the forrester wave™: in-memory data grids, q3 2015 · hazelcast, ibm, oracle, pivotal software,...

The Forrester Wave™: In-Memory Data Grids, Q3 2015There Is No Better Way To Achieve Blazing Fast Performance At Scale

by Mike GualtieriSeptember 23, 2015

For ApplicAtion DevelopMent & Delivery proFeSSionAlS

ForreSTer.coM

Key takeawayseleven In-Memory Data Grids Vie For enterprise AdoptionAmong the commercial and open source in-memory data and compute grid vendors Forrester evaluated, we found seven leaders, three Strong performers, and one contender.

Use In-Memory Data Grids To Make everything Faster At ScaleData read/write latency saps app performance, especially when disks and networks are involved. in-memory grids use the fastest memory on the planet -- rAM -- to exponentially reduce latency in a scale-out architecture.

Build IMDGs Into Your Application From The outsetAD&D pros should not make the mistake of turning to iMDGs only when performance at scale becomes an issue. it will become an issue sooner or later.

Why read this reportBlazing fast application performance — it’s what customers want and what every application development professional (AD&D) needs to deliver. there is no better way to achieve it than to use random access memory (rAM), which will give you low latency data access combined with distributed computing for reliability and scale. Start here with Forrester’s 32-criteria evaluation of 11 in-memory data grid (iMDG) products from vendors: Alachisoft, GigaSpaces, GridGain Systems, Hazelcast, iBM, oracle, pivotal Software, red Hat, Scaleout Software, Software AG, and tiBco Software.

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table of contents

Performance And Scale Never Go Out Of Style

In-Memory Data Grids Provide Performance At Scale

Get Started With one of these Use cases

In-Memory Data Grids Evaluation Overview

evaluation criteria: current offering, Strategy, And Market presence

iMDG Wave evaluation Assessed the capabilities of 11 vendor products

Enterprises Have Lots Of Solid Choices

Vendor Profiles

leaders

Strong performers

contenders

Supplemental Material

notes & resources

Forrester conducted product evaluations in July 2015 and interviewed 11 vendor companies: Alachisoft, GigaSpaces, GridGain Systems, Hazelcast, iBM, oracle, pivotal Software, red Hat, Scaleout Software, Software AG, and tiBco Software.

related research Documents

the Forrester Wave™: Big Data Streaming Analytics platforms, Q3 2014

Market overview: in-Memory Data platforms

For ApplicAtion DevelopMent & Delivery proFeSSionAlS

The Forrester Wave™: In-Memory Data Grids, Q3 2015There Is No Better Way To Achieve Blazing Fast Performance At Scale

by Mike Gualtieriwith Holger Kisker, ph.D., Mark Grannan, Sophia christakis, and ian Mcpherson

September 23, 2015

http://www.forrester.com/go?objectid=RES113442



http://www.forrester.com/go?objectid=BIO1858



For ApplicAtion Development & Delivery proFessionAls

The Forrester Wave™: In-Memory Data Grids, Q3 2015september 23, 2015

© 2015 Forrester research, inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

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There Is No Better Way To Achieve Blazing Fast Performance At Scale

performance And Scale never Go out of Style

customers won’t wait. they want apps to be instantly responsive. they also want apps that serve them well with increasing functionality. these requirements are polar opposites that often result in Sophie’s Choice compromises.1 compromise no more! AD&D professionals can design and implement the most dazzling, personalized customer experience into their apps, but it is all for naught if the performance of the app is subpar. App experiences have to be wonderful, which also means they must be blazing fast at any scale. this is a challenging combination of design goals because:

› Modern app experiences require more and faster data access. Apps naturally evolve to provide users with more information access and transactions. this information is often siloed in multiple databases, Apis, and other applications with different read/write rates. the problem is that the app’s read/write access will never be faster than the slowest data source. exacerbating the issue is that apps often have to access this data more frequently to provide advanced functionality like real-time updates.

