Data Sources JS APIs

Data Sources JS APIs

The Data Source JS APIs allows you to interact and make any sort of change to your app’s Data Sources from the app itself.

The fliplet-datasources package contains the following namespaces:

Data Sources

Get the list of data sources for the current organization

Use the get function to fetch the list of data sources for the current organization. You can optionally pass a list of attributes to return.

Fliplet.DataSources.get({ attributes: ['id', 'name'] }).then(function (dataSources) {
  // dataSources is an array of data sources
});

Get the list of data sources in use by the current app

Use the appId and includeInUse options together to get the list of data sources owned or in use by the current app.

Fliplet.DataSources.get({
  appId: Fliplet.Env.get('masterAppId'),
  includeInUse: true
}).then(function (dataSources) {
 // dataSources is an array of data sources in use by the current app
});

Get a data source by ID

Use the getById function to fetch details about a data source by its ID. You can optionally pass a list of attributes to return.

Fliplet.DataSources.getById(123, {
  attributes: ['name', 'hooks', 'columns']
}).then(function (dataSource) {

});

Create a new data source

Use the create function to programmatically create a new data source.

Fliplet.DataSources.create({ name: 'foo' }).then(function (dataSource) {
  // The data source has been created
});

If you don’t want your data source to be displayed in the Data Source Manager in Fliplet Studio (available under the “App data” menu in the top header), simply add a specific type to it when it’s being created, e.g. type: 'comments'.

The following example creates a data source with 2 columns and 2 entries. It also attaches the data source to the current app and organization.

Fliplet.DataSources.create({
  name: 'foo',

  // Optionally attach the data source to a specific app or organization
  appId: Fliplet.Env.get('appId'),
  organizationId: Fliplet.Env.get('organizationId'),

  // Define a type (String) to avoid showing the data source in the data source manager
  type: null,

  // Define the columns for the data source
  columns: ['Email' ,'Name'],

  // Optionally define the initial data source entries to create for the data source
  entries: [
    {
      Name: 'John Doe',
      Email: 'johndoe@example.com'
    },
    {
      Name: 'Jane Doe',
      Email: 'janedoe@example.com'
    }
  ],

  // Optionally define access rules
  accessRules: [
    { type: ['select', 'insert', 'update', 'delete'], allow: 'all' }
  ]
}).then(function (dataSource) {
  // The data source has been created
});

Connect to a data source by ID

Fliplet.DataSources.connect(dataSourceId).then(function (connection) {
  // check below for the list of instance methods for the connection object
});

Fliplet apps on mobile devices attempt to connect to the offline bundled data sources by default. You can optionally prevent a data source from being bundled by editing its settings in Fliplet Studio, but this can also be custom coded when connecting to the data source.

Providing the offline: false parameter instructs the JS API to only connect to the live online data source to Fliplet APIs:

// Advanced connection passing options as second parameter
Fliplet.DataSources.connect(dataSourceId, {
  offline: false // disable querying offline on mobile devices
}).then(function (connection) {
  // check below for the list of instance methods for the connection object
});

Once you get a connection, you can use the instance methods described below to find, insert, update and delete data source entries.

Connect to a data source by Name

You can also connect to a datas ource by its name (case-sensitive) using the connectByName method.

Fliplet.DataSources.connectByName("Attendees").then(function (connection) {
  // check below for the list of instance methods for the connection object
});

Connection instance methods

Fetch records from a data source

Fetch all records

// use "find" with no options to get all entries
connection.find().then(function (records) {
  // records is an array
});

Fetch records with a query

Querying options are based on the Sift.js operators, which mimic MongoDB querying operators. Here are the supported operators from Sift.js:

  • $in, $nin, $exists, $gte, $gt, $lte, $lt, $eq, $ne, $iLike, $mod, $all, $and, $or, $nor, $not, $size, $type, $regex, $elemMatch

The following operators and values are optimized to perform better with Fliplet’s database.

  • Operators: $or, $and, $gte, $lte, $gt, $lt, $eq
  • Values: strings and numbers

Fliplet also supports a custom $filters operator with some unique conditional logic such as case-insensitive match or date & time comparison. See example below.

