MongoDB Query Builder

USE CASE

MongoDB Query Builder

Case Description

MongoDB Query Builder is a tool that allows users to search for error logs in a MongoDB database. It provides a user-friendly interface for constructing and executing queries, and allows users to filter, sort, and export the results for further analysis.

This use case describes how to use MongoDB Query Builder to search for error logs, and provides an overview of the steps involved. It also discusses some potential questions that a data scientist might ask of the error log collection in MongoDB.

Before You Start

A suitable collection for the MongoDB logs use case could be called "logs", and would contain documents representing individual logs. The structure of the documents in the collection would depend on the specific details of the logs, but could include fields for the timestamp, log level, error message, and other relevant information. The documents could also include references to other documents in the same collection or in other collections, allowing for the representation of complex relationships between logs.

For example, a document in the "logs" collection could have the following structure:

Logs

{

timestamp: <timestamp of when the log was generated>, 

log_leven: <level of the log, such as "error" or "warning">, 

eror_message: <description of the error that occurred>, 

user: <reference to the user who generated the log>, 

system: <reference to the system on which the log was generated>

To create the "logs" collection on MongoDB, you would first need to connect to your MongoDB instance using the mongo shell or another client application. Once connected, you can use the following command to create the collection:

This will create the "logs" collection in the currently-selected database. You can then use the insertOne or insertMany methods to insert documents into the collection, representing the logs you want to store.

For example, to insert a single log into the "logs" collection, you could use the following command:

Logs

db.createCollection("logs")

db.logs.insertOne({
  timestamp: <timestamp of when the log was generated>,
  log_level: <level of the log, such as "error" or "warning">,
  error_message: <description of the error that occurred>,
  user: <reference to the user who generated the log>,
  system: <reference to the system on which the log was generated>

APPLICATION

Step-by-Step MongoDB Query Generation

All Databases

Manual Table

CSV Schema

DDL Script

ERD Diagram

Connector

Type

Name

Content

Manual Table

E-Commerce - Playground

Column, Column, Column, Column, Column, Column,

Manual Table

Travel Agencies - Playground

Column, Column, Column, Column, Column, Column,

Manual Table

Retail - Playground

Column, Column, Column, Column, Column, Column,

Manual Table

Real Estate - Playground

Column, Column, Column, Column, Column, Column,

Manual Table

Healthcare - Playground

Column, Column, Column, Column, Column, Column,

Manual Table

Social Media - Playground

Column, Column, Column, Column, Column, Column,

Manual Table

Library System - Playground

Column, Column, Column, Column, Column, Column,

CSV Schema

Lorem Ipsum CSV

version 1.0

@totalColumns 9

/*---------------------------------------------------------------------------------------------------------------------------------------------------------------------------

|This schema is for the validation of technical environment metadata csv files according to the specification given for Lot 2 of the Scanning and Transcription Framework |

|Invitation To Tender document, Appendix D, in particular implementing the restrictions and consistency checks given on page 255. |

|The data in this file is a fairly general description of (software) tools used to process images, so in fact there are few hard and fast restrictions: |

|Most fields are allowed to be any length and may contain any combination of numerals, word characters, whitespace, hyphens, commas and full stops, any exception are noted |

|below. However, as the schema stands, each field must contain some value, it cannot be empty. | *

|This schema was used to validate test results supplied by potential suppliers |

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------*/

//the version number above is the version of the schema language, not the version of this particular schema file

//each line of the csv file being tested must contain 9 columns (fields)

batch_code: length(1,16) regex("^[0-9a-zA-Z]{1,16}$") //1st condition, must be between 1 and 16 characters long,

// and (implicitly multiple conditions are joined by a logical AND

// unless another boolean is provided)

// 2nd condition restricts to alphanumeric characters as specified in ITT p256

company_name: regex("[-/0-9\w\s,.]+")

image_deskew_software: regex("[-/0-9\w\s,.]+")

image_split_software: regex("[-/0-9\w\s,.]+")

image_crop_software: regex("[-/0-9\w\s,.]+")

jp2_creation_software: regex("[-/0-9\w\s,.]+")

uuid_software: regex("[-/0-9\w\s,.]+")

embed_software: regex("[-/0-9\w\s,.]+")

image_inversion_software: regex("[-/0-9\w\s,.]+")

DDL Script

Lorem Ipsum DDL