› Scale and concurrency stress architectural components. the number of global smartphone subscribers is expected to reach 3.5 billion by 2019, crossing the 50% mark for smartphone penetration by population in 2017 and reaching 59% by 2019, up from 28% in 2013.2 As enterprises develop apps to become customers’ digital favorites, the number of users and frequency of use will expand even faster than the internet/mobile adoption rate. Apps that rely on architectural components that cannot scale-out will suffer not only in performance, but may not even work under the load of so many concurrent users.

in-Memory Data Grids provide performance At Scale

it’s the perfect storm — increasing app functionality, more concurrent users, and legacy back-end componentry. in-memory data and compute grids are perfectly designed to help AD&D professionals get through this storm with colors flying proudly. Forrester defines in-memory data grids (iMDG) as:

Software tools and technologies that are architected to use chip-based random access memory (RAM) distributed across multiple nodes to accelerate performance and achieve scalability of data access and compute.

the concept of in-memory data grids to provide performance at scale is simple:

› Use rAM to reduce latency of data read/write access. Aside from central processing unit (cpU) registers, the fastest way on the planet to read/write data is to use chip-based rAM. rAM is 58,000 times faster than disk and 2,000 times faster than solid-state drives (SSD).3 As the price of rAM continues to drop with even faster performance, just the thought of in-memory no longer conjures up dollar signs. Many in-memory grids can also extend storage capacity to SSD, which is also becoming faster with each successive generation.




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› Distribute data across nodes to scale linearly. Modern applications must not only be highly performant, they must also scale to handle large amounts of data and large numbers of users. in-memory grids implement a distributed architecture to additively support an increased storage capacity by adding additional nodes (servers) to the grid (see Figure 1). For example, if a single node in the grid has 256MB rAM, then the grid capacity could be expanded to 1tB by creating a grid with 4 nodes (256MB x 4 ~ 1tB). Actual usable storage capacity will be less than 1tB because in-memory grids can also make multiple copies of the data to achieve high-availability.

› replicate data across nodes to achieve high-availability. one of the frightening characteristics of rAM compared to disk is that data stored in rAM is lost forever while data stored on disk is magnetically etched in perpetuity. to overcome this vulnerability, in-memory data grids replicate the data to one or more nodes (see Figure 2). if a node goes down, a copy of the data is available on another node in the grid. new non-volatile rAM (nvrAM) chips are becoming available that store data even after the power is lost. However, this will not alleviate the need for replicas in grids to achieve high-availability because even if the data is preserved the application is still dead in the water if the node is down.

› Synchronize data across data centers for disaster recovery. like all applications, in-memory grids are vulnerable to data center failures whether it is an enterprise data center or a cloud data center. in-memory grids can be configured to support wide-area network (WAn) replication to copy data across data centers (see Figure 3). in the event that a data center goes down, the application can seamlessly access data from the other data center. Some in-memory grids can also be configured active/active to not only support disaster recovery, but also to distribute data access load geographically.




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FIGUre 1 in-Memory Data Grids provide Blazing Fast Access to Data And can Scale linearly

Node 1 Node 2 Node 3

Scale out

In-memory data grid

Applications

Future

RAM RAM RAM

FIGUre 2 iMDGs replicate Data to one or More nodes to Achieve High-Availability

Node 1

Copy 2

Data 1

Node 2

Data 2

Copy 1

Data replication




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FIGUre 3 iMDGs can Synchronize Data Across Data centers For Disaster recovery capability

Node 4

Node 1

Node 2

Node 3

WANreplication

Data center 1

Node 4

Node 1

Node 2

Node 3

Data center 2

Get Started With one of These Use cases

Business technology professionals often discover and then implement an in-memory grid only after they experience a critical application performance problem. in-memory grids are, in fact, a perfect solution to dramatically boost performance and scale for existing applications. However, they are extremely versatile data platforms that can satisfy a number of key use cases whose common thread is blazing fast performance and linear scale. Make an in-memory grid a standard architectural component in your application infrastructure for any or all of the following:

› cache to overcome legacy data bottlenecks. the most popular use case for in-memory grids is to act as a speedy object store to mitigate performance and scale issues with relational database management systems (rDBMS). Many web and mobile apps access data from one or more rDBMSes that often lead to performance and/or scale issues because traditional rDBMS are designed to scale-up rather than scale-out. More concurrency, more complex queries, and more transactions all strain the scale-up architecture to a breaking point. An in-memory grid can be added to the application architecture as a blazing fast cache for read/write data operations between the application and the rDBMS. caching is not limited to rDBMS. Data from mainframes, file systems, and other data sources can also be cached in the grid. Sometimes the performance bottleneck occurs when all of these sources must be read/write together.

