{"id":1371,"date":"2018-09-10T04:27:56","date_gmt":"2018-09-10T11:27:56","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/ycsb-json-benchmarking-json-databases-by-extending-ycsb\/"},"modified":"2018-09-10T04:27:56","modified_gmt":"2018-09-10T11:27:56","slug":"ycsb-json-benchmarking-json-databases-by-extending-ycsb","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/pt\/ycsb-json-benchmarking-json-databases-by-extending-ycsb\/","title":{"rendered":"Using YCSB to Benchmark JSON Databases"},"content":{"rendered":"\n<p><a href=\"https:\/\/queue.acm.org\/detail.cfm?id=1036486\">Bruce\u00a0Lindsay<\/a>\u00a0<a href=\"https:\/\/sigmod.org\/publications\/interviews\/pdf\/p71-column-winslet.pdf\">once said<\/a>, &#8220;There are three things important in the database world: Performance, Performance, and Performance&#8221;.\u00a0 Most enterprise architects know, as we progress in database features and architectures, it&#8217;s important to measure performance in an open way so they can compare total cost of ownership reliably.<\/p>\n\n\n\n<p><a href=\"https:\/\/www2.cs.duke.edu\/courses\/fall13\/cps296.4\/838-CloudPapers\/ycsb.pdf\">YCSB<\/a> did a great job of benchmarking datastores serving the &#8220;Cloud OLTP&#8221; applications. These data stores were simple with simple get, put, delete operations.\u00a0 The original <a href=\"https:\/\/github.com\/brianfrankcooper\/YCSB\">YCSB benchmark<\/a> consists of a simple insert, update, delete, and scan operations on a simple document of 10 key-values; workloads are defined with a mix of these operations with various percentages.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.json.org\">JSON<\/a> databases like <a href=\"https:\/\/www.couchbase.com\">Couchbase<\/a> and <a href=\"https:\/\/www.mongodb.com\">MongoDB<\/a> have a more advanced data model with scalars, nested objects, arrays, arrays of objects, arrays and arrays of objects.\u00a0 JSON databases also have more sophisticated <a href=\"https:\/\/docs.couchbase.com\/server\/5.5\/n1ql\/n1ql-language-reference\/index.html\">query<\/a> language, indexes, and capabilities. In addition to CRUD operations, applications routinely use the declarative query languages in these databases to search, paginate, and run reports.\u00a0 So, to help architects to evaluate platforms effectively, we need an additional benchmark to measure these capabilities in addition to the basic CRUD operations. <span>This YCSB tutorial explains its capabilities in filling the gap.<\/span><\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p><a href=\"https:\/\/www.cs.duke.edu\/courses\/fall13\/cps296.4\/838-CloudPapers\/ycsb.pdf\">YCSB paper<\/a> states: We also hope to foster the development of additional cloud benchmark suites that represent other classes of applications by making our benchmark tool available via open source. In this regard, a key feature of the YCSB framework\/tool is that it is extensible\u2014it supports easy definition of new workloads, in addition to making it easy to benchmark new systems.<\/p>\n<\/blockquote>\n\n\n\n<p>This benchmark extends YCSB to JSON databases by extending existing operations to JSON and then defining new operations and new workloads.<\/p>\n\n\n\n<p><strong><span>Here&#8217;s the outline.<\/span><\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><span>Introduction<\/span><\/li>\n\n\n<li><span>Data Model<\/span><\/li>\n\n\n<li><span>Benchmark Operations<\/span><\/li>\n\n\n<li><span>Benchmark Workloads<\/span><\/li>\n\n\n<li><span>YCSB-JSON implementation<\/span><\/li>\n\n\n<li><span>How to run YCSB-JSON?<\/span><\/li>\n\n\n<li><span>References<\/span><\/li>\n\n<\/ol>\n\n\n\n<h5 class=\"wp-block-heading\"><strong><span>1. Introduction<\/span><\/strong><\/h5>\n\n\n\n<p><span>YCSB was developed to measure the performance of scalable NoSQL key-value datastores. YCSB infrastructure does that job well.\u00a0 YCSB uses a simple flat key-value. Couchbase uses a JSON model, which customers use to massively interactive applications.\u00a0 We\u2019ve built and are building features into the product to enable customers to build these applications effectively. We need performance measurements for these use cases.<\/span><\/p>\n\n\n\n<p><span>There are additional databases supporting JSON model: MongoDB, DocumentDB, DynamoDB, RethinkDB, Oracle NoSQL.\u00a0 When running YCSB on JSON databases (Couchbase, MongoDB, etc), the driver simply stores and retrieves strings in the JSON key-value structure. All of these databases require a new benchmark to measure processing of rich structure of JSON (nested objects, arrays) and operations like paging, grouping, aggregations.