NoSQL startup Couchbase will launch ‘SQL for documents’ this summer

23 Mar 2015 | Author: | No comments yet »

: Multi-Dimensional Scaling by #Couchbase: scale up and scale out with database service isolation.

News today from the database vendor Couchbase that it is giving its customers much more flexibility when it comes to finely tuning database scalability.NoSQL database company Couchbase is adding new scaling technology in its next major release of Couchbase Server to increase application performance as well as reduce costs. Multi-Dimensional Scaling provides the option to isolate database query, index and data services, so that hardware resources can be independently assigned and optimized on a per node basis, as application requirements change. “Unlike MongoDB, Oracle, Cassandra, and other databases that have a limiting ‘one size fits all’ approach to scaling, Couchbase is enabling organizations to precisely provision hardware to meet application performance requirements,” said Bob Wiederhold, CEO, Couchbase. “With Multi-Dimensional Scaling, enterprises can independently assign and scale the index, query and data services to specific servers. The upcoming Couchbase 4.0 changed architecture so that three very different types of database tasks can run on different sets of servers optimized to do those tasks — an alternative to using the same set of servers (a single node) to accomplish all three core tasks of read/write, indexing, and queries.

Within the security industry, it has often been said, “It is easier to teach a developer about security than it is to teach a security researcher about development.” Pure security researchers have often seen only the failures in the industry. Readers will likely know that traditional databases follow a tabular approach (rows and columns) and, while this makes them great for structure data (name, rank, serial number), it makes them less well suited for unstructured data. Couchbase Server 4.0 with Multi-Dimensional Scaling eliminates these problems by enabling enterprises to run database services on separate hardware and assign right-sized servers for each service.

Multi-Dimensional Scaling is configurable during runtime, giving organizations the ability to deploy one configuration at launch, and then change the scaling based on application performance needs. Read/write is memory oriented; indexing is disk-intensive; querying needs a lot of CPU horsepower. “Enterprises are faced with a broad range of data processing requirements, for which they have traditionally relied on extending the relational model and, more recently, combined a variety of specialist NoSQL databases,” Matt Aslett, research director at 451 Research, said in an email. “Our research suggests that enterprises are making strategic investments in more agile, multi-model databases that serve a variety of needs. Phase 1 was about grass roots developer adoption with developers downloading the code, installing it in order to explore the technology and start using it on lightweight applications. If they have never been exposed to large-scale development, then they don’t have a robust understanding of the complex challenges that face developers in secure code development. Phase 2 kicked off in early 2013, as enterprises began using NoSQL in increasing numbers under mission-critical applications that they are operating at scale, which in turn drove a put a higher priority on scalability and high performance to support those applications.

And while some of these products are differentiated through the way they work (object store versus document store, for example), others would see to be a “me too” approach. Simply put, SQL and NoSQL databases both collect, organize and accept queries for information, therefore both are susceptible to malicious code injections.

The second phase was around 2013, with enterprises believing NoSQL had evolved to the point where it was suitable for more mission critical use cases. If you dig into NoSQL databases, you quickly realize that there are a wide variety of query formats, from SQL-esque queries (Cassandra), to JSON-based queries (MongoDB, DynamoDB), to assembly-esque queries (Redis). Organizations will have full flexibility to use Multi-Dimensional Scaling or to deploy standard distributed scaling with all services running on the same hardware, and can change on the fly. We have very strong scalability and performance, differentiation against MongoDB.” Beginning early next year, Weiderhold believes the third phase will be in full gear: wide deployment of NoSQL. “Strategic evaluations are done, the technology is considered stable enough to be deployed widely.

The impacts of that being that relational databases perform well with high query complexity and low scalability while traditional NoSQL offerings do the opposite – performing best with low query complexity and higher data scalability. Major parts of the infrastructure software stack are changing right now.” Enterprise IT is undergoing an evolution architecturally with the move toward cloud shaping the next generation. Web scale, mobile apps, and Internet of Things all depend on doing things a little differently in terms of databases, with NoSQL more suited to this world than relational databases. “Relational databases have added features that appear to be similar to NoSQL, but they’re missing the point,” said Weiderhold. “They’re fundamentally different architectures and trade-offs.” The 4.0 release will be out this summer and feature other enhancements, but Couchbase is currently focused on getting the word out about the new multi-dimensional scaling capability.

For Couchbase, it ideally means that more customers will fulfill their requirements with one product (Couchbase) given its increasing flexibility to cover their different use-cases. The team was absolutely correct in rejecting my initial changes because the design needed to be improved so that it could be sustainable over the long term. Developers around the world use the Couchbase platform to build enterprise web, mobile, and IoT applications that support massive data volumes in real time. In the process of trying to commit to the project, I learned a lot more about tools such as MongoDB and Django than I ever would have learned skimming security best practice documentation. This experience will make me more effective within Adobe when talking to product teams using these tools, since I will better understand their language and concerns.

If you look at the people leading the industry at companies such as Google, Etsy and iSec Partners, many are respected because they are also keeping their hands on the keyboards and are speaking from direct knowledge.

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