DOCSLIB.ORG

5 Signs Your Cache + Database Architecture May Be Obsolete

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
5 Signs Your Cache + Database Architecture May Be Obsolete 5 Signs Your Cache + Database Architecture May Be Obsolete Executive Overview For years using an external caching layer with an RDBMS was the accepted conventional wisdom for attaining performance and reliability. Data was kept in memory by the cache to reduce the amount of time spent querying the underlying data store. As data volumes grew, NoSQL databases were substituted for the RDBMS to provide horizontal scaling to clusters and to keep latency down. Then “Digital Transformation” happened. “The growth in servers needed to handle the In the blink of an eye, simple web applications have become systems of growth in data could no longer be funded by engagement (SoE) that are now serving billions of objects, with millions the growth in revenue.” of contextual data points and enabling rich interactions and -CTO, Digital Identity Firm engagement - all in a few milliseconds. Data has grown in both velocity and volume - the more data, delivered faster, the richer the engagement and the better the decision. Larger datasets of up to 40TB to 100TB are now common, according to Forrester Research1, and there is no end in sight to data growth or to transaction velocity. The only way to support modern velocities simultaneously with deal with growing data volumes that much data in the cache is to be stuck in a never-ending cycle of adding cache nodes and deploying increasingly -complex cache management systems and strategies. This approach ignores the high cost of external DRAM caching layers as their continual expansion is , for core principles that huge and growing data sets mean an external caching layer is unfundable over time and requires significant investments in managing complex data lifecycle issues such as cache consistency and correctness which are not core non-core issues. Translation: the cache is unaffordable, untrustable, and unstable at those volumes. It’s time to challenge conventional wisdom. Even though it’s still common practice today, the harsh reality is that using an external cache layer as a fundamental component of a System of Engagement (SoE), where huge scale, ultrafast response, and rock-solid reliability are critical success factors, is a last decade approach for solving next decade problems. Instead of more memory, or a better cache, perhaps what’s needed is a better data architecture - one which uses the fact that systems of engagement typically don’t require; and doesn’t rely on an external caching layer for performance, reliability, or scale: Hybrid Memory Architecture. Forrester defines hybrid memory architecture as a database architecture that combines the use of DRAM and SSD and combines DRAM's parallelism and SSD's fast, random bulk storage capability to provide near DRAM performance with SSD reliability and lower cost, including integrated internal DRAM caches. A hybrid memory database provides automation to simplify the access and processing of data, and support for transactional, operational and analytical workloads. It leverages SSD and flash natively in an optimized and engineered manner. Fortunately, the Aerospike database, built with hybrid memory architecture, allows you to eliminate that external caching layer while simultaneously handling internet-scale data volumes at sub-millisecond response times and with significantly fewer servers. It is used in production and trusted by industry-leading organizations to power their Systems of Engagement. 1 “Forrester Study on Hybrid Memory NoSQL Architecture for Mission-Critical, Real-Time Systems of Engagement” 2017 Forrester Research, Noel Yuhanna © 2017 Aerospike, Inc. All rights reserved. Aerospike and the Aerospike logo 2525 E. Charleston Road, Suite 201 Are trademarks or registered trademarks of Aerospike. All other names and Mountain View, CA 94043 trademarks are for identification purposes and are the property of their respective owners. Tel: 408 462 AERO (2376) www.aerospike.com 5 Signs Your Cache + Database Architecture May Be Obsolete 5 Signs Your Cache + Database Architecture May Be Obsolete What are the 5 signs your external cache + database architecture may be obsolete? Your caching nodes are growing at an uncontrolled pace and you’re preparing yet another 1 unplanned budget variance request - Is your growth in data volume or access patterns driving ever-larger caching clusters (30 nodes or more)? - Are you frequently requesting budget for more memory or servers, over and above your original projections? It takes hours or even days to repopulate your cache after a disruption, and that is now part of 2 your daily operations - Do you frequently hold new nodes or servers out of production while the cache gets repopulated? For how long? - Are you worried that your data will be out of date while the cache gets rebuilt? You have implemented a cache-first architecture, but you still can’t meet your SLAs. 3 - What’s causing you to miss your SLAs? At what cost? - Even if you are meeting your SLAs today, will you be