<\/span><\/p>\n\n\n\n<p><span>The purpose of YCSB-JSON is to extend the YCSB benchmark to measure JSON database capability to cover these two things: <\/span><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><span>Operations representative of massively interactive applications.<\/span>\n<ul>\n<li><span>Operations on the JSON data model, including nested objects, arrays.<\/span><\/li>\n<\/ul>\n<\/li>\n\n\n<li><span>Create workloads that represent operations from these applications.<\/span><\/li>\n\n<\/ol>\n\n\n\n<p><span>See these customer use cases:<\/span><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><span><a href=\"https:\/\/www.couchbase.com\/customers\/marriott\/\">Marriott<\/a> <\/span><span>built its reservation system on IBM Mainframe and DB2. They\u2019ve run into cost, performance challenges as more and more customer try to browse the available inventory.\u00a0 Systems on DB2 was originally built to take reservations from a phone-in system or from agents. The look to book ratio is low. Today, this ratio is high since the number of lookup requests has gone up exponentially. \u00a0 This has increased the database cost dramatically as well.\u00a0 Marriott moved all of its inventory data to Couchbase with continuous synchronization from its mainframe systems; web applications use Couchbase for the lookup\/search operations.<\/span><\/li>\n\n\n<li><span><a href=\"https:\/\/www.couchbase.com\/customers\/\">Cars.com<\/a> is a portal to list and sell cars. They have the listing data on Oracle.\u00a0 When they serve it up on the web, they not only have to present the basic car information but also provide additional insights like how many users are looking into a car or have saved it in their wish list. This is a way of increasing the engagement and sense of urgency.\u00a0 All the data required for these interactive operations are stored in Couchbase.<\/span><\/li>\n\n<\/ol>\n\n\n\n<p><span>More generally, the massively interactive applications include the following:<\/span><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><span>Browse rooms availability, pricing details, amenities <\/span><i><span>(lookups by end customers) <\/span><\/i><\/li>\n\n\n<li><span>Browse information on car make\/model or repair shops <\/span><i><span>(enable web-scale consumers &amp; partners)<\/span><\/i><\/li>\n\n\n<li><span>Provide information to the customer in context \u00a0<\/span><i><span>(location-based services)<\/span><\/i><\/li>\n\n\n<li><span>Serve both Master Data and Transactional Data <\/span><i><span>(at scale)<\/span><\/i><\/li>\n\n<\/ol>\n\n\n\n<p><span>To support these requirements, the applications &amp; databases do the following:<\/span><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><span>Query offload from high-cost Systems of Record (mainframe, Oracle) databases <\/span>\n<ul>\n<li><i><span>(reservations &amp; revenue apps)<\/span><\/i><\/li>\n<\/ul>\n<\/li>\n\n\n<li><span>Opening up back-office functions \u00a0to web \/ mobile access <\/span>\n<ul>\n<li><i><span>(enable web users to check room details)<\/span><\/i><\/li>\n<\/ul>\n<\/li>\n\n\n<li><span>Scale database\/queries with better TCO \u00a0<\/span>\n<ul>\n<li><i><span>(scale mainframes with commodity servers) <\/span><\/i><\/li>\n<\/ul>\n<\/li>\n\n\n<li><span>Modernize legacy systems with capabilities demanded by new collaboration\/engagement applications <\/span>\n<ul>\n<li><i><span>(browse inventory, flight, room availability, departmental analysis)<\/span><\/i><\/li>\n<\/ul>\n<\/li>\n\n<\/ol>\n\n\n\n<p>The new benchmark needs to measure the performance of queries implementing these operations.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><strong><span>2. Data Model<\/span><\/strong><\/h5>\n\n\n\n<p>We&#8217;ve taken customer and orders as two distinct collections of JSON documents.\u00a0 Each order has a reference to its customer.<\/p>\n\n\n\n<p>Below are the sample customer and order document.\u00a0 This has been generated via the\u00a0fakeit data generator.\u00a0 This tool is available at:\u00a0<a href=\"https:\/\/github.com\/bentonam\/fakeit\">https:\/\/github.com\/bentonam\/fakeit<\/a><\/p>\n\n\n\n<p>See the appendix for the YAML file used to define the data model and domain.