{"id":4295,"date":"2017-12-04T18:26:28","date_gmt":"2017-12-05T02:26:28","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=4295"},"modified":"2025-11-07T04:29:38","modified_gmt":"2025-11-07T12:29:38","slug":"comparing-couchbase-views-couchbase-n1ql-indexing","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/comparing-couchbase-views-couchbase-n1ql-indexing\/","title":{"rendered":"Comparing Couchbase Views with Couchbase N1QL & Indexing."},"content":{"rendered":"

As Couchbase data platform evolved, services like N1QL and GSI Indexing handled the use cases Couchbase VIEWS used to handle and much more.\u00a0 It’s logical to ask the comparative question between them.\u00a0 Here is a table comparing both.\u00a0 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.<\/p>\n

\"\"<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Topic<\/strong><\/th>\nCouchbase Map-Reduce Views<\/strong><\/th>\nCouchbase N1QL+GSI<\/strong><\/th>\n<\/tr>\n
Approach<\/td>\nBased 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.<\/td>\nBased on declarative N1QL query (SQL for JSON).\u00a0 Uses appropriate indexes to optimize execution and executed dynamically by orchestrating Query-Index-data services.\u00a0 N1QL enables easily writeable and readable queries for JSON.\u00a0 Because it\u2019s inspired by SQL, it\u2019s flexible, composable.\u00a0 Because, it\u2019s extended for JSON, it works on rich JSON data.\u00a0 Uses 4-valued boolean logic (true, false,NULL, MISSING)<\/td>\n<\/tr>\n
More Info<\/td>\nCouchbase Docs:\u00a0https:\/\/bit.ly\/2jQrY11<\/a><\/td>\n\n1. https:\/\/query.couchbase.com<\/a>
\n2. https:\/\/www.couchbase.com\/blog\/n1ql-practical-guide-second-edition\/<\/a><\/td>\n<\/tr>\n
Querying<\/td>\nQuery based on<\/strong><\/p>\n

\n1. Single key
\n2. Set of keys
\n3. Start-End key
\n4. Start-End document keys
\n5. Group BY, Aggregation
\n6. Pagination<\/p>\n<\/td>\n

Query Statements<\/strong><\/p>\n

1. SELECT
\n2. INSERT
\n3. UPDATE
\n4. DELETE
\n5. MERGE
\n6. INFER
\n7. EXPLAIN
\nQuery Operations:<\/strong>
\n1. Single key
\n2. Set of keys
\n3. Range keys
\n4. Range of document keys
\n5. Arbitrarily complex predicates
\n6. INNER JOIN, LEFT OUTER JOIN
\n7. NEST, UNNEST
\n8. GROUP BY
\n9. Aggregation
\n10. Pagination (OFFSET, LIMIT)
\n11. Optimization
\n12. ORDER BY
\n13. HAVING
\n14. Subqueries (correlated, non-correlated)
\n15. Derived tables
\n16. SET operations: UNION, UNION ALL, EXCEPT, EXCEPT ALL, INTERSECT
\n17. Highly composable queries, meaning these operations can be simply combined with each other to express complex business questions and operations easily.<\/p>\n<\/td>\n<\/tr>\n

Indexing<\/td>\nSimple index for views.<\/td>\n\n \t1. Primary Index
\n \t2. Named primary index
\n \t3. Secondary index
\n \t4. Composite Secondary Index
\n 5. Functional index
\n \t6. Array Index
\n \t7. ALL array
\n \t8. ALL DISTINCT array
\n \t9. Partial Index
\n \t10. Adaptive Index
\n \t11. Duplicate Indices
\n \t12. Covering Index<\/p>\n<\/td>\n<\/tr>\n
Partitioning<\/td>\nAligned to data partitioning.<\/td>\nIndependent services.<\/p>\n

N1QL and GSI scales independent of Data service and each other.<\/span><\/td>\n<\/tr>\n

Scale<\/td>\nScales with data service<\/td>\nIndependent scaling via Multidimensional scaling (MDS)<\/td>\n<\/tr>\n
Fetch with document key<\/td>\nBecause the data is partitioned on document key, fetches the document directly from the node<\/td>\nSpecify the query via USE KEYS clause.<\/p>\n

Because the data is partitioned on document key, fetches the document directly from the node<\/span><\/td>\n<\/tr>\n

