<p>Today marks a milestone: we have achieved the goal we set when Found joined forces with Elastic back in March last year by making the entire Elastic stack available to our hosted Elasticsearch customers. First, we integrated Kibana 4 in July, Shield for security in September, Watcher for alerting in October, and today, we're excited to announce that the Elastic monitoring plugin, Marvel, is now available on Found, our hosted Elasticsearch product. This is a major engineering milestone for us as it means that all of our Elastic&nbsp;plugins are now available on Found.</p><p><a href="/products/marvel">Marvel</a> is a monitoring plugin that lets you view and analyze the health and performance of your Elasticsearch clusters in real time as well as historically. The <a href="/blog/shield-watcher-and-marvel-2-0-ga-released">newly released Marvel 2.0</a> is built on top of Kibana 4, which features a complete UI redesign.</p><p>To start using Marvel on Found, all you need to do is to enable Marvel and then select a cluster to which you want to ship your Marvel metrics data. We recommend creating a dedicated Elasticsearch cluster for this so it doesn't interfere with any production clusters you are running. Next, you simply launch Kibana on your Marvel cluster and select the Marvel app on the Kibana menu bar to access the key metrics of your clusters, indexes, and nodes. No additional configuration is needed. That's it — just Marvel-ous.</p><p><img src="https://api.contentstack.io/v2/assets/575e4d130342dfd738264868/download?uid=bltf9bc2b8d35b7e4be?uid=bltf9bc2b8d35b7e4be" data-sys-asset-uid="blt4bb4ba85bc986dd7" alt="marvel-config.png" style="max-width: 100%;"/></p><p><br /></p><p><img src="https://api.contentstack.io/v2/assets/575e4d1312798d8e381fec5f/download?uid=blt2b960727a9b34104?uid=blt2b960727a9b34104" data-sys-asset-uid="blt596e10e319884251" alt="Marvel_blog_1.png" style="max-width: 100%;"/></p><p>Want to try it out? Sign up for a <a href="/found/signup">free trial now</a>!<br /></p><p>This milestone signifies an end, but also a beginning. As a highly engineering-driven company, there's always going to be a ‘but wait, there's more', so let's have a peek at what's on the horizon. But first let's take a quick look at how we got to where we are today.</p><h2>Developer's Best Friend in the Cloud</h2><p>A few years ago, we were a tiny startup in Norway with some&nbsp;wonderful customers who were all fanatic Elasticsearch users looking&nbsp;for a better way to host and manage their Elasticsearch clusters than simply doing it themselves on AWS. We stood up to the challenge, and back&nbsp;in 2012 we launched the public beta of our hosted Elasticsearch service. We hit challenges early on and ‘failed fast', which gave us a wealth of experience enabling us to execute well in a fast moving cloud space. Our mission was to be the developer's best friend, by hiding complexity while remaining flexible, and last but not least, by delivering a rock solid, reliable hosted Elasticsearch service. It was still early days in the hosted Elasticsearch domain when we went GA in 2013, but the product showed significant traction in the following months. By the time Elastic acquired Found short of one year ago, we had hundreds of paying customers. Since then, the ride has been simply amazing.</p><h2>A Dream Come True: Found + Elastic</h2><p>We were secretly hoping to one day become a part of ‘the core Elasticsearch team', and we had good reasons to believe that Elastic was planning to enter the hosted Elasticsearch market. As it turned out, we were right and Elastic had such faith in what we built that they scrapped the plan of building their own service and instead ‘popped the question'. In March 2015, <a href="https://www.elastic.co/blog/welcome-found">we merged together with the Elastic team</a>. Usually, acquisitions tend to be rather bumpy, but based on strikingly similar culture,&nbsp;ethos, and a vision for the future, it was simply a great fit for us and our customers. One result was that many more and also larger companies signed up for Found as they entrusted in the fact that the company who created Elasticsearch was now supporting the Found hosted Elasticsearch service. Today, we are humbled that we are hosting three times as many clusters as before the acquisition and honored to have awesome customers such as HotelTonight, Docker and Instacart enjoying the benefits of Elasticsearch in the cloud.</p><h2>Story complete with Marvel</h2><p>With a steadily growing cloud team in 2015 and access to the people who built the Elastic products, we have finally packaged all Elastic products into our hosted Elasticsearch solution. As Elastic continues to enhance ‘the stack’, as well as develop new products, we can assure you that all of these goodies will be packaged with our hosted Elasticsearch service. Already, new Elasticsearch versions and Kibana apps - such as Sense and Timelion - are made available on Found as soon as they are released.</p><h2>Hosted Elasticsearch in Your Clouds</h2><p>If you see us with shades, it's because the future's so bright... With the usage of Elasticsearch snowballing and more and more businesses adopting the cloud model, we're in a good place. But complacency is not in our DNA. We're all about helping developers, devops, and our customers start and grow their businesses, and what we have come to realize is that despite the increasing demand for Elasticsearch as a Service, there's an unmet demand that we can help deliver: a fully packaged Elastic stack for on-premise environments and private clouds. So if you choose to run Elasticsearch in your own data centers, you can look forward to a downloadable, feature-complete version of the Elastic Stack to run in your own datacenter or private cloud, built by a team with many years of experience managing thousands of clusters. Our goal is to let you use Elastic's products to solve even more challenges and accelerate your business further – without having to expand your operations teams or ship data out of your data center. So stay tuned! And we promise, there's lots more ‘Found 2.0' awesomeness to come.</p><p>Cecilie Myhre and Morten Ingebrigtsen,<br />Directors of Marketing and Product Management (Found Team)</p>