<\/p>\n\n\n<p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; wrap=&#8221;true&#8221; show-plain=&#8221;1&#8243; scroll=&#8221;true&#8221; expand=&#8221;true&#8221; whitespace-before=&#8221;2&#8243; whitespace-after=&#8221;2&#8243; lang=&#8221;js&#8221; range=&#8221;1-20&#8243; decode=&#8221;true&#8221;]Sample customer document<br \/>\nDocument Key: 100_advjson<br \/>\n{<br \/>\n  &#8220;_id&#8221;: &#8220;100_advjson&#8221;,<br \/>\n  &#8220;doc_id&#8221;: 100,<br \/>\n  &#8220;gid&#8221;: &#8220;48a8e177-15e5-5116-95d0-41478601bbdd&#8221;,<br \/>\n  &#8220;first_name&#8221;: &#8220;Stella&#8221;,<br \/>\n  &#8220;middle_name&#8221;: &#8220;Jackson&#8221;,<br \/>\n  &#8220;last_name&#8221;: &#8220;Toy&#8221;,<br \/>\n  &#8220;ballance_current&#8221;: &#8220;$1084.94&#8221;,<br \/>\n  &#8220;dob&#8221;: &#8220;2016-05-11&#8221;,<br \/>\n  &#8220;email&#8221;: &#8220;Alysson83@yahoo.com&#8221;,<br \/>\n  &#8220;isActive&#8221;: true,<br \/>\n  &#8220;linear_score&#8221;: 31,<br \/>\n  &#8220;weighted_score&#8221;: 40,<br \/>\n  &#8220;phone_country&#8221;: &#8220;fr&#8221;,<br \/>\n  &#8220;phone_by_country&#8221;: &#8220;01 80 03 25 39&#8221;,<br \/>\n  &#8220;age_group&#8221;: &#8220;child&#8221;,<br \/>\n  &#8220;age_by_group&#8221;: 12,<br \/>\n  &#8220;url_protocol&#8221;: &#8220;http&#8221;,<br \/>\n  &#8220;url_site&#8221;: &#8220;twitter&#8221;,<br \/>\n  &#8220;url_domain&#8221;: &#8220;gov&#8221;,<br \/>\n  &#8220;url&#8221;: &#8220;https:\/\/www.twitter.gov\/Stella&#8221;,<br \/>\n  &#8220;devices&#8221;: [<br \/>\n    &#8220;EE-245&#8221;,<br \/>\n    &#8220;FF-012&#8221;,<br \/>\n    &#8220;GG-789&#8221;,<br \/>\n    &#8220;HH-246&#8221;<br \/>\n  ],<br \/>\n  &#8220;linked_devices&#8221;: [<br \/>\n    [<br \/>\n      &#8220;AA-038&#8221;,<br \/>\n      &#8220;BB-577&#8221;<br \/>\n    ],<br \/>\n    [<br \/>\n      &#8220;OO-565&#8221;,<br \/>\n      &#8220;KK-448&#8221;,<br \/>\n      &#8220;FF-281&#8221;<br \/>\n    ],<br \/>\n    [<br \/>\n      &#8220;BB-495&#8221;,<br \/>\n      &#8220;AA-374&#8221;<br \/>\n    ],<br \/>\n    [<br \/>\n      &#8220;BB-609&#8221;,<br \/>\n      &#8220;VV-899&#8221;,<br \/>\n      &#8220;LL-675&#8221;,<br \/>\n      &#8220;BB-291&#8221;<br \/>\n    ],<br \/>\n    [<br \/>\n      &#8220;CC-048&#8221;<br \/>\n    ]<br \/>\n  ],<br \/>\n  &#8220;address&#8221;: {<br \/>\n    &#8220;street&#8221;: &#8220;6392 Crona Rue Curve&#8221;,<br \/>\n    &#8220;city&#8221;: &#8220;Simeonland&#8221;,<br \/>\n    &#8220;zip&#8221;: &#8220;98316&#8221;,<br \/>\n    &#8220;country&#8221;: &#8220;Bahrain&#8221;,<br \/>\n    &#8220;prev_address&#8221;: {<br \/>\n      &#8220;street&#8221;: &#8220;9063 Johns Islands Divide&#8221;,<br \/>\n      &#8220;city&#8221;: &#8220;South Jayme&#8221;,<br \/>\n      &#8220;zip&#8221;: &#8220;34950-8194&#8221;,<br \/>\n      &#8220;country&#8221;: &#8220;Bulgaria&#8221;,<br \/>\n      &#8220;property_current_owner&#8221;: {<br \/>\n        &#8220;first_name&#8221;: &#8220;Weston&#8221;,<br \/>\n        &#8220;middle_name&#8221;: &#8220;Clyde&#8221;,<br \/>\n        &#8220;last_name&#8221;: &#8220;Considine&#8221;,<br \/>\n        &#8220;phone&#8221;: &#8220;(665) 343-9468&#8221;<br \/>\n      }<br \/>\n    }<br \/>\n  },<br \/>\n  &#8220;children&#8221;: [<br \/>\n    {<br \/>\n      &#8220;first_name&#8221;: &#8220;Darrel&#8221;,<br \/>\n      &#8220;gender&#8221;: null,<br \/>\n      &#8220;age&#8221;: 10<br \/>\n    },<br \/>\n    {<br \/>\n      &#8220;first_name&#8221;: &#8220;Shea&#8221;,<br \/>\n      &#8220;gender&#8221;: null,<br \/>\n      &#8220;age&#8221;: 6<br \/>\n    }<br \/>\n  ],<br \/>\n  &#8220;visited_places&#8221;: [<br \/>\n    {<br \/>\n      &#8220;country&#8221;: &#8220;Iran&#8221;,<br \/>\n      &#8220;cities&#8221;: [<br \/>\n        &#8220;Heidenreichshire&#8221;,<br \/>\n        &#8220;West Luciano&#8221;,<br \/>\n        &#8220;Haroldmouth&#8221;,<br \/>\n        &#8220;West Jakeburgh&#8221;<br \/>\n      ]<br \/>\n    },<br \/>\n    {<br \/>\n      &#8220;country&#8221;: &#8220;Comoros&#8221;,<br \/>\n      &#8220;cities&#8221;: [<br \/>\n        &#8220;New Valliemouth&#8221;,<br \/>\n        &#8220;East Kaleighland&#8221;<br \/>\n      ]<br \/>\n    },<br \/>\n    {<br \/>\n      &#8220;country&#8221;: &#8220;Israel&#8221;,<br \/>\n      &#8220;cities&#8221;: [<br \/>\n        &#8220;East Kali&#8221;,<br \/>\n        &#8220;Pabloport&#8221;<br \/>\n      ]<br \/>\n    },<br \/>\n    {<br \/>\n      &#8220;country&#8221;: &#8220;French Guiana&#8221;,<br \/>\n      &#8220;cities&#8221;: [<br \/>\n        &#8220;North Zachary&#8221;,<br \/>\n        &#8220;Kielmouth&#8221;<br \/>\n      ]<br \/>\n    }<br \/>\n  ]<br \/>\n}<\/p>\n<p>See the appendix for the YAML file used to define the data model and domain.<br \/>\n[\/crayon]<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><strong><span>3. Benchmark Operations:<\/span><\/strong><\/h5>\n\n\n\n<p><span>The first four operations are the same as standard YCSB, except this is on JSON documents. Rest of the operations are new.<\/span><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><span><strong>Insert<\/strong>: Insert a new JSON document. <\/span><\/li>\n\n\n<li><span><strong>Update<\/strong>: Update a JSON document by replacing the value of one scalar field. <\/span><\/li>\n\n\n<li><span><strong>Read<\/strong>: Read a JSON document, either one randomly chosen field or all fields.