I would like to clarify definitions for the following technologies:
- In-Memory Distributed Cache
- In-Memory Data Grid
- In-Memory Database
These three terms are, surprisingly, often used interchangeably and yet technically and historically they represent very different products and serve different, sometimes very different, use cases.
It’s also important to note that there’s no specifications or industry standards on what cache, or data grid or database should be (unlike java application servers and JEE, for example). There was and still is an attempt to standardize caching via JSR107 but it has been years (almost a decade) in the making and it is hopelessly outdated by now (I’m on the expert group).
Tricycle vs. Bike vs. Motorcycle
First of all, let me clarify that I am discussing caches, data grids and databases in the context of in-memory, distributed architectures. Traditional disk-based databases and non-distributed in-memory caches or databases are out of scope for this article.
Chronologically, caches, data grids and databases were developed in that order: starting from simple caching to more complex data grids and finally to distributed in-memory databases. The first distributed caches appeared in the late 1990s, data grids emerged around 2002-2003 and in-memory databases have really came to the forefront in the last 5 years.
All of these technologies are enjoying a significant boost in interest in the last couple years thanks to explosive growth in-memory computing in general fueled by 30% YoY price reduction for DRAM and cheaper Flash storage.
Despite the fact that I believe that distributed caching is rapidly going away, I still think it’s important to place it in its proper historical and technical context along with data grids and databases.
In-Memory Distributed Caching
The primary use case for caching is to keep frequently accessed data in process memory to avoid constantly fetching this data from disk, which leads to the High Availability (HA) of that data to the application running in that process space (hence, “in-memory” caching).
Most of the caches were built as distributed in-memory key/value stores that supported a simple set of ‘put’ and ‘get’ operations and optionally some sort of read-through and write-through behavior for writing and reading values to and from underlying disk-based storage such as an RDBMS. Depending on the product, additional features like ACID transactions, eviction policies, replication vs. partitioning, active backups, etc. also became available as the products matured.
These fundamental data management capabilities of distributed caches formed the foundation for the technologies that came later and were built on top of them such as In-Memory Data Grids.
In-Memory Data Grid
The feature of data grids that distinguishes them from distributed caches was their ability to support co-location of computations with data in a distributed context and consequently provided the ability to move computation to data. This capability was the key innovation that addressed the demands of rapidly growing data sets that made moving data to the application layer increasing impractical. Most of the data grids provided some basic capabilities to move the computations to the data.
This new and very disruptive capability also marked the start of the evolution of in-memory computing away from simple caching to a bona-fide modern system of record. This evolution culminated in today’s In-Memory Databases.
The feature that distinguishes in-memory databases over data grids is the addition of distributed MPP processing based on standard SQL and/or MapReduce, that allows to compute over data stored in-memory across the cluster.
Just as data grids were developed in response to rapidly growing data sets and the necessity to move computations to data, in-memory databases were developed to respond to the growing complexities of data processing. It was no longer enough to have simple key-based access or RPC type processing. Distributed SQL, complex indexing and MapReduce-based processing across TBs of data in-memory are necessary tools for today’s demanding data processing.
Adding distributed SQL and/or MapReduce type processing required a complete re-thinking of data grids, as focus has shifted from pure data management to hybrid data and compute management.
What is common about Oracle and SAP when it comes to In-Memory Computing? Both see this technology as merely a high performance addition to SQL-based database products. This is shortsighted and misses a significant point.
SQL Is Not Enough For New Payloads
It is interesting to note that as the NoSQL movement sails through the “trough of disillusionment,” traditional SQL and transactional datastores are re-gaining some of the attention. But, importantly, the return to SQL, even based on in-memory technology, is limiting for many newer payload types. In-Memory Computing will play a role which is much more significant than that of a mere SQL database accelerator.
Let’s take high performance computations as an example. Use cases abound: anything from traditional MonteCarlo simulations, video and audio processing, to NLP and image processing software. All can benefit greatly from in-memory processing and gain critical performance improvements – yet for systems like this a SQL database is of little, if any, help at all. In fact, SQL has absolutely nothing to do with these use cases – they require traditional HPC processing along the lines of MPI, MapReduce or MPP — and none of these are features of either Oracle or SAP Hana databases.
