The Importance of a Data Format: Part 1 — Current State Problems

JSON is a really simple format. It's very easy to work with it, interchange it, read it, etc. Here is the full JSON format definition:

object = {} | { members }
members = pair | pair , members
pair = string : value
array = [] | [ elements ]
elements = value | value , elements
value = string | number | object | array | true | false | null

So far, so good. But, JSON also has a few major issues. In particular, JSON requires you to read and parse the entire document (at least until the part you actually care about) before you can do something with it. Reading JSON documents into memory and actually working with them means loading and parsing the whole thing, and typically requires the use of dictionaries to get fast access to the data. Let us look at this typical document:

{
  "firstName": "John",
  "lastName": "Smith",
  "address": {
  "state": "NY",
  "postalCode": "10021-3100"
},
"children": [{"firstName": "Alice"}]
}

How would this look in memory after parsing?

Dictionary (root)
firstName –> John
lastName –> Smith
address –> Dictionary
state –> NY
postalCode –> 10021-3100
children –> array
[0] –> Dictionary
firstName –> Alice

So, that is three dictionaries and an array (even assuming we ignore all the strings). Using Newtonsoft.Json, the above document takes 3,840 bytes in managed memory (measured using ObjSize in WinDBG). The size of the document is 126 bytes as text. The reason for the different sizes is dictionaries. Here is 320 bytes allocation:

new Dictionary<string,Object>{ {“test”, “tube”} };

And, as you can see, this adds up fast. For a database that mostly deals with JSON data, this is a pretty important factor. Controlling memory is a very important aspect of the work of a database. And, JSON is really inefficient in this regard. For example, imagine that we want to index documents by the names of the children. That is going to force us to parse the entire document, incurring a high penalty in both CPU and memory. We need a better internal format for the data.

In my next post, I’ll go into details on this format and what constraints we are working under.