<\/span><\/li>\n\n\n<li><span><strong>Delete<\/strong>: Delete a JSON document with a given key.<\/span><\/li>\n\n\n<li><span><strong>Scan<\/strong>: Scan JSON documents in order, starting at a randomly chosen record key. The number of records to scan is randomly chosen (LIMIT).<\/span><\/li>\n\n\n<li><span><span><strong>Search<\/strong><\/span>: Search JSON documents based on range predicates on 3 fields (customizable to n fields).<\/span><\/li>\n\n\n<li><span><span><strong>Page<\/strong><\/span>: Paginate result set of a query with predicate on a field in the document.<\/span>\n<ul>\n<li><span>All customers in zip with randomly chosen OFFSET and LIMIT in SQL, N1QL.<\/span><\/li>\n<\/ul>\n<\/li>\n\n\n<li><span><span><strong>NestScan<\/strong><\/span>: Query JSON documents based on a predicate on a 1-level nested field.<\/span><\/li>\n\n\n<li><span><strong><span>ArrayScan<\/span><\/strong>: Query JSON documents based on a predicate within the single-level array field.<\/span><\/li>\n\n\n<li><span><strong><span>ArrayDeepScan<\/span><\/strong>: Query JSON documents based on a predicate within a two-level array field (array of arrays).<\/span><\/li>\n\n\n<li><span><span><strong>Report<\/strong><\/span>: Query customer order details for customers in specific zipcode.<\/span>\n<ul>\n<li><span>Each customer has multiple orders.<\/span><\/li>\n<li><span>Order document has order details.<\/span><\/li>\n<\/ul>\n<\/li>\n\n\n<li><span><strong><span>Report2<\/span><\/strong>: Generate sales order summary for a given day, group by zip.<\/span><\/li>\n\n\n<li><span><span><strong>Load<\/strong><\/span>: Data loading.<\/span><\/li>\n\n\n<li><span><span><strong>Sync<\/strong><\/span>: Data streaming and synchronization from another system.<\/span><\/li>\n\n\n<li><span><span><strong>Aggregate<\/strong><\/span>: Do some grouping and aggregation.<\/span><\/li>\n\n<\/ol>\n\n\n\n<h5 class=\"wp-block-heading\"><span><strong>For Couchbase: Benchmark Operations implementation examples<\/strong><\/span><\/h5>\n\n\n\n<p><span>The first four operations are the same as standard YCSB, except this is on JSON documents. Rest of the operations are new.<\/span><\/p>\n\n\n\n<p>Couchbase implements YCSB in two modes.<\/p>\n\n\n\n<p>KV=true.\u00a0 KV stands for key-value. The simple YCSB operations INSERT, UPDATE, and DELETE can be implemented via KV APIs instead of queries.\u00a0 Setting KV=true means, use the KV API and KV=false means use the <a href=\"https:\/\/docs.couchbase.com\/server\/5.5\/n1ql\/n1ql-language-reference\/index.html\">N1QL<\/a> (SQL for JSON) query. See the tutorial for N1QL at <a href=\"https:\/\/query-tutorial.couchbase.com\">https:\/\/query-tutorial.couchbase.com<\/a><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><span><strong>Insert<\/strong>: Insert a new JSON document. <\/span><\/li>\n\n<\/ol>\n\n\n<p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to insert<br \/>\nKV=false: INSERT INTO customer VALUES(&#8230;)[\/crayon]<\/p>\n\n\n\n<p><span><span><strong>2. Update<\/strong>: Update a JSON document by replacing the value of one scalar field.<\/span><\/span><\/p>\n\n\n<p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; whitespace-before=&#8221;1&#8243; whitespace-after=&#8221;1&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to UPDATE a single document.<br \/>\nKV=false: UPDATE customer SET field1 = value USE KEYS [documentkey]<span style=\"font-weight: 400\"><strong>Read<\/strong>: Read a JSON document, either one randomly chosen field in the document or all the fields.<\/span>[\/crayon]<\/p>\n\n\n<p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; whitespace-before=&#8221;1&#8243; whitespace-after=&#8221;1&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to fetch a single document.<br \/>\nKV=false: SELECT * FROM customer USE KEYS [documentkey][\/crayon]<\/p>\n\n\n\n<p><strong>3. Read: <\/strong>Fetch a JSON document with a given key.<\/p>\n\n\n<p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; whitespace-before=&#8221;1&#8243; whitespace-after=&#8221;1&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to fetch a single document.<br \/>\nKV=false: SELECT * FROM customer USE KEYS [documentkey][\/crayon]<\/p>\n\n\n\n<p><strong>4. Delete: <\/strong>Delete a JSON document with a given key.<\/p>\n\n\n<p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; whitespace-before=&#8221;1&#8243; whitespace-after=&#8221;1&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to fetch a single document.