Mongodb json query example1/20/2024 ![]() The output shows here that we have inserted the image file in a JSON object successfully. In this section we will give you plenty of REST API examples using MongoDB like query syntax. restdb.io uses plain URLs with simple parameters and JSON documents to query your database. Finally, we insert the documents into the collection student using the insertMany() method.Ĭonst img1Data = fs.readFileSync('G:\StudentProfile.PNG') Ĭonst img2Data = fs.readFileSync('G:\StudentCard.PNG') Ĭonst image1Base64 = img1Data.toString('base64') Ĭonst image2Base64 = img2Data.toString('base64') Querying your database is an essential part of any application. ![]() Creates output data in Extended JSON v2.0 (Canonical mode) if used with -jsonFormat. Creates output data in Extended JSON v2.0 (Relaxed mode) by default. That is, to restore data files created with a specific version of mongodump, use the corresponding version of mongorestore. In this example, we require to retrieve the first JSON object from the employees key. JSON document database is a type of NoSQL database that stores data as JSON documents. In general, use corresponding versions of mongodump and mongorestore. This object contains an array of documents, each containing the name of the image and its Base64-encoded data. Path mode: It controls the output of a JSONQUERY() function in case of an invalid JSON string using the LAX and Strict arguments Example 1: Get the JSON object from a JSON string. Then, we convert each Buffer into a Base64-encoded string and create a json object. ![]() We loaded two image files StudentProfile.PNG and StudentCard.PNG into variables mgData1 and imgData2, respectively, and read them into Buffer objects. However, we can put multiple image files at the same time in a JSON file.Ĭonsidering the following example. For example, Zephyr built a platform that integrates diverse healthcare data using a document database (MongoDB) and. Storing data in multiple databases is referred to as polyglot persistence. You can also query to find all restaurants contained in a given neighborhood. For example, take the specification of the spherical square defined by the longitude latitude points (0,0). Sometimes the answer to a data problem is not one type of NoSQL database but multiple data stores. MongoDBs geospatial indexing allows you to efficiently execute spatial queries on a collection that contains geospatial shapes and points. You can use jsonSchema in query conditions for read and write operations to find documents in the collection that satisfy the specified schema. Moreover, we have seen how to put an image file in a JSON object with the prior examples. This is one example of a use case for a graph database. ![]()
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