A few examples to get you started:

// Find records where column "sum" is greater than 10 and column "name"
// is either "Nick" or "Tony"
connection.find({
  where: {
    sum: { $gt: 10 },
    name: { $in: ['Nick', 'Tony'] }
  }
});

// Find a case insensitive and partial match to the "Email" column. For e.g. it will match with bobsmith@email.com or Bobsmith@email.com or smith@email.com
connection.find({
  where: {
    Email: { $iLike: 'BobSmith@email.com' }
  }
});

// Find records where column "email" matches the domain "example.org"
connection.find({
  where: {
    email: { $regex: /example\.org$/i }
  }
});

// Nested queries using the $or operator: find records where either "name" is "Nick"
// or "address" is "UK" and "name" is "Tony"
connection.find({
  where: {
    $or: [
      { name: 'Nick' },
      { address: 'UK', name: 'Tony' }
    ]
  }
});

// Find records where the column "country" is not "Germany" or "France"
// and "createdAt" is on or after a specific date
connection.find({
  where: {
    country: { $nin: ['Germany', 'France'] },
    createdAt: { $gte: '2018-03-20' }
  }
});

// Use Fliplet's custom $filters operator
// The "==" and "contains" conditions are optimized to perform better with Fliplet's database
connection.find({
  where: {
    // Find entries that match ALL of the following conditions
    $filters: [
      // Find entries with a case insensitive match on the column
      {
        column: 'Email',
        condition: '==',
        value: 'user@email.com'
      },
      // Find entries where the column does not match the value
      {
        column: 'Email',
        condition: '!=',
        value: 'user@email.com'
      },
      // Find entries where the column is greater than the value
      {
        column: 'Size',
        condition: '>',
        value: 10
      },
      // Find entries where the column is greater than or equal to the value
      {
        column: 'Size',
        condition: '>=',
        value: 10
      },
      // Find entries where the column is less than the value
      {
        column: 'Size',
        condition: '<',
        value: 10
      },
      // Find entries where the column is less than or equal to the value
      {
        column: 'Size',
        condition: '<=',
        value: 10
      },
      // Find entries with a case insensitive partial match on the column
      {
        column: 'Email',
        condition: 'contains',
        value: '@email.com'
      },
      // Find entries where the column is empty based on _.isEmpty()
      {
        column: 'Tags',
        condition: 'empty'
      },
      // Find entries where the column is not empty based on _.isEmpty()
      {
        column: 'Tags',
        condition: 'notempty'
      },
      // Find entries where the column is in between 2 numeric values (inclusive)
      {
        column: 'Size',
        condition: 'between',
        value: {
          from: 10,
          to: 20
        }
      },
      // Find entries where the column is one of the values
      {
        column: 'Category',
        condition: 'oneof',
        // value can also be a CSV string
        value: ['News', 'Tutorial']
      },
      // Find entries where the column matches a date comparison
      {
        column: 'Birthday',
        // Use dateis, datebefore or dateafter to match
        // dates before and after the comparison value
        condition: 'dateis',
        value: '1978-04-30'
        // Optionally provide a unit of comparison:
        //  - year
        //  - quarter
        //  - month
        //  - week
        //  - day
        //  - hour
        //  - minute
        //  - second
        // unit: 'month'
      },
      // Find entries where the column is before the a certain time of the day
      {
        column: 'Start time',
        condition: 'datebefore',
        value: '17:30'
      },
      // Find entries where the column is after a timestamp
      {
        column: 'Birthday',
        condition: 'dateafter',
        // Provide a full timestamp for comparison in YYYY-MM-DD HH:mm format
        value: '2020-03-10 13:03'
      },
      // Find entries where the column is between 2 dates (inclusive)
      {
        column: 'Birthday',
        condition: 'datebetween',
        from: {
          value: '1978-01-01'
        },
        to: {
          value: '1978-12-31'
        }
      }
    ]
  }
});

Filter the columns returned when finding records

Use the attributes array to optionally define a list of the columns that should be returned for the records.

