{"id":6760,"date":"2019-05-26T23:50:29","date_gmt":"2019-05-27T06:50:29","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=6760"},"modified":"2025-06-13T17:32:38","modified_gmt":"2025-06-14T00:32:38","slug":"the-unreasonable-effectiveness-of-sql-in-nosql-databases","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/the-unreasonable-effectiveness-of-sql-in-nosql-databases\/","title":{"rendered":"The Unreasonable Effectiveness of SQL in NoSQL Databases"},"content":{"rendered":"

Business applications have requirements: take customer orders, deliver customer orders, track shipping, generate inventory report, end of the day\/month\/quarter business report, generate business dashboards and more. \u00a0These requirements evolve slowly. They remain even when you choose a NoSQL database<\/a>.<\/span><\/p>\n

On NoSQL databases, challenges are addressed by a multitude of technologies and workarounds. Here are some of them:<\/span><\/p>\n

    \n
  1. Copying the data with a different key to make the scans easier.<\/span><\/li>\n
  2. Fetch all of the data needed to the application and then generate reports<\/li>\n
  3. Load the data into relational databases to generate the report.<\/span><\/li>\n
  4. Products provide workarounds with <\/span>map-reduce views<\/span><\/a>, <\/span>APIs<\/span><\/a>, etc.<\/span><\/li>\n
  5. Finally, SQL itself has been <\/span>unreasonably effective<\/span><\/a> for both structured and semi-structured data. NoSQL databases like <\/span>Couchbase<\/span><\/a>, <\/span>Cassandra<\/span><\/a>, <\/span>CosmosDB<\/span><\/a> have extended SQL for JSON and wide-column data model.<\/span><\/li>\n<\/ol>\n

    NoSQL has evolved from “NO SQL” to Not Only SQL.\u00a0 If you’re interested in some of the evolutionary background combined with a history of SQL evolution to support semi-structured data, I suggest the following interviews.<\/p>\n

      \n
    1. Ravi Mayuram’s interview with Don Chamberlin[2017]:\u00a0 https:\/\/youtu.be\/-U_UjqnhMBI?t=3492<\/a><\/li>\n
    2. A panel discussion between Ravi Mayuram, Don Chamberlin and Prof. Mike Carey [2018]:\u00a0 https:\/\/www.youtube.com\/watch?v=LAlDe1w7wxc<\/a><\/li>\n<\/ol>\n

      A lot of NoSQL databases claim “SQL Support”. The SQL standard<\/span><\/a> is wide and deep, covered in <\/span>nine voluminous books<\/span><\/a>. No one, neither <\/span>Oracle<\/span><\/a> nor <\/span>SQL Server<\/span><\/a>, supports everything in the standard despite decades of work. So, the NoSQL databases have a long way to go in catching up. So, a detailed evaluation of the SQL support is worth your while.\u00a0<\/span><\/p>\n

      Here are the criteria you should use to evaluate the SQL support in NoSQL databases.<\/p>\n

        \n
      1. Language support: What statements, data types, operations (joins, grouping, aggregation, windowing, pagination, etc)<\/li>\n
      2. Indexing support:\u00a0 Indexes are key to performance, especially in the workloads for interactive applications.<\/li>\n
      3. Optimizer: Query rewrite, choosing the right access path, creating the optimal query execution path is what makes SQL a successful 4GL.\u00a0 Some have a rule-based optimizer, some have a cost-based optimizer,\u00a0some others have both.\u00a0 Evaluating the quality of the optimizer is critical.\u00a0 Typical benchmarks (TPC-C, TPC-DS, YCSB, YCSB-JSON) won\u2019t help you here.<\/li>\n
      4. As the saying goes: \u201d There are three things important in databases: performance, performance, and performance\u201d. It\u2019s important to measure the performance of your workload.\u00a0\u00a0YCSB<\/a>\u00a0and the extended\u00a0YCSB-JSON<\/a>\u00a0will make this evaluation easier.<\/li>\n
      5. SDKs: Rich SDKs and language support speed up your development.<\/li>\n
      6. \u00a0BI tool support: For large data analysis, support from BI tools usually via standard database connectivity drivers is important.<\/li>\n<\/ol>\n