able to meet them as your data and customer size grows? Your cache has grown to where you need clustering - and that scares you 4 - Do you have the time, talent, and experience to rearchitect your new system for clustering? - Are you worried that the new architecture may not support further - especially unexpected - growth? You’ve “solved” your cache stampede problem by code revision - again – and you now have to 5 update code libraries; or push a cache proxy update – again - Do you experience cache stampede often? - Have you tried different algorithms to solve the problem? - Is it becoming hard to convince your development groups to use the “latest” fix for this problem? What Is Hybrid Memory Architecture and Why Is It important? The cache + database architecture was never designed to handle the volume and latency requirements for today’s systems of engagement. An RDBMS just doesn’t scale horizontally and time-consuming complex joins and queries are rarely necessary. First-generation NoSQL databases are built for horizontal scale but can’t maintain sub- millisecond response times. 5 Signs Your Cache + Database Architecture May Be Obsolete Why is hybrid memory architecture important? “Hybrid memory architecture is a new approach that leverages both volatile memory (DRAM) and nonvolatile memory such as SSD and flash to deliver consistent, trusted, reliable and low-latency access to support existing and new generations of transactional, operational, and analytical applications. Implementing this type of architecture allows organizations to move from a two-tier in-memory architecture to a single-tier structure that simplifies the movement and storage of data without requiring a caching layer. Early-adopters are seeing several benefits to their business like a lower cost of ownership, huge reductions in their server footprint, simplified administration and improved scalability.”2 Hybrid memory architecture has the following characteristics: 1. Stores indices in memory (DRAM) and data on SSD storage, reducing server count by as much as a factor of 10. 2. Has multi-threaded, massively parallel systems for predictable performance. 3. Significantly improves uptime and availability, without complex and error-prone manual DevOps processes. 4. Automatically distributes both data and traffic to all the nodes in a cluster. Clients automatically do load balancing for improved performance and correctness. 5. Provides better data consistency, because external cache unreliability is no longer an issue.3 Aerospike Database hybrid memory architecture eliminates the need for the out-dated and costly external cache layer; it’s the best way to architect systems of engagement. The result is significantly better performance, reliability and uptime, as well as simplified datacenter operations. “The business benefits of making this leap (to hybrid memory architecture) can be far-reaching and position an organization for the next wave of digital business growth in a world of instant analytic insights on real-time data.”4 Five Signs Your Cache + Database Architecture Is Obsolete Sign #1: Your caching nodes are growing at an uncontrolled pace and you’re preparing yet another unplanned budget variance request As your business grows, so does your data and the size of your cache, at a rate that’s directly proportional. As the value of engagement increases, new applications and projects clamor for database access, increasing transaction volumes and cache working set sizes. Server counts need to keep up, even though budgets were set at the beginning of the year. When growth happens beyond expectations it must be addressed or your System of Engagement won’t be able to keep up. 2 “Hybrid Memory Architecture Drives Real-Time Systems of Engagement” Forrester Research, May 2017 3 Ibid. 4 “Delivering Digital Business Value Using Practical Hybrid Transactional/Analytical Processing” Gartner, Inc. Published: 27 April 2017 ID: G00314780 5 Signs Your Cache + Database Architecture May Be Obsolete As noted above, our customers who were using the old cache + database architecture found that funding this growth was simply unsustainable. Business growth means a non-linear growth in data as more information is compiled or queried per customer and per transaction. Add to that the use of additional data sources for better analysis (more data is always considered better when it comes to transactional analytics) and your cache will rapidly and happily expand to consume the budget available - and then some. The operative word here in the quote to the left is “when” and “When your cache runs out of not “if.” The companies at the marquee 2017 Redis conference memory…” who presented “solutions” to their cache management issues -(overheard from multiple presenters at ranged in size from a few dozen employees to some of the RedisConf2017) largest, well-known companies on the planet. They all acknowledged that the issues and strategies for dealing with cache management were serious and common enough to warrant presenting them to other users. No one using an external cache, it seems, is immune from the challenge of running out of memory.