› cache for transient data. Many applications create transient data such as session data or shared data such as online gaming or other apps that share data. in-memory data grid can cache the transient data.




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› Primary store for modern apps. there is no reason why an in-memory grid cannot be the system of record (Sor) for applications. in-memory grids provide superfast data access, scale-out architecture, compute capabilities, and high-availability through replication. Modern applications have these requirements from the get-go. Savvy application architects include in-memory grids from the start.

› NoSQL database at in-memory speed. Are you considering a noSQl database such as Apache cassandra, Apache HBase, or MongoDB data store for your next app?4 Full stop. AD&D professionals should also consider an in-memory data grid for the exact same purpose. in-memory data grids are flexible key-value stores just like noSQl databases. they can be configured to support either atomicity, consistency, isolation, and durability (AciD) transactions or eventual consistency, which is a plus over many noSQl databases that only support eventual-consistency.

› Data services fabric for real-time data integration. enterprises are saddled with a long legacy of siloed applications, each with its own data source. Multiple modern applications often require data from a combination of these sources and they need it in real time. For example, a 360-degree view of the customer that is assembled from several siloed applications. An in-memory data grid is an excellent way to integrate these source and make it available at in-memory speeds.

› compute grid at in-memory speed. in-memory data grids are not just about data. Most of the products also provide frameworks such as Mapreduce and others to process arbitrarily complex batch process on the data in the grid at in-memory speeds. in-memory data grids can be an alternative to Hadoop and Spark for batch processing while at the same time acting as a Sor for one or more applications.5

in-Memory Data Grids evaluation overview

to assess the state of the market and see how the vendors stack up against each other, Forrester evaluated the strengths and weaknesses of top in-memory data and compute grid vendors.

evaluation criteria: current offering, Strategy, And Market Presence

After examining past research, user requirements, and vendor interviews, we developed a comprehensive set of evaluation criteria. We evaluated vendors against 32 criteria, which we grouped into three high-level buckets:

› current offering. We evaluated each product’s architecture, scalability, performance, high-availability, platform administration, core data grid and compute capabilities, development tools, and other features to establish the capabilities of the vendor’s current offering. All products evaluated must have been publicly available by May 1, 2015.




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› Strategy. We reviewed each vendor’s strategy to assess their ability to compete and grow in the in-memory data and compute grid market. Key criteria include Forrester’s level of confidence in the vendor’s ability to execute on its stated strategy and support current and future customers. Forrester also reviewed each vendor’s product road map to assess how it will affect the vendor’s competitive position compared to the other vendors in this evaluation.

› Market presence. to determine each vendor’s market presence we evaluated overall vendor revenue, iMDG revenue, number of paying customers, global distribution of paying customers, market awareness of the vendor’s product, and partnerships with other technology and services firms.

IMDG Wave evaluation Assessed The capabilities of 11 Vendor Products

Forrester included 11 vendors in the assessment: Alachisoft, GigaSpaces, Gridgain Systems, Hazelcast, iBM, oracle, pivotal Software, red Hat, Scaleout Software, Software AG, and tiBco Software. each of these vendors has (see Figure 4):

› core IMDG functionality. the vendors included in this evaluation provide an in-memory grid (cluster) architecture for data operations stored in rAM. the solution must scale to handle the requirements of the world’s largest enterprises, organizations, and government agencies.

› original, general-purpose technology. the products included in this evaluation are general purpose iMDG products that aren’t embedded or functionally focused within domain-specific applications. For proprietary platforms, vendors must be the creator and owner of the technology. For open source platforms, vendors must be the primary steward for the open source community and may also include and/or offer proprietary add-ons.

› Has a significant number of customers using the platform. each vendor must have at least 10 paying customers using their product in production (most have well over that and a few have over 1,000 paying customers). each vendor must also provide at least one customer reference that is willing to be interviewed by Forrester about their experience using the product.