        In this article, I’ll compare and contrast the SQL language support in Cassandra, CosmosDB, Couchbase, and MongoDB via their respective implementations.\u00a0 To be sure, MongoDB doesn’t support SQL but have some comparative commands<\/a>.<\/span><\/p>\n

        I’ve divided the analysis into multiple sections.\u00a0 WordPress formatting makes these tables too big.\u00a0 Here is a PDF version that’s compact and easy to read. [Click on the image to view the PDF]<\/p>\n

        \"\"<\/a>

        Summary of the SQL Support in Cassandra, CosmosDB, Couchbase, and MongoDB.<\/p><\/div>\n

         <\/p>\n

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         <\/p>\n

        SQL Support approach: \u00a0<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\n SQL is a declarative language, select-join-project operations serving as the foundation.<\/span><\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nCQL: SQL inspired language for Cassandra.<\/span><\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nSupports SQL along with MongoDB API, Gremlin (for the graph), etc. Simple select-from-where-order-by support. \u00a0<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nN1QL: SQL for JSON.\u00a0<\/span><\/p>\n

        Couchbase has two implementations of N1QL: query service and analytics service.<\/td>\n<\/tr>\n

        MongoDB<\/span><\/td>\nJavascript based, simplified SQL command based query.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        INPUT and OUTPUT<\/strong><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\nINPUT: Set of rows (tuples)<\/span><\/p>\n

        OUTPUT: A set of rows (tuples)<\/span><\/td>\n<\/tr>\n

        Cassandra<\/span><\/td>\nINPUT: Sets of rows<\/span><\/p>\n

        OUTPUT: Set of rows<\/span><\/td>\n<\/tr>\n

        CosmosDB<\/span><\/td>\nINPUT: Sets of JSON<\/span><\/p>\n

        OUTPUT: Set of JSON<\/span><\/td>\n<\/tr>\n

        Couchbase<\/span><\/td>\nINPUT: Sets of JSON<\/span><\/p>\n

        OUTPUT: Set of JSON<\/span><\/td>\n<\/tr>\n

        MongoDB<\/span><\/td>\nINPUT: Sets of JSON<\/span><\/p>\n

        OUTPUT: Set of JSON<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: FROM Clause<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\nSpecifies the datasource tables (relations)<\/span><\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nFROM clause with only one table allowed. \u00a0Now joins, subqueries or expressions are allowed. \u00a0From clause interpretation is same as SQL.<\/span><\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nFROM clause supports a single collection and self joins (same as UNNEST in Couchbase). \u00a0\u00a0<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nFROM clause with multiple keyspaces (subset of bucket), subqueries, expressions. \u00a0Same as SQL<\/span><\/td>\n<\/tr>\n
        MongoDB<\/span><\/td>\ndb.t1.find() for single table SELECT.<\/span><\/p>\n

        db.t1.aggregate() is used as the generalized query framework. aggregate() can join with additional collections using $lookup operator.\u00a0 There can be multiple $lookup operators in the aggregation pipeline framework making it the closest cousin of the SQL FROM clause.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: WHERE Clause<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nCriteria for selecting a row<\/td>\n<\/tr>\n
        Cassandra<\/td>\nStandard boolean expressions. No subqueries.<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nSame as SQL<\/td>\n<\/tr>\n
        Couchbase<\/td>\nStandard boolean expressions and subqueries.<\/td>\n<\/tr>\n
        MongoDB<\/td>\ndb.t1.find({x:10});<\/p>\n

        aggregate() has the $match clause.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: SELECT Clause<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nProjection clause<\/td>\n<\/tr>\n
        Cassandra<\/td>\nSELECT clause is same as SQL.<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nSELECT clause is same as SQL.<\/td>\n<\/tr>\n
        Couchbase<\/td>\nSELECT clause is same as SQL.<\/td>\n<\/tr>\n
        MongoDB<\/td>\ndb.t1.find({x:10}, {a:1, b:1})<\/p>\n