// use "find" with "attributes" to filter the columns returned
connection.find({ attributes: ['Foo', 'Bar'] }).then(function (records) {
  // records is an array
});

You can also use this by passing an empty array as an efficient method to count the number of entries without requesting much data from the server:

connection.find({ attributes: [] }).then(function (records) {
  // use records.length as the number of records
});

Fetch records with pagination

You can use the limit and offset parameters to filter down the returned entries to a specific chunk (page) of the Data Source.

// use limit and offset for pagination
connection.find({
  limit: 50,
  offset: 10
});

Full example:

Fliplet.DataSources.connect(123).then(function (connection) {
  return connection.find({ limit: 1000 }).then(function (results) {

  });
});

Moreover, the includePagination parameter enables the response to return the count of total entries in the Data Source:

connection.find({
  limit: 50,
  offset: 10,
  includePagination: true
}).then(function (response) {
  // response.entries []
  // response.pagination = { total, limit, offset }
});

Note that when using the above parameter, the returned object from the find() method changes from an array of records to an object with the following structure:

{
  "entries": [],
  "dataSourceId": 123456,
  "count": 50,
  "pagination": {
    "total": 1000,
    "limit": 50,
    "offset": 10
  }
}

Join data from other dataSources

View documentation for joining data from other data sources

Run aggregation queries

You can use the built-in Mingo library to run complex aggregation queries or projections on top of Data Sources. Mingo operations can be provided to the find method via the aggregate attribute:

// This example groups records by values found on a sample column "myColumnName"
// and counts the matches for each value
connection.find({
  aggregate: [
    {
      $group: {
        _id: '$data.myColumnName',
        count: { $sum: 1 }
      }
    }
  ]
});

Please refer to the Mingo documentation to read more about all the different usages and types of aggregation queries.

Sort / order the results

Use the order array of arrays to specify the sorting order for the returned entries.

You can order by:

  • Fliplet columns: id, order, createdAt, deletedAt, updatedAt
  • Entry columns, using the data. prefix (e.g. data.Email)

The order direction is either ASC for ascending ordering or DESC for descending ordering.

The order array accepts a list of arrays, where each includes the column and sorting order:

// Sort records by their created time (first records are newer)
connection.find({
  where: { Office: 'London' },
  order: [
    ['createdAt', 'DESC']
  ]
}).then(function (records) {
  // ...
});

// Sort records alphabetically by their last name first and then first name
connection.find({
  where: { Office: 'London' },
  order: [
    ['data.LastName', 'ASC'],
    ['data.FirstName', 'ASC']
  ]
}).then(function (records) {
  // ...
});

Find a specific record

The findOne method allows you to look for up to one record, limiting the amount of entries returned if you’re only looking for one specific entry.

connection.findOne({
  where: { name: 'John' }
}).then(function (record) {
  // record is either the found entry "object" or "undefined"
});

Find a record by its ID

This is a code snippet for finding a record in a specific Data Source by its ID.

The findById() method accepts a single parameter, which is the ID of the entry to search for in the Data Source. Once the entry has been found, it will be returned as a record object in the response, and the code inside the promise callback function will be executed.

connection.findById(1).then(function (record) {
  // records is the found object
});

Replace the contents of the data source with new records

connection.replaceWith([
  { id: 1, name: 'Alice' },
  { id: 2, name: 'Bob', email: 'bob@example.org' }
]).then(function onComplete() {

});

Insert an array of new records into a data source

connection.append([
  { id: 3, name: 'Nick' },
  { id: 4, name: 'Ian', email: 'ian@example.org' }
]).then(function onComplete() {

});

Using connection.append(entriesArray) also triggers “insert “hooks for each created entry. This can be turned off (it defaults to true via the options second parameter) as follows:

connection.append([{ name: 'Nick' }], { runHooks: false })

Commit changes at once to a data source

Use connection.commit(Array) to commit more than one change at once to a data source. You can use this to insert, update and delete entries at the same time with a single request. This makes it very efficient in terms of both minimizing the network requests and computation required from both sides.

