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+++ b/content/docs/0.9.4/aql/externaldata.html
@@ -1,13 +1,13 @@
 <!DOCTYPE html>
 <!--
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+    <meta name="Date-Revision-yyyymmdd" content="20181011" />
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@@ -26,8 +26,8 @@
 
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-      <li id="projectVersion" class="pull-right">Version: 0.9.4</li>
+        <li id="publishDate">Last Published: 2018-10-11</li>
+      <li id="projectVersion" class="pull-right">Version: 0.9.5-SNAPSHOT</li>
       <li class="pull-right"><a href="../index.html" title="Documentation 
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@@ -40,26 +40,26 @@
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       <li class="nav-header">AsterixDB Primer</li>
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class="none"></span>Option 2: using AQL</a></li>
+    <li><a href="../sqlpp/primer-sqlpp.html" title="Using SQL++"><span 
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+      <li class="nav-header">Queries</li>
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-    <li><a href="../feeds/tutorial.html" title="Support for Data 
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+    <li><a href="../feeds.html" title="Data Ingestion with Feeds"><span 
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     <li><a href="../udf.html" title="User Defined Functions"><span 
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+    <li><a href="../sqlpp/filters.html" title="Filter-Based LSM Index 
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+    <li><a href="../sqlpp/fulltext.html" title="Support of Full-text 
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+    <li><a href="../sqlpp/similarity.html" title="Support of Similarity 
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+      <li class="nav-header">Deprecated</li>
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Language (AQL)"><span class="none"></span>Queries: The Asterix Query Language 
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 </ul>
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-<!DOCTYPE html>
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class="none"></span>User Defined Functions</a></li>
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Index Acceleration</a></li>
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-<h1>Filter-Based LSM Index Acceleration</h1>
-<div class="section">
-<h2><a name="Table_of_Contents"></a><a name="toc" id="toc">Table of 
Contents</a></h2>
-<ul>
-
-<li><a href="#Motivation">Motivation</a></li>
-<li><a href="#FiltersInAsterixDB">Filters in AsterixDB</a></li>
-<li><a href="#FiltersAndMergePolicies">Filters and Merge Policies</a></li>
-</ul></div>
-<div class="section">
-<h2><a name="Motivation_.5BBack_to_TOC.5D"></a><a name="Motivation" 
id="Motivation">Motivation</a> <font size="4"><a href="#toc">[Back to 
TOC]</a></font></h2>
-<p>Traditional relational databases usually employ conventional index 
structures such as B+ trees due to their low read latency.  However, such 
traditional index structures use in-place writes to perform updates, resulting 
in costly random writes to disk. Today&#x2019;s emerging applications often 
involve insert-intensive workloads for which the cost of random writes 
prohibits efficient ingestion of data. Consequently, popular NoSQL systems such 
as Cassandra, HBase, LevelDB, BigTable, etc. have adopted Log-Structured Merge 
(LSM) Trees as their storage structure. LSM-trees avoids the cost of random 
writes by batching updates into a component of the index that resides in main 
memory &#x2013; an <i>in-memory component</i>. When the space occupancy of the 
in-memory component exceeds a specified threshold, its entries are 
<i>flushed</i> to disk forming a new component &#x2013; a <i>disk 
component</i>. As disk components accumulate on disk, they are periodically 
merged together subject to
  a <i>merge policy</i> that decides when and what to merge. The benefit of the 
LSM-trees comes at the cost of possibly sacrificing read efficiency, but, it 
has been shown in previous studies that these inefficiencies can be mostly 
mitigated.</p>
-<p>AsterixDB has also embraced LSM-trees, not just by using them as primary 
indexes, but also by using the same LSM-ification technique for all of its 
secondary index structures. In particular, AsterixDB adopted a generic 
framework for converting a class of indexes (that includes conventional B+ 
trees, R trees, and inverted indexes) into LSM-based secondary indexes, 
allowing higher data ingestion rates. In fact, for certain index structures, 
our results have shown that using an LSM-based version of an index can be made 
to significantly outperform its conventional counterpart for <i>both</i> 
ingestion and query speed (an example of such an index being the R-tree for 
spatial data).</p>
-<p>Since an LSM-based index naturally partitions data into multiple disk 
components, it is possible, when answering certain queries, to exploit 
partitioning to only access some components and safely filter out the remaining 
components, thus reducing query times. For instance, referring to our <a 
href="primer.html#ADM:_Modeling_Semistructed_Data_in_AsterixDB">TinySocial</a> 
example, suppose a user always retrieves tweets from the <tt>TweetMessages</tt> 
dataset based on the <tt>send-time</tt> field (e.g., tweets posted in the last 
24 hours). Since there is not a secondary index on the <tt>send-time</tt> 
field, the only available option for AsterixDB would be to scan the whole 