        $project operator in the aggregation pipeline<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: CTE – Common Table Expression<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\nDynamically defined data source (table, resultset)<\/span><\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nUnsupported<\/span><\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nUnsupported<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nWITH clause; same as SQL (in v6.5). Recursive CTE is unsupported<\/span><\/td>\n<\/tr>\n
        MongoDB<\/span><\/td>\nUnsupported<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: Subquery<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nSubquery: Subqueries in the FROM clause, WHERE clause, anywhere an expression is allowed.<\/td>\n<\/tr>\n
        Cassandra<\/td>\nUnsupported<\/td>\n<\/tr>\n
        Cosmosdb<\/td>\nUnsupported<\/td>\n<\/tr>\n
        Couchbase<\/td>\nSupports both correlated and non-correlated subqueries.<\/td>\n<\/tr>\n
        MongoDB<\/td>\nUnsupported in find(). Can add $match in the pipeline, but not exactly an equivalent of a subquery.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: GROUP BY<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nGroup the rows based on one or more expressions. Quite useful in reporting and aggregation for groups.<\/td>\n<\/tr>\n
        Cassandra<\/td>\nSupported; Same as SQL.<\/td>\n<\/tr>\n
        Cosmosdb<\/td>\nUnsupported. Can only do the aggregation on the whole resultset.<\/td>\n<\/tr>\n
        Couchbase<\/td>\nSupported; Similar as SQL.<\/td>\n<\/tr>\n
        MongoDB<\/td>\n$group operator in aggregate() pipeline<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: HAVING clause<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nFiltering after the aggregation.<\/td>\n<\/tr>\n
        Cassandra<\/td>\nUnsupported<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nUnsupported<\/td>\n<\/tr>\n
        Couchbase<\/td>\nHAVING clause; Same as SQL<\/td>\n<\/tr>\n
        MongoDB<\/td>\n$match after the grouping and aggregation.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: ORDER BY Clause<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nThe final order of the results produced by the query block<\/td>\n<\/tr>\n
        Cassandra<\/td>\nORDER BY clause; Same as SQL.<\/td>\n<\/tr>\n
        Cosmosdb<\/td>\nORDER BY clause; Same as SQL.<\/td>\n<\/tr>\n
        Couchbase<\/td>\nORDER BY clause; Same as SQL.<\/td>\n<\/tr>\n
        MongoDB<\/td>\ndb.t1.find().sort({a:1, b:-1});<\/p>\n

        aggregate() has $sort to specify the result order.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: LIMIT, OFFSET Clause<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nUsed for pagination of the resultset<\/td>\n<\/tr>\n
        Cassandra<\/td>\n“LIMIT is supported.<\/p>\n

        OFFSET is unsupported.”<\/td>\n<\/tr>\n

        CosmosDB<\/span><\/td>\nLIMIT (TOP) and OFFSET clause; Similar to SQL<\/td>\n<\/tr>\n
        Couchbase<\/td>\nLIMIT and OFFSET clause; Same as SQL<\/td>\n<\/tr>\n
        MongoDB<\/td>\nskip(), limit() methods with find(). $offset, $limit with aggregate().<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: JOIN clause<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nINNER JOIN, LEFT\/RIGHT\/FULL outer joins.<\/td>\n<\/tr>\n
        Cassandra<\/td>\nJoins are unsupported. \u00a0Applications will have to model the data to avoid joins or do the joins in the application layer.<\/td>\n<\/tr>\n
        Cosmosdb<\/td>\nOnly self JOINs. \u00a0No INNER\/LEFT\/RIGHT\/etc joins.<\/td>\n<\/tr>\n
        Couchbase<\/td>\nSupports INNER, LEFT OUTER, NEST, UNNEST and limited RIGHT outer. \u00a0Same syntax as SQL. FULL OUTER join is unsupported.<\/td>\n<\/tr>\n
        MongoDB<\/td>\nLimited LEFT OUTER JOIN only via $lookup operator. \u00a0No join on array elements or expressions.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: Aggregation<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\nAggregation<\/span><\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nSimple aggregation on the whole result is supported. \u00a0Aggregation with GROUP BY is unsupported.<\/span><\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nSimple aggregation on the whole result is supported. \u00a0Aggregation with GROUP BY is unsupported.<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nSUM, AVG, COUNT, MAX, MIN, VARIANCE: same as SQL<\/span><\/td>\n<\/tr>\n
        MongoDB<\/span><\/td>\n$sum, $count, $avg with grouping support<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT: aggregate functions<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\n Simple aggregation on the whole result is supported. \u00a0Aggregation with GROUP BY is unsupported.<\/span><\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nUnsupported<\/span><\/td>\n<\/tr>\n
        Cosmosdb<\/span><\/td>\nUnsupported<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nSupports SQL Standard window analytical functions in 6.5.<\/span><\/td>\n<\/tr>\n
        MongoDB<\/span><\/td>\nUnsupported<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SELECT : Window (analytics\/aggregation) functions<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\nWindow functions for running totals using the OVER() clause<\/span><\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nUnsupported<\/span><\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nUnsupported<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nSupports SQL Standard window analytical functions in 6.5.<\/span><\/p>\n

