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Converts tabular row data (as from SQL joins, flat JSON, etc) to deep tree graphs based on simple column naming conventions.
Converts row data (in JSON/associative array format) to object/tree structure based on column naming conventions.
##Why?
Most of us still have our hands in traditional relational databases (e.g. MySQL). While the normalized tables do a fine job of representing the parent/child relationships, the joined SQL results do not. In fact, they look more like an Excel spreadsheet than anything. This presents us with a problem when trying to supply a nice deep object graph for applications.
Using a traditional ORM is slow (either many fragmented SQL calls, slow object hydration of models, or both). Beyond that, for a lightweight API, you don't want to have to first pick an ORM and then model out all your relationships. For complex queries, especially where results are filtered by multiple columns across multiple tables, it becomes even more troublesome, or borderline impossible to use these model helpers.
The problem is, you can write the complex deeply joined SQL call that has all the results you wanted - but you can't get it back into an object graph so it looks/behaves like something other than data vomit.
Now you can.
npm install treeize
treeize.grow(flatData, options)
- takes your results/rows of flat associative data and returns a full object graph.treeize.getOptions()
- returns global options for the lib.treeize.setOptions(options)
- sets global options for the lib. For example, to use a path delimiter of '>' instead of ':', call treeize.setOptions({ delimiter: '>' })
id
suggests an id
attribute on a root element, whereas name+first
implies a first
attribute on a name
object within a root element.:
delimiter (default) to seperate path nodes. To change this, use the treeize.set([options])
function.This library has several assumptions that make it possible.
In this short series of examples, we'll take a standard "join dump", originally keyed (via attribute names) to organize by movie - and demonstrate how other organizations can be easily derived from the same original feed... by simply modifying the column/attribute names in the output.
In this example, we'll take our dump (as if from a CSV or SQL result) - and name the keys to
group by movies (as if for an /api/movies
).
var treeize = require('treeize');
var movieDump = [
{
"title": "The Prestige",
"director": "Christopher Nolan",
"actors:name": "Christian Bale",
"actors:as": "Alfred Borden"
},
{
"title": "The Prestige",
"director": "Christopher Nolan",
"actors:name": "Hugh Jackman",
"actors:as": "Robert Angier"
},
{
"title": "The Dark Knight Rises",
"director": "Christopher Nolan",
"actors:name": "Christian Bale",
"actors:as": "Bruce Wayne"
},
{
"title": "The Departed",
"director": "Martin Scorsese",
"actors:name": "Leonardo DiCaprio",
"actors:as": "Billy"
},
{
"title": "The Departed",
"director": "Martin Scorsese",
"actors:name": "Matt Damon",
"actors:as": "Colin Sullivan"
}
];
var movies = treeize.grow(movieDump);
/*
'movies' now contains the following:
[
{
"director": "Christopher Nolan",
"title": "The Prestige",
"actors": [
{
"as": "Alfred Borden",
"name": "Christian Bale"
},
{
"as": "Robert Angier",
"name": "Hugh Jackman"
}
]
},
{
"director": "Christopher Nolan",
"title": "The Dark Knight Rises",
"actors": [
{
"as": "Bruce Wayne",
"name": "Christian Bale"
}
]
},
{
"director": "Martin Scorsese",
"title": "The Departed",
"actors": [
{
"as": "Billy",
"name": "Leonardo DiCaprio"
},
{
"as": "Colin Sullivan",
"name": "Matt Damon"
}
]
}
]
*/
Taking the same feed, but modifying the target paths through the attribute/column
names we can completely transform the data (as you would for another API endpoint,
for example). This time we'll organize the data by directors, as you would for
and endpoint like /api/directors
.
Notice the feed is left unchanged - only the attribute names have been modified to define their new target path. In this case, by changing the base node to the directors name (instead of the movie name), we group everything by director at a high level.
var treeize = require('treeize');
var moviesDump = [
{
"movies:title": "The Prestige",
"name": "Christopher Nolan",
"workedWith:name": "Christian Bale",
"workedWith:as": "Alfred Borden"
},
{
"movies:title": "The Prestige",
"name": "Christopher Nolan",
"workedWith:name": "Hugh Jackman",
"workedWith:as": "Robert Angier"
},
{
"movies:title": "The Dark Knight Rises",
"name": "Christopher Nolan",
"workedWith:name": "Christian Bale",
"workedWith:as": "Bruce Wayne"
},
{
"movies:title": "The Departed",
"name": "Martin Scorsese",
"workedWith:name": "Leonardo DiCaprio",
"workedWith:as": "Billy"
},
{
"movies:title": "The Departed",
"name": "Martin Scorsese",
"workedWith:name": "Matt Damon",
"workedWith:as": "Colin Sullivan"
}
];
var directors = treeize.grow(movieDump);
/*
'directors' now contains the following:
[
{
"name": "Christopher Nolan",
"workedWith": [
{
"as": "Alfred Borden",
"name": "Christian Bale"
},
{
"as": "Robert Angier",
"name": "Hugh Jackman"
},
{
"as": "Bruce Wayne",
"name": "Christian Bale"
}
],
"movies": [
{
"title": "The Prestige"
},
{
"title": "The Dark Knight Rises"
}
]
},
{
"name": "Martin Scorsese",
"workedWith": [
{
"as": "Billy",
"name": "Leonardo DiCaprio"
},
{
"as": "Colin Sullivan",
"name": "Matt Damon"
}
],
"movies": [
{
"title": "The Departed"
}
]
}
]
*/
FAQs
Converts tabular row data (as from SQL joins, flat JSON, etc) to deep object graphs based on simple column naming conventions - without the use of an ORM or models.
The npm package treeize receives a total of 433 weekly downloads. As such, treeize popularity was classified as not popular.
We found that treeize demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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