Or take streaming and CEP as another example. Tremendous growth in sensory, machine-to-machine and social data, generated in real time, makes streaming and CEP one of the fastest growing use cases for big data processing. Ability to ingest hundreds of thousands of events per seconds and process them in real time has practically nothing to do with traditional SQL databases – but everything to do with in-memory computing. In fact – these systems require a completely different approach of sliding window processing, streaming indexing and complex distributed workflow management – none of which are capabilities of either Oracle or SAP Hana.
Nonetheless, SQL processing was, is, and always will be with us. Ironically, it is now getting back on some of the pundits’ radars. For example, in data warehousing, where Hadoop can be used as a massive data store of record, SQL can play well. In-Memory Computing, however, plays a greater role than just a cache for a large datastore. New payload types require different processing approaches – and all benefit from the dramatic performance improvements brought by in-memory computing.
At GridGain, we are keenly aware of the self evident point: In-Memory Computing is much more significant than just getting a slow SQL database to go faster. Our end-to-end product suite delivers many additional benefits of in-memory computing, handles use cases that are impossible to address in the traditional database world. And there’s so much more to come.
We are happy to announce the general availability release for GridGain 5.2 which includes updates to all products in the platform:
- In-Memory HPC 5.2
- In-Memory Database 5.2
- In-Memory Streaming 2.0
- In-Memory Accelerator for Hadoop 2.0
We anticipate this being the last mid-point release in the platform before we roll out 6.0 line Q114 or Q214 (we are still planning to have bi-weekly service releases going forward as usual).
During past months we’ve been working very diligently to improve the general usability of our products: from first impressions, to POCs, to production use. Despite the fact that GridGain has enjoyed a stellar record on this front for years – the platform’s size is growing rapidly (we are now at almost 4x size of the entire Hadoop codebase, for example) – and we need to make sure that size and complexity don’t overshadow the simplicity and usability our products enjoyed so far.
We’ve added many features and enhancements: better error messages, automatic configuration conflict detection, automatic backward compatibility checks, and better documentation.
Work in this direction will continue. We listen to our customers and pay attention to how they use our products. We make improvements every sprint.
One of the biggest improvement in the last 6 months is performance for non-transactional use cases. GridGain has been winning every benchmark when it comes to distributed ACID transactions – but we haven’t had same winning margins when it came to simpler, non-transactional payloads.
It’s fixed now.
We are currently running over 50 benchmarks against every competitive database and data grid products (all seven of them) and currently are winning over 95% of them with some as much as 3-4x. That includes 100% of distributed ACID transactional use cases and most of of the non-transactional use cases (EC, simple automicity, local-only transactions, etc.)
GridGain still holds a record of achieving 1 Billion TPS on 10 commodity Dell R610 blades. The records was achieved in a open tender and is verifiable. No other product has yet achieved this level of performance.
There’s plenty of exciting stuff that we’ve been working on for the past 6-9 months that will be made public early next year when GridGain 6.0 platform will roll out. Some features have trickled out to the public – but most have been kept tight for the next release.
As always, grab your free download of GridGain at http://www.gridgain.com/download and check out our constantly growing documentation center for all your screencasts, videos, white papers, and technical documentation: http://www.gridgain.com/documentation
In the last 12 months we observed a growing trend that use cases for distributed caching are rapidly going away as customers are moving up stack… in droves.
Let me elaborate by highlighting three points that when combined provide a clear reason behind this observation.
Databases Caught Up With Distributed Caching
In the last 3-5 years traditional RDBMSs and new crop of simpler NewSQL/NoSQL databases have mastered the in-memory caching and now provide comprehensive caching and even general in-memory capabilities. MongoDB and CouchDB, for example, can be configured to run mostly in-memory (with plenty caveats but nonetheless). And when Oracle 12 and SAP HANA are in the game (with even more caveats) – you know it’s a mainstream already.
There’s simply less reasons today for just caching intermediate DB results in memory as data sources themselves do a pretty decent job at that, 10GB network is often fast enough and much faster IB interconnect is getting cheaper. Put it the other way, performance benefits of distributed caching relative to the cost are simpler not as big as they were 3-5 years ago.
Emerging “Caching The Cache” anti-pattern is a clear manifestation of this conundrum. And this is not only related to historically Java-based caching products but also to products like Memcached. It’s no wonder that Java’s JSR107 has been such a slow endeavor as well.
Customers Demand More Sophisticated Products
In the same time as customers moving more and more payloads to in-memory processing they are naturally starting to have bigger expectations than the simple key/value access or full-scan processing. As MPP style of processing on large in-memory data sets becoming a new “norm” these customers are rightly looking for advanced clustering, ACID distributed transactions, complex SQL optimizations, various forms of MapReduce – all with deep sub-second SLAs – as well as many other features.