<tt>TweetMessages</tt> dataset and then apply the predicate as a 
post-processing step. However, if disk components of the primary index were 
tagged with the minimum and maximum timestamp values of the objects they 
contain, we could utilize the tagged information to directly access the primary 
index and prune comp
 onents that do not match the query predicate. Thus, we could save substantial 
cost by avoiding scanning the whole dataset and only access the relevant 
components. We simply call such tagging information that are associated with 
components, filters. (Note that even if there were a secondary index on 
<tt>send-time</tt> field, using filters could save substantial cost by avoiding 
accessing the secondary index, followed by probing the primary index for every 
fetched entry.) Moreover, the same filtering technique can also be used with 
any secondary LSM index (e.g., an LSM R-tree), in case the query contains 
multiple predicates (e.g., spatial and temporal predicates), to obtain similar 
pruning power.</p></div>
-<div class="section">
-<h2><a name="Filters_in_AsterixDB_.5BBack_to_TOC.5D"></a><a 
name="FiltersInAsterixDB" id="FiltersInAsterixDB">Filters in AsterixDB</a> 
<font size="4"><a href="#toc">[Back to TOC]</a></font></h2>
-<p>We have added support for LSM-based filters to all of AsterixDB&#x2019;s 
index types. To enable the use of filters, the user must specify the 
filter&#x2019;s key when creating a dataset, as shown below:</p>
-<div class="section">
-<div class="section">
-<h4><a name="Creating_a_Dataset_with_a_Filter"></a>Creating a Dataset with a 
Filter</h4>
-
-<div>
-<div>
-<pre class="source">    create dataset Tweets(TweetType) primary key tweetid 
with filter on send-time;
-</pre></div></div>
-
-<p>Filters can be created on any totally ordered datatype (i.e., any field 
that can be indexed using a B+ -tree), such as integers, doubles, floats, 
UUIDs, datetimes, etc.</p>
-<p>When a dataset with a filter is created, the name of the filter&#x2019;s 
key field is persisted in the <tt>Metadata.Dataset</tt> dataset (which is the 
metadata dataset that stores the details of each dataset in an AsterixDB 
instance) so that DML operations against the dataset can recognize the 
existence of filters and can update them or utilize them accordingly. Creating 
a dataset with a filter in AsterixDB implies that the primary and all secondary 
indexes of that dataset will maintain filters on their disk components. Once a 
filtered dataset is created, the user can use the dataset normally (just like 
any other dataset). AsterixDB will automatically maintain the filters and will 
leverage them to efficiently answer queries whenever possible (i.e., when a 
query has predicates on the filter&#x2019;s key).</p></div></div></div>
-<div class="section">
-<h2><a name="Filters_and_Merge_Policies_.5BBack_to_TOC.5D"></a><a 
name="FiltersAndMergePolicies" id="FiltersAndMergePolicies">Filters and Merge 
Policies</a> <font size="4"><a href="#toc">[Back to TOC]</a></font></h2>
-<p>The AsterixDB default merge policy, the prefix merge policy, relies on 
component sizes and the number of components to decide which components to 
merge. This merge policy has proven to provide excellent performance for both 
ingestion and queries. However, when evaluating our filtering solution with the 
prefix policy, we observed a behavior that can reduce filter effectiveness. In 
particular, we noticed that under the prefix merge policy, the disk components 
of a secondary index tend to be constantly merged into a single component. This 
is because the prefix policy relies on a single size parameter for all of the 
indexes of a dataset. This parameter is typically chosen based on the sizes of 
the disk components of the primary index, which tend to be much larger than the 
sizes of the secondary indexes&#x2019; disk components. This difference caused 
the prefix merge policy to behave similarly to the constant merge policy (i.e., 
relatively poorly) when applied to secondary indexes in 
 the sense that the secondary indexes are constantly merged into a single disk 
component. Consequently, the effectiveness of filters on secondary indexes was 
greatly reduced under the prefix-merge policy, but they were still effective 
when probing the primary index.  Based on this behavior, we developed a new 
merge policy, an improved version of the prefix policy, called the 
correlated-prefix policy. The basic idea of this policy is that it delegates 
the decision of merging the disk components of all the indexes in a dataset to 
the primary index. When the policy decides that the primary index needs to be 
merged (using the same decision criteria as for the prefix policy), then it 
will issue successive merge requests to the I/O scheduler on behalf of all 
other indexes associated with the same dataset. The end result is that 
secondary indexes will always have the same number of disk components as their 
primary index under the correlated-prefix merge policy. This has improved query 
perfo
 rmance, since disk components of secondary indexes now have a much better 
chance of being pruned.</p></div>
-        </div>
-      </div>
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-    <hr/>
-    <footer>
-      <div class="container-fluid">
-        <div class="row-fluid">
-<div class="row-fluid">Apache AsterixDB, AsterixDB, Apache, the Apache
-        feather logo, and the Apache AsterixDB project logo are either
-        registered trademarks or trademarks of The Apache Software
-        Foundation in the United States and other countries.