found-alerts-watcher-elasticsearch-as-a-search-full-bleed.jpg

fetch.png

Hosted Elasticsearch: Monitoring Now Available on Found

Using Kibana for Business Intelligence

<html><head></head><body><p><strong>UPDATE:</strong> This article refers to our hosted Elasticsearch offering by an older name, Found. Please note that Found is now known as Elastic Cloud.</p>
<h2>What Does an Index Look Like?</h2>
<p>In the previous article we looked at how search engines parses, analyzes and tokenizes text. In this article we will explore in more detail what an index looks like.
</p><section id="terms-words-and-tokens" class="level2">
<h2><a href="#terms-words-and-tokens">Terms, Words and Tokens</a></h2>
<p>Read the previous article here: <a href="/blog/found-indexing-for-beginners-part2/" title="/blog/found-indexing-for-beginners-part2">Document parsing and tokenization</a>
</p>
<p>After we have tokenized the text, we are left with a list of tokens that the search engine will store in the index. In our cookbook example, the tokens are the actual words in the recipes that we want to make searchable. As you may have noticed, we often use the terms <em>word</em>, <em>term</em> and <em>token</em> interchangeably and in the broadest sense when talking about indexing. For example, we use these terms for actual words, numbers, abbreviations, paths (in the example with the path-tokenizer) and so on. Strictly speaking, a <em>token</em> is the name of a unit that we derive from the tokenizer, and the token therefore depends on the tokenizer. A token is not necessarily a word, but a <em>word</em> is normally a token when dealing with text. When we store the token in the index, it is usually called a <em>term</em>.
</p>
</section><section id="the-forward-index" class="level2">
<h2><a href="#the-forward-index">The Forward Index</a></h2>
<p>The most obvious way to build up an index, is to store a list of all terms for each document that we are indexing:
</p>
<table>
<thead>
<tr class="header">
	<th style="text-align: left;">Document
	</th>
	<th style="text-align: left;">Terms
	</th>
</tr>
</thead>
<tbody>
<tr class="odd">
	<td style="text-align: left;">Grandma&#x2019;s tomato soup
	</td>
	<td style="text-align: left;">peeled, tomatoes, carrot, basil, leaves, water, salt, stir, and, boil, &#x2026;
	</td>
</tr>
<tr class="even">
	<td style="text-align: left;">African tomato soup
	</td>
	<td style="text-align: left;">15, large, tomatoes, baobab, leaves, water, store, in, a, cool, place, &#x2026;
	</td>
</tr>
<tr class="odd">
	<td style="text-align: left;">Good ol&#x2019; tomato soup
	</td>
	<td style="text-align: left;">tomato, garlic, water, salt, 400, gram, chicken, fillet, cook, for, 15, minutes, &#x2026;
	</td>
</tr>
</tbody>
</table>
<p>This is called a <em>forward index</em>. The forward index is pretty fast when indexing, as new documents are appended to the index and no rebuilding of the index is required. It is, however, not very efficient when querying because querying requires the search engine to look through all entries in the index for a specfic term in order to return all documents containing the term.
</p>
</section><section id="inverted-index" class="level2">
<h2><a href="#inverted-index">Inverted Index</a></h2>
<p>To make the query process faster, it is much smarter to sort the index by terms, like this:
</p>
<table>
<thead>
<tr class="header">
	<th style="text-align: left;">Term
	</th>
	<th style="text-align: left;">Documents
	</th>
</tr>
</thead>
<tbody>
<tr class="odd">
	<td style="text-align: left;">baobaob
	</td>
	<td style="text-align: left;">African tomato soup
	</td>
</tr>
<tr class="even">
	<td style="text-align: left;">basil
	</td>
	<td style="text-align: left;">Grandma&#x2019;s tomato soup
	</td>
</tr>
<tr class="odd">
	<td style="text-align: left;">leaves
	</td>
	<td style="text-align: left;">African tomato soup, Grandma&#x2019;s tomato soup
	</td>
</tr>
<tr class="even">
	<td style="text-align: left;">salt
	</td>
	<td style="text-align: left;">African tomato soup, Good ol&#x2019; tomato soup
	</td>
</tr>
<tr class="odd">
	<td style="text-align: left;">tomato
	</td>
	<td style="text-align: left;">African tomato soup, Good ol&#x2019; tomato soup, Grandma&#x2019;s tomato soup
	</td>
</tr>
<tr class="even">
	<td style="text-align: left;">water
	</td>
	<td style="text-align: left;">African tomato soup, Good ol&#x2019; tomato soup, Grandma&#x2019;s tomato soup
	</td>
</tr>
<tr class="odd">
	<td style="text-align: left;">&#x2026;
	</td>
	<td style="text-align: left;">&#x2026;
	</td>
</tr>
</tbody>
</table>
<p>This type of index is called an <em>inverted index</em>, namely because it is an inversion of the forward index. With the inverted index, we only have to look for a term once to retrieve a list of all documents containing the term. As mentioned in the first article in this series, conventional textbook indexing is based on inverted index.
</p>
<p>Often both the forward and inverted index are used in search engines, where the inverted index is built by sorting the forward index by its terms.
</p>
<p>In some search engines the index includes additional information such as frequency of the terms, e.g. how often a term occurs in each document, or the position of the term in each document. The frequency of a term is often used to calculate the relevance of a search result, whereas the position is often used to facilitate searching for phrases in a document.
</p>
<p>In the next article, we will look at how you can build up more sophisticated indexes to speed up and improve the relevancy of your search results.
</p>
<p>Previous: <a href="/blog/found-indexing-for-beginners-part2/" title="/blog/found-indexing-for-beginners-part2">Document Parsing and Tokenization</a>
</p>
</section></body></html>