Load more

Recommended publications
  • Optimized Database Performance Contents Your Customers Need Lots of Data, Fast
    Improve customer experience in B2B SaaS with optimized database performance Contents Your customers need lots of data, fast Your customers need lots of data, fast ......................... 2 Business to business applications such as human resources (HR) portals, collaboration tools and customer relationship management (CRM) Memory and storage: completing the pyramid .......... 3 systems must all provide fast access to data. Companies use these tools Traditional tiers of memory and storage ....................... 3 to increase their productivity, so delays will affect their businesses, and A new memory tier .................................................................. 4 ultimately their loyalty to the software provider. Additional benefits of the 2nd generation Intel® As data volumes grow, business to business software as a service (B2B SaaS) providers need to ensure their databases not only have the Xeon® Scalable processor .....................................................4 capacity for lots of data, but can also access it at speed. Improving caching with Memory Mode .......................... 5 Building services that deliver the data customers need at the moment More responsive storage with App Direct Mode ........ 6 they need it can lead to higher levels of customer satisfaction. That, in turn, could lead to improved loyalty, the opportunity to win new Crawl, walk, run ......................................................................... 8 business, and greater revenue for the B2B SaaS provider. The role of SSDs ......................................................................
    [Show full text]
  • Grade Database Architecture for Mission-‐Critical, Real
    WHITE PAPER Building an Enterprise-Grade Database Architecture for Mission-Critical, Real-Time Applications Table of contents A new generation of applications – Systems of engagement ......................................... 3 New challenges – Always on at the speed of now .......................................................... 3 Shortcomings of conventional technologies ................................................................... 4 Limitations of relational databases .............................................................................. 4 Emergence of NoSQL ................................................................................................... 4 Challenges of adding a caching layer ........................................................................... 5 A better way – In-memory speed without a caching layer ............................................. 7 The industry’s first SSD-optimized NoSQL database architecture ............................... 7 How it works ................................................................................................................... 8 Exploiting cost and durability breakthroughs in flash memory technology ................ 9 Enterprise-grade NoSQL has arrived ............................................................................... 9 Financial services – Position system of record and risk ............................................... 9 More sample use cases ................................................................................................
    [Show full text]
  • Aerospike Multi-Site Clustering: Globally Distributed, Strongly Consistent, Highly Resilient
    Aerospike Multi-site Clustering: Globally Distributed, Strongly Consistent, Highly Resilient Transactions at Scale Contents Executive Summary ......................................................................................................... 3 Applications and Use Cases ........................................................................................... 3 Fundamental Concepts.................................................................................................... 5 The Aerospike Approach ................................................................................................. 6 Core Technologies ........................................................................................................... 7 Rack Awareness ..................................................................................................................................... 7 Strong, Immediate Data Consistency ..................................................................................................... 8 Operational Scenarios ..................................................................................................... 9 Healthy Cluster ....................................................................................................................................... 9 Failure Situations .................................................................................................................................. 10 Summary.........................................................................................................................
    [Show full text]
  • Aerospike Vs. Cassandra - a Brief Comparison
    Benchmark Summary Aerospike vs. Cassandra - a Brief Comparison As a leading NoSQL database, Aerospike delivers fast, predictable performance at scale at a much lower Total Cost of Ownership (TCO) than other alternatives. Our tests with Apache Cassandra showed that Aerospike delivered 15.4x better transaction throughput, 14.8x better 99th percentile read latency and 12.8x better 99th percentile update latency. And Aerospike did that at less than 10% of Cassandra’s cost. Perhaps that’s hard to believe. Why not explore the details yourself? Benchmark Summary We tested Aerospike 4.6.0.4 Community Edition and Apache Cassandra 3.11.4 using the Yahoo Cloud Serving Benchmark (YCSB). To run a balanced mix of read and write operations for 24 hours on 2.65 TB of unique user data with a replication factor of 2, the total user data was 5.3 TB. Our environment included three Dell® R730xd rack-mounted servers with 128 GB of DRAM and two 14 core Xeon processors (56 hyperthreads) on each server. Each server utilized two 1.6 TB Micron 9200 Max NVMe SSDs for data and ran Centos Linux version 7.6. As Table 1 presents Aerospike’s throughput and latency advantages at the 99th percentile and 95th percentile, which were unmatched. Read Results Update Results Throughput 95th 99th Throughput 95th 99th Transactions Percentile Percentile Transactions Percentile Percentile (per second) Latency Latency (per second) Latency Latency (µsecs) (µsecs) (µsecs) (µsecs) Apache 23,999 8,487 17,187 23,999 8,319 20,109 Cassandra 3.11.4 Aerospike CE 369,998 744 1,163 369,998 1,108 1,574 4.6.0.4 Aerospike 15.4x 11.4x 14.8x 15.4x 7.51x 12.8x advantage better better better better better better Table 1: YCSB results summary Maybe you’re wondering why Cassandra didn’t fare better.
