por Query in SQL and NoSQL databases is one of the most important elements in the management of these relational and non-relational databases., since they condition the consultation dynamics in the big data management process. This allows a series of functions, such as moving data or quickly updating it from queries.
Therefore, understanding how it works is essential if you plan to become a Big Data expert. In the course of this post, we will explain how Query SQL works in SQL and NoSQL databases as part of the Big Data worldthrough the Postgres and MongoDB platforms.
What is Query in databases?
Within the databases, Query refers to the repetition of data that is stored. Besides, Query is also the query term that results in a SERP within search engines.. In this way, this tool aims to facilitate the results returned to a request.
Query in SQL and NoSQL databases is a technique to obtain the data that you plan to analyze. For example, you can request all the user’s data and, in addition, you can select the table or request a specific sheet where the addresses are found.
On the other hand, you can consult all records or all rounds that match a user’s identifier. Immediately, You will be presented with search matches.
Now, suppose you are looking for perform a SQL Query on a database that, by design, is slow, since it has a lot of data; Even so, it would only take a couple of seconds.
This can happen in your app, where suddenly people start logging in because you’ve added a feature new. Due to the number of users using your system, it becomes slower. This is a common case andas a solution, a cache is used. If you’re wondering where this is cached, the answer is in RAM memory, so it is very fast and simple.
Query in SQL and NoSQL databases
Query in SQL and NoSQL databases has a series of variants and function options depending on the interest with which you plan to develop data analysis. However, you may find changes in the development of its functions depending on whether it is a relational or non-relational database. Therefore, through some examples, we will show you a comparison between similar Query operations in SQL and NoSQL databases.
In this way, we will illustrate each of the functions (Simple Query, Query with Like, Query with >, Count, plus the creation of new collections/tables) through its implementation in Postgres as SQL and MongoDB as NoSQL.
In the first instance, for the query It is necessary to start by carrying out a setup in both databases, as we show below:
simple query
First of all, you can find the simple query, which refers to the query without conditions of a certain factor of the query. In the following image, you will notice how a simple query in SQL and NoSQL databases:
Query with LIKE
This search function consists of following the query nomenclature by column, operator and value for the query in SQL. Below, we share an example of how its development works in Postgres and MongoDB.
Query with > (greater than)
This function lies in the comparison of two values, so that it is established if one of the values is greater or less than another. When the function >it is marked as TRUE; If not and the second value is greater, then it will give you a Fake.
Count in Postgres and MongoDB
Count It fulfills the task of returning the number of values and rows from a set of values and rows of queries in SQL. In the following example, you will see How Count works as a Query strategy in SQL and NoSQL databases:
New collections/tables
On the other hand, away from queries in SQL, you can also implement new collections/tables on Postgres and Mongo platforms. Now, we illustrate how this function is achieved:
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In this post, we have taught you How Query SQL works in SQL and NoSQL databases, through a series of examples and images that illustrate how each of them is used from Postgres and MongoDB. No however, There is still much to learn about the management of Big Data today, since many more tools, systems and languages are increasingly implemented to manage it.
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