-        All other marks mentioned may be trademarks or registered
-        trademarks of their respective owners.
-      </div>
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Home">Documentation Home</a></li>
-        </ul>
-      </div>
-      <div class="row-fluid">
-        <div id="leftColumn" class="span2">
-          <div class="well sidebar-nav">
-    <ul class="nav nav-list">
-      <li class="nav-header">Get Started - Installation</li>
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class="none"></span>Option 1: using NCService</a></li>
-    <li><a href="../ansible.html" title="Option 2: using Ansible"><span 
class="none"></span>Option 2: using Ansible</a></li>
-    <li><a href="../aws.html" title="Option 3: using Amazon Web 
Services"><span class="none"></span>Option 3: using Amazon Web Services</a></li>
-      <li class="nav-header">AsterixDB Primer</li>
-    <li><a href="../sqlpp/primer-sqlpp.html" title="Option 1: using 
SQL++"><span class="none"></span>Option 1: using SQL++</a></li>
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class="none"></span>Option 2: using AQL</a></li>
-      <li class="nav-header">Data Model</li>
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class="none"></span>The Asterix Data Model</a></li>
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-      <li class="nav-header">Queries - AQL</li>
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-    <li><a href="../aql/externaldata.html" title="Accessing External 
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-    <li><a href="../udf.html" title="User Defined Functions"><span 
class="none"></span>User Defined Functions</a></li>
-    <li><a href="../aql/filters.html" title="Filter-Based LSM Index 
Acceleration"><span class="none"></span>Filter-Based LSM Index 
Acceleration</a></li>
-    <li><a href="../aql/similarity.html" title="Support of Similarity 
Queries"><span class="none"></span>Support of Similarity Queries</a></li>
-</ul>
-          <hr />
-          <div id="poweredBy">
-            <div class="clear"></div>
-            <div class="clear"></div>
-            <div class="clear"></div>
-            <div class="clear"></div>
-<a href=".././" title="AsterixDB" class="builtBy"><img class="builtBy"  
alt="AsterixDB" src="../images/asterixlogo.png"    /></a>
-            </div>
-          </div>
-        </div>
-        <div id="bodyColumn"  class="span10" >
-<!--
- ! Licensed to the Apache Software Foundation (ASF) under one
- ! or more contributor license agreements.  See the NOTICE file
- ! distributed with this work for additional information
- ! regarding copyright ownership.  The ASF licenses this file
- ! to you under the Apache License, Version 2.0 (the
- ! "License"); you may not use this file except in compliance
- ! with the License.  You may obtain a copy of the License at
- !
- !   http://www.apache.org/licenses/LICENSE-2.0
- !
- ! Unless required by applicable law or agreed to in writing,
- ! software distributed under the License is distributed on an
- ! "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- ! KIND, either express or implied.  See the License for the
- ! specific language governing permissions and limitations
- ! under the License.