    [Show full text]
  • Drivescale Architecture for Data Center Scale Composability
    DriveScale Architecture for Data Center Scale Composability by Brian Pawlowski, Chris Unkel, Jim Hanko, Jean-François Remy 1 Overview of talk § Defining composable infrastructure § What the administrator sees § DriveScale design and architecture § Future directions § Recapitulation § Questions 2 Defining composable infrastructure 3 DriveScale – Disaggregate then Compose Captive, fixed DAS Composable DAS DAS Disaggregate DAS DAS DAS DAS Purchase Time Right sized Defined Infrastructure Software Defined Infrastructure 4 Composable Infrastructure – blade chassis scale • Single vendor hardware • Low scalability 10,000 Servers 100,000 Drives 5 Composable Infrastructure – data center scale 10,000 Servers 100,000 Drives • Choose optimized dense diskless servers, optimized storage platforms for HDD and SSD • Multivendor – mix and match • Multiprotocol Ethernet (iSCSI, ROCEv2, NVME over TCP) for wide variety of use cases • Composer platform server(s) configuration: dual socket x86, 128GB DRAM, SSD 6 Composable Infrastructure at scale Compose any compute to any drive Ethernet to storage adapters PhysicalLogical View View Hadoop Cassandra Aerospike 7 Think Bare Metal Virtualized Infrastructure for Linux, at scale – but without the VMs. 8 The devil is in the details § Breaking up (disaggregation) is easy (in my pocket) § Composable Infrastructure – at scale – is hard • Create secure durable bindings between servers and drives • Plumb end-to-end Linux storage stack over multiple protocols, multipathing, file systems, RAID configurations, hardware vendors
    [Show full text]
  • Aerospike: Architecture of a Real-Time Operational DBMS
    Aerospike: Architecture of a Real-Time Operational DBMS V. Srinivasan Brian Bulkowski Wei-Ling Chu Sunil Sayyaparaju Andrew Gooding Rajkumar Iyer Ashish Shinde Thomas Lopatic Aerospike, Inc. [email protected] ABSTRACT correct advertisement to a user, based on that user’s behavior. You In this paper, we describe the solutions developed to address key can see the basic architecture of the ecosystem illustrated in technical challenges encountered while building a distributed Figure 1. database system that can smoothly handle demanding real-time workloads and provide a high level of fault tolerance. Specifically, we describe schemes for the efficient clustering and data partitioning for the automatic scale out of processing across multiple nodes and for optimizing the usage of CPUs, DRAM, SSDs and networks to efficiently scale up performance on one node. The techniques described here were used to develop Aerospike (formerly Citrusleaf), a high performance distributed database system built to handle the needs of today’s interactive online services. Most real-time decision systems that use Aerospike require very high scale and need to make decisions within a strict SLA by reading from, and writing to, a database containing billions of data items at a rate of millions of operations per second with sub-millisecond latency. For over five years, Aerospike has been continuously used in over a hundred successful production deployments, as many enterprises have discovered that it can Figure 1: RTB technology stack substantially enhance their user experience. In order to participate in the real-time bidding [22] process, every participant in this ecosystem needs to have a high-performance 1.
    [Show full text]
  • Memcached, Redis, and Aerospike Key-Value Stores Empirical
    Memcached, Redis, and Aerospike Key-Value Stores Empirical Comparison Anthony Anthony Yaganti Naga Malleswara Rao University of Waterloo University of Waterloo 200 University Ave W 200 University Ave W Waterloo, ON, Canada Waterloo, ON, Canada +1 (226) 808-9489 +1 (226) 505-5900 [email protected] [email protected] ABSTRACT project. Thus, the results are somewhat biased as the tested DB The popularity of NoSQL database and the availability of larger setup might be set to give more advantage of one of the systems. DRAM have generated a great interest in in-memory key-value We will discuss more in Section 8 (Related Work). stores (kv-store) in the recent years. As a consequence, many In this work, we conduct a thorough experimental evaluation by similar kv-store store projects/products has emerged. Besides the comparing three major key-value stores nowadays, namely Redis, benchmarking results provided by the KV-store developers which Memcached, and Aerospike. We first elaborate the databases that are usually tested under their favorable setups and scenario, there we tested in Section 3. Then, the evaluation methodology are very limited comprehensive resources for users to decide comprises experimental setups, i.e., single and cluster mode; which kv-store to choose given a specific workload/use-case. To benchmark setups including the description of YCSB, dataset provide users with an unbiased and up-to-date insight in selecting configurations, types workloads (i.e., read-heavy, balanced, in-memory kv-stores, we conduct a study to empirically compare write-heavy), and concurrent access; and evaluation metrics will Redis, Memcached and Aerospike on equal ground by trying to be discussed in Section 4.
