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 intended for developers and architects familiar with both them and not as an introductory article. Use the links here to learn more and play with the respective features.

 Topic
Couchbase Map-Reduce Views
Couchbase N1QL+GSI
Approach Based on user-defined map() and reduce() functions that operate on data in the background. Because map() and reduce() is written in Javascript, you can code complex logic within those functions. Based on declarative N1QL query (SQL for JSON).  Uses appropriate indexes to optimize execution and executed dynamically by orchestrating Query-Index-data services.  N1QL enables easily writeable and readable queries for JSON.  Because it’s inspired by SQL, it’s flexible, composable.  Because, it’s extended for JSON, it works on rich JSON data.  Uses 4-valued boolean logic (true, false,NULL, MISSING)
More Info Couchbase Docs: http://bit.ly/2jQrY11
  1. http://query.couchbase.com
  2. https://blog.couchbase.com/n1ql-practical-guide-second-edition/

 

Querying Query based on

  1. Single key
  2. Set of keys
  3. Start-End key
  4. Start-End document keys
  5. Group BY, Aggregation
  6. Pagination

 

Query Statements

  1. SELECT
  2. INSERT
  3. UPDATE
  4. DELETE
  5. MERGE
  6. INFER
  7. EXPLAIN

Query Operations:

  1. Single key
  2. Set of keys
  3. Range keys
  4. Range of document keys
  5. Arbitrarily complex predicates
  6. INNER JOIN, LEFT OUTER JOIN
  7. NEST, UNNEST
  8. GROUP BY
  9. Aggregation
  10. Pagination (OFFSET, LIMIT)
  11. Optimization
  12. ORDER BY
  13. HAVING
  14. Subqueries (correlated, non-correlated)
  15. Derived tables
  16. SET operations: UNION, UNION ALL, EXCEPT, EXCEPT ALL, INTERSECT
  17. Highly composable queries, meaning these operations can be simply combined with each other to express complex business questions and operations easily.
Indexing Simple index for views.
  1. Primary Index
  2. Named primary index
  3. Secondary index
  4. Composite Secondary Index
  5. Functional index
  6. Array Index
  7. ALL array
  8. ALL DISTINCT array
  9. Partial Index
  10. Adaptive Index
  11. Duplicate Indices
  12. Covering Index
Partitioning Aligned to data partitioning. Independent services.

N1QL and GSI scales independent of Data service and each other.

Scale Scales with data service Independent scaling via Multidimensional scaling (MDS)
Fetch with document key Because the data is partitioned on document key, fetches the document directly from the node Specify the query via USE KEYS clause.

Because the data is partitioned on document key, fetches the document directly from the node

Fetch with Index key Scatter-Gather Each index scan on a single node; Data on multiple nodes.

Post processing in Query node

Range scan Scatter-Gather Index scan on a single node.

Post processing in Query node

Grouping, aggregation Built-in with Views API Built into N1QL
Caching File system Index buffer pool

Data cache

Storage Couchstore Plasma storage engine (5.0 & above)

Memory Optimized Index (4.5 and above)

ForestDB (community)

Availability Replica Based 5.0: Replicas

4.x: Equivalent Indexes

Query Latency

(Simple queries)

10 milliseconds to 100 milliseconds 5 milliseconds+
Query Throughput

(Simple queries)

3K to 4K queries per second 40K queries per second
Scalability Moderate (scaling tied to data service) High (independent scaling of index and query services: MDS)
Applicability Aggregations, best of large scale aggregations for low and moderate latency requirements.  Map-reduce operations on the data is done in the background as the data is modified. Best for attribute based lookup, range scans, complex select-join-project-array

Operations.  Supports grouping, aggregation and ordering — these operations are done dynamically as part of query execution.

Application requirements Report on well defined metrics

Large scale aggregations

Latency sensitive

Secondary key lookups

Range Scans

Operational aggregations

Filtered queries

Ad-hoc queries with complex predicates, joins, aggregations, app search, pagination, secondary key based updates.

Spatial Supported via Spatial Views Not directly.

https://dzone.com/articles/speed-up-spatial-search-in-couchbase-n1ql

Consistency Stale = UPDATE_AFTER

Stale = OK

Stale = FALSE

Unbounded (stale = OK)

AT_PLUS (read your own writes)

REQUEST_PLUS (read after index updates up to now(). Stale = False).

Tools Web console Web console, Developer workbench, Query monitoring, Query Profiling, Visual explain, INFER.

 

Posted by Keshav Murthy

Keshav Murthy is a Senior Director at Couchbase R&D. Previously, he was Senior Director of Product Management at MapR, Senior Architect for IBM, with more than 20 years experience in database design & development. He lead the SQL and NoSQL query processing team IBM Informix database. He received a President’s Club award 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 and holds eight US patents.

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