// # Recline Backends // // Backends are connectors to backend data sources and stores // // Backends are implemented as Backbone models but this is just a // convenience (they do not save or load themselves from any remote // source) this.recline = this.recline || {}; this.recline.Model = this.recline.Model || {}; (function($, my) { // ## Backbone.sync // // Override Backbone.sync to hand off to sync function in relevant backend Backbone.sync = function(method, model, options) { return model.backend.sync(method, model, options); } // ## wrapInTimeout // // Crude way to catch backend errors // Many of backends use JSONP and so will not get error messages and this is // a crude way to catch those errors. function wrapInTimeout(ourFunction) { var dfd = $.Deferred(); var timeout = 5000; var timer = setTimeout(function() { dfd.reject({ message: 'Request Error: Backend did not respond after ' + (timeout / 1000) + ' seconds' }); }, timeout); ourFunction.done(function(arguments) { clearTimeout(timer); dfd.resolve(arguments); }) .fail(function(arguments) { clearTimeout(timer); dfd.reject(arguments); }) ; return dfd.promise(); } // ## BackendMemory - uses in-memory data // // This is very artificial and is really only designed for testing // purposes. // // To use it you should provide in your constructor data: // // * metadata (including headers array) // * documents: list of hashes, each hash being one doc. A doc *must* have an id attribute which is unique. // // Example: // //
// // Backend setup
// var backend = Backend();
// backend.addDataset({
// metadata: {
// id: 'my-id',
// title: 'My Title',
// headers: ['x', 'y', 'z'],
// },
// documents: [
// {id: 0, x: 1, y: 2, z: 3},
// {id: 1, x: 2, y: 4, z: 6}
// ]
// });
// // later ...
// var dataset = Dataset({id: 'my-id'});
// dataset.fetch();
// etc ...
//
my.BackendMemory = Backbone.Model.extend({
initialize: function() {
this.datasets = {};
},
addDataset: function(data) {
this.datasets[data.metadata.id] = $.extend(true, {}, data);
},
sync: function(method, model, options) {
var self = this;
if (method === "read") {
var dfd = $.Deferred();
if (model.__type__ == 'Dataset') {
var rawDataset = this.datasets[model.id];
model.set(rawDataset.metadata);
model.docCount = rawDataset.documents.length;
dfd.resolve(model);
}
return dfd.promise();
} else if (method === 'update') {
var dfd = $.Deferred();
if (model.__type__ == 'Document') {
_.each(self.datasets[model.dataset.id].documents, function(doc, idx) {
if(doc.id === model.id) {
self.datasets[model.dataset.id].documents[idx] = model.toJSON();
}
});
dfd.resolve(model);
}
return dfd.promise();
} else if (method === 'delete') {
var dfd = $.Deferred();
if (model.__type__ == 'Document') {
var rawDataset = self.datasets[model.dataset.id];
var newdocs = _.reject(rawDataset.documents, function(doc) {
return (doc.id === model.id);
});
rawDataset.documents = newdocs;
dfd.resolve(model);
}
return dfd.promise();
} else {
alert('Not supported: sync on BackendMemory with method ' + method + ' and model ' + model);
}
},
query: function(model, queryObj) {
var numRows = queryObj.size;
var start = queryObj.offset;
var dfd = $.Deferred();
results = this.datasets[model.id].documents;
// not complete sorting!
_.each(queryObj.sort, function(item) {
results = _.sortBy(results, function(doc) {
var _out = doc[item[0]];
return (item[1] == 'asc') ? _out : -1*_out;
});
});
var results = results.slice(start, start+numRows);
dfd.resolve(results);
return dfd.promise();
}
});
my.backends['memory'] = new my.BackendMemory();
// ## BackendWebstore
//
// Connecting to [Webstores](http://github.com/okfn/webstore)
//
// To use this backend ensure your Dataset has a webstore_url in its attributes.
my.BackendWebstore = Backbone.Model.extend({
sync: function(method, model, options) {
if (method === "read") {
if (model.__type__ == 'Dataset') {
var base = model.get('webstore_url');
var schemaUrl = base + '/schema.json';
var jqxhr = $.ajax({
url: schemaUrl,
dataType: 'jsonp',
jsonp: '_callback'
});
var dfd = $.Deferred();
wrapInTimeout(jqxhr).done(function(schema) {
headers = _.map(schema.data, function(item) {
return item.name;
});
model.set({
headers: headers
});
model.docCount = schema.count;
dfd.resolve(model, jqxhr);
})
.fail(function(arguments) {
dfd.reject(arguments);
});
return dfd.promise();
}
}
},
query: function(model, queryObj) {
var base = model.get('webstore_url');
var data = {
_limit: queryObj.size
, _offset: queryObj.offset
};
var jqxhr = $.ajax({
url: base + '.json',
data: data,
dataType: 'jsonp',
jsonp: '_callback',
cache: true
});
var dfd = $.Deferred();
jqxhr.done(function(results) {
dfd.resolve(results.data);
});
return dfd.promise();
}
});
my.backends['webstore'] = new my.BackendWebstore();
// ## BackendDataProxy
//
// For connecting to [DataProxy-s](http://github.com/okfn/dataproxy).