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FIGUre 4 evaluated vendors: product information

Vendor

Alachisoft

GigaSpaces

GridGain Systems

Hazelcast

IBM

Oracle

Pivotal Software

Red Hat

ScaleOut Software

Software AG

TIBCO Software

Product

NCache

XAP

GridGain In-Memory Data Fabric Enterprise Edition

Hazelcast

IBM WebSphere eXtreme Scale

Oracle Coherence

Pivotal GemFire

Red Hat JBoss Data Grid

ScaleOut StateServer

Terracotta In-Memory Data Fabric Platform

ActiveSpaces

Version

4.4 SP1

10.1

v. 7.0.x

3.4

v8.6

12c (12.1.3)

v8.1

6.4

5.3.1.250

4.3

2.1.5

enterprises Have lots of Solid choices

Forrester’s evaluation of iMDG platforms uncovered a market with seven leaders, three Strong performers, and one contenders (see Figure 5):

› Leaders have breadth and depth. oracle, Software AG, iBM, red Hat, pivotal Software, GigaSpaces, and Hazelcast are leaders. these vendors offer the most comprehensive set of features to accommodate the broadest use cases and have good to excellent strategies.

› Strong Performers are solid choices, but may lack in one or more areas. GridGain Systems, Alachisoft, and Scaleout Software are Strong performers. these vendors are may lack some “edge” features that the leaders have but are excellent choices for most use cases.

› contenders require more engineering to fill gaps. tiBco Software is a contender. tiBco Software has focused its product road map on using ActiveSpaces to accelerate its other platforms. if tiBco Software wishes to compete in this market, it will have to implement features that meet or exceed the other iMDG vendors.




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this evaluation of the in-memory data and compute grid market is intended to be a starting point only. We encourage clients to view detailed product evaluations and adapt criteria weightings to fit their individual needs through the Forrester Wave excel-based vendor comparison tool. clients can also schedule an inquiry with Forrester analyst Mike Gualtieri to have a conversation about the market and specific vendor products.

FIGUre 5 Forrester Wave™: in-Memory Data Grids, Q3 2015

Contenders LeadersStrong

Performers

StrategyWeak Strong

Currentoffering

Weak

Strong

Go to Forrester.com to

download the

Forrester Wave tool for

more detailed product

evaluations, feature

comparisons, and

customizable rankings.

Market presence

Alachisoft

GigaSpaces

GridGainSystems

Hazelcast

IBM

Oracle

PivotalSoftware

RedHat

ScaleOut Software

Software AG

TIBCO Software

Challengers




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FIGUre 5 Forrester Wave™: in-Memory Data Grids, Q3 2015 (cont.)

CURRENT OFFERING Architecture Platform administration Security Data features Compute features Development STRATEGY Acquisition Ability to execute Implementation support Solution road map MARKET PRESENCE Company �nancials Customer base Partnerships

4.484.504.005.004.804.404.00

3.381.503.005.004.00

2.881.504.002.00

2.852.755.001.003.100.402.50

3.505.003.003.003.00

1.881.002.751.00

Forr

este

r’sW

eigh

ting

50%30%15%5%

30%10%10%

50%25%25%25%25%

0%25%50%25%

3.513.254.003.003.204.204.00

2.882.503.003.003.00

1.130.001.751.00

3.723.253.003.004.004.405.00

3.755.003.003.004.00

2.381.002.753.00

4.254.004.005.004.803.604.00

4.504.005.005.004.00

4.133.504.005.00

4.624.754.005.005.004.404.00

4.754.005.005.005.00

4.884.505.005.00

3.604.003.003.003.204.404.00

4.504.005.005.004.00

3.383.004.751.00

3.853.754.003.003.703.605.00

4.505.005.005.003.00

3.003.003.502.00

3.573.255.001.003.404.203.50

2.631.503.003.003.00

2.001.003.001.00

4.394.254.003.005.003.605.00

4.755.005.005.004.00

4.754.005.005.00

2.381.883.001.003.001.902.25

2.130.503.003.002.00

3.003.503.751.00

All scores are based on a scale of 0 (weak) to 5 (strong).

