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backend.js

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) {
  my.backends = {};

  Backbone.sync = function(method, model, options) {
    return my.backends[model.backendConfig.type].sync(method, model, options);
  }

BackendMemory - uses in-memory data

To use you should:

A. provide metadata as model data to the Dataset

B. Set backendConfig on your dataset with attributes:

  • type: 'memory'

  • data: hash with 2 keys:

    • headers: list of header names/labels
    • rows: list of hashes, each hash being one row. A row must have an id attribute which is unique.

    Example of data:

       {
       headers: ['x', 'y', 'z']
     , rows: [
         {id: 0, x: 1, y: 2, z: 3}
       , {id: 1, x: 2, y: 4, z: 6}
     ]
   };

  my.BackendMemory = Backbone.Model.extend({
      sync: function(method, model, options) {
        var self = this;
        if (method === "read") {
          var dfd = $.Deferred();
          if (model.__type__ == 'Dataset') {
            var dataset = model;
            dataset.set({
              headers: dataset.backendConfig.data.headers
            });
            dataset.docCount = dataset.backendConfig.data.rows.length;
            dfd.resolve(dataset);
          }
          return dfd.promise();
        } else if (method === 'update') {
          var dfd = $.Deferred();
          if (model.__type__ == 'Document') {
            _.each(model.backendConfig.data.rows, function(row, idx) {
              if(row.id === model.id) {
                model.backendConfig.data.rows[idx] = model.toJSON();
              }
            });
            dfd.resolve(model);
          }
          return dfd.promise();
        } else if (method === 'delete') {
          var dfd = $.Deferred();
          if (model.__type__ == 'Document') {
            model.backendConfig.data.rows = _.reject(model.backendConfig.data.rows, function(row) {
              return (row.id === model.id);
            });
            dfd.resolve(model);
          }
          return dfd.promise();
        } else {
          alert('Not supported: sync on BackendMemory with method ' + method + ' and model ' + model);
        }
      },
      getDocuments: function(model, numRows, start) {
        if (start === undefined) {
          start = 0;
        }
        if (numRows === undefined) {
          numRows = 10;
        }
        var dfd = $.Deferred();
        rows = model.backendConfig.data.rows;
        var results = rows.slice(start, start+numRows);
        dfd.resolve(results);
        return dfd.promise();
      }
  });
  my.backends['memory'] = new my.BackendMemory();

BackendWebstore

Connecting to Webstores

To use this backend set backendConfig on your Dataset as:

{
  'type': 'webstore',
  'url': url to relevant Webstore table
}

  my.BackendWebstore = Backbone.Model.extend({
    sync: function(method, model, options) {
      if (method === "read") {
        if (model.__type__ == 'Dataset') {
          var dataset = model;
          var base = dataset.backendConfig.url;
          var schemaUrl = base + '/schema.json';
          var jqxhr = $.ajax({
            url: schemaUrl,
              dataType: 'jsonp',
              jsonp: '_callback'
          });
          var dfd = $.Deferred();
          jqxhr.then(function(schema) {
            headers = _.map(schema.data, function(item) {
              return item.name;
            });
            dataset.set({
              headers: headers
            });
            dataset.docCount = schema.count;
            dfd.resolve(dataset, jqxhr);
          });
          return dfd.promise();
        }
      }
    },
    getDocuments: function(model, numRows, start) {
      if (start === undefined) {
        start = 0;
      }
      if (numRows === undefined) {
        numRows = 10;
      }
      var base = model.backendConfig.url;
      var jqxhr = $.ajax({
        url: base + '.json?_limit=' + numRows,
          dataType: 'jsonp',
          jsonp: '_callback',
          cache: true
      });
      var dfd = $.Deferred();
      jqxhr.then(function(results) {
        dfd.resolve(results.data);
      });
      return dfd.promise();
    }
  });
  my.backends['webstore'] = new my.BackendWebstore();

BackendDataProxy

For connecting to DataProxy-s.

Set a Dataset to use this backend:

dataset.backendConfig = {
  // required
  url: {url-of-data-to-proxy},
  format: csv | xls,
}

When initializing the DataProxy backend you can set the following attributes:

  • dataproxy: {url-to-proxy} (optional). Defaults to http://jsonpdataproxy.appspot.com

Note that this is a read-only backend.

 
  my.BackendDataProxy = Backbone.Model.extend({
    defaults: {
      dataproxy: 'http://jsonpdataproxy.appspot.com'
    },
    sync: function(method, model, options) {
      if (method === "read") {
        if (model.__type__ == 'Dataset') {
          var dataset = model;
          var base = my.backends['dataproxy'].get('dataproxy');

TODO: should we cache for extra efficiency

 
          var data = {
            url: dataset.backendConfig.url
            , 'max-results':  1
            , type: dataset.backendConfig.format
          };
          var jqxhr = $.ajax({
            url: base
            , data: data
            , dataType: 'jsonp'
          });
          var dfd = $.Deferred();
          jqxhr.then(function(results) {
            dataset.set({
              headers: results.fields
            });
            dfd.resolve(dataset, jqxhr);
          });
          return dfd.promise();
        }
      } else {
        alert('This backend only supports read operations');
      }
    },
    getDocuments: function(dataset, numRows, start) {
      if (start === undefined) {
        start = 0;
      }
      if (numRows === undefined) {
        numRows = 10;
      }
      var base = my.backends['dataproxy'].get('dataproxy');
      var data = {
        url: dataset.backendConfig.url
        , 'max-results':  numRows
        , type: dataset.backendConfig.format
      };
      var jqxhr = $.ajax({
        url: base
        , data: data
        , dataType: 'jsonp'
      });
      var dfd = $.Deferred();
      jqxhr.then(function(results) {
        var _out = _.map(results.data, function(row) {
          var tmp = {};
          _.each(results.fields, function(key, idx) {
            tmp[key] = row[idx];
          });
          return tmp;
        });
        dfd.resolve(_out);
      });
      return dfd.promise();
    }
  });
  my.backends['dataproxy'] = new my.BackendDataProxy();

Google spreadsheet backend

Connect to Google Docs spreadsheet. For write operations

 
  my.BackendGDoc = Backbone.Model.extend({
    sync: function(method, model, options) {
      if (method === "read") { 
        var dfd = $.Deferred(); 
        var dataset = model;

        $.getJSON(model.backendConfig.url, function(d) {
          result = my.backends['gdocs'].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(); }
    },

    getDocuments: function(dataset, start, numRows) { 
      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));