"Compactly condensing" an abundance of boolean values in JSON through manual effort

We are dealing with a data model that consists of 600 boolean values per entity. This entire dataset needs to be transmitted from a node.js backend to an Angular frontend via JSON.

Considering the internal nature of this API (not public), our primary focus is on performance and bandwidth optimization rather than adhering strictly to best practices.

Seeking input on potential optimization strategies, here are some options under consideration:

  • Converting it into a bitfield and utilizing a massive (600-bit) BigInt. However, concerns exist regarding potential performance drawbacks associated with this approach.

  • Partitioning the 600 bits into 10 integers (given that JS integers are 64-bit) and inserting them into an array within the JSON payload.

  • Transforming the binary blob through Base64 encoding (assumed to be decoded into a UInt8Array).

  • Exploring the use of Protobuf, though the complexity of implementation may outweigh its benefits considering time constraints and reluctance to make significant architecture changes.

Note: An absence of compression on the server side due to infrastructure limitations necessitates tackling this issue at the data level.

Thank you!

javascriptjsonserializationdata-structures

Answer №1

When Evan suggests transforming your boolean values into single characters (true="t" and false="f"), you can create a string of 600 characters that can be split into three strings of 200 characters each. Once this string reaches the front end, you can easily concatenate it and use a simple regular expression to convert it back to Boolean values.

function convertBooleanString(str)
   {
      tmpArray = str.split('').map((value) =>{
        return value == "t"?true:false;
      });
      return tmpArray;
   };

This conversion function can be adjusted and enhanced as needed. I hope this explanation helps!

Answer №2

Is there a specific sorting method that can be applied? If certain boolean values consistently appear with related data, it may be beneficial to organize and streamline them.

Consider the purpose of the data - caching or memoization could improve efficiency depending on usage frequency. Keep in mind that caching may require additional space.

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