In this series on Collation support in MongoDB, we've been learning how to specify collation in MongoDB using the Navicat for MongoDB GUI administration and development tool. Part I provided a brief introduction to the concept of collation, covered the fields that govern collation in MongoDB, as well as got into some of the specifics of the first three fields, namely Locale, Case Level, and Case First. Today's blog will describe the rest of the fields.
Collation involves a set of language-specific rules for string comparison, such as those for lettercase and accent marks. Your run of the mill sorting is fine for simple entries made up of alphanumeric characters, but once you include special characters, such as @, #, $, % (etc) and è, é, ê, ö (etc, etc), it becomes imperative that you specify your own collation.
In relational databases, a view is a searchable data subset that is defined by a query. Views are sometimes referred to as "virtual tables" because they don't store data, but can be queried just like tables. MongoDB recently introduced views in version 3.4. In today's blog, we'll learn how to create a view in MongoDB using Navicat for MongoDB GUI administration and development tool.
Schema Analysis is useful in verifying your schemas, visualizing data distributions and for identifying data outliers. Available only for MongoDB, the Navicat for MongoDB Collection and Data Viewer toolbars include command buttons for analyzing your collection schema and document data.
In today's blog, we'll be exploring Navicat for MongoDB's analysis tool.
In the last couple of blogs, we learned how each of Navicat for MongoDB's Collection views - Grid, Tree, and JSON - provide a different set of command buttons for performing operations that are tailored to that particular view. In the last blog, we learned about transactions, filtering, and sorting. In today's blog, we'll be covering how to expand array values, colorize cells, and migrating data between MongoDB and other databases.
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