Not all of the available Bson types listed in the BsonType class are supported, but all of the types you’d normally want to use in testing are supported. If you are using mongosh, see Iterate a Cursor in. To learn how to iterate through documents in a cursor, refer to your drivers documentation. The query results are not returned as an array of documents. Sometimes, simply verifying that a field value exists (i.e., != null) is good enough. When you run a find operation with a MongoDB driver or mongosh, the command returns a cursor that manages query results. Which fields to skip assertion for is a decision that is closely tied to your particular use case. The _id field is the easiest example for this feature application, but there are certainly others. At the first step, DTM Data Generator for JSON makes a set of test JSON files in 'd:resultsJsonFiles' folder. At the same time, trying to guess or verify the actual value that the database might come up with for the _id field is not worth the effect. We can have high confidence that the MongoDB database will create the proper value for this field when a new document is inserted. When figuring out what value to check for is especially difficult, the risk/reward ratio can become such that it’s better to ignore this field (or verify it manually in the body of the test) instead of trying to come up with an expected value that would work. Sometimes, we can have very high confidence in correctness of a field such that the value of verifying it is not particular great. There is one fundamental rule that is followed when using the assertion JSON: If a document specified in the assertion JSON is missing a field, that field is ignored during the assertion process. "name" : "Bob" Special rule for assertions ![]() In addition, the optional comparator field can inform how mongoUnit should compare the expected to the actual value.īy default, and if omitted, the comparator value is always =, but the following are all of the comparison operator values it supports: It causes MongoDB and the mongoUnit framework to handle these as STRING data type, which is not usually good enough for interacting with its values. jsonlist: jsonmap: - we use this for MongoDb. However, when it comes to OBJECT_ID and DATE_TIME Bson types, relying on the automatic type interpretation is usually not a good idea. Fill database tables with generated random data for testing your application. For example, the name field is specified directly as "name": "Bob The Builder". Just like when it comes to seeding the database, outside of OBJECT_ID and DATE_TIME Bson types, it’s rarely necessary to specify the BSON types explicitely and one can rely on the automatic type interpretation. The BSON_TYPE is the Bson type that directly corresponds to the enum names/constants of the BsonType class, which is part of the MongoDB Java driver. The configurable $$ in the type specification field name is a configuration trigger to let mongoUnit know that this is a special case that needs interpretation and not just a regular field in a document. The above is read: “Assert that T17:05:36.132Z < actual value”. This tool is in beta version, if you find a bug, please let me know in the comments.[ ![]() The basic generator is easier to use, but does not allow you to generate complex data.It is ideal for generating CSV data that you want to integrate into a database.This tool also provides an API to generate data. This generator can generate a variety of data types, including names, addresses, email addresses. MongoDB queries must be valid JSON documents, using the same field multiple times in a query is not allowed using PanacheQL as it would generate an invalid. These generators are a bit complicated to use, you have to be comfortable with this type of data. This type of data that approximates real data helps to find bugs more easily.Also, if you have to give a presentation, using realistic data can help understanding.Īdvanced test data generators in JSON and XML format allow to generate complex data with sub-objects / tags. drag a folder with files, you need to zip it and upload in Test Data section and use it like so. This powerful tool is 100% online and allows you to quickly generate realistic test data (datasets). We often need test data to validate that our applications respect the functional rules, and also that they hold the load with a large volume of data.
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