    [Show full text]
  • Key Data Modeling Techniques
    Key Data Modeling Techniques Piyush Gupta Director Customer Enablement Aerospike Aerospike is a "Record Centric", Distributed, NoSQL Database. 2 AEROSPIKE SUMMIT ‘19 | Proprietary & Confidential | All rights reserved. © 2019 Aerospike Inc Relational Data Modeling – Table Centric Schema, 3rdNF 3 AEROSPIKE SUMMIT ‘19 | Proprietary & Confidential | All rights reserved. © 2019 Aerospike Inc NoSQL Modeling: Record Centric Data Model § De-normalization implies duplication of data § Queries required dictate Data Model § No “Joins” across Tables (No View Table generation) § Aggregation (Multiple Data Entry) vs Association (Single Data Entry) § “Consists of” vs “related to” 4 AEROSPIKE SUMMIT ‘19 | Proprietary & Confidential | All rights reserved. © 2019 Aerospike Inc Before jumping into modeling your data ... What do you want to achieve? § Speed at Scale. § Need Consistency & Multi-Record Transactions? § Know your traffic. § Know your data. Model your data: § Even a simple key-value lookup model can be optimized to significantly reduce TCO. § Will you need secondary indexes? § List your Queries upfront. § Design de-normalized data model to address the queries. Data Modeling is tightly coupled with reducing the Total Cost of Operations. 5 AEROSPIKE SUMMIT ‘19 | Proprietary & Confidential | All rights reserved. © 2019 Aerospike Inc Aerospike Architecture Related Decisions [ Proprietary & Confidential || © 2017 Aerospike Inc. All rights reserved. 6 ] Namespaces – Select one or more. [Data Storage Options] § Storage: RAM– fastest, File storage slowest. SSDs: RAM like performance. § ALL FLASH: TCO advantage for petabyte stores/small size records. Latency penalty. 7 AEROSPIKE SUMMIT ‘19 | Proprietary & Confidential | All rights reserved. © 2019 Aerospike Inc Aerospike API Features for Data Modeling API Features to exploit for Data Modeling: § Write policy - GEN_EQUAL for Compare-and-Set (Read-Modify-Write).
    [Show full text]
  • Accelerating Memcached on AWS Cloud Fpgas
    Accelerating Memcached on AWS Cloud FPGAs Jongsok Choi, Ruolong Lian, Zhi Li, Andrew Canis, and Jason Anderson LegUp Computing Inc. 88 College St., Toronto, ON, Canada, M5G 1L4 [email protected] ABSTRACT In recent years, FPGAs have been deployed in data centres of major cloud service providers, such as Microsoft [1], Ama- zon [2], Alibaba [3], Tencent [4], Huawei [5], and Nimbix [6]. This marks the beginning of bringing FPGA computing to the masses, as being in the cloud, one can access an FPGA from anywhere. A wide range of applications are run in the cloud, includ- ing web servers and databases among many others. Mem- cached is a high-performance in-memory object caching sys- Figure 1: Memcached deployment architecture. tem, which acts as a caching layer between web servers and databases. It is used by many companies, including Flicker, Wikipedia, Wordpress, and Facebook [7, 8]. Another historic impediment to FPGA accessibility lies In this paper, we present a Memcached accelerator imple- in their programmability or ease-of-use. FPGAs have tra- mented on the AWS FPGA cloud (F1 instance). Compared ditionally been difficult to use, requiring expert hardware to AWS ElastiCache, an AWS-managed CPU Memcached designers to use low-level RTL code to design circuits. In service, our Memcached accelerator provides up to 9× bet- order for FPGAs to become mainstream computing devices, ter throughput and latency. A live demo of the Memcached they must be as easy to use as CPUs and GPUs. There has accelerator running on F1 can be accessed on our website [9].
