather inefficient in updating or inserting values in multiple columns.
  • Transactional locking is also less efficient in columnar model.
  • Required de-compression and re-compression further degrades performance.
  • KV storage model, on the other hand, provides an ideal model for individual updates as individual objects can be accessed, locked and updated very effectively.
  • Lack of compression in GridGain IMDB makes updates go even faster than in columnar model with compression.

As an example, GridGain just won a public tender for one of the biggest financial institutions in the world achieving 1 billion transactional updates per second on 10 commodity blades costing less than $25K all together. That transactional performance and associated TCO is clearly not the territory any columnar database can approach.

For OLAP workloads the picture is less obvious. HANA is heavily biased towards OLAP processing, and GridGain IMDB is neutral towards it. Both GridGain IMDB and SAP HANA provides comprehensive data partitioning capabilities and allow for processing parallelization – MPP traits necessary for scale out OLAP processing. I believe the actual difference observed by the customers will be driven primarily by three factors rooted deeply in differences between columnar and KV implementations in respective products:

  • Optimizations around data affinity and co-location.
  • Optimizations around the distribution overhead.
  • Optimizations around indexing of partitioned data.

Unfortunately – there’s no way to provide any generalized guidance on performance difference here… We always recommend to try both in your particular scenario, pay attention to specific configuration and tuning around three points mentioned above – and see what results you’ll get. It does take time and resources – but you may be surprised by your findings!

Re-Imagining Ultimate Performance

It’s been somewhat quiet here and on GridGain side for a few months – and we’ve had a good reason for it.

We’ve just announced closing a $10M series B investment and bringing new awesome investor with it. In the last 6 months not only we’ve closed new round of investment, we’ve rebuilt and tripled our sales and business development team, retooled our marketing, released new products, and have 3 other products in the development pipeline to be announced this year. We’ve been busy…

But I think the most important thing we’ve accomplished so far is the crystallization and validation of our vision and strategy around end-to-end stack for In-Memory Computing.

In-Memory Computing

Kirill Sheynkman, one of our board members and investor, probably put it the best: “In-Memory Computing is characterized by using high-performance, integrated, distributed memory systems to manage and transact on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based technologies”.

And yet – In-Memory Computing is not a feature or just a product – it is a new way to compute and store data, a type of revolution we haven’t witnessed since the early seventies when IBM released “winchester” disk IBM 3340 and the era of HDDs has officially began. Today we are in same transitional period moving away from HDDs/SSDs or other block devices to a new era of DRAM-based storage and it creates a tidal wave of innovation in software.

Just as development of cheap HDDs pushed forward database industry in early seventies and the SQL was born – today the relentless data growth coupled with real time requirements for data processing necessitate move to in-memory processing, massive parallelization and unstructured data.

Unlike companies around us – here at GridGain we strongly believe that In-Memory Computing is a paradigm shift. It’s not just a single product, enhancement or feature add-on – it’s a new way to think about the ways we deal with exponentially growing data sets and the different types of payloads this data explosion brings in.

Here at GridGain we want to lead this revolution and we have the vision and technology to do just that…

End-to-End In-Memory Computing Stack

Most of today’s business applications dealing with large data sets (outside of legacy batch processing) are built around three fundamental types of payloads:

  • Database or system of records,
  • High performance, parallelized computations, and
  • Real time, high frequency streaming & CEP data processing.

These three types of payloads (or combination of them) is what at the core of practically every big data end user system built today. Providing in-memory products directly addressing these three types of payloads is what makes GridGain an end-to-end In-Memory Computing stack:

in-memory-ecosystem-white-big

What also important is that everything that GridGain has was built in-house – without any exception. We didn’t acquire some fledging startup, got “merged” into something else, or acqua-hired some dying open source project to quickly fill the gap in the product line – every product we have is built by the same team, from the same mold and came about in course of natural evolution of our product line in the last 7 years.

That’s why you have absolutely zero learning curve when moving from product to product. Our customers often note just how cohesive and unified our products “feel” to them: familiar APIs, principles and concepts, same configuration, same management, same installation, same documentation… and the same engineers helping them with support.

Platforms don’t get built by haphazardly stitching together random pieces of software – they grow organically overtime by dedicated teams.

Integrated Products

Few years ago we’ve noticed one class of customers that would loved to get benefits of in-memory computing but just didn’t have the appetite for the development and simply shy away from using any in-memory computing products all together.

Instead of loosing these customers (like everyone else) we’ve decided to pick some of the most frequent use cases we come across and provide highly integrated, plug-n-play products for them, a.k.a accelerators, so that they can enjoy benefits of the in-memory computing without any need for any development or any changes in their systems what-so-ever.

That’s how In-Memory Hadoop® and In-Memory MongoDB® Accelerators came about. And that’s how cloud storage accelerators are coming about in a few months.

A unique characteristic of GridGain’s integrated products is the “no assembly required” nature in which they integrate. They deliver all the scalability and performance advantages of GridGain’s In-Memory Computing stack with zero code changes and minimal configuration changes to the host products.

Management & Monitoring

No end-to-end stack can be truly called this way without single and unified management and monitoring system. GridGain provides the #1 devops support among any in-memory computing products with its Visor Administration Console. GridGain’s Visor is GUI-based and CLI-based system that provides deep runtime management, monitoring, and operational support for running any GridGain products in production context.

visor_dash2

Time Is Now

Einstein got it right when he said imagination is more important than knowledge. At GridGain, we’ve re-imagined ultimate performance as In-Memory Computing so that you can re-imagine your company for today’s increasingly competitive business environment.

GridGain understands that In-Memory Computing is more than the latest tech trend. It’s the next major shift for an increasingly hyper business world in which organizations face problems that traditional technology can’t even fathom, much less solve. In-Memory Computing is a step all organizations must take to remain competitive, and we’re ready to take that step with you.

You’ll never need to analyze less data. The speed of business will never be slower. Your business challenges will never be simpler. Now is the time for In-Memory Computing – only GridGain gives you a complete solution without any compromises.