<br \/>\nKV=false: DELETE FROM customer USE KEYS [documentkey][\/crayon]<\/p>\n\n\n\n<p><span><strong>5. Scan<\/strong>: Scan JSON documents in order, starting at a randomly chosen record key. The number of records to scan is randomly chosen (LIMIT).<\/span><\/p>\n\n\n<p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; wrap=&#8221;true&#8221; whitespace-before=&#8221;1&#8243; whitespace-after=&#8221;1&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=TRUE:<br \/>\nSELECT META().id FROM customer WHERE META().id &gt; \u201cval\u201d ORDER BY META().id LIMIT &lt;num&gt;<br \/>\nFetch the actual documents directly using KV calls from the benchmark driver.<\/p>\n<p>KV=false: SELECT * FROM customer WHERE META().id &gt; \u201cval\u201d ORDER BY META().id LIMIT &lt;num&gt;[\/crayon]<\/p>\n\n\n\n<p><span><strong><span>6. Page<\/span><\/strong>: Paginate result set of a query with predicate on a field in the document.<\/span><\/p>\n\n\n<p>[crayon font-size=&#8221;14&#8243; line-height=&#8221;17&#8243; wrap=&#8221;true&#8221; scroll=&#8221;true&#8221; whitespace-before=&#8221;2&#8243; whitespace-after=&#8221;2&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]All customers in address.zip with randomly chosen OFFSET and LIMIT in SQL, N1QL<br \/>\nKV=TRUE:<br \/>\nSELECT META().id FROM customer WHERE address.zip = \u201cvalue\u201d OFFSET &lt;num&gt; LIMIT &lt;num&gt;<br \/>\nFetch the actual documents directly using KV calls from the benchmark driver.<\/p>\n<p>KV=false: SELECT * FROM customer WHERE address.zip = \u201cvalue\u201d OFFSET &lt;num&gt; LIMIT &lt;num&gt;[\/crayon]<\/p>\n\n\n\n<p><span><span><strong>7. Search<\/strong><\/span>: Search JSON documents based on range predicates on <\/span><span>3 fields <\/span><span>(customizable to n fields).<\/span><\/p>\n\n\n<p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;17&#8243; wrap=&#8221;true&#8221; scroll=&#8221;true&#8221; whitespace-before=&#8221;2&#8243; whitespace-after=&#8221;2&#8243; lang=&#8221;default&#8221; decode=&#8221;true&#8221;]All customers WHERE (country = \u201cvalue1\u201d AND age_group = \u201cvalue2\u201d and YEAR(dob) = \u201cvalue\u201d )<br \/>\nAll customers retrieved with randomly chosen OFFSET and LIMIT in SQL, N1QL<\/p>\n<p>KV=TRUE:<br \/>\nSELECT META().id FROM customer WHERE country = \u201cvalue1\u201d AND age_group = \u201cvalue2\u201d and YEAR(dob) = \u201cvalue\u201d ORDER BY country, age_group, YEAR(dob) OFFSET &lt;num&gt; LIMIT &lt;num&gt;<br \/>\nFetch the actual documents directly using KV calls from the benchmark driver.<\/p>\n<p>KV=false: SELECT * FROM customer WHERE WHERE country = \u201cvalue1\u201d AND age_group = \u201cvalue2\u201d and YEAR(dob) = \u201cvalue\u201d ORDER BY country, age_group, YEAR(dob) OFFSET &lt;num&gt; LIMIT &lt;num&gt;<br \/>\n[\/crayon]<\/p>\n\n\n\n<p><span><span><strong>8. NestScan<\/strong><\/span>: Query JSON documents based on a predicate on a 1-level nested field.<\/span><\/p>\n\n\n<p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;17&#8243; wrap=&#8221;true&#8221; scroll=&#8221;true&#8221; whitespace-before=&#8221;2&#8243; whitespace-after=&#8221;2&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=TRUE:<br \/>\nSELECT META().id FROM customer WHERE address.prev_address.zip = \u201cvalue\u201d LIMIT &lt;num&gt;<br \/>\nFetch the actual documents directly using KV calls from the benchmark driver.<\/p>\n<p>KV=false: SELECT * FROM customer WHERE address.prev_address.zip = \u201cvalue\u201d LIMIT &lt;num&gt;<br \/>\n[\/crayon]<\/p>\n\n\n\n<p><span><span><strong>9. ArrayScan<\/strong><\/span>: Query JSON documents based on a predicate within the single-level array field.<\/span><\/p>\n\n\n\n<p><p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to insert<br \/>\nKV=false: INSERT INTO customer VALUES(&#8230;)[\/crayon]<\/p>\n0<\/p>\n\n\n\n<p><span><span><strong>10. ArrayDeepscan<\/strong><\/span>: Query JSON documents based on a predicate within a two-level array field (array of arrays).<\/span><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><span>Get me list of all customers who have visited Paris, France.<\/span><\/li>\n\n<\/ul>\n\n\n\n<p><span><strong>KV=true:<\/strong> <\/span><\/p>\n\n\n\n<p><p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to insert<br \/>\nKV=false: INSERT INTO customer VALUES(&#8230;)[\/crayon]<\/p>\n1<\/p>\n\n\n\n<p><span>Fetch the actual documents directly using KV calls from the benchmark driver.<\/span><\/p>\n\n\n\n<p><strong>KV=false: <\/strong><\/p>\n\n\n\n<p><p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to insert<br \/>\nKV=false: INSERT INTO customer VALUES(&#8230;)[\/crayon]<\/p>\n2<\/p>\n\n\n\n<p><span><span><strong>11. Report<\/strong><\/span>: Query customer order details for customers in specific zipcode.