        See details: https:\/\/www.couchbase.com\/blog\/json-to-insights-fast-and-easy\/<\/a><\/p>\n

        https:\/\/www.couchbase.com\/blog\/get-a-bigger-picture-with-n1ql-window-functions-and-cte\/<\/a><\/td>\n<\/tr>\n

        MongoDB<\/span><\/td>\nUnsupported<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        INSERT: \u00a0Single row\/document insert.<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\nInsert a single row<\/span><\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nINSERT statement<\/span><\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nAPI Insert<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nINSERT statement<\/span><\/td>\n<\/tr>\n
        MongoDB<\/span><\/td>\ndb.t1.save()<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        INSERT: \u00a0Multiple row\/document insert.<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nINSERT statement<\/td>\n<\/tr>\n
        Cassandra<\/td>\nUnsupported<\/td>\n<\/tr>\n
        Cosmosdb<\/td>\nUnsupported<\/td>\n<\/tr>\n
        Couchbase<\/td>\nINSERT with Multiple documents<\/td>\n<\/tr>\n
        MongoDB<\/td>\ndb.t1.insert()<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        DELETE Statement<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nDelete one or more documents<\/td>\n<\/tr>\n
        Cassandra<\/td>\nDELETE statement; Same as SQL<\/td>\n<\/tr>\n
        Cosmosdb<\/td>\nAPI delete<\/td>\n<\/tr>\n
        Couchbase<\/td>\nDELETE statement; Same as SQL<\/td>\n<\/tr>\n
        MongoDB<\/td>\ndb.t1.delete()<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        UPSERT statement<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nINSERT. \u00a0UPDATE if exists.<\/td>\n<\/tr>\n
        Cassandra<\/td>\nUnsupported<\/td>\n<\/tr>\n
        Cosmosdb<\/td>\nUnsupported<\/td>\n<\/tr>\n
        Couchbase<\/td>\nUPSERT statement.<\/td>\n<\/tr>\n
        MongoDB<\/td>\nUnsupported<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        UPDATE Statement<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\n<\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nUPDATE; Same as SQL<\/span><\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nAPI update<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nUPDATE; Same as SQL<\/span><\/td>\n<\/tr>\n
        MongoDB<\/span><\/td>\ndb.t1.update()<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        MERGE: Merge one relation (set of rows) to another.<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nMerge a set of rows (documents) into another.<\/td>\n<\/tr>\n
        Cassandra<\/td>\nUnsupported<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nUnsupported<\/td>\n<\/tr>\n
        Couchbase<\/td>\nMERGE statement, same as SQL.<\/td>\n<\/tr>\n
        MongoDB<\/td>\nUnsupported<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        PREPARE statement<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nParse, analyze and create an execution plan.<\/td>\n<\/tr>\n
        Cassandra<\/td>\nSupported. \u00a0\u00a0I see excamples of preparedStatement() in Java SDK.dd<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nUnsupported<\/td>\n<\/tr>\n
        Couchbase<\/td>\nSupported; PREPARE<\/td>\n<\/tr>\n
        MongoDB<\/td>\nUnsupported<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        EXECUTE<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nExecute an ad-hoc or prepared statement.<\/td>\n<\/tr>\n
        Cassandra<\/td>\nSupported in Java.<\/td>\n<\/tr>\n
        Cosmosdb<\/td>\nUnsupported<\/td>\n<\/tr>\n
        Couchbase<\/td>\nSupported, similar to SQL.<\/td>\n<\/tr>\n
        MongoDB<\/td>\nUnsupported<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        GRANT\/REVOKE<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nGrant\/REVOKE permissions for specific operation on the data set<\/td>\n<\/tr>\n
        Cassandra<\/td>\nGRANT, REVOKE<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nAPI support<\/td>\n<\/tr>\n
        Couchbase<\/td>\nGRANT ROLE, REVOKE ROLE<\/td>\n<\/tr>\n
        MongoDB<\/td>\nTBD??<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        DESCRIBE statement<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nDescribes the schema of a table<\/td>\n<\/tr>\n
        Cassandra<\/td>\nDESCRIBE<\/td>\n<\/tr>\n
        Cosmosdb<\/td>\nUnsupported<\/td>\n<\/tr>\n
        Couchbase<\/td>\nINFER describes the schema of the documents<\/td>\n<\/tr>\n
        MongoDB<\/td>\nCompass tool — graphical only.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        TRUNCATE statement<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nTruncates the data in the table without altering security or physical schema.<\/td>\n<\/tr>\n
        Cassandra<\/td>\nTRUNCATE<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nUnsupported<\/td>\n<\/tr>\n
        Couchbase<\/td>\nFLUSH operation<\/td>\n<\/tr>\n
        MongoDB<\/td>\nUnsupported. \u00a0Workaround via remove collection, recreate with the same security settings.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        Value Logic (boolean values)<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nTrue, False, NULL (Unknown)<\/td>\n<\/tr>\n
        Cassandra<\/td>\nTrue, False, NULL (Unknown)<\/td>\n<\/tr>\n
        Cosmosdb<\/td>\nTrue, False, NULL (Unknown)<\/td>\n<\/tr>\n
        Couchbase<\/td>\nTrue, False, NULL (Unknown), Missing<\/p>\n

