|By Nikita Ivanov||
|December 29, 2014 12:00 PM EST||
A few months ago, I spoke at the conference where I explained the difference between caching and an in-memory data grid. Today, having realized that many people are also looking to better understand the difference between two major categories in in-memory computing: In-Memory Database and In-Memory Data Grid, I am sharing the succinct version of my thinking on this topic - thanks to a recent analyst call that helped to put everything in place
Skip to conclusion to get the bottom line.
Let's clarify the naming and buzzwords first. In-Memory Database (IMDB) is a well-established category name and it is typically used unambiguously.
It is important to note that there is a new crop of traditional databases with serious In-Memory "options". That includes MS SQL 2014, Oracle's Exalytics and Exadata, and IBM DB2 with BLU offerings. The line is blurry between these and the new pure In-Memory Databases, and for the simplicity I'll continue to call them In-Memory Databases.
In-Memory Data Grids (IMDGs) are sometimes (but not very frequently) called In-Memory NoSQL/NewSQL Databases. Although the latter can be more accurate in some case - I am going to use the In-Memory Data Grid term in this article, as it tends to be the more widely used term.
Note that there are also In-Memory Compute Grids and In-Memory Computing Platforms that include or augment many of the features of In-Memory Data Grids and In-Memory Databases.
Confusing, eh? It is... and for consistency - going forward we'll just use these terms for the two main categories:
- In-Memory Database
- In-Memory Data Grid
It is also important to nail down what we mean by "In-Memory". Surprisingly - there's a lot of confusion here as well as some vendors refer to SSDs, Flash-on-PCI, Memory Channel Storage, and, of course, DRAM as "In-Memory".
In reality, most vendors support a Tiered Storage Model where some portion of the data is stored in DRAM (the fastest storage but with limited capacity) and then it gets overflown to a verity of flash or disk devices (slower but with more capacity) - so it is rarely a DRAM-only or Flash-only product. However, it's important to note that most products in both categories are often biased towards mostly DRAM or mostly flash/disk storage in their architecture.
Bottom line is that products vary greatly in what they mean by "In-Memory" but in the end they all have a significant "In-Memory" component.
It's easy to start with technical differences between the two categories.
Most In-Memory Databases are your father's RDBMS that store data "in memory" instead of disk. That's practically all there's to it. They provide good SQL support with only a modest list of unsupported SQL features, shipped with ODBC/JDBC drivers and can be used in place of existing RDBMS often without significant changes.
In-Memory Data Grids typically lack full ANSI SQL support but instead provide MPP-based (Massively Parallel Processing) capabilities where data is spread across large cluster of commodity servers and processed in explicitly parallel fashion. The main access pattern is key/value access, MapReduce, various forms of HPC-like processing, and a limited distributed SQL querying and indexing capabilities.
It is important to note that there is a significant crossover from In-Memory Data Grids to In-Memory Databases in terms of SQL support. GridGain, for example, provides pretty serious and constantly growing support for SQL including pluggable indexing, distributed joins optimization, custom SQL functions, etc.
Speed Only vs. Speed + Scalability
One of the crucial differences between In-Memory Data Grids and In-Memory Databases lies in the ability to scale to hundreds and thousands of servers. That is the In-Memory Data Grid's inherent capability for such scale due to their MPP architecture, and the In-Memory Database's explicit inability to scale due to fact that SQL joins, in general, cannot be efficiently performed in a distribution context.
It's one of the dirty secrets of In-Memory Databases: one of their most useful features, SQL joins, is also is their Achilles heel when it comes to scalability. This is the fundamental reason why most existing SQL databases (disk or memory based) are based on vertically scalable SMP (Symmetrical Processing) architecture unlike In-Memory Data Grids that utilize the much more horizontally scalable MPP approach.
It's important to note that both In-Memory Data Grids and In-Memory Database can achieve similar speed in a local non-distributed context. In the end - they both do all processing in memory.
But only In-Memory Data Grids can natively scale to hundreds and thousands of nodes providing unprecedented scalability and unrivaled throughput.