List of input parameters:

  • entries: (required array): the list of entries to insert or update ({ data } for insert and { id, data } for updates).
  • append: (optional boolean, defaults to false): set to true to keep existing remote entries not sent in the updates to be made. When this is set to false you will essentially be replacing the whole data source with just the data you are sending.
  • delete: (optional array): the list of entry IDs to remove (when used in combination with append: true).
  • extend (optional boolean, defaults to false): set to true to enable merging the local columns you are sending with any existing columns for the affected data source entries.
  • runHooks (optional array) the list of hooks (insert or update) to run on the data source during the operation.
  • returnEntries (optional boolean, defaults to true): set to false to stop the API from returning all the entries in the data source

The following sample request applies the following changes to the data source:

  • inserts a new entry
  • updates the entry with ID 123 merging its data with the new added column(s)
  • deletes the entry with ID 456
connection.commit({
  entries: [
    // Insert a new entry
    { data: { foo: 'bar' } },

    // Update the entry with ID 123
    { id: 123, data: { foo: 'barbaz' } }
  ],

  // Delete the entry with ID 456
  delete: [456],

  // Ensure existing entries are unaffected
  append: true,

  // Keep remote columns not sent with
  // the updates of entry ID 123
  extend: true,

  // Do not return the whole data source after updating the data.
  // Keep this as "false" to speed up the response.
  returnEntries: false
});

Insert a single record into the data source

To insert a record into a data source, use the connection.insert method by passing the data to be inserted as a JSON object or a FormData object.

// Using a JSON object
connection.insert({
  id: 3,
  name: 'Bill'
});

// Using a FormData object
connection.insert(FormData);

Note: the dataSourceId and dataSourceEntryId are reserved keys and should not be used in the input JSON.

The second parameter of the connection.insert function accepts various options as described below:

Options: folderId

When FormData is used as first parameter, your record gets uploaded using a multipart request. If your FormData contains files, you can specify the MediaFolder where files should be stored to using the folderId parameter:

connection.insert(FormData, {
  folderId: 123
});

Options: ack

If you want to make sure the local (offline) database on the device also gets updated as soon as the server receives your record you can use the ack (which abbreviates the word acknowledge) parameter:

connection.insert({ foo: 'bar' }, {
  // this ensure the local database gets updated straight away, without
  // waiting for silent updates (which can take up to 30 seconds to be received).
  ack: true
});

Update a record (entry)

Updating a data source entry is done via the connection.insert method by providing its ID and the update to be applied.

connection.update(123, {
  name: 'Bill'
});

You can also pass a FormData object to upload files using a multipart request. When uploading files, you can also specify the MediaFolder where files should be stored to:

connection.update(123, FormData, {
  mediaFolderId: 456
});

Import a file into the data sources

connection.import(FormData).then(function onSuccess() {});

Remove a record by its ID

Use the removeById method to remove a entry from a data source given its ID.

connection.removeById(1).then(function onRemove() {});

Remove entries matching a query

Set type to delete and specify a where clause. This will query the data source and delete any matching entries.

connection.query({
  type: 'delete',
  where: { Email: 'test@fliplet.com' }
});

Get unique values for a column

Use the getIndex method to get unique values for a given column of the Data Source:

connection.getIndex('name').then(function onSuccess(values) {
  // array of unique values
});

Get unique values for multiple columns at once

Use the getIndexes method to get unique values for a given array of columns of the Data Source:

connection.getIndexes(['name','email']).then(function onSuccess(values) {
  // an object having key representing each index and the value being the array of values
  // e.g. { name: ['a', 'b'], email: ['c', 'd'] }
});

Define views to filter a data source

View documentation on how to define views to filter data of a data source

Configurable operations

Automatically generate a unique ID for your entries

You can instruct the system to automatically generate a GUID (also known as UUID) to all entries you insert by simply defining the guid key for a data source definition in Fliplet Studio (under the “App Data” section) and specifying the target column:

{ "guid": "myPrimaryGuidColumn" }

When this is set, all entries will automatically get a random 36-characters GUID once they get saved in the data source.

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