Indexing for Beginners, Part 3

<html><head></head><body><p><strong>UPDATE:</strong> This article refers to our hosted Elasticsearch offering by an older name, Found. Please note that Found is now known as Elastic Cloud.</p><h2>Document Parsing and Tokenization</h2><p>We continue the Indexing for beginners article with an introduction to the document parsing process and use some space to explain the concept of tokenization and tokens.
</p><section id="document-parsing" class="level2">
<h2><a href="#document-parsing">Document Parsing</a></h2>
<p>Read part 1 of this article here: <a href="/blog/found-indexing-for-beginners-part1/" title="/blog/found-indexing-for-beginners-part1/">What is an index?</a>
</p>
<p>Say you are running a recipe website where visitors can add their own recipes. Obviously, you want the recipes to be searchable, so you set up a search engine to take care of this. Every time someone creates or uploads a new recipe to the website, the search engine is tasked with making the new recipe searchable. The first step in this process is is to scan and process the text in the recipe. This process is generally called <em>document parsing</em>. Other common names for this process are <em>text processing</em>, <em>text analysis</em>, <em>text mining</em> and <em>content analysis</em>. In the document parsing step the search engine creates a list of the words - or <em>terms</em> - in the recipe, accompanied by a reference - or mapping - to the recipes containing these terms. This list is basically what makes up the index. Lastly, the search engine will save the index to disk to be able to retrieve it later. Also, parts of the index will reside in memory, to make the search process faster.
</p>
<section id="full-text-search-engines" class="level3">
<h3><a href="#full-text-search-engines">Full-Text Search Engines</a></h3>
<p>Search engines that scan and process the entire text in a document prior to indexing, are so-called full-text search engines. Lucene, which Elasticsearch and Solr are built on top of, is an example of a full-text search engine.
</p>
</section>
<section id="tokenization" class="level3">
<h3><a href="#tokenization">Tokenization</a></h3>
<p>When the recipe text is scanned and fed into our search engine, the first thing the search engine has to do is to make sense of the data it receives. This might seem trivial to us, but for a computer it&apos;s not that simple. When you and I read a text, we automatically break up the text into characters, words and sentences. This is a process we have learned and automated through years of practice. We know that words are separated by spaces, and that sentences are separated by punctuation marks. Computers, on the other hand, has no such built-in understanding of so-called <em>natural languages</em><a href="#fn1" class="footnoteRef" id="fnref1"><sup>1</sup></a>. For a computer, the text is just a stream of characters where a white space, a punctuation mark or the character &apos;t&apos; has no special meaning. Therefore, computers has to be programmed to break up text into their distinct elements, such as words and sentences. This process is called <em>tokenization</em> and the different chunks, usually words, that constitute the text are called <em>tokens</em>. Here is an example how a sentence is tokenized:
</p>
<p>The sentence
</p>
<pre>&lt;code&gt;Heat slowly, stir gently while adding twenty-five grams of flour.&lt;/code&gt;
</pre><br><br>
<p>is split into the following tokens
</p>
<pre>&lt;code&gt;[heat] [slowly] [stir] [gently] [while] [adding] [twenty-five] [grams] [of] [flour]&lt;/code&gt;
</pre><p><br></p>
<p><br>
</p>