Distributed caching simply doesn’t cut it: it’s a one thing to live without a distributed hash map for your web sessions – but it’s completely different story to approach mission critical enterprise data processing without transactional data center replication, comprehensive computational and data load balancing, SQL support or complex secondary indexes for MPP processing.
Apples and oranges…
Focus Shifting to Complex Data Processing
And not only customers move more and more data to in-memory processing but their computational complexity grows as well. In fact, just storing data in-memory produces no tangible business value. It is the processing of that data, i.e. computing over the stored data, is what delivers net new business value – and based on our daily conversations with prospects the companies across the globe are getting more sophisticated about it.
Distributed caches and to a certain degree data grids missed that transition completely. While concentrating on data storage in memory they barely, if at all, provide any serious capabilities for MPP or MPI-based or MapReduce or SQL-based processing of the data – leaving customers scrambling for this additional functionality. What we are finding as well is that just SQL or just MapReduce, for instance, is often not enough as customers are increasingly expecting to combine the benefits of both (for different payloads within their systems).
Moreover, the tight integration between computations and data is axiomatic for enabling “move computations to the data” paradigm and this is something that simply cannot be bolted on existing distributed cache or data grid. You almost have to start form scratch – and this is often very hard for existing vendors.
And unlike the previous two points this one hits below the belt: there’s simply no easy way to solve it or mitigate it.
So, what’s next? I don’t really know what the category name will be. May be it will be Data Platforms that would encapsulate all these new requirements – may be not. Time will tell.
At GridGain we often call our software end-to-end in-memory computing platform. Instead of one do-everything product we provide several individual but highly integrated products that address every major type of payload of in-memory computing: from HPC, to streaming, to database, and to Hadoop acceleration.
It is an interesting time for in-memory computing. As a community of vendors and early customers we are going through our first serious transition from the stage where simplicity and ease of use were dominant for the early adoption of the disruptive technology – to a stage where growing adaption now brings in the more sophisticated requirements and higher customer expectations.
As vendors – we have our work cut out for us.
As any fast growing technology In-Memory Computing has attracted a lot of interest and writing in the last couple of years. It’s bound to happen that some of the information gets stale pretty quickly – while other is simply not very accurate to being with. And thus myths are starting to grow and take hold.
I want to talk about some of the misconceptions that we are hearing almost on a daily basis here at GridGain and provide necessary clarification (at least from our our point of view). Being one of the oldest company working in in-memory computing space for the last 7 years we’ve heard and seen all of it by now – and earned a certain amount of perspective on what in-memory computing is and, most importantly, what it isn’t.
Let’s start at… the beginning. What is the in-memory computing? Kirill Sheynkman from RTP Ventures gave the following crisp definition which I like very much:
“In-Memory Computing is based on a memory-first principle utilizing high-performance, integrated, distributed main memory systems to compute and transact on large-scale data sets in real-time – orders of magnitude faster than traditional disk-based systems.”
The most important part of this definition is “memory-first principle”. Let me explain…
Memory-first principle (or architecture) refers to a fundamental set of algorithmic optimizations one can take advantage of when data is stored mainly in Random Access Memory (RAM) vs. in block-level devices like HDD or SSD.
RAM has dramatically different characteristics than block-level devices including disks, SSDs or Flash-on-PCI-E arrays. Not only RAM is ~1000x times faster as a physical medium, it completely eliminates the traditional overhead of block-level devices including marshaling, paging, buffering, memory-mapping, possible networking, OS I/O, and I/O controller.
Let’s look at example: say you need to read a single record in your program.
In in-memory context your code will be compiled to interact with memory controller and read it directly from local RAM in the exact format you need (i.e. your object representation in particular programming language) – in most cases that will result in a simple pointer arithmetic. If you use proper vectorized execution technique – you’ll often read it from L2 cache of your CPUs. All in all – we are talking about nanoseconds and this performance is guaranteed for all cases.
If you read the same record form block-level device – you are in for a very different ride… Your code will have to deal with OS I/O, buffered read, I/O controller, seek time of the device, and de-marshaling back the byte stream that you get from it to an object representation that you actually need. In worst case scenario – we’re talking dozen milliseconds. Note that SSDs and Flash-on-PCI-E only improves portion of the overhead related to seek time of the device (and only marginally).