Ala

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Gig

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Grid

Gai

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yste

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Haz

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IBM

Ora

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Piv

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Sof

twar

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Red

Hat

Sca

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Sof

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TIB

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vendor profiles

Leaders

› oracle innovates to stay on top of the category it invented. 2007 seems like a generation ago in technology years, but that is when oracle acquired tangosol arguably the company that created the market for in-memory data grids that were more than just data caches. the iMDG that started it all continues to set the standard for all other iMDG vendors.

› Software AG makes Terracotta a seamless digital business component. terracotta was one of the first iMDGs that originated in the open source community. terracotta has a unique architecture compared to the other vendors in that it uses a striping architecture instead of a peer-to-peer architecture to achieve high-availability. Software AG has made terracotta a key component to accelerate its entire suite of products.




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› IBM’s engineering prowess shines through. not surprisingly, iBM eXtreme Scale is a capable solution for any large enterprise. of particular note is eXtreme Scale’s WAn solution to support active/active access to the grid across multiple data centers.

› red Hat provides the purest of the open source solutions. red Hat fine-tuned JBoss data grid to work seamlessly with JBoss. it is also a very capable iMDG for a wide range of applications that require acceleration.

› Pivotal Software open sources GemFire to spur community innovation. GemFire has it all — data and compute features that have been tested under the most challenging conditions. GemFire was one of the original iMDG that was quickly adopted by financial customers due to its compute and data capability. pivotal Software has contributed GemFire to the Apache open source community as Geode.

› GigaSpaces platform is up to any task. if you can make it on Wall Street, you can make it anywhere. GigaSpaces was the first vendor to create an iMDG for Java that is used by investment banks and other industries. they are also one of the few leading pure-play iMDG vendors.

› Hazelcast gives the big enterprise vendors a run for their money. open source and venture backed, Hazelcast went from an obscure, free open source option to a “taking names and kicking ass” enterprise option. venture capital and professional management made all the difference for Hazelcast. it is still open source, but now run by former terracotta cto Greg luck. Hazelcast is one of the few pure-play vendors that is a leader.

Strong Performers

› GridGain Systems is the compute grid trailblazer. iMDG’s, in general, suffer from a caching stereotype. GridGain Systems smashes that stereotype by offering a general purpose compute grid. if you need an iMDG and a super-computer all in one then take a look at GridGain Systems. GridGain Systems open sourced its core technology by contributing it to the Apache ignite project which is now a top-level Apache Software Foundation project.

› Alachisoft excels for Microsoft shops, but has fast plans to branch out. Alachisoft is an amazingly easily iMDG solution to implement and is one of the only server-side Windows solution. even though Alachisoft has been around since 2003, they remain a hidden gem due to their lack of marketing. But, every .net shop should add Alachisoft to their short list of iMDG’s because it can immediately accelerate .net web application. Alachisoft plans to extend their expertise to the open source world by implementing their core iMDG in Java to address that market.

› Scaleout Software is pleasurably easy to implement. if you need fast results, try Scaleout Software. Scaleout Software offers all the key features of an iMDG but also provides simple installation and configuration tools. Scaleout Software also distinguishes itself by providing a Mapreduce framework for the grid.




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contenders

› TIBco Software accelerates their platform portfolio with ActiveSpaces. tiBco Software’s focus for ActiveSpaces has always been to accelerate other tiBco Software products. thus, it has not focused on a feature-for-feature competition with the other vendors in this Forrester Wave evaluation. ActiveSpaces has a strong base, but needs to add more features to become a Strong performer.

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Supplemental Material

online resource

the online version of Figure 5 is an excel-based vendor comparison tool that provides detailed product evaluations and customizable rankings.

Data Sources Used In This Forrester Wave

Forrester used a combination of 32 data sources to assess the strengths and weaknesses of each solution:

http://forr.com/1einFan

http://www.forrester.com/Analyst-Advisory/-/E-MPL172




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› Vendor surveys. Forrester surveyed vendors on their capabilities as they relate to the evaluation criteria. once we analyzed the completed vendor surveys, we conducted vendor calls where necessary to gather details of vendor qualifications.

› Product briefings and demos. We asked vendors to conduct briefings and demonstrations of their product’s functionality. We used findings from these product briefings and demos to validate details of each vendor’s product capabilities.

