Showing posts with label azuresearch. Show all posts
Showing posts with label azuresearch. Show all posts

Reading Notes #227

Suggestion of the week


Reading Notes #219

AzureSDK2-8-2Suggestion of the week




Reading Notes #205





Reading Notes #204

AzureConLabsSuggestion of the week





  • Going Back to One (Alexandre Brisebois) - Organize our work to become a performer, could be easily done in Windows 10.
  • Static Site or CMS? - (Brian Rinaldi) - Nice post that gives insights to answer one of the most frequent questions when people start a blog/website.

Reading Notes #194

IMG_20150716_225401Suggestion of the week


Customers can replicate on-premises workloads to Azure with Azure Site Recovery for 31 days at no charge




~ Frank

Reading Notes #170

2015-01-15 19.08.05Suggestion of the week


  • Data Pipeline Guidance (patterns & practices) - Interesting project using Azure Eventhub's best practices in an IoT context.




  • Removing Distractions - Interesting post where the author share is experience while trying to be less connected.

~Frank B.

The making of: Franky's Notes Azure Search - part 2

This post concludes The making of: Franky’s Notes Azure Search. In the previous post, I build a console application in .Net using the RedDog.Search library, to populate an index in my Azure Search Service with my notes.
In this post, I’m sharing with you how I created the user interface to query my notes. To know more about the Azure Search REST API, all the documentation is available online.


For this part of the project, we will use the azure-search javascript client of Richard Astbury available on Github. The idea is to build a nice user interface (UI) that will provide a simple and efficient way to search. Since the code will be in JavaScript, it’s strongly suggested to use a query key instead of a master key. These keys can be managed from the Azure Portal.

Azure Search Query Keys

Creating the Interface

First, we need to get the azure-search. To get it, you can whether download the file azure-search.min.js on Github or by execute npm install azure-search from a Node.js console.
Now we need a simple HTML page with a form, a textbox and a button.
            <link  href="css/bootstrap.min.css" rel="stylesheet">
            <!--[if lt IE 9]>
                <script src="scripts/html5shiv.min.js"></script>
                <script src="scripts/respond.min.js"></script>
                <input id="txtSearch" placeholder="Search">
                <button id="btnSearch" type="button">Search</button>

            <div id="result"></div>

            <script src="scripts/jquery.min.js"></script>
            <script src="scripts/bootstrap.min.js"></script>
            <script src="scripts/azure-search.min.js"></script>

                var client = AzureSearch({
                  url: "",
                  key:"DB7B9D1C53EC08932D8A8D5A1406D8CA" // - query only

As you can see I’m creating the AzureSearch client using my query key from before. Afterwards, we create a Search function to retrieve the search criteria from the textbox and pass it to the client. A dynamic function is used as a callback that receives the parameter noteList which is an array of matching documents. We finally just need to loop through the result to build a nice output.
    function Search(){

        var _searchCriteria = $("#txtSearch").val();   
        var _objSearch = {search: _searchCriteria, $orderby:'title desc'};'notes', _objSearch, function(err, noteList){
            var $divResult = $("div#result");
            $divResult.html( "<div class='panel-heading'><h3 class='panel-title'>" + noteList.length + " Result(s)</h3></div><div class='panel-body'>" );

            if(noteList.length > 0){

                var _strResult = "";
                _strResult = "<ul class='list-group'>";

                for(var key in noteList){
                    var fNote = noteList[key];

                    _strResult += = "<li class='list-group-item'><a href='" + fNote.url + "' target='_blank'>" + fNote.title + "</a><p>" + fNote.note + "</p></li>";

                _strResult += "</ul></div>";
                $divResult.append( _strResult );

If we put all this together, we got a nice result.

Franky's Notes Search UI

Live Demo


I really had a lot of fun creating these little applications. I found the client incredibly easy to use. I hope it will help you to get ideas and moving forward to use Azure Search. Any comments, suggestions and/or questions are welcome.

~ Frank Boucher


The making of: Franky's Notes Azure Search - part 1

For a long time now, I'm thinking about creating an API that will allow to search easily through my notes. When Azure Search came public few weeks ago, I knew it was what this project needed to come alive. In this post, I will share how I did it, and more importantly, show how incredibly easy it was to do.

What's Azure Search?