Taking advantage of these differences and optimizing your software accordingly – is what memory-first principle is all about.
Now, let’s get to the myths.
Myth #1: It’s Too Expensive
This is one of the most enduring myths of in-memory computing. Today – it’s simply not true. Five or ten years ago, however, it was indeed true. Look at the historical chart of USD/MB storage pricing to see why:
The interesting trend is that price of RAM is dropping 30% every 12 months or so and is solidly on the same trajectory as price of HDD which is for all practical reasons is almost zero (enterprises care more today about heat, energy, space than a raw price of the device).
The price of 1TB RAM cluster today is anywhere between $20K and $40K – and that includes all the CPUs, over petabyte of disk based storage, networking, etc. CIsco UCS, for example, offers very competitive white-label blades in $30K range for 1TB RAM setup: http://buildprice.cisco.com/catalog/ucs/blade-server Smart shoppers on eBay can easily beat even the $20K price barrier (as we did at GridGain for our own recent testing/CI cluster).
In a few years from now the same 1TB TAM cluster setup will be available for $10K-15K – which makes it all but commodity at that level.
And don’t forget about Memory Channel Storage (MCS) that aims to revolutionize storage by providing the Flash-in-DIMM form factor – I’ve blogged about it few weeks ago.
Myth #2: It’s Not Durable
This myths is based on a deep rooted misunderstanding about in-memory computing. Blame us as well as other in-memory computing vendors as we evidently did a pretty poor job on this subject.
The fact of the matter is – almost all in-memory computing middleware (apart from very simplistic ones) offer one or multiple strategies for in-memory backups, durable storage backups, disk-based swap space overflow, etc.
More sophisticated vendors provide a comprehensive tiered storage approach where users can decide what portion of the overall data set is stored in RAM, local disk swap space or RDBMS/HDFS – where each tier can store progressively more data but with progressively longer latencies.
Yet another source of confusion is the difference between operational datasets and historical datasets. In-memory computing is not aimed at replacing enterprise data warehouse (EDW), backup or offline storage services – like Hadoop, for example. In-memory computing is aiming at improving operational datasets that require mixed OLTP and OLAP processing and in most cases are less than 10TB in size. In other words – in-memory computing doesn’t suffer from all-or-nothing syndrome and never requires you to keep all data in memory.
If you consider the totally of the data stored by any one enterprise – the disk still has a clear place as a medium for offline, backup or traditional EDW use cases – and thus the durability is there where it always has been.
Myth #3: Flash Is Fast Enough
The variations of this myth include the following:
- Our business doesn’t need this super-fast processing (likely shortsighted)
- We can mount RAM disk and effectively get in-memory processing (wrong)
- We can replace HDDs with SSDs to get the performance (depends)
Mounting RAM disk is a very poor way of utilizing memory from every technical angle (see above).
As far as SSDs – for some uses cases – the marginal performance gain that you can extract from flash storage over spinning disk could be enough. In fact – if you are absolutely certain that the marginal improvements is all you ever need for a particular application – the flash storage is the best bet today.
However, for a rapidly growing number of use cases – speed matters. And it matters more and for more businesses every day. In-memory computing is not about marginal 2-3x improvement – it is about giving you 10-100x improvements enabling new businesses and services that simply weren’t feasible before.
There’s one story that I’ve been telling for quite some time now and it shows a very telling example of how in-memory computing relates to speed…
Around 6 years ago GridGain had a financial customer who had a small application (~1500 LOC in Java) that took 30 seconds to prepare a chart and a table with some historical statistical results for a given basket of stocks (all stored in Oracle RDBMS). They wanted to put it online on their website. Naturally, users won’t wait for half a minute after they pressed the button – so, the task was to make it around 5-6 seconds. Now – how do you make something 5 times faster?
We initially looked at every possible angle: faster disks (even SSD which were very expensive then), RAID systems, faster CPU, rewriting everything in C/C++, running on different OS, Oracle RAC – or any combination of thereof. But nothing would make an application run 5x faster – not even close… Only when we brought the the dataset in memory and parallelized the processing over 5 machines using in-memory MapReduce – we were able to get results in less than 4 seconds!
The morale of the story is that you don’t have to have NASA-size problem to utilize in-memory computing. In fact, every day thousands of businesses solving performance problem that look initially trivial but in the end could only be solved with in-memory computing speed.