<\/span><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\n<p><p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to insert<br \/>\nKV=false: INSERT INTO customer VALUES(&#8230;)[\/crayon]<\/p>\n3<\/p>\n<\/li>\n\n<\/ul>\n\n\n\n<p><span><strong><span>12. Report2<\/span><\/strong>: Generate sales order summary for a given day, group by zip.<\/span><\/p>\n\n\n\n<p><p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to insert<br \/>\nKV=false: INSERT INTO customer VALUES(&#8230;)[\/crayon]<\/p>\n4<\/p>\n\n\n\n<p><span><span><strong>13. Load<\/strong><\/span>: Data loading.<\/span><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><span>LOAD 1 million documents.<\/span><\/li>\n\n\n<li><span>LOAD 10 million documents.<\/span><\/li>\n\n<\/ul>\n\n\n\n<p><span><span><strong>14. Sync<\/strong><\/span>: Data streaming and synchronization from another system<\/span><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><span>Need to measure the data sync performance.<\/span>\n<ol>\n<li><span>Sync 1 million documents. 50% update, 50% insert.<\/span><\/li>\n<li><span>Sync 10 million documents. 80% update, 20% insert.<\/span><\/li>\n<\/ol>\n<\/li>\n\n\n<li><span>Ideally, this sync would be done from Kafka or some other connector pulling data from a different source.<\/span><\/li>\n\n<\/ul>\n\n\n\n<p><span><span><strong>15. Aggregate<\/strong><\/span>: Do some grouping and aggregation.<\/span><\/p>\n\n\n\n<p><p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to insert<br \/>\nKV=false: INSERT INTO customer VALUES(&#8230;)[\/crayon]<\/p>\n5<\/p>\n\n\n\n<p>\u00a0<\/p>\n\n\n\n<p><p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to insert<br \/>\nKV=false: INSERT INTO customer VALUES(&#8230;)[\/crayon]<\/p>\n6<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><span><strong>4. Benchmark Workloads<\/strong><\/span><\/h5>\n\n\n\n<p><span>Workloads are a combination of these operations.<\/span><\/p>\n\n\n\n<p><span>To begin with, the workload definition can reuse the definitions of the YCSB definition: workload-A through workload-E. Details are available at <\/span><a href=\"https:\/\/github.com\/brianfrankcooper\/YCSB\/wiki\/Core-Workloads\"><span>https:\/\/github.com\/brianfrankcooper\/YCSB\/wiki\/Core-Workloads<\/span><\/a><span>.\u00a0 We\u2019ll need to define additional workloads with a combination of operations defined above.<\/span><\/p>\n\n\n\n<p><span>Workload SA is the same as workload A on the new model. <\/span><span>Ditto with workload B through F. \u00a0We\u2019ll call them SB through SF to differentiate from the workload B through F.<\/span><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<tbody>\n<tr>\n<td><span><strong>Workload<\/strong><\/span><\/td>\n<td><span><strong>Operations<\/strong><\/span><\/td>\n<td><span><strong>Record selection<\/strong><\/span><\/td>\n<td><span><strong>Application Example<\/strong><\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SA &#8212; Update heavy<\/span><\/td>\n<td><span>Read: 50%<\/span>\n<p><span>Update 50%<\/span><\/p><\/td>\n<td><span>Zipfian<\/span><\/td>\n<td><span>Session store recording recent actions in a user session<\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SB &#8212; Read heavy<\/span><\/td>\n<td><span>Read: 95%<\/span>\n<p><span>Update: 5%<\/span><\/p><\/td>\n<td><span>Zipfian<\/span><\/td>\n<td><span>Photo tagging; add a tag is an update, but most operations<\/span>\n<p><span>Update: 5% are to read tags<\/span><\/p><\/td>\n<\/tr>\n<tr>\n<td><span>SC &#8212; Read only<\/span><\/td>\n<td><span>Read: 100%<\/span><\/td>\n<td><span>Zipfian<\/span><\/td>\n<td><span>User profile cache, where profiles are constructed elsewhere (e.g., Hadoop)<\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SD &#8212; Read latest<\/span><\/td>\n<td><span>Read: 95%<\/span>\n<p><span>Insert 5%<\/span><\/p><\/td>\n<td><span>Latest<\/span><\/td>\n<td><span>User status updates; people want to read the latest statuses<\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SE &#8212; Short ranges<\/span><\/td>\n<td><span>Scan: 95%<\/span>\n<p><span>Insert: 5%<\/span><\/p><\/td>\n<td><span>Zipfian\/Uniform<\/span><\/td>\n<td><span>Threaded conversations, where each scan is for the posts in a given thread (assumed to be clustered by thread id)<\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SF &#8212; Read, modify, write<\/span><\/td>\n<td><span>Read: 50%<\/span>\n<p><span>Write: 50%<\/span><\/p><\/td>\n<td><span>Zipfian<\/span><\/td>\n<td><span>user database, where user records are read and modified by the user or to record user activity.