![]() NumberDecimal("") constructor is now usable to explicitly specify 128-bit decimal-based floating-point values capable of emulating decimal rounding with exact precision. Normally, mongo shell (and Robo 3T shell) treats all numbers as 64-bit floating-point double values by default. ![]() NumberDecimal (Decimal128) is added as new data type in MongoDB 3.4. New Data Type: NumberDecimal (Decimal128) Support ![]() By the time of writing this blog post, MongoDB offers free, easily configurable and usable 3.4 cluster at Mongo Atlas web site.Įxample MongoDB 3.4 cluster view on Mongo Atlas web site:Įxample MongoDB 3.4 cluster view on Robo 3T:Ģ. Some major evolution in capabilities and enhancements in MongoDB 3.4 are "native graph computation, faceted navigation, rich real-time analytics, geo-distributed MongoDB zones, elastic clustering".įinally, some words about "Free Mongo DB 3.4 Cluster by Mongo Atlas". (More about this topic can be seen in section 2 in this blog) This functionality is intended for applications that handle monetary data, such as financial, tax, and scientific computations. Some key features in MongoDB 3.4 are linearizable read concerns, views, collation and new data type NumberDecimal (Decimal128).Ī quick word about new data type NumberDecimal (Decimal128), it is added to represent 128-bit decimal-based floating-point values capable of emulating decimal rounding with exact precision. After being busy developing and releasing some critical features like SSL and Replica Sets, finally with this version Robo 3T also supports MongoDB 3.4. Since the latest version of MongoDB version 3.4 released, we were very eager to support this new MongoDB version as soon as possible. Tool Chain Improvements: Modern C++14 features are enabled Security Improvement: OpenSSL version upgradeĭ. Stability Improvement: Fix for a crash on MACOS SierraĬ. ECMAScript 2015 aka ES6 Support (Modernized JavaScript Implementation)ī.New Data Type: NumberDecimal (Decimal128) Support.One important change worth to mention is that we changed our name from Robomongo to Robo 3T because of trademark reasons (more details about this topic can be seen in section Renaming from Robomongo to Robo 3T). We are very happy to announce the release of Robo 3T 1.1 with MongoDB 3.4 Support! In this release we have some fundamental changes, improvements and fixes. ask yourself if you made the right architecture decisions when you find yourself frequently using $lookup.Menu Robomongo is now Robo 3T, with MongoDB 3.4 support 14 June 2017 That's a lot of $lookups.and starts to beg the question "WHY NOT JUST USE SQL". This will require one UGLY Mongo aggregation where you aggregate likes, comments, etc for a given post. Let's say you want to display a users most liked blog posts. If you use MongoDb for this use case, you can quickly find yourself in trouble (especially if each entity is represented by it's own collection!) This is a lot of RELATIONSHIPS to manage. A typical blog comes with Users, Posts, Comments, Likes, Followers, etc. To further illustrate, let's say you set up a blog using Mongo. Remember that one of the key advantages of MongoDb is faster performance with unstructured data.the irony. Remember that a $lookup operation comes with the cost of performing a JOIN operation on your collections. While it's good that $lookup exists in MongoDb, it goes against every reason why you would use MongoDB vs another relational datastore (MySQL). Remember that $lookup can be used for both equality checks (first example) as well as more complex sub-queries (second example).ĭepending on which type of query you run, $lookup will take slightly different parameters (let, pipeline vs localField, foreignField).įor more on the aggregation pipeline, be sure to check out this 5 minute guide to the MongoDB aggregation framework. You've now seen two examples of using $lookup in the aggregation pipeline. This query adds an additional condition for returning comments whose likes count is greater than the corresponding post's likes.īy running this query, we now know if any posts have comments with more likes than the post itself :). Like the first example, this query returns the comments whose postTitle matches the title field for each post. We reference fields in the foreign collection with a single $. Notice how we use $$ to refer to the variables we defined in let. The examples are based on this sample data. We'll provide an example of each scenario. $lookup supports both basic equality matches as well as uncorrelated sub-queries. ![]() $lookup works by returning documents from a "joined" collection as a sub-array of the original collection. $lookup allows you to perform joins on collections in the same database. Preface: New to the aggregation pipeline? Check out our 5 minute explanation before diving into these $lookup examples.Īlso be sure to understand indexing and it's performance implications before reviewing these examples. ![]()
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