Shay gave the team the lowdown on the latest functionality, and Arie outlined interesting cutomers and use-cases. Shay also talked about how Kibana and Logstash can make pushing data in and out of indexes easier.

Comperio is really interested in the opportunities that Elasticsearch opens up for visualizing large datasets, particularly those generated by large distributed electronic systems. We will definitely be following up these opportunities with our customers, and hope to bounce some ideas off of the Elastisearch guys again soon.

0.3.0 fixes lots niggles with indexing, and gives a pretty hefty improvement to memory usage. There is also a built in matcher for creating cool auto-suggest and auto-complete controls based on the content of the corpus.

In related news there is now a family of crawling tools which allow you to scrape, process and index web content into your Forage server. Check out forage-fetch, forage-document-processor and forage-indexer on the forage.js GitHub group.

As always, feedback, pull requests, comments, praise, criticism and beer are most welcome.

Henceforth, said search server shall be known as Forage. Check it out here and update all favourites and bookmarks accordingly.

In related news, Nor.. sorry Forage is now 10% as popular as Solr on Github. Thanks to all users and contributors who are putting Forage through its paces. We love you.

Norch is a search engine written for Node.js. Norch uses the Node search-index module which is in turn written using the super fast levelDB library that Google open-sourced in 2011.

The aim of Norch is to make a simple, fast search server, that requires minimal configuration to set up. Norch sacrifices complex functionality for a limited robust feature set, that can be used to set up a freetext search engine for most enterprise scenarios.

Currently Norch features

• Full text search
• Stopword removal
• Faceting
• Filtering
• Relevance weighting (tf-idf)
• Field weighting
• Paging (offset and resultset length)

Solrstrap is a Query-Result interface for Solr- it is intended to be a starting point for those building web interfaces that talk to Solr, or a very lightweight admin tool for querying Solr in a Googleish fashion.

Cool things about Solrstrap:

• Requires only local installation- easy to set up
• Access to all Bootstrap functionality. Can be easily extended in a Bootstrappy way.
• Blazing fast
• Uses less bandwidth

Use it as you see fit. Merciless criticism and fawning praise equally welcome.

Handlebars provides the power necessary to let you build semantic templates effectively with no frustration.

Handlebars.js has become the defacto standard for templating in JavaScript, filling the void left from the abandonment and non-replacement of the jQuery template project. Handlebars.js is an implementation of the mighty platform-independant mustache project

JavaScript templating makes it a lot easier to implement cool interface functionality on your search result page such as responsive design, autoscrolling and more. Handlebars make JS templating a cinch.

You get the benefit of Handlebars.js on webpages that have a lot of repeating code such as lists, search-results and libraries.

In order to see Solr and Handlebars.js in action click here or copy and paste the code below into an empty .html file.

<b>Handlebars.js and Solr Demo</b><br>
Type in something reutersey and click the <b>search</b> button!<br>
<input id="searchterm" />
<button id="search">search</button>
<div id="rs"></div>
<script src="//ajax.googleapis.com/ajax/libs/jquery/1.8.0/jquery.min.js"></script>
<script src="//cdnjs.cloudflare.com/ajax/libs/handlebars.js/1.0.0.beta6/handlebars.min.js"></script>
<script id="hit-template" type="text/x-handlebars-template">
  <div class="entry">
    <b>{{title}}</b><br>
    {{text}}
  </div>
</script>
<script type='text/javascript'>
  var hitTemplate = Handlebars.compile($("#hit-template").html());
  $("#search").click(function(){
    $("#rs").empty();
    $.getJSON("http://evolvingweb.ca/solr/reuters/select/?q=" + $("#searchterm").val() + "&wt=json&json.wrf=?&indent=true", function(result){
      for (var i = 0; i < result.response.docs.length; i++) {
        $("#rs").append(hitTemplate({title: result.response.docs[i].title, text: result.response.docs[i].text}));
      }
    });
  });
</script>

One of the live demos has also been uploaded here to demonstrate how a Solr server can be rendered using JavaScript.

Here is a quick demo that demonstrates how to create an “instant search” service using JSON, ajax and JQuery. This instant search application uses the excellent OpenSearch interface provided by Wikipedia. Use this code as a starting point for developing prettier result pages.

The Wikipedia query API is described in some detail here.

Click here to see the demo, or copy and paste the code below into a blank HTML document.

<script type="text/javascript" src="http://code.jquery.com/jquery-latest.js"></script>

Search Wikipedia!

<input id="searchterm" type="text" /> <button id="search">search</button>

<script type="text/javascript">// <![CDATA[
      $("#searchterm").keyup(function(e){
        var q = $("#searchterm").val();
        $.getJSON("http://en.wikipedia.org/w/api.php?callback=?",
        {
          srsearch: q,
          action: "query",
          list: "search",
          format: "json"
        },
        function(data) {
          $("#results").empty();
          $("#results").append("

Results for <b>" + q + "</b>

");
          $.each(data.query.search, function(i,item){
            $("#results").append("
<div><a href='http://en.wikipedia.org/wiki/" + encodeURIComponent(item.title) + "'>" + item.title + "</a>" + item.snippet + "</div>
");
          });
        });
      });

// ]]></script>

This image search app provides a neat solution to the history/speed issue by simply displaying results in a push-pop stack. As you type in new search terms, the image thumbnails are prepended to the resultset. Your search history stays on the page, but is simply pushed further down as new thumbnails appear. Its is really fast and simple to browse your image search history.

Also, this search app operates entirely in 30 lines of HTML and JavaScript by using a JSON feed to the flickr API. Simple and elegant.

Click here for the demo

Or copy and paste the code below this line:

<html>
  <head>
    <script src="http://code.jquery.com/jquery-latest.js"></script>
    <title>Comperio Super Simple Image Search</title>
  </head>
  <body>
    Search for <b>cats, dogs, cakes</b>, or anything else that takes your fancy :-)
    <br />
    <input id="searchterm" />
    <button id="search">search</button>
    <div id="results"></div>
    <script>
      $("#search").click(function(){
        $.getJSON("http://api.flickr.com/services/feeds/photos_public.gne?jsoncallback=?",
        {
          tags: $("#searchterm").val(),
          tagmode: "any",
          format: "json"
        },
        function(data) {
          $.each(data.items, function(i,item){
            $("<img/>").attr("src", item.media.m).prependTo("#results");
            if ( i == 10 ) return false;
          });
        });
      });
    </script>
  </body>
</html>

The video is 70mins long and roughly divided into three sections: 1) the challenges you face as an implementer (quite interesting), 2) the nuts and bolts of how you overcome this, and 3) Some real life examples. At the end of the presentation I showcase some really nice SharePoint search front-ends that Comperio made for Microsoft a few months ago.

How do you make great UX? What can be done to make your Fast Search For SharePoint application a joy to use? Can Scottish people make themselves understood to Americans? What tips would you give the up and coming UX designer?