Mongoid parent child relationship inventory

ruby on rails - Accessing parent-child attributes in Mongoid - Stack Overflow

When a child embedded document can belong to more than one type of parent document, you can. tell Mongoid to support this by adding the ``as`` option to the . Compare top NoSQL solutions – Couchbase Server v, MongoDB v, and DataStax Customers with orders, orders with inventory, inventory with suppliers , This supported joins of in a child-to-parent relationship. Childhood adversity, parental stress and depression of negligent mothers. Parent-child Relationship Inventory (PCRI) Author Gerard, A. B. Author Affiliation .

We simply exported the data from Couchbase and imported to a mongo database called travel-sample. In MongoDB, the 5 different types of the document landmark, route, airline, airport, hotel are stored in 5 collections with respective names.

This is a fairly simple left outer join query joining two collections and then simply counting the total number of documents produced. List the airports and landmarks in the same city, ordered by the airports. Then do the sort and final projection.

The join clause for this query is a bit more complex, with two predicates airport. This requires a cumbersome pipeline syntax on the MongoDB query. And because you need to differentiate between the two collections, you need another let stage to create the local variables for airport attributes. Like before, it requires an additional match clause to eliminate non-matching empty airline docs, followed by grouping and sorting. Find all the hotels and landmarks in Yosemite. Hotels should have at least 5 likes.

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N1QL expressed the query in characters. MongoDB required characters. All this for a two table join. As the complexity increases, the ratio increases as well since the MongoDB query is written in a procedural way. This is just like Example 4, but do it faster!

(PCRI™) Parent-Child Relationship Inventory™ | WPS

The default join method in Couchbase N1QL is nested loop join. This works fine when you have a smaller number of documents involved on each side of the join. When you have a larger data set, typically in reporting queries, nested loop join slows down. Key Considerations with Data Modeling The key considerations you have to think about while modeling data that will be stored and managed in MongoDB are: Read and write operations When designing a data model for MongoDB, it is important to know your application patterns.

They will help you to better understand how data will be created and used.

Group 2 Parent Children Relationship

Based on that understanding, you should be in a better position to improve the design of your data model applying the data model design patterns that are the best fit for the patterns of your application. One of the main questions are: How your data will grow and change over time?

What kinds of queries your application will perform? Are there any concurrency related constrains you should look at? Documents can grow by either adding new fields to them, or adding new elements to its array fields, or by frequently updating them. MongoDB has a document size limit of 16 MB. MongoDB will move documents to accommodate their new space requirements.

Document moves are generally slow and can also fragment space where the file with document's collection resides. When MongoDB writes to a document or updates a field in a document it has to read document sequentially in order to come to the fields to be updated or to add a field with its value to the document.

If the document has many fields, this sequental access to the document fields will take longer.

Joining JSON: Comparing Couchbase N1QL and MongoDB

If you are dealing with large number of documents during some application operations it can add up to lots of time just spent on moving through the document to access specific fields or to add fileds to the document. If an array field is indexed, one document in the collection is responsible for a separate entry in that index for each and every element in its array. So inserting or deleting a document with a element array, if that array is indexed, is like inserting or deleting documents in terms of the amount of indexing work required.

Since the BSON data format manipulates documents with a linear memory scan, so that finding elements all the way at the end of a large array takes a long time, and most operations dealing with such a document would be slow. Atomicity There is no concept of transaction in MongoDB. All operations that create or change data e. If fields of a document have to be modified together, all of them have to be embedded in a single document in order to guarantee atomicity. MongoDB does not support multi-document transactions.

Distributed transactions and distributed join operations are two main challenges associated with distributed database design.

By not supporting these features MongoDB has been able to implement highly scalable and efficient atomic sharding solution. Sharding Design a data model to include a field that will be exclusively used as a shard key to enable balanced distribution of data with good support for write scaling and query isolation.

First analyze your application read and write operations to get a full understanding of its writing and data retrieval patterns. You will need to consider performance, complexity and flexibility of your solution in order to come up with the most appropriate model.