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SG &#8212; Page heavy<\/span><\/td>\n<td><span>Page: 90%<\/span>\n<p><span>Insert: 5%<\/span><\/p>\n<p><span>Update:5%<\/span><\/p><\/td>\n<td><span>Zipfian<\/span><\/td>\n<td><span>User database, where new users are added, existing records are updated, pagination queries on the system.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SH &#8212; Search heavy<\/span><\/td>\n<td><span>Search: 90%<\/span>\n<p><span>Insert: 5%<\/span><\/p>\n<p><span>Update: 5%<\/span><\/p><\/td>\n<td><span>Zipfian<\/span><\/td>\n<td><span>User database, where new users are added, existing records are updated, search queries on the system.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SI &#8212; NestScan heavy<\/span><\/td>\n<td><span>Nestscan: 90%<\/span>\n<p><span>Insert: 5%<\/span><\/p>\n<p><span>Update: 5%<\/span><\/p><\/td>\n<td><span>Zipfian<\/span><\/td>\n<td><span>User database, where new users are added, existing records are updated, nestscan queries on the system.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SJ &#8212; Arrayscan heavy<\/span><\/td>\n<td><span>Arrayscan: 90%<\/span>\n<p><span>Insert: 5%<\/span><\/p>\n<p><span>Update: 5%<\/span><\/p><\/td>\n<td><span>Zipfian<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span>SK &#8212; ArrayDeepscan heavy<\/span><\/td>\n<td><span>ArrayDeepScan: 90%<\/span>\n<p><span>Insert: 5%<\/span><\/p>\n<p><span>Update: 5%<\/span><\/p><\/td>\n<td><span>Zipfian<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span>SL &#8212; Report<\/span><\/td>\n<td><span>Report: 100%<\/span><\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span>SL &#8212; Report2<\/span><\/td>\n<td><span>Report2: 100%<\/span><\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span>SLoad &#8212; Load <\/span><\/td>\n<td><span>Load: 100%<\/span><\/td>\n<td><span>Everything<\/span><\/td>\n<td><span>Data load to setup SoE<\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SN &#8212; Aggregate<\/span>\n<p><span>(SN1, SN2)<\/span><\/p><\/td>\n<td><span>Aggregation: 90%<\/span>\n<p><span>Insert: 5%<\/span><\/p>\n<p><span>Update: 5%<\/span><\/p><\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span>SMIX &#8212; Mixed workload<\/span><\/td>\n<td><span>Page:20%<\/span>\n<p><span>Search:20%<\/span><span><br>\n<\/span><span>Nestscan:15%<\/span><\/p>\n<p><span>Arrayscan:15%<\/span><\/p>\n<p><span>ArrayDeepscan:10%<\/span><\/p>\n<p><span>Aggregate: 10%<\/span><\/p>\n<p><span>Report: 10%<\/span><\/p><\/td>\n<td><\/td>\n<td><span>See below.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span>SSync &#8212; Sync<\/span><\/td>\n<td><span>Sync: 100%<\/span>\n<p><span>Merge\/Update: 70%<\/span><\/p>\n<p><span>New\/Insert: 30%<\/span><\/p><\/td>\n<td><\/td>\n<td><span>Continuous sync of data from other systems to systems of engagement. See below.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<p>\u00a0<\/p>\n\n\n\n<p><span>Example Configuration for YCSB\/JSON Workload<\/span><\/p>\n\n\n\n<p><p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to insert<br \/>\nKV=false: INSERT INTO customer VALUES(&#8230;)[\/crayon]<\/p>\n7<\/p>\n\n\n\n<p><span><strong>Acknowledgments<\/strong><\/span><\/p>\n\n\n\n<p>Thanks to <strong>Raju Suravarjjala,\u00a0<\/strong>Couchbase Senior director for QE and Performance, for pushing us to do this and the entire performance team for supporting this effort.\u00a0The YCSB-JSON benchmark was developed in collaboration with\u00a0<strong>Alex Gyryk,\u00a0<\/strong>Couchbase Principal Performance Engineer.\u00a0 He developed the data models for customer and orders used in this paper and implemented the operations and workloads in YCSB-JSON for Couchbase and MongoDB.\u00a0 The YCSB-JSON implementation is available at:\u00a0<a href=\"https:\/\/github.com\/couchbaselabs\/YCSB\">https:\/\/github.com\/couchbaselabs\/YCSB<\/a><\/p>\n\n\n\n<p>Thanks to\u00a0<strong>Aron Benton,\u00a0<\/strong>Couchase Solution Architect, for developing an easy to use and efficient JSON data generator, fakeit.\u00a0 He developed this prior to joining Couchbase. It is available at:\u00a0<a href=\"https:\/\/github.com\/bentonam\/fakeit\">https:\/\/github.com\/bentonam\/fakeit<\/a><\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><span><strong>Next part<\/strong><\/span><\/h5>\n\n\n\n<h5 class=\"wp-block-heading\">In the next article on YCSB-JSON, Alex will explain the implementations of this benchmark for Couchbase and MongoDB.