{"id":8813,"date":"2020-06-23T07:00:22","date_gmt":"2020-06-23T14:00:22","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=8813"},"modified":"2025-06-13T18:44:13","modified_gmt":"2025-06-14T01:44:13","slug":"analyze-this-mongodb-couchbase-analytics","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/analyze-this-mongodb-couchbase-analytics\/","title":{"rendered":"Analyze This: MongoDB & Couchbase Analytics."},"content":{"rendered":"

The purpose of computing is insight, not numbers.\u00a0 — <\/span>Richard Hamming<\/span><\/a><\/p><\/blockquote>\n

The spiral of running the business, analyzing what to change & what to change to, and then changing the business is an eternal one. Do the right analysis, your spiral will get larger.\u00a0 Else, you\u2019ll spiral down.<\/span><\/p>\n

Couchbase<\/span><\/a>, like the other pioneers of NoSQL systems, was created to address extreme scale, performance, and availability requirements of the web 2.0 world. From the simple key-value, Couchbase has evolved to handle <\/span>query<\/span><\/a>, <\/span>search<\/span><\/a> and <\/span>analytics<\/span><\/a> — at scale. Each of them is purpose-built engines integrated via Couchbase\u2019s <\/span>multi-dimensional<\/span><\/a> architecture.\u00a0 The query and analytics service both talk N1QL. Why build two distinct engines that talk the same language?\u00a0 Because…<\/span><\/p>\n

One Size Fits All: An Idea Whose Time Has Come and Gone.\u00a0 — <\/span>Michael Stonebraker<\/span><\/a><\/p><\/blockquote>\n

Query engine was built for operational workload and the Analytics engine for the analysis workload.\u00a0 We\u2019ve <\/span>compared<\/span><\/a> the two engines and given the <\/span>guidance<\/span><\/a>.\u00a0 MongoDB has followed a similar path from being a clustered database handling simple workload to complex workload for analytics and queries on data lakes.\u00a0<\/span><\/p>\n

Last year, MongoDB announced analytic nodes in their clusters for analytic processing.\u00a0 In this blog, we compare and contrast the two engines for the analytics use case.<\/span><\/p>\n

Couchbase: High-Level Architecture<\/strong><\/h5>\n

\"\"<\/a><\/p>\n

Inside Couchbase Analytics: High-level Architecture<\/strong>
\n\"\"<\/a><\/h5>\n
MongoDB Analytics Nodes:<\/strong>\"\"<\/a><\/h5>\n

Let’s compare and contrast the analytics support in MongoDB Analytic nodes and Couchbase Analytics.<\/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\n\n\n\n\n\n\n
<\/td>\nMongoDB Analytic nodes<\/span><\/td>\nCouchbase Analytics<\/span><\/td>\n<\/tr>\n
Docs<\/span><\/td>\nhttps:\/\/docs.atlas.mongodb.com\/reference\/replica-set-tags\/<\/span><\/a><\/td>\nhttps:\/\/docs.couchbase.com\/server\/6.5\/analytics\/introduction.html<\/span><\/a><\/td>\n<\/tr>\n
Architecture<\/span><\/td>\nUse a set of Secondary replica nodes with a complete copy of the operational data. The query language is the same (MQL); query processing is the same as the operational workload.<\/span><\/td>\nDistinct Analytics nodes which have a user-defined subset of the operational data. The query language is the same (N1QL); query processing is designed for larger datasets (see below).<\/span><\/td>\n<\/tr>\n
Architecture Details<\/span><\/td>\nAtlas Mapped Analytics Nodes<\/span><\/a><\/td>\nCouchbase Analytics: NoETL for Scalable NoSQL Data Analysis<\/span><\/a><\/td>\n<\/tr>\n
Data Model<\/span><\/td>\nBSON<\/span><\/td>\nJSON<\/span><\/td>\n<\/tr>\n
Query Language<\/span><\/td>\nMQL<\/span><\/a> – MongoDB Query Language<\/span><\/td>\nN1QL<\/span><\/a> – Non 1st Normal-form Query Language; SQL for JSON<\/span><\/td>\n<\/tr>\n
Query page<\/span><\/td>\nMongoDB Query<\/span><\/a><\/td>\nAnalytics Query<\/span><\/a><\/td>\n<\/tr>\n
Query processing<\/span><\/td>\nSame as operational query processing, using mongos and mongod for distributed query processing.\u00a0<\/span><\/td>\nAnalytics engine designed for massive parallel processing (MPP) of the data. Each N1QL\u00a0<\/span><\/td>\n<\/tr>\n
Query optimizer<\/span><\/td>\nShape-Based Optimizer; Requires <\/span>plan management<\/span><\/a>.<\/span><\/td>\nRule-Based Optimizer. No plan management required.\u00a0<\/span><\/td>\n<\/tr>\n
Explain<\/span><\/td>\nText and graphical.<\/span><\/td>\nText and graphical.<\/span><\/td>\n<\/tr>\n
Indexing<\/span><\/td>\nNeed to create the index in the operational and have it copied over.<\/span><\/td>\nAnalytics only Indexing<\/span><\/td>\n<\/tr>\n
Parallel processing<\/span><\/td>\nEach Mongod node runs the basic operations and mongos combines it (e.g. final group and aggregation).\u00a0<\/span><\/td>\nTo handle complex analytics queries efficiently, and to deliver<\/span><\/p>\n