- !-->
-<h1>AsterixDB  Support of Full-text search queries</h1>
-<div class="section">
-<h2><a name="Table_of_Contents"></a><a name="toc" id="toc">Table of 
Contents</a></h2>
-<ul>
-
-<li><a href="#Motivation">Motivation</a></li>
-<li><a href="#Syntax">Syntax</a></li>
-<li><a href="#FulltextIndex">Creating and utilizing a Full-text index</a></li>
-</ul></div>
-<div class="section">
-<h2><a name="Motivation_.5BBack_to_TOC.5D"></a><a name="Motivation" 
id="Motivation">Motivation</a> <font size="4"><a href="#toc">[Back to 
TOC]</a></font></h2>
-<p>Full-Text Search (FTS) queries are widely used in applications where users 
need to find records that satisfy an FTS predicate, i.e., where simple 
string-based matching is not sufficient. These queries are important when 
finding documents that contain a certain keyword is crucial. FTS queries are 
different from substring matching queries in that FTS queries find their query 
predicates as exact keywords in the given string, rather than treating a query 
predicate as a sequence of characters. For example, an FTS query that finds 
&#x201c;rain&#x201d; correctly returns a document when it contains 
&#x201c;rain&#x201d; as a word. However, a substring-matching query returns a 
document whenever it contains &#x201c;rain&#x201d; as a substring, for 
instance, a document with &#x201c;brain&#x201d; or &#x201c;training&#x201d; 
would be returned as well.</p></div>
-<div class="section">
-<h2><a name="Syntax_.5BBack_to_TOC.5D"></a><a name="Syntax" 
id="Syntax">Syntax</a> <font size="4"><a href="#toc">[Back to 
TOC]</a></font></h2>
-<p>The syntax of AsterixDB FTS follows a portion of the XQuery FullText Search 
syntax. Two basic forms are as follows:</p>
-
-<div>
-<div>
-<pre class="source">    ftcontains(Expression1, Expression2, {FullTextOption})
-    ftcontains(Expression1, Expression2)
-</pre></div></div>
-
-<p>For example, we can execute the following query to find tweet messages 
where the <tt>message-text</tt> field includes &#x201c;voice&#x201d; as a word. 
Please note that an FTS search is case-insensitive. Thus, &#x201c;Voice&#x201d; 
or &#x201c;voice&#x201d; will be evaluated as the same word.</p>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    for $msg in dataset TweetMessages
-    where ftcontains($msg.message-text, &quot;voice&quot;, 
{&quot;mode&quot;:&quot;any&quot;})
-    return {&quot;id&quot;: $msg.id}
-</pre></div></div>
-
-<p>The DDL and DML of TinySocial can be found in <a 
href="primer.html#ADM:_Modeling_Semistructed_Data_in_AsterixDB">ADM: Modeling 
Semistructed Data in AsterixDB</a>.</p>
-<p>The same query can be also expressed in the SQL++.</p>
-
-<div>
-<div>
-<pre class="source">    use TinySocial;
-
-    select element {&quot;id&quot;:msg.id}
-    from TweetMessages as msg
-    where TinySocial.ftcontains(msg.`message-text`, &quot;voice&quot;, 
{&quot;mode&quot;:&quot;any&quot;})
-</pre></div></div>
-
-<p>The <tt>Expression1</tt> is an expression that should be evaluable as a 
string at runtime as in the above example where <tt>$msg.message-text</tt> is a 
string field. The <tt>Expression2</tt> can be a string, an (un)ordered list of 
string value(s), or an expression. In the last case, the given expression 
should be evaluable into one of the first two types, i.e., into a string value 
or an (un)ordered list of string value(s).</p>
-<p>The following examples are all valid expressions.</p>
-
-<div>
-<div>
-<pre class="source">   ... where ftcontains($msg.message-text, 
&quot;sound&quot;)
-   ... where ftcontains($msg.message-text, &quot;sound&quot;, 
{&quot;mode&quot;:&quot;any&quot;})
-   ... where ftcontains($msg.message-text, [&quot;sound&quot;, 
&quot;system&quot;], {&quot;mode&quot;:&quot;any&quot;})
-   ... where ftcontains($msg.message-text, {{&quot;speed&quot;, 
&quot;stand&quot;, &quot;customization&quot;}}, 
{&quot;mode&quot;:&quot;all&quot;})
-   ... where ftcontains($msg.message-text, let $keyword_list := 
[&quot;voice&quot;, &quot;system&quot;] return $keyword_list, 
{&quot;mode&quot;:&quot;all&quot;})
-   ... where ftcontains($msg.message-text, $keyword_list, 
{&quot;mode&quot;:&quot;any&quot;})
-</pre></div></div>
-
-<p>In the last example above, <tt>$keyword_list</tt> should evaluate to a 
string or an (un)ordered list of string value(s).</p>
-<p>The last <tt>FullTextOption</tt> parameter clarifies the given FTS request. 
If you omit the <tt>FullTextOption</tt> parameter, then the default value will 
be set for each possible option. Currently, we only have one option named 
<tt>mode</tt>. And as we extend the FTS feature, more options will be added. 