        https:\/\/docs.couchbase.com\/server\/4.0\/n1ql\/n1ql-language-reference\/booleanlogic.html<\/a><\/td>\n<\/tr>\n

        MongoDB<\/td>\nTrue, False, NULL (Unknown)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        Query Optimizer: Type of Optimizer<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nRule-based and cost-based optimizer. \u00a0Does query rewrites, index selection, join ordering, join type selection and position of the tablers (inner\/outer, hash-table build\/probe)<\/td>\n<\/tr>\n
        Cassandra<\/td>\nRule-based optimizer. \u00a0Index selection for the single table is done since there are no joins.<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nRule-based optimizer mainly does index selection.<\/td>\n<\/tr>\n
        Couchbase<\/td>\nRule-based optimizer, index(es) selection. \u00a0Block Nested Loop join by default but supports hash join via user hint in the query.<\/td>\n<\/tr>\n
        MongoDB<\/td>\n\u201cShape-based\u201d optimizer according to the docs. \u00a0Each new query is matched with a query based on the \u201cshape\u201d. \u00a0The first time a query is run, the optimizer does index selection, but when there are multiple candidates, it\u2019ll run multiple queries concurrently to see who returns the results first.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        Query Optimizer: Index Selection<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nYes<\/td>\n<\/tr>\n
        Cassandra<\/td>\nYes<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nYes<\/td>\n<\/tr>\n
        Couchbase<\/td>\nYes<\/td>\n<\/tr>\n
        MongoDB<\/td>\nYes<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        Query Optimizer: Query Rewrite<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\nRewrite parts of the query to logical equivalent to better performance. E.g. Subquery rewrite, view folding, join type conversion, constant expression evaluation, etc.<\/span><\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nNone<\/span><\/td>\n<\/tr>\n
        Cosmosdb<\/span><\/td>\nNo known rewrites<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nBasic query rewrite. \u00a0LEFT OUTER to INNER when applicable, constant expression evaluation.<\/span><\/td>\n<\/tr>\n
        MongoDB<\/span><\/td>\nNone<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        Query Optimization: JOIN Type <\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nChoose the most efficient index, from the available join types.<\/td>\n<\/tr>\n
        Cassandra<\/td>\nNot applicable since joins are unsupported.<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nNot clearly documented.<\/td>\n<\/tr>\n
        Couchbase<\/td>\nNested loop by default. Hash join by user hint.<\/td>\n<\/tr>\n
        MongoDB<\/td>\nJust the nested loop is supported.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        TRANSACTION support. \u00a0<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\nACID support with multi-row and multi-statement support.<\/span><\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nNo<\/span><\/td>\n<\/tr>\n
        Cosmosdb<\/span><\/td>\nYes<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nNo<\/span><\/td>\n<\/tr>\n