the desired scale-up and speed-up properties, the Analytics Service<\/span><\/p>\n

employs the same kinds of state-of-the-art, shared-nothing MPP<\/span><\/p>\n

(massively parallel processing) based query processing strategies [<\/span>From the VLDB paper]<\/span><\/a><\/td>\n<\/tr>\n

Indexing<\/span><\/td>\nLocal indexing\u00a0<\/span><\/td>\nLocal indexing<\/span><\/td>\n<\/tr>\n
Joins – Language<\/span><\/td>\n$lookup<\/span><\/a> operator supports simple equality joins between two collections; Only simple scalar fields are allowed. Arrays need to be unwound before joins.<\/span><\/p>\n
    \n
  1. One of the two collections CANNOT be sharded.\u00a0 That means, can\u2019t join on large collections.<\/span><\/li>\n
  2. Need a separate pipeline stage for simple non-equality joins. That means, the queries are inefficient and takes lot of resources;\u00a0<\/span><\/li>\n
  3. This is roughly equivalent to LEFT OUTER JOINs in SQL.\u00a0 Users will have to do additional pipeline processing to get the INNER JOIN and other joins.<\/span><\/li>\n<\/ol>\n<\/td>\n
INNER JOIN, LEFT OUTER JOIN, NEST and UNNEST operations.<\/span><\/p>\n
    \n
  1. Standard SQL syntax<\/span><\/li>\n
  2. Supports sharded dataset by default.<\/span><\/li>\n
  3. Supports equality and arbitrarily complex join expressions.<\/span><\/li>\n<\/ol>\n<\/td>\n<\/tr>\n
Query processing:data size<\/span><\/td>\nIntermediate stages of aggregate() pipeline cannot be more than <\/span>100 MiB<\/span><\/a> in size. Query writers\/users should use a special flag to allow this.<\/span><\/td>\nNo Limitations; When the intermediate data (e.g. hash table, sort data) gets bigger, it\u2019s spilled over to the disk.<\/span><\/td>\n<\/tr>\n
Query processing: Join type<\/span><\/td>\n(roughly) LEFT OUTER JOIN<\/span><\/td>\nINNER JOIN<\/span><\/p>\n

LEFT OUTER JOIN<\/span><\/td>\n<\/tr>\n

Search<\/span><\/td>\nSupports search within the query. Uses Atlas search on the cloud and basic B-tree based search on-prem.\u00a0<\/span><\/td>\nAnalytics service doesn\u2019t have a built-in search.\u00a0 We need to use query service with FTS for combining search within a query.<\/span><\/td>\n<\/tr>\n
Queries supported<\/span><\/td>\nfind() and aggregate()<\/span><\/td>\nSELECT statement (from SQL and SQL++)<\/span><\/td>\n<\/tr>\n
JOIN types (Language)<\/span><\/td>\n$lookup — this is roughly LEFT OUTER JOIN via\u00a0<\/span><\/td>\nINNER JOIN<\/span><\/p>\n