Speed also matters in the raw sense as well. Look at this diagram from Stanford about relative performance of disks, flash and RAM:
As DRAM closes its pricing gap with flash such dramatic difference in raw performance will become more and more pronounced and tangible for business of all sizes.
Myth #4: It’s About In-Memory Databases
This is one of those mis-conceptions that you hear mostly from analysts. Most analysts look at SAP HANA, Oracle Exalytics or something like QlikView – and they conclude that this is all that in-memory computing is all about, i.e. database or in-memory caching for faster analytics.
There’s a logic behind it, of course, but I think this is rather a bit shortsighted view.
First of all, in-memory computing is not a product – it is a technology. The technology is used to built products. In fact – nobody sells just “in-memory computing” but rather products that are built with in-memory computing.
I also think that in-memory databases are important use case… for today. They solve a specific use case that everyone readily understands, i.e. faster system of records. It’s sort of a low hanging fruit of in-memory computing and it gets in-memory computing popularized.
I do, however, think that the long term growth for in-memory computing will come from streaming use cases. Let me explain.
Streaming processing is typically characterized by a massive rate at which events are coming into a system. Number of potential customers we’ve talked to indicated to us that they need to process a sustained stream of up to 100,000 events per second with out a single event loss. For a typical 30 seconds sliding processing window we are dealing with 3,000,000 events shifting by 100,000 every second which have to be individually indexed, continuously processed in real-time and eventually stored.
This downpour will choke any disk I/O (spinning or flash). The only feasible way to sustain this load and corresponding business processing is to use in-memory computing technology. There’s simply no other storage technology today that support that level of requirements.
So we strongly believe that in-memory computing will reign supreme in streaming processing.
GridGain just posted service point releases for In-Memory HPC and In-Memory Database products version 5.1.6. If you are currently running either of these two products we recommend to update. This point release includes performance improvements and number of bug fixes:
CLIENT_ONLYmode for partitioned cache.
ATOMICatomicity mode for better performance for non-transactional use.
- New optional
GridOptimizedMarshallableinterface to improve optimized marshaller.
- New one-phase commit in
TRANSACTIONALmode for basic
- New automatic back-pressure control for async operations.
- Multiple fixes/enhancements to Visor Management Console.
Release notes available.
What does the relatively new acronym MCI have to do with the accelerated adoption of in-memory computing? I’d say everything.
MCI stands for Memory Channel Interface storage (a.k.a MCS – 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, MCI 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 DDR3 RDIMM (32 DIMMs)||1000-10,000x||$20,000|
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.
MCI-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 MCI and PCI-E storage, what makes MCI 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.
MCI 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, MCI provides direct improvements over PCI-E storage. Diablo Technology, the pioneer behind MCI technology, claims that MCI 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.
Another important characteristic of MCI 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.
FOR IMMEDIATE RELEASE
FOSTER CITY, Calif., Aug. 13, 2013 /PRNewswire/ – GridGain™ Systems today announced it has expanded its management team, tapping the talents of Andy Sacks as Executive Vice President of Sales, Lisa Bergamo as Vice President of Marketing, and Jeff Stacey as Global Head of Business Development, to build on its current momentum in In-Memory Computing. The announcement follows its recent closing of $10 million in Series B venture financing in a round led by global venture capital firm Almaz Capital, with continued participation from previous investor RTP Ventures.
The team additions mark key elements of GridGain’s three-pronged promise to use its new capital to rapidly expand sales, marketing and new product development to meet the growing need for In-Memory Computing in big data environments. “The market need for new technology that can handle the 2.5 quintillion bytes of data that businesses generate has perfectly aligned with GridGain’s ability to offer unprecedented computing power,” said Nikita Ivanov, Founder and CEO, GridGain. “Andy Sacks, Lisa Bergamo and Jeff Stacey bring the skills and experience to capitalize this moment and offer In-Memory technology to every organization.”
As Executive Vice President of Sales, Andy Sacks brings more than 20 years of enterprise sales experience in developing direct and indirect routes to market. He comes to GridGain from Red Hat, Inc., where he spent over 8 years developing and leading sales teams, delivering substantial company revenue. Prior to Red Hat, he held sales leadership roles at Bluestone Software (acquired by HP), RightWorks (acquired by i2) and Inktomi (acquired by Yahoo! and Verity).