        MongoDB<\/span><\/td>\nYes, in 4.0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        Indexes<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/td>\nData structures maintained to speed up the query performance. \u00a0Indexes are<\/td>\n<\/tr>\n
        Cassandra<\/td>\nSupports primary, secondary, array-indexes. \u00a0Need to install and index the data on SOLR for a search index.<\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nIndexes everything by default: scalars, arrays. \u00a0No support for the search index.<\/td>\n<\/tr>\n
        Couchbase<\/td>\nSupports primary, secondary, composite, functional, adaptive, search, spatial, partitioned and replica index. \u00a0Indexes are eventually consistent.<\/td>\n<\/tr>\n
        MongoDB<\/td>\nSupports primary, secondary, composite, search, spatial, partitioned and replica index.\u00a0 The search index is simplistically created on a B-Tree.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        SQL: Datatype support.<\/b><\/p>\n\n\n\n\n\n\n\n
        SQL<\/span><\/td>\nExtensive numerical, character, date-time data type support.<\/span><\/td>\n<\/tr>\n
        Cassandra<\/span><\/td>\nNumeric, decimal, double. Int, float, varint, Timestamp, collection (set, list) <\/span><\/td>\n<\/tr>\n
        CosmosDB<\/span><\/td>\nJSON data types: numeric, string, boolean, object, arrays<\/span><\/td>\n<\/tr>\n
        Couchbase<\/span><\/td>\nJSON data types: numeric, string, boolean, object, arrays<\/span><\/td>\n<\/tr>\n
        MongoDB<\/span><\/td>\nJSON data types: numeric, string, boolean, object, arrays and custom extensions for the timestamp datatype.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        Conclusion:<\/strong><\/p>\n

        The popular NoSQL databases have tried to extend and support the declarative SQL for the respective data model and architectures. So, it’s important to understand the capabilities, limitations of the features and architecture during the evaluation.<\/p>\n

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

          \n
        1. The Unreasonable Effectiveness of SQL<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

          Business applications have requirements: take customer orders, deliver customer orders, track shipping, generate inventory report, end of the day\/month\/quarter business report, generate business dashboards and more. \u00a0These requirements evolve slowly. They remain even when you choose a NoSQL database. On […]<\/p>\n","protected":false},"author":55,"featured_media":6985,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1814,2294,1819,1812],"tags":[1886,2136,1261,1590,1309],"ppma_author":[8929],"class_list":["post-6760","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-application-design","category-analytics","category-data-modeling","category-n1ql-query","tag-cassandra","tag-cosmosdb","tag-json","tag-microsoft","tag-mongodb"],"acf":[],"yoast_head":"\nThe Unreasonable Effectiveness of SQL in NoSQL Databases<\/title>\n<meta name=\"description\" content=\"Compare and contrast SQL language support in Cassandra, CosmosDB, Couchbase, and MongoDB via their respective implementations.\u00a0\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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