//
// When initializing the DataProxy backend you can set the following attributes:
//
// * dataproxy: {url-to-proxy} (optional). Defaults to http://jsonpdataproxy.appspot.com
//
// Datasets using using this backend should set the following attributes:
//
// * url: (required) url-of-data-to-proxy
// * format: (optional) csv | xls (defaults to csv if not specified)
//
// Note that this is a **read-only** backend.
my.BackendDataProxy = Backbone.Model.extend({
defaults: {
dataproxy_url: 'http://jsonpdataproxy.appspot.com'
},
sync: function(method, model, options) {
var self = this;
if (method === "read") {
if (model.__type__ == 'Dataset') {
var base = self.get('dataproxy_url');
// TODO: should we cache for extra efficiency
var data = {
url: model.get('url')
, 'max-results': 1
, type: model.get('format') || 'csv'
};
var jqxhr = $.ajax({
url: base
, data: data
, dataType: 'jsonp'
});
var dfd = $.Deferred();
wrapInTimeout(jqxhr).done(function(results) {
model.set({
headers: results.fields
});
dfd.resolve(model, jqxhr);
})
.fail(function(arguments) {
dfd.reject(arguments);
});
return dfd.promise();
}
} else {
alert('This backend only supports read operations');
}
},
query: function(dataset, queryObj) {
var base = this.get('dataproxy_url');
var data = {
url: dataset.get('url')
, 'max-results': queryObj.size
, type: dataset.get('format')
};
var jqxhr = $.ajax({
url: base
, data: data
, dataType: 'jsonp'
});
var dfd = $.Deferred();
jqxhr.done(function(results) {
var _out = _.map(results.data, function(doc) {
var tmp = {};
_.each(results.fields, function(key, idx) {
tmp[key] = doc[idx];
});
return tmp;
});
dfd.resolve(_out);
});
return dfd.promise();
}
});
my.backends['dataproxy'] = new my.BackendDataProxy();
// ## Google spreadsheet backend
//
// Connect to Google Docs spreadsheet.
//
// Dataset must have a url attribute pointing to the Gdocs
// spreadsheet's JSON feed e.g.
//
//
// var dataset = new recline.Model.Dataset({
// url: 'https://spreadsheets.google.com/feeds/list/0Aon3JiuouxLUdDQwZE1JdV94cUd6NWtuZ0IyWTBjLWc/od6/public/values?alt=json'
// },
// 'gdocs'
// );
//
my.BackendGDoc = Backbone.Model.extend({
sync: function(method, model, options) {
var self = this;
if (method === "read") {
var dfd = $.Deferred();
var dataset = model;
$.getJSON(model.get('url'), function(d) {
result = self.gdocsToJavascript(d);
model.set({'headers': result.header});
// cache data onto dataset (we have loaded whole gdoc it seems!)
model._dataCache = result.data;
dfd.resolve(model);
})
return dfd.promise(); }
},
query: function(dataset, queryObj) {
var dfd = $.Deferred();
var fields = dataset.get('headers');
// zip the field headers with the data rows to produce js objs
// TODO: factor this out as a common method with other backends
var objs = _.map(dataset._dataCache, function (d) {
var obj = {};
_.each(_.zip(fields, d), function (x) { obj[x[0]] = x[1]; })
return obj;
});
dfd.resolve(objs);
return dfd;
},
gdocsToJavascript: function(gdocsSpreadsheet) {
/*
:options: (optional) optional argument dictionary:
columnsToUse: list of columns to use (specified by header names)
colTypes: dictionary (with column names as keys) specifying types (e.g. range, percent for use in conversion).
:return: tabular data object (hash with keys: header and data).
Issues: seems google docs return columns in rows in random order and not even sure whether consistent across rows.
*/
var options = {};
if (arguments.length > 1) {
options = arguments[1];
}
var results = {
'header': [],
'data': []
};
// default is no special info on type of columns
var colTypes = {};
if (options.colTypes) {
colTypes = options.colTypes;
}
// either extract column headings from spreadsheet directly, or used supplied ones
if (options.columnsToUse) {
// columns set to subset supplied
results.header = options.columnsToUse;
} else {
// set columns to use to be all available
if (gdocsSpreadsheet.feed.entry.length > 0) {
for (var k in gdocsSpreadsheet.feed.entry[0]) {
if (k.substr(0, 3) == 'gsx') {
var col = k.substr(4)
results.header.push(col);
}
}
}
}
// converts non numberical values that should be numerical (22.3%[string] -> 0.223[float])
var rep = /^([\d\.\-]+)\%$/;
$.each(gdocsSpreadsheet.feed.entry, function (i, entry) {
var row = [];
for (var k in results.header) {
var col = results.header[k];
var _keyname = 'gsx$' + col;
var value = entry[_keyname]['$t'];
// if labelled as % and value contains %, convert
if (colTypes[col] == 'percent') {
if (rep.test(value)) {
var value2 = rep.exec(value);
var value3 = parseFloat(value2);
value = value3 / 100;
}
}
row.push(value);
}
results.data.push(row);
});
return results;
}
});
my.backends['gdocs'] = new my.BackendGDoc();
}(jQuery, this.recline.Model));