All posts by Keshav Murthy

Keshav Murthy is a Senior Director at Couchbase of N1QL R&D. Previously, he was a Senior Director at MapR, Senior Architect for IBM, with more than 20 years experience in database design & development. He lead the SQL and NoSQL R&D team at IBM Informix. He has received two President's Club awards at Couchbase, two Outstanding Technical Achievement Awards at IBM. Keshav has a bachelors degree in Computer Science and Engineering from the University of Mysore, India, holds eight US patents and invented databases for systems of engagement.

Divide and Conquer: Couchbase GSI Index partitioning.

In Couchbase, data is always partitioned using the consistent hash value of the document key into vbukets which are stored on the data nodes.  Couchbase Global Secondary Index (GSI) abstracts the indexing operations and runs as a distinct service within...

/ April 11, 2018

Develop with Agility, Develop at any Scale with Couchbase on Cisco UCS

Author’s note: This excerpt from the Couchbase on Cisco UCS® solution brief is published on behalf of Cisco. Full solution brief for Couchbase on Cisco UCS® is available later in this blog. As the world migrates to a digital economy, businesses are...

/ February 16, 2018

Using Google Artificial Intelligence Services in Couchbase N1QL

“If you’re not using deep learning, you should be.” – Jeff Dean, Google Fellow. Google has started out with a mission to empower everything and everyone with Artificial Intelligence (A.I.).  It has open-sourced Tensorflow and supporting libraries to enable developers...

/ February 10, 2018

Couchbase for Oracle developers — Part 3 : Database Objects

Here’s the home page for the series: https://blog.couchbase.com/couchbase-oracle-developers-part-1-overview/ Oracle DBAs work with clusters, machines, storage systems, disks, etc. Oracle developers and their applications work with databases, tables, rows, columns, partitions, users, data types within the Oracle database system.  Let’s compare and...

/ January 27, 2018

Couchbase for Oracle Developers – Part 2 : Architecture

Back in November, Raju Suravarjjala, (Sr. Director, Couchbase QE & Performance) and I  presented “Mindmap: Oracle to Couchbase For Developers”. You can see the slide deck here: http://bit.ly/2mPdJfo. Overview article for the series is at is at https://blog.couchbase.com/couchbase-oracle-developers-part-1-overview/  This series is written in...

/ January 22, 2018

Couchbase for Oracle Developers – Part 1 : Overview

Back in November, Raju Suravarjjala,(Sr. Director, Couchbase QE & Performance) and I  presented “Mindmap: Oracle to Couchbase For Developers”. You can see the slide deck here: http://bit.ly/2mPdJfo. Overview article for the series is at is at https://blog.couchbase.com/couchbase-oracle-developers-part-1-overview/  This series is written in collaboration...

/ January 17, 2018

Database Pagination: Using OFFSET and Keyset in N1QL.

Read the pagination background in my previous article: https://blog.couchbase.com/optimizing-database-pagination-using-couchbase-n1ql/ Pagination is the task of dividing the potential result into pages and retrieving the required pages, one by one on demand.  Using OFFSET and LIMIT is the easy way to write...

/ January 11, 2018

Comparing Couchbase Views with Couchbase N1QL & Indexing.

As Couchbase data platform evolved, services like N1QL and GSI Indexing handled the use cases Couchbase VIEWS used to handle and much more.  It’s logical to ask the comparative question between them.  Here is a table comparing both.  This is...

/ December 4, 2017

Create the Right Index, Get the Right Performance.

Introduction There are three things important in database systems: performance, performance, performance.  For NoSQL database systems, there are three important things: performance at scale, performance at scale, performance at scale. Understanding the index options, creating the right index, with the...

/ November 12, 2017

Optimizing Database Pagination using Couchbase N1QL.

Background: How does Google do it? When you google something or anything, it gives you back top relevant results, tells you an approximate number of documents for your topic — all under a second.   Here are some high-level pointers: https://www.google.com/search/howsearchworks/algorithms/...

/ November 2, 2017