LEFT OUTER JOIN\u00a0<\/span><\/td>\n<\/tr>\n

JOIN types (Implementation)<\/span><\/td>\n\n
    \n
  1. Join only between one sharded and another non-sharded collection.<\/span><\/li>\n
  2. Nested loop (NL) only. (NL is bad for performance while handling large data).<\/span><\/li>\n
  3. Intermediate results are limited to 100MB memory.\u00a0 User will have to know the size and use options to allow spill over.<\/span><\/li>\n<\/ol>\n<\/td>\n
\n
    \n
  1. Joins sharded datasets; All of the datasets are sharded by default.<\/span><\/li>\n
  2. Supports nested loop, broadcast and parallel hash join<\/span><\/li>\n
  3. Supports both Nested Loop join and Hash join.\u00a0<\/span><\/li>\n
  4. Use Hash join by default — well suited for large scale data processing.\u00a0<\/span><\/li>\n
  5. No size limitations on the intermediate data.<\/span><\/li>\n<\/ol>\n<\/td>\n<\/tr>\n
Aggregation<\/span><\/td>\nSupports the common grouping and aggregation via aggregate() method.<\/span><\/td>\nSupports the common grouping and aggregation via GROUP BY and respective aggregations. See below for Windowed aggregates.\u00a0<\/span><\/td>\n<\/tr>\n
Windowed aggregate Functions: <\/span>Probably, the Coolest SQL Feature.<\/span><\/a><\/td>\nUnavailable.<\/span><\/td>\nFully <\/span>supported<\/span><\/a>.<\/span><\/p>\n

RANK()<\/span><\/p>\n

PERCENT_RANK()<\/span><\/p>\n

DENSERANK()<\/span><\/p>\n

ROW_NUMBER()<\/span><\/p>\n

CUME_DIST()<\/span><\/p>\n

FIRST_VALUE()<\/span><\/p>\n

LAST_VALUE()<\/span><\/p>\n

NTH_VALUE()<\/span>
\n<\/span>LAG()<\/span><\/p>\n

LEAD()<\/span><\/p>\n

NTILE()<\/span><\/p>\n

RATIO_TO_REPORT()<\/span><\/td>\n<\/tr>\n

Analyzing data from Multi-clusters<\/span><\/td>\nAll the data analyzed is from a single MongoDB cluster.<\/span><\/td>\n6.5: All the data analyzed is from a single Couchbase cluster.<\/span><\/p>\n

6.6: Can ingest and analyze the data from multiple Couchbase clusters.<\/span><\/td>\n<\/tr>\n

External data<\/span><\/td>\nSupports query processing on S3 data. Supports BSON, CSV, TSV, Avro, and Parquet formats.<\/span><\/td>\n6.6: Supports external JSON, CSV and TSV data in S3<\/span><\/td>\n<\/tr>\n
External data sources<\/span><\/td>\nSupports additional datasources via JDBC driver. Integrated with the aggregation pipeline via, you\u2019ve to wait for it, <\/span>$sql operator<\/span><\/a>.<\/span><\/td>\nNone except the ones mentioned above.\u00a0<\/span><\/td>\n<\/tr>\n
Subqueries<\/span><\/td>\nSubqueries via the aggregation pipeline.<\/span><\/td>\nStandard SQL subqueries.<\/span><\/td>\n<\/tr>\n
Query plan<\/span><\/td>\n$explain<\/span><\/td>\nEXPLAIN<\/span><\/td>\n<\/tr>\n
DataViz<\/span><\/td>\nBuilt-in MongoDB charts<\/span><\/td>\nNo built-in DataViz<\/span><\/td>\n<\/tr>\n
Business intelligence\u00a0<\/span><\/td>\nKnowi<\/span><\/a><\/p>\n

Tableau and other ODBC, JDBC compliant BI engines.<\/span><\/td>\n

Knowi<\/span><\/a><\/p>\n

Tableau and other ODBC, JDBC compliant BI engines.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

References:<\/b><\/p>\n

    \n
  1. Comparing Two SQL-Based Approaches for Querying JSON: SQL++ and SQL:2016<\/span><\/a><\/li>\n
  2. SQL to NoSQL \u2013 7 Metrics to Compare Query Language<\/span><\/a><\/li>\n
  3. Couchbase Analytics: NoETL for Scalable NoSQL Data Analysis<\/span><\/a><\/li>\n<\/ol>\n

     <\/p>\n","protected":false},"excerpt":{"rendered":"

    The purpose of computing is insight, not numbers.\u00a0 — Richard Hamming The spiral of running the business, analyzing what to change & what to change to, and then changing the business is an eternal one. 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