MySQL and MongoDB

Since MySQL 5.7.8, it supports a native JSON data type.

I guess that the line between SQL and NoSQL, MySQL and MongoDB, become not very clear.

 

 

Validate JSON files

The following tools can be used to validate the JSON files:

---

• freeformatter.com
• jsonformat.com 

The files generated directly from MongoDB Query tool are not exactly JSON files.

JSON to Parquet Conversion

Creating parquet files is now part of the optimization process to improve the query performance in Spark.  It is useful to store the data in parquet files as way to prepare data for query.

JSON is a popular form in web apps.  NoSQL databases, such as MongoDB, allow the developers to directly store data in the format such as JSON to maintain the nested structure.  This way the OLTP apps development and performance can be optimized.

The remaining challenge is to convert the JSON files as parquet files.

MongoDB Query (3)

4. Use OR in the filter criteria

When the criteria are AND-ed together, you can use comma.

When the criteria are OR-ed together, we need to use OR.

db.Person.find(
  $or:[
    {"Job":"DBA"},
    {"Age":30}
  ]
)

The conditions are put into an Array.

 

MongoDB Query (2)

3. More than one criteria

db.Person.find(
  {"Job":"DBA","age":30}
)

The different criteria can be connected using comma.

Some questions:

1. How to use OR?
2. How to use the comparison criteria, other the equal comparison? How about Great Than, Less Than, In, Like(Contain), StartsWith, Not Equal, etc.