› customer reference calls. to validate product and vendor qualifications, Forrester also conducted reference calls or conducted surveys with at least one of each vendor’s current customers.

The Forrester Wave Methodology

We conduct primary research to develop a list of vendors that meet our criteria to be evaluated in this market. From that initial pool of vendors, we then narrow our final list. We choose these vendors based on: 1) product fit; 2) customer success; and 3) Forrester client demand. We eliminate vendors that have limited customer references and products that don’t fit the scope of our evaluation.

After examining past research, user need assessments, and vendor and expert interviews, we develop the initial evaluation criteria. to evaluate the vendors and their products against our set of criteria, we gather details of product qualifications through a combination of lab evaluations, questionnaires, demos, and/or discussions with client references. We send evaluations to the vendors for their review, and we adjust the evaluations to provide the most accurate view of vendor offerings and strategies.

We set default weightings to reflect our analysis of the needs of large user companies — and/or other scenarios as outlined in the Forrester Wave document — and then score the vendors based on a clearly defined scale. these default weightings are intended only as a starting point, and we encourage readers to adapt the weightings to fit their individual needs through the excel-based tool. the final scores generate the graphical depiction of the market based on current offering, strategy, and market presence. Forrester intends to update vendor evaluations regularly as product capabilities and vendor strategies evolve. For more information on the methodology that every Forrester Wave follows, go to http://www.forrester.com/marketing/policies/forrester-wave-methodology.html.

Integrity Policy

All of Forrester’s research, including Forrester Wave evaluations, is conducted according to our integrity policy. For more information, go to http://www.forrester.com/marketing/policies/integrity-policy.html.




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endnotes1 the expression “Sophie’s choice,” from the novel and film of the same name, has come to mean an impossibly

difficult choice in a no-win situation.

2 Forrester’s mobile and smartphone adoption forecast details the global installed base and subscriber data. it includes data on business-owned smartphone subscribers and subscribers by operating system (2011 to 2014 only), as well as age and income ownership demographics. it also includes historical data going back to 2009 plus a five-year forecast. See the “Forrester research World Mobile And Smartphone Adoption Forecast, 2014 to 2019 (Global)” Forrester report.

3 For performance difference calculation between rAM, SSD, and disk the following access times are used: rAM 50ns, SSD 100,000ns, and disk 2,900,000ns. performance can vary greatly due to specific operations and other factors. A comparison between SSD and disk performance can be found on Wikipedia. Source: “Solid-state drive,” Wikipedia (https://en.wikipedia.org/wiki/Solid-state_drive#comparison_with_other_technologies).

rAM performance can be found here on Wikipedia. Source: vangie Beal, “access time,” Webopedia (http://www.webopedia.com/terM/A/access_time.html).

4 noSQl document databases are optimized to store, access, and manage semi-structured information like documents. in Forrester’s 57 criteria evaluation of noSQl document database vendors, we evaluated MongoDB, Marklogic, cloudant, and couchbase. We scored factors like performance, scalability, integration, security, high availability, workload management, and form factor. this report details our findings on how well each solution fulfills the criteria and where it stands in relation to other offerings. See the “the Forrester Wave™: noSQl Document Databases, Q3 2014” Forrester report.

Key-value databases can handle web scale — thousands of servers and millions of users — with extremely fast, optimized storage and retrieval. top use cases for key-value database include social and mobile apps, scale-out apps, Web 2.0, line-of-business (loB) apps, big data apps, and operational and analytical apps. to assess the state of the noSQl key-value database market, Forrester evaluated the strengths and weakness across 57 criteria of seven leading solutions. this report details our findings on how well each solution fulfills the criteria and where it stands in relation to other offerings. See the “the Forrester Wave™: noSQl Key-value Databases, Q3 2014” Forrester report.

5 Apache Spark is an open source cluster computing platform designed to process big data as efficiently as possible. you’ll often hear Hadoop and Spark mentioned in the same breath. that’s because, although they are independent platforms in their own right, they have an evolving, symbiotic relationship. Application development and delivery professionals (AD&D) must understand the key differences and synergies between this next-generation cluster-computing power couple to make informed decisions about their big data strategy and investments. See the “Apache Spark is powerful And promising” Forrester report.







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