<p>We see that the tokenizer used here knows how to separate the words from each other by looking for spaces and punctuation characters. The hyphen is a bit more tricky though; should we split <em>twenty-five</em> into two tokens: [twenty] and [five], or should it be only one token: [twenty-five]? And what if you want to match a search for 25?
</p>
<p>Tokenizers can be quite sophisticated, and they should be able to handle text constructs such as abbreviations (ca., kg., dr.), numbers (10 or ten, 3x10^9 or 3 billion), hyphens (twenty-five), end-of-line-hyphens (a word split across two lines), lexical hyphens (pre-, multi-), contractions (I&apos;m, it&apos;s), compound words (motorboat or Kraftfahrzeug), and so on. Additionally, in many cases the tokenizer needs to be language aware. This is especially true when tokenizing non-phonetic languages, where one grapheme<a href="#fn2" class="footnoteRef" id="fnref2"><sup>2</sup></a> might indicate an entire word or a syllable, for example in Chinese or Japanese.
</p>
<p>There are many specialized tokenizers, for example CamelCase tokenizer, URL tokenizer, path tokenizer and N-gram tokenizer. These are designed to handle special language constructs, especially in relation to artificial languages like mathematics or programming languages.
</p>
<p>Just to give you an idea how these tokenizers work, here is an example of a path tokenizer, where the path:
</p>
<pre>&lt;code&gt;C:\WINDOWS\system32\rundll32.exe&lt;/code&gt;
</pre><br><br>
<p>is split into the following tokens:
</p>
<pre>&lt;code&gt;[c:] [c:/windows] [c:/windows/system32] [c:/windows/system32/rundll32.exe]&lt;/code&gt;
</pre><br><br>
</section>
<section id="stop-words" class="level3">
<h3><a href="#stop-words">Stop Words</a></h3>
<p>Sometimes we want to avoid certain words from being indexed. For instance, in many cases it would make no sense to store the words on, for, a, the, us, who etc. in the index. Such terms are called stop words. However, in most modern search engines, stop words are not problematic, and by indexing stop words it&apos;s possible to search for the band &quot;The Who&quot; or exact phrases and even entire sentences.
</p>
</section>
<section id="relevancy" class="level3">
<h3><a href="#relevancy">Relevancy</a></h3>
<p>Essentially, all the tokens - in our case all the words in our Tomato recipes - are stored as terms in the index, together with the mapping to all occurrences of the terms. For example, if a visitor searches for the term &quot;tomato&quot;, the search request will return every recipes containing the word &quot;tomato&quot;, and it will certainly include more than just tomato soup recipes.
</p>
<p>This rudimentary way of indexing does not take into account the relevancy of the search results. In fact, one of the most important considerations when setting up a search engine is how you tell the search engine to process and analyze the text, and how to build the indexes, as this will heavily influence how you construct your queries and filters (explained later), which in turn will determine the relevancy of the search results. We will talk more about relevancy and relevance models in an upcoming article.
</p>
<p>Previous: <a href="/blog/found-indexing-for-beginners-part1/" title="/blog/found-indexing-for-beginners-part1/">What is an index?</a>
</p>
<p>Next: <a href="/blog/found-indexing-for-beginners-part3/" title="/blog/found-indexing-for-beginners-part3/">What does an index look like?</a>
</p>
<div class="references">
</div>
</section>
</section><section class="footnotes">
<hr>
<ol>
	<li id="fn1">Languages that we humans speak or write are generally called natural languages.<a href="#fnref1">&#x21A9;</a></li>
	<li id="fn2">A grapheme is the smallest unit in a written language, for instance the character &apos;w&apos; in the Latin alphabet, or the sign representing the word &apos;table&apos; in Chinese.<a href="#fnref2">&#x21A9;</a></li>
</ol>
</section></body></html>