\u00a0 The source code for the implementation is available\u00a0at:\u00a0<a href=\"https:\/\/github.com\/couchbaselabs\/YCSB\">https:\/\/github.com\/couchbaselabs\/YCSB<\/a><\/h5>\n\n\n\n<h5 class=\"wp-block-heading\"><span><strong>References<\/strong><\/span><\/h5>\n\n\n\n<ol class=\"wp-block-list\">\n<li><span>Benchmarking Cloud Serving Systems with YCSB: <\/span><a href=\"https:\/\/www.cs.duke.edu\/courses\/fall13\/cps296.4\/838-CloudPapers\/ycsb.pdf\"><span>https:\/\/www.cs.duke.edu\/courses\/fall13\/cps296.4\/838-CloudPapers\/ycsb.pdf<\/span><\/a><\/li>\n\n\n<li><span>JSON: <\/span><a href=\"https:\/\/json.org\"><span>https:\/\/json.org<\/span><\/a><\/li>\n\n\n<li><span>JSON Generator: <\/span><span><a href=\"https:\/\/www.json-generator.com\/\">https:\/\/www.json-generator.com\/<\/a><\/span><\/li>\n\n\n<li>YCSB-JSON Implementation: <a href=\"https:\/\/github.com\/couchbaselabs\/YCSB\">https:\/\/github.com\/couchbaselabs\/YCSB<\/a><\/li>\n\n<\/ol>\n\n\n\n<h5 class=\"wp-block-heading\"><span><strong>Appendix<\/strong><\/span><\/h5>\n\n\n\n<p><b>YAML to generate the customer dataset.<\/b><\/p>\n\n\n\n<p><p>[crayon theme=&#8221;github&#8221; font-size=&#8221;14&#8243; line-height=&#8221;16&#8243; lang=&#8221;mysql&#8221; decode=&#8221;true&#8221;]KV=true: KV call to insert<br \/>\nKV=false: INSERT INTO customer VALUES(&#8230;)[\/crayon]<\/p>\n8<\/p>\n\n\n\n<p>\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Bruce\u00a0Lindsay\u00a0once said, &#8220;There are three things important in the database world: Performance, Performance, and Performance&#8221;.\u00a0 Most enterprise architects know, as we progress in database features and architectures, it&#8217;s important to measure performance in an open way so they can compare total cost of ownership reliably. YCSB did a great job of benchmarking datastores serving the [&hellip;]<\/p>\n","protected":false},"author":55,"featured_media":1370,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[127,179,54,189,17,18,64],"tags":[329,175,30,44,6,330],"ppma_author":[291],"class_list":["post-1371","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-application-design","category-couchbase-architecture","category-couchbase-server","category-data-modeling","category-performance","category-n1ql-query","category-tools-sdks","tag-benchmark","tag-database","tag-json","tag-mongodb","tag-nosql-database","tag-ycsb"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.6 (Yoast SEO v27.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Using YCSB to Benchmark JSON Databases - The Couchbase Blog<\/title>\n<meta name=\"description\" content=\"This post covers the YCSB benchmark and provides examples of benchmark operations, workloads, YCSB-JSON implementation and explains how to run YCSB-JSON.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.couchbase.com\/blog\/pt\/ycsb-json-benchmarking-json-databases-by-extending-ycsb\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Using YCSB to Benchmark JSON Databases\" \/>\n<meta property=\"og:description\" content=\"This post covers the YCSB benchmark and provides examples of benchmark operations, workloads, YCSB-JSON implementation and explains how to run YCSB-JSON.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/pt\/ycsb-json-benchmarking-json-databases-by-extending-ycsb\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:published_time\" content=\"2018-09-10T11:27:56+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/5\/2026\/05\/Screen-Shot-2018-09-10-at-4.21.45-AM.png\" \/>\n\t<meta property=\"og:image:width\" content=\"2048\" \/>\n\t<meta property=\"og:image:height\" content=\"498\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Keshav Murthy\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@rkeshavmurthy\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Keshav Murthy\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"9 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/ycsb-json-benchmarking-json-databases-by-extending-ycsb\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/ycsb-json-benchmarking-json-databases-by-extending-ycsb\\\/\"},\"author\":{\"name\":\"Keshav Murthy\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#\\\/schema\\\/person\\\/c261644262bf98e146372fe647682636\"},\"headline\":\"Using YCSB to Benchmark JSON Databases\",\"datePublished\":\"2018-09-10T11:27:56+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/ycsb-json-benchmarking-json-databases-by-extending-ycsb\\\/\"},\"wordCount\":2841,\"commentCount\":6,\"publisher\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/ycsb-json-benchmarking-json-databases-by-extending-ycsb\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/wp-content\\\/uploads\\\/sites\\\/5\\\/2026\\\/05\\\/Screen-Shot-2018-09-10-at-4.21.45-AM.png\",\"keywords\":[\"benchmark\",\"database\",\"JSON\",\"mongodb\",\"NoSQL Database\",\"ycsb\"],\"articleSection\":[\"Application Design\",\"Couchbase Architecture\",\"Couchbase Server\",\"Data Modeling\",\"High Performance\",\"SQL++ \\\/ N1QL Query\",\"Tools &amp; 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