Please note that the format of <tt>FullTextOption</tt> is a record, thus you 
need to put the option(s) in a record <tt>{}</tt>. The <tt>mode</tt> option 
indicates whether the given FTS query is a conjunctive (AND) or disjunctive 
(OR) search request. This option can be either <tt>&#x201c;any&#x201d;</tt> or 
<tt>&#x201c;all&#x201d;</tt>. The default value for <tt>mode</tt> is 
<tt>&#x201c;all&#x201d;</tt>. If one specifies <tt>&#x201c;any&#x201d;</tt>, a 
disjunctive search will be conducted. For example, the following query will 
find documents whose <tt>message-text</tt> field contains &#x201c;sound&#x201d; 
or &#x201c;system&#x201d;, so a document will be returned if it contains eit
 her &#x201c;sound&#x201d;, &#x201c;system&#x201d;, or both of the keywords.</p>
-
-<div>
-<div>
-<pre class="source">   ... where ftcontains($msg.message-text, 
[&quot;sound&quot;, &quot;system&quot;], {&quot;mode&quot;:&quot;any&quot;})
-</pre></div></div>
-
-<p>The other option parameter,<tt>&#x201c;all&#x201d;</tt>, specifies a 
conjunctive search. The following examples will find the documents whose 
<tt>message-text</tt> field contains both &#x201c;sound&#x201d; and 
&#x201c;system&#x201d;. If a document contains only &#x201c;sound&#x201d; or 
&#x201c;system&#x201d; but not both, it will not be returned.</p>
-
-<div>
-<div>
-<pre class="source">   ... where ftcontains($msg.message-text, 
[&quot;sound&quot;, &quot;system&quot;], {&quot;mode&quot;:&quot;all&quot;})
-   ... where ftcontains($msg.message-text, [&quot;sound&quot;, 
&quot;system&quot;])
-</pre></div></div>
-
-<p>Currently AsterixDB doesn&#x2019;t (yet) support phrase searches, so the 
following query will not work.</p>
-
-<div>
-<div>
-<pre class="source">   ... where ftcontains($msg.message-text, &quot;sound 
system&quot;, {&quot;mode&quot;:&quot;any&quot;})
-</pre></div></div>
-
-<p>As a workaround solution, the following query can be used to achieve a 
roughly similar goal. The difference is that the following queries will find 
documents where <tt>$msg.message-text</tt> contains both &#x201c;sound&#x201d; 
and &#x201c;system&#x201d;, but the order and adjacency of 
&#x201c;sound&#x201d; and &#x201c;system&#x201d; are not checked, unlike in a 
phrase search. As a result, the query below would also return documents with 
&#x201c;sound system can be installed.&#x201d;, &#x201c;system sound is 
perfect.&#x201d;, or &#x201c;sound is not clear. You may need to install a new 
system.&#x201d;</p>
-
-<div>
-<div>
-<pre class="source">   ... where ftcontains($msg.message-text, 
[&quot;sound&quot;, &quot;system&quot;], {&quot;mode&quot;:&quot;all&quot;})
-   ... where ftcontains($msg.message-text, [&quot;sound&quot;, 
&quot;system&quot;])
-</pre></div></div>
-</div>
-<div class="section">
-<h2><a 
name="Creating_and_utilizing_a_Full-text_index_.5BBack_to_TOC.5D"></a><a 
name="FulltextIndex" id="FulltextIndex">Creating and utilizing a Full-text 
index</a> <font size="4"><a href="#toc">[Back to TOC]</a></font></h2>
-<p>When there is a full-text index on the field that is being searched, rather 
than scanning all records, AsterixDB can utilize that index to expedite the 
execution of a FTS query. To create a full-text index, you need to specify the 
index type as <tt>fulltext</tt> in your DDL statement. For instance, the 
following DDL statement create a full-text index on the 
TweetMessages.message-text attribute.</p>
-
-<div>
-<div>
-<pre class="source">create index messageFTSIdx on TweetMessages(message-text) 
type fulltext;
-</pre></div></div></div>
-        </div>
-      </div>
-    </div>
-    <hr/>
-    <footer>
-      <div class="container-fluid">
-        <div class="row-fluid">
-<div class="row-fluid">Apache AsterixDB, AsterixDB, Apache, the Apache
-        feather logo, and the Apache AsterixDB project logo are either
-        registered trademarks or trademarks of The Apache Software
-        Foundation in the United States and other countries.
-        All other marks mentioned may be trademarks or registered
-        trademarks of their respective owners.
-      </div>
-        </div>
-      </div>
-    </footer>
-  </body>
-</html>

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diff --git a/content/docs/0.9.4/aql/js-sdk.html 
b/content/docs/0.9.4/aql/js-sdk.html
index 50700e9..c5902e4 100644
--- a/content/docs/0.9.4/aql/js-sdk.html
+++ b/content/docs/0.9.4/aql/js-sdk.html
@@ -1,13 +1,13 @@
 <!DOCTYPE html>
 <!--
- | Generated by Apache Maven Doxia Site Renderer 1.8.1 from 
src/