As Vice President of Marketing, Lisa Bergamo brings to GridGain more than 25 years of technology marketing, branding, and public relations experience. Bergamo, who has consistently delivered results for emerging technology companies over the past two decades, was founder and vice president of marketing for SOASTA, Inc., developer of an award-winning, on-demand cloud service for load and performance testing of websites and applications. Bergamo has also held executive marketing positions at Infochimps, Symplified, GlobalFluency, Sagent and CyberSource Corporation, and venture capital firm Canaan Partner. She currently serves as a mentor for start-up accelerator Founders Pad.
Jeff Stacey brings to his new position as Global Head of Business Development twenty years of experience at companies like Dell, IBM, SAP/Business Objects and Oracle, as well as smaller emerging technology firms, launching large scale analytic products into the marketplace. Partnering with system integrators, resellers and analytic solution providers, he repeatedly won, managed and grew technology ecosystems from zero to over $100M in global revenue.
“For enterprises, whether their data becomes a value prop or pain point depends largely on whether or not they embrace In-Memory technology,” said Ivanov. “From this moment, data will only become more unruly, problems more complicated, and time a more precious commodity. Companies need new technology to handle a new scope of data challenges, and GridGain provides the full stack.”
GridGain’s complete In-Memory Computing platform enables organizations to conquer challenges that traditional technology can’t even fathom. While most organizations now ingest infinitely more data than they can possibly make sense of, GridGain’s customers leverage a new level of real-time computing power that allows them to easily innovate ahead of the accelerating pace of business.
Built from the ground up, GridGain’s product line delivers all the high performance benefits of In-Memory Computing in a simple, intuitive package. From high performance computing, streaming and database to Hadoop and MongoDB accelerators, GridGain provides a complete end-to-end stack for low-latency, high performance computing for each and every category of payloads and data processing requirements. Fortune 500 companies, top government agencies and innovative mobile and web companies use GridGain to achieve unprecedented performance and business insight. GridGain is headquartered in Foster City, California. Learn more at http://www.gridgain.com.
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.
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.
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!
It’s been somewhat quiet here on the GridGain front for a few months, and for good reason!
We just announced closing a $10M Series B investment and bringing an awesome new investor on board. In the last 6 months we not only closed the new round, we also rebuilt and tripled our sales and business development team, retooled our marketing, released new products, and have 3 other products in the development pipeline scheduled for announcement later this year.
But I think the most important thing we’ve accomplished so far is the crystallization and validation of our vision and strategy around our end-to-end stack for In-Memory Computing.
Kirill Sheynkman, one of our board members and an 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”.
In-Memory Computing is a new way to compute and store data, a type of revolution we haven’t witnessed since the early 1970s when IBM released the “winchester” disk IBM 3340 and the era of HDDs 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 — creating a tidal wave of innovation in software.
Just as the development of cheap HDDs pushed forward the database industry in the 1970s and SQL was born, today relentless data growth coupled with real time requirements for data processing necessitate a 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 different way to think about how we deal with exponentially growing data sets and unrelenting appetite for actionable and real-time data intelligence and analysis.
Here at GridGain we are leading 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 to process three different types of payload:
- Database or system of record,
- High performance, parallelized computations, and
- Real time, high frequency streaming and CEP data processing
These three types of payload (or combination of them) are at the core of practically every big data end user system built today. Providing in-memory products directly addressing these three types of payload is what makes GridGain an end-to-end In-Memory Computing stack
What’s also important is that we have built our product line from the ground up. We didn’t acquire some fledging startup, gain pieces of technology from a merger, or revamp some dying open source project to quickly fill a gap in the product line. Every product we have is built by the same team, from the same base and came about as a 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 providing top-notch support.
Platforms don’t get built by haphazardly stitching together random pieces of software. They grow organically over time in the hands of dedicated product and engineering teams.
A few years ago we noticed one class of customers that want the increased speed and scalability benefits of in-memory computing but just didn’t have the appetite for the development and simply shied away from using any in-memory computing products at all.
Instead of losing these customers, we’ decided to pick some of the most common use cases and create highly integrated, plug-n-play product, i.e accelerators, so that they can enjoy the benefits of in-memory computing without the need for development cycles or potential changes in their systems.
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 and Monitoring
No stack can be truly considered end-to-end without incorporating a single and unified management and monitoring system. GridGain prides itself on providing the #1 DevOps support technology among any in-memory computing company with its Visor Administration Console. GridGain’s Visor is a GUI-and CLI-based system that provides deep runtime management, monitoring, and operational command and control for running any production GridGain product.
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.