    [Show full text]
  • Best Practices
    Cloud Container Engine Best Practices Issue 01 Date 2021-09-07 HUAWEI TECHNOLOGIES CO., LTD. Copyright © Huawei Technologies Co., Ltd. 2021. All rights reserved. No part of this document may be reproduced or transmitted in any form or by any means without prior written consent of Huawei Technologies Co., Ltd. Trademarks and Permissions and other Huawei trademarks are trademarks of Huawei Technologies Co., Ltd. All other trademarks and trade names mentioned in this document are the property of their respective holders. Notice The purchased products, services and features are stipulated by the contract made between Huawei and the customer. All or part of the products, services and features described in this document may not be within the purchase scope or the usage scope. Unless otherwise specified in the contract, all statements, information, and recommendations in this document are provided "AS IS" without warranties, guarantees or representations of any kind, either express or implied. The information in this document is subject to change without notice. Every effort has been made in the preparation of this document to ensure accuracy of the contents, but all statements, information, and recommendations in this document do not constitute a warranty of any kind, express or implied. Issue 01 (2021-09-07) Copyright © Huawei Technologies Co., Ltd. i Cloud Container Engine Best Practices Contents Contents 1 Checklist for Deploying Containerized Applications in the Cloud...............................1 2 Cluster.......................................................................................................................................
    [Show full text]
  • Ultra-High Performance Nosql Benchmarking
    Ultra-High Performance NoSQL Benchmarking Analyzing Durability and Performance Tradeoffs Denis Nelubin, Director of Technology, Thumbtack Technology Ben Engber, CEO, Thumbtack Technology Overview As companies deal with ever larger amounts of data and increasingly demanding workloads, a new class of databases has taken hold. Dubbed “NoSQL”, these databases trade some of the features used by traditional relational databases in exchange for increased performance and/or partition tolerance. But as NoSQL solutions have proliferated and differentiated themselves (into key-value stores, document databases, graph databases, and “NewSQL”), trying to evaluate the database landscape for a particular class of problem becomes more and more difficult. In this paper we attempt to answer this question for one specific, but critical, class of functionality – applications that need the highest possible raw performance for a reliable storage engine. There have been a few attempts to provide standardized tools to measure performance or other characteristics, but these have been hobbled by the lack of a clear mandate on exactly what they’re testing, plus an inability to measure load at the highest volumes. In addition, there is an implicit tradeoff between the consistency and durability requirements of an application and the maximum throughput that can be processed. What is needed is not an attempt to quantify every NoSQL solution into one artificial bucket, but a more systemic analysis of how some of these databases can achieve under assumptions that mirror real-world application needs. We attempted to provide a comprehensive answer to one specific set of use cases for NoSQL databases -- consumer-facing applications which require extremely high throughput and low latency, and whose information can be represented using a key-value schema.
    [Show full text]
  • Snapdeal Case Study
    In-memory NoSQL Database fueled by Aerospike Snapdeal, India’s Largest Online Marketplace, Accelerates RESULTS Personalized Web Experience With Aerospike In-memory Aerospike database maintains sub-millisecond In India, Internet retail accounted for $3 billion in 2013 and is expected to latency on Amazon Elastic swell to $22 billion in the next five years. Still, with a total consumer market Compute Cloud (EC2) while managing 100 million-plus valued at $450 billion, India has been called the world’s last major frontier for objects stored in 8GB of DRAM e-commerce. Leading the industry in addressing this opportunity is Snapdeal, to support real-time dynamic India’s largest online marketplace. pricing. Snapdeal empowers sellers across the country to provide consumers with a Predictable low latency with 95- fully responsive and intuitive online shopping experience through an advanced 99% of transactions complet- ing within 10 milliseconds—es- platform that merges logistics subsystems with cutting-edge online and sential for enabling a responsive mobile payment models. The platform has a wide range of products from customer experience. thousands of national, international and regional brands. Aerospike’s highly efficient use Snapdeal.com now has a network of more than 20,000 sellers, of resources enables Snapdeal to cost effectively deploy on serving 20 million-plus members—one out of every six Internet just two servers in Amazon EC2. users in India. Powering this platform is the Aerospike flash and DRAM- optimized in-memory NoSQL data- Full replication across the base. By harnessing the real-time “There has been no need for main- Amazon EC2 servers ensures big data processing capabilities of tenance with Aerospike database; business continuity.
    [Show full text]