在房间数据库中存储复杂数据类型的替代方法

Question

Existing approach : Currently we use TypeConverters to help database to store and retrieve complex data type (POJO class objects). But that involves serializing and deserializing the objects, which seems unnecessary when we only need a simple primitive data type like int, string, float, etc.

My approach : I am thinking of an approach of breaking down the complex data type to primitive ones and create separate columns to store them. When we need a simple primitive type from the database, then we won't have to go through the process of deserializing complex objects.

I have tried the my approach and it is working but I'm not sure of the corner cases that may arise while implementing this approach in big projects.

I am still new to this, need help in finding pros and cons of my approach.

Answer 1

There are some who advocate storing a representation of objects as a single column. This is fine if you just want to store and retrieve the objects and then work with the objects. The code itself can often be shorter.

If you want to manipulate the underlying values (fields in the object) embedded in the representation via the power of SQLite then matters can get quite complex and perhaps inefficient due to the much higher likelihood of full table scans due to the likely lack of available/usable indexes.

eg if myvalue is a value within a representation (typically JSON) then to find rows with this value you would have to use something like

@Query("SELECT * FROM the_table WHERE the_one_for_many_values_column LIKE '%myvalue%'")

or

@Query("SELECT * FROM the_table WHERE instr(the_one_for_may_values_column,'myvalue')

as opposed to myvalue being stored in a column of it's own (say the_value) then

@Query("SELECT * FROM the_table WHERE the_value LIKE 'myvalue')

• the former two have the flaw that if myvalue is stored elsewhere within the representation then that row is also included even. Other than the fact that LIKE is case independent the third is an EXACT.
• an index on the the_value column may improve performance

Additionally the representation will undoubtedly add bloat (separators and descriptors of the values) and thus will require more storage. This compounded as the same data would often be stored multiple times whilst a normalised relational approach may well store just 1 instance of the data (with just up to 8 bytes per repetition if a 1-M relationship (indexes excluded)).

• 32 bytes of bloat and the maximum needed to cater for a many-many relationship (8 bytes parent 8 bytes child and two 8 bytes columns in the mapping table) will be exceeded.

As the SQLite API utilises Cursors (buffering) to retrieve extracted data, then with a greater storage requirement fewer rows can be held at once by a CursorWindow (the limited sized buffer that is loaded by x rows of output). There is also greater potential, again due to the bloat, of a single row being larger than the CursorWindow permits.

In short for smaller simpler projects not interested greatly in performance, then storing representations via TypeConverters could be the more convenient and practical approach. For larger more complex projects then unleashing the relational aspects of SQLite via data that is related rather than embedded within a representation could well be the way to go.