Indexing for Beginners, Part 2

<html><head></head><body><p><strong>UPDATE:</strong> This article refers to our hosted Elasticsearch offering by an older name, Found. Please note that Found is now known as Elastic Cloud.</p><p>The Missing Document on Search Engine Indexing
</p><section id="what-is-an-index" class="level2">
<h2><a href="#what-is-an-index">What is an Index?</a></h2>
<blockquote>
	&#x201C;If you don&#x2019;t find it in the index, look very carefully through the entire catalog.&#x201D; &#x2013; Sears, Roebuck, and Co., Consumers&#x2019; Guide 1897
</blockquote>
<p>The concept of an index is pretty straightforward. I&#x2019;m sure you are familiar with indexes from textbooks, so we will use this as a point of reference for the following introductory explanation.
</p>
<p>Here&#x2019;s an everyday scenario: you want to make tomato soup but don&#x2019;t know how. So you pick up that dusty cookbook you were given for Christmas. But hey, it happens to be a bible size cookbook covering all things foody. Surely you don&#x2019;t want to start on page 1 and read through the majority of the cookbook to find out how to make tomato soup, right? Luckily for you, some clever person in the 16th century came up with the headsmacking idea of indexing books!
</p>
<p>Here is a sample of what a cookbook index might look like:
</p>
<pre>&lt;code&gt;Tartar, beef tartar&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;page 67
Tomato chutney&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;page 645
Tomato soup&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;page 23, 78
Umami burger&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;&#x2026;page 378&lt;/code&gt;
</pre>
<p>An index in a textbook is basically a mapping between words or phrases in the book, for instance &#x201C;tomato soup&#x201D;, and the page or pages where you can find the word or phrase. By convention and most widely used is the back-of-the-book index, sorted alphabetically. Evidently, this makes it quick and easy to look up a word or phrase and subsequently locate the mapped content.
</p>
<p>Indexing by search engines works pretty much the same way. A search engine creates mappings between terms, in the above example recipe names, and occurrences of the terms, in the above example the pages in the cookbook where you can find the recipes.
</p>
<p>Just as it&#x2019;s a tedious and cumbersome task to read through an entire cookbook to find a specific recipe, it is equally inefficient for a computer to scan through a pile of documents or a database every time it is asked to find all occurrences of a term. For large databases or mountains of documents, this process could take several minutes, or even hours. Indexing has therefore been introduced to speed up the search process, or information retrieval (IR) process, as it is generally called.
</p>
<p>Somewhat akin to the alphabetical sorting of the index in a cookbook, a search engine&#x2019;s index is stored using specific formats - or data structures - to make it fast to locate and find the search term and their mappings. Moreover, storing the index in memory (RAM) is a sure-fire way to speed up the lookup process. Imagine if you could memorize all the indexes of your entire stack of textbooks!
</p>
<p>Next: <a href="/blog/found-indexing-for-beginners-part2/" title="/blog/found-indexing-for-beginners-part2">Document Parsing and Tokenization</a>
</p>
</section></body></html>

Indexing for Beginners, Part 1

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