Currently in preview, Azure Search is a cloud-based search-as-a-service that provides a set of REST APIs defined in terms of HTTP requests and responses, in OData JSON format.

Getting Started

From the Azure Portal, let's create an Azure Search Service by clicking the plus button on the bottom left of the screen. Select the Search option, and fill-up the options.


Application to populate my Azure Search service

First, we will need some data. My weekly posts Reading Notes are generated with a Ruby script that I did few years ago. You can read more about it on First step with Ruby: Kindle Clipping Extractor. Basically, the script extracts my notes from my Kindle and build a collection of notes grouped in different categories to generate a markdown file. That can easily be done by adding a new Json output file. Here is a quick view this output.
  "json_class": "FrankyNotes",
  "categories": {
    "dev": [
        "id": 77077357,
        "title": "Customize the MVC 5 Application Users’ using ASP.Net Identity 2.0",
        "author": "Dhananjay kumar",
        "url": "",
        "note": "Need to get the fukk article",
        "tags": "dev,frankysnotes,readingnotes160",
        "date": "2014/10/17",
        "category": "dev"
        "id": 77156372,
        "title": "Custom Login Scopes, Single Sign-On, new ASP.NET Web API – updates to 

Now that we have some data, we need to create an index and be able to add document in it. A console application will be perfect for this job. At the time of writing this post, two libraries exist to interact with the Microsoft Azure Search REST API. For this part of the project, we will use the RedDog.Search library available on Github, since it's a .Net library.

Note: To create an index or upload documents you will need an admin key.


First, we need to create an Index. Let's keep it simple and just create the index with all the properties of the json object. Here the code of my function CreateNoteIndex.
public IndexManagementClient Client
        if (_client == null){
            _client = new IndexManagementClient(ApiConnection.Create("frankysnotes", "AdminKey"));
        return _client;

public async Task<string> CreateNoteIndex()
    var createResult = await Client.CreateIndexAsync(new Index("notes")
        .WithStringField("id", opt => opt.IsKey().IsRetrievable())
        .WithStringField("title", opt => opt.IsRetrievable().IsSearchable())
        .WithStringField("author", opt => opt.IsRetrievable().IsSearchable())
        .WithStringField("url", opt => opt.IsRetrievable().IsSearchable(false))
        .WithStringField("note", opt => opt.IsRetrievable().IsSearchable())
        .WithStringField("tags", opt => opt.IsRetrievable().IsFilterable().IsSearchable())
        .WithStringField("date", opt => opt.IsRetrievable().IsSearchable())
        .WithStringField("category", opt => opt.IsRetrievable().IsFilterable().IsSearchable())
    if (createResult.IsSuccess)
        return "Index Reseted successfully";

To be able to search by note instead of by post, I decided to break down the file in multiple documents containing one note by document. After what, it was really easy to upload the documents into the index.
public async Task<string> AddNotes(string filepath)
    var docs = new List<IndexOperation>();
    FrankysNotes notes = DeserializeFNotes(filepath);

    foreach (var category in notes.categories)
        foreach (var fNote in notes.categories[category])
            var doc = ConvertfNote(fNote);

    var result = await Client.PopulateAsync("notes", docs.ToArray<IndexOperation>());

    return "File uploaded successfully";

private FrankysNotes DeserializeFNotes(string filepath)
    var jsonStr = File.ReadAllText(filepath);
    var serializer = new JavaScriptSerializer();

    var notes = serializer.Deserialize<FrankysNotes>(jsonStr);
    return notes;

private IndexOperation ConvertfNote(FrankysNote fnote)
    var doc = new IndexOperation(IndexOperationType.Upload, "id",
                    .WithProperty("title", fnote.title)
                    .WithProperty("url", fnote.url)
                    .WithProperty("note", fnote.note)
                    .WithProperty("tags", fnote.tags)
                    .WithProperty("category", fnote.category);
    return doc;

To keep the code as clear as possible, I removed all validations and error management. The json file is deserialized, then looping through all notes I build a list of IndexOperation. And Finally I upload all the notes with Client.PopulateAsync("notes", docs.ToArray<IndexOperation>());

Wrapping up

Using the RedDog.Search library to push documents in Azure Search Index was extremely easy. In fact, it's that simplicity that pushed me to share my discovery. In the next part of the series, I will create a simple HTML page to do real query.

Stay tune...

~ Frank Boucher