Category: Global Secondary Index
Index Advisor Service for N1QL (June refresh)
This is our June drop for Index Advisor service for N1QL after fixing some of the bugs that were found after our last refresh in May. We plan to keep improvising the Index Advisor service(What is it?). The fixes in...
Announcing Spring Data Couchbase 4.0
Spring Data Couchbase 4.0 a completely new perspective
Flexible Query & Indexing for Flexible JSON Model.
Use N1QL when you’re in a JSON pickle. — Confucius For the JSON data model, the advice is to think of collections as tables, JSON document as denormalized rows and field names as columns – roughly. All this holds in...
Index Advisor Service for N1QL (March refresh)
This is our March drop for Index Advisor service for N1QL after fixing some of the bugs that were found after our last refresh in Feb. We plan to keep improvising the Index Advisor service(What is it?). The fixes in...
FTS and N1QL: Better MongoDB in Operator Performance Querying Multiple Arrays
Learn the advantage of using FTS over GSI for array indexing with an example that requires querying multiple arrays with MongoDB in operator performance.
Part 2: N1QL: To Query or To Analyze?
When you need to query documents using SQL, there are two options available in Couchbase. The Query service and the Analytics service. Our blog, N1QL: To Query or To Analyze? provides a detailed overview of both services. I highly recommend...
Index Advisor Service for Couchbase N1QL(Feb refresh)
Follow-up blog for recent defects fixed in Couchbase Index advisor provides secondary index recommendations for SQL-like queries on JSON documents to improve query performance.
Couchbase 6.5 – RMS for Indexing Service
Couchbase 6.5 release includes an extensive list of Enterprise Grade Database Query capability that allows customers to expand the adoption of NoSQL database into traditional database applications. The release has added transactional capability, Analytical Window functions, user defined JS functions,...
Top Posts
- Data Modeling Explained: Conceptual, Physical, Logical
- What are Embedding Models? An Overview
- Data Analysis Methods: Qualitative vs. Quantitative Techniques
- Application Development Life Cycle (Phases and Management Models)
- What Is Data Analysis? Types, Methods, and Tools for Research
- App Development Costs (A Breakdown)
- Data Normalization vs. Denormalization Comparison
- What are Vector Embeddings?
- The Importance of Data Preprocessing in Machine Learning (ML)