Replace Database vs. Change Application
Apart from scalability, there is another difference that is important for uses cases where In-Memory Data Grids or In-Memory Database are tasked with speeding up existing systems or applications.
An In-Memory Data Grid always works with an existing database providing a layer of massively distributed in-memory storage and processing between the database and the application. Applications then rely on this layer for super-fast data access and processing. Most In-Memory Data Grids can seamlessly read-through and write-through from and to databases, when necessary, and generally are highly integrated with existing databases.
In exchange - developers need to make some changes to the application to take advantage of these new capabilities. The application no longer "talks" SQL only, but needs to learn how to use MPP, MapReduce or other techniques of data processing.
In-Memory Databases provide almost a mirror opposite picture: they often requirereplacing your existing database (unless you use one of those In-Memory "options" to temporary boost your database performance) - but will demand significantly less changes to the application itself as it will continue to rely on SQL (albeit a modified dialect of it).
In the end, both approaches have their advantages and disadvantages, and they may often depend in part on organizational policies and politics as much as on their technical merits.
The bottom line should be pretty clear by now.
If you are developing a green-field, brand new system or application the choice is pretty clear in favor of In-Memory Data Grids. You get the best of the two worlds: you get to work with the existing databases in your organization where necessary, and enjoy tremendous performance and scalability benefits of In-Memory Data Grids - both of which are highly integrated.
If you are, however, modernizing your existing enterprise system or application the choice comes down to this:
You will want to use an In-Memory Database if the following applies to you:
- You can replace or upgrade your existing disk-based RDBMS
- You cannot make changes to your applications
- You care about speed, but don't care as much about scalability
In other words - you boost your application's speed by replacing or upgrading RDBMS without significantly touching the application itself.
On the other hand, you want to use an In-Memory Data Grid if the following applies to you:
- You cannot replace your existing disk-based RDBMS
- You can make changes to (the data access subsystem of) your application
- You care about speed and especially about scalability, and don't want to trade one for the other
In other words - with an In-Memory Data Grid you can boost your application's speed and provide massive scale by tweaking the application, but without making changes to your existing database.
It can be summarized it in the following table:
|In-Memory Data Grid||In-Memory Database|
|Existing RDBMS||Unchanged||Changed or Replaced|
A strange thing is happening along the way to the Internet of Things, namely far too many devices to work with and manage. It has become clear that we'll need much higher efficiency user experiences that can allow us to more easily and scalably work with the thousands of devices that will soon be in each of our lives. Enter the conversational interface revolution, combining bots we can literally talk with, gesture to, and even direct with our thoughts, with embedded artificial intelligence, whic...
Feb. 21, 2017 07:45 AM EST Reads: 248
The buzz continues for cloud, data analytics and the Internet of Things (IoT) and their collective impact across all industries. But a new conversation is emerging - how do companies use industry disruption and technology enablers to lead in markets undergoing change, uncertainty and ambiguity? Organizations of all sizes need to evolve and transform, often under massive pressure, as industry lines blur and merge and traditional business models are assaulted and turned upside down. In this new da...
Feb. 21, 2017 07:45 AM EST
It is one thing to build single industrial IoT applications, but what will it take to build the Smart Cities and truly society changing applications of the future? The technology won’t be the problem, it will be the number of parties that need to work together and be aligned in their motivation to succeed. In his Day 2 Keynote at @ThingsExpo, Henrik Kenani Dahlgren, Portfolio Marketing Manager at Ericsson, discussed how to plan to cooperate, partner, and form lasting all-star teams to change the...
Feb. 21, 2017 07:30 AM EST Reads: 4,129
"I think that everyone recognizes that for IoT to really realize its full potential and value that it is about creating ecosystems and marketplaces and that no single vendor is able to support what is required," explained Esmeralda Swartz, VP, Marketing Enterprise and Cloud at Ericsson, in this SYS-CON.tv interview at @ThingsExpo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Feb. 21, 2017 07:15 AM EST
When building large, cloud-based applications that operate at a high scale, it’s important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. “Fly two mistakes high” is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Lee A...
Feb. 21, 2017 07:00 AM EST Reads: 5,225
“We're a global managed hosting provider. Our core customer set is a U.S.-based customer that is looking to go global,” explained Adam Rogers, Managing Director at ANEXIA, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Feb. 21, 2017 04:30 AM EST Reads: 1,421
SYS-CON Events announced today that Linux Academy, the foremost online Linux and cloud training platform and community, will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Linux Academy was founded on the belief that providing high-quality, in-depth training should be available at an affordable price. Industry leaders in quality training, provided services, and student certification passes, its goal is to c...
Feb. 21, 2017 03:45 AM EST Reads: 871
Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at 20th Cloud Expo, Ed Featherston, director/senior enterprise architect at Collaborative Consulting, will discuss the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine.
Feb. 21, 2017 03:30 AM EST Reads: 3,588
Manufacturers are embracing the Industrial Internet the same way consumers are leveraging Fitbits – to improve overall health and wellness. Both can provide consistent measurement, visibility, and suggest performance improvements customized to help reach goals. Fitbit users can view real-time data and make adjustments to increase their activity. In his session at @ThingsExpo, Mark Bernardo Professional Services Leader, Americas, at GE Digital, discussed how leveraging the Industrial Internet and...
Feb. 21, 2017 03:30 AM EST Reads: 7,563
Cognitive Computing is becoming the foundation for a new generation of solutions that have the potential to transform business. Unlike traditional approaches to building solutions, a cognitive computing approach allows the data to help determine the way applications are designed. This contrasts with conventional software development that begins with defining logic based on the current way a business operates. In her session at 18th Cloud Expo, Judith S. Hurwitz, President and CEO of Hurwitz & ...
Feb. 21, 2017 03:00 AM EST Reads: 1,610
910Telecom exhibited at the 19th International Cloud Expo, which took place at the Santa Clara Convention Center in Santa Clara, CA, in November 2016. Housed in the classic Denver Gas & Electric Building, 910 15th St., 910Telecom is a carrier-neutral telecom hotel located in the heart of Denver. Adjacent to CenturyLink, AT&T, and Denver Main, 910Telecom offers connectivity to all major carriers, Internet service providers, Internet backbones and exchanges.
Feb. 21, 2017 02:45 AM EST Reads: 1,359
Whether you like it or not, DevOps is on track for a remarkable alliance with security. The SEC didn’t approve the merger. And your boss hasn’t heard anything about it. Yet, this unruly triumvirate will soon dominate and deliver DevSecOps faster, cheaper, better, and on an unprecedented scale. In his session at DevOps Summit, Frank Bunger, VP of Customer Success at ScriptRock, discussed how this cathartic moment will propel the DevOps movement from such stuff as dreams are made on to a practic...
Feb. 21, 2017 02:00 AM EST Reads: 4,577
As software becomes more and more complex, we, as software developers, have been splitting up our code into smaller and smaller components. This is also true for the environment in which we run our code: going from bare metal, to VMs to the modern-day Cloud Native world of containers, schedulers and micro services. While we have figured out how to run containerized applications in the cloud using schedulers, we've yet to come up with a good solution to bridge the gap between getting your contain...
Feb. 21, 2017 01:15 AM EST Reads: 3,283
The modern software development landscape consists of best practices and tools that allow teams to deliver software in a near-continuous manner. By adopting a culture of automation, measurement and sharing, the time to ship code has been greatly reduced, allowing for shorter release cycles and quicker feedback from customers and users. Still, with all of these tools and methods, how can teams stay on top of what is taking place across their infrastructure and codebase? Hopping between services a...
Feb. 21, 2017 01:00 AM EST Reads: 6,280
Niagara Networks exhibited at the 19th International Cloud Expo, which took place at the Santa Clara Convention Center in Santa Clara, CA, in November 2016. Niagara Networks offers the highest port-density systems, and the most complete Next-Generation Network Visibility systems including Network Packet Brokers, Bypass Switches, and Network TAPs.
Feb. 21, 2017 12:30 AM EST Reads: 727