Fetch with Index key<\/td>\nScatter-Gather<\/td>\nEach index scan on a single node; Data on multiple nodes.<\/p>\n

Post processing in Query node<\/span><\/td>\n<\/tr>\n

Range scan<\/td>\nScatter-Gather<\/td>\nIndex scan on a single node.<\/p>\n

Post processing in Query node<\/span><\/td>\n<\/tr>\n

Grouping, aggregation<\/td>\nBuilt-in with Views API<\/td>\nBuilt into N1QL<\/td>\n<\/tr>\n
Caching<\/td>\nFile system<\/td>\nIndex buffer pool<\/p>\n

Data cache<\/span><\/td>\n<\/tr>\n

Storage<\/td>\nCouchstore<\/td>\nPlasma storage engine (5.0 & above)<\/p>\n

Memory Optimized Index (4.5 and above)<\/span><\/p>\n

ForestDB (community)<\/span><\/td>\n<\/tr>\n

Availability<\/td>\nReplica Based<\/td>\n5.0: Replicas<\/p>\n

4.x: Equivalent Indexes<\/span><\/td>\n<\/tr>\n

Query Latency<\/p>\n

(Simple queries)<\/span><\/td>\n

10 milliseconds to 100 milliseconds<\/td>\n5 milliseconds+<\/td>\n<\/tr>\n
Query Throughput<\/p>\n

(Simple queries)<\/span><\/td>\n

3K to 4K queries per second<\/td>\n40K queries per second<\/td>\n<\/tr>\n
Scalability<\/td>\nModerate (scaling tied to data service)<\/td>\nHigh (independent scaling of index and query services: MDS)<\/td>\n<\/tr>\n
Applicability<\/td>\nAggregations, best of large scale aggregations for low and moderate latency requirements.\u00a0 Map-reduce operations on the data is done in the background as the data is modified.<\/td>\nBest for attribute based lookup, range scans, complex select-join-project-array<\/p>\n

Operations.\u00a0 Supports grouping, aggregation and ordering — these operations are done dynamically as part of query execution.<\/span><\/td>\n<\/tr>\n

Application requirements<\/td>\nReport on well defined metrics<\/p>\n

Large scale aggregations<\/span><\/p>\n

Latency sensitive<\/span><\/td>\n

Secondary key lookups<\/p>\n

Range Scans<\/span><\/p>\n

Operational aggregations<\/span><\/p>\n

Filtered queries<\/span><\/p>\n

Ad-hoc queries with complex predicates, joins, aggregations, app search, pagination, secondary key based updates.<\/span><\/td>\n<\/tr>\n

Spatial<\/td>\nSupported via Spatial Views<\/td>\nNot directly.<\/p>\n

https:\/\/dzone.com\/articles\/speed-up-spatial-search-in-couchbase-n1ql<\/a><\/span><\/td>\n<\/tr>\n

Consistency<\/td>\nStale = UPDATE_AFTER<\/p>\n

Stale = OK<\/p>\n

Stale = FALSE<\/td>\n

Unbounded (stale = OK)<\/p>\n

AT_PLUS (read your own writes)<\/span><\/p>\n

REQUEST_PLUS (read after index updates up to now(). Stale = False).<\/td>\n<\/tr>\n

Tools<\/td>\nWeb console<\/td>\nWeb console, Developer workbench, Query monitoring, Query Profiling, Visual explain, INFER.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

As Couchbase data platform evolved, services like N1QL and GSI Indexing handled the use cases Couchbase VIEWS used to handle and much more.\u00a0 It’s logical to ask the comparative question between them.\u00a0 Here is a table comparing both.\u00a0 This is […]<\/p>\n","protected":false},"author":55,"featured_media":4301,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1814,1815,1821,1819,9417,1812],"tags":[1696,1248,1641],"ppma_author":[8929],"class_list":["post-4295","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-application-design","category-best-practices-and-tutorials","category-couchbase-architecture","category-data-modeling","category-performance","category-n1ql-query","tag-indexing","tag-mapreduce","tag-secondary-indexing"],"acf":[],"yoast_head":"\nComparing Couchbase Views with Couchbase N1QL & Indexing<\/title>\n<meta name=\"description\" content=\"The blog focuses on the table comparison between Couchbase Map-Reduce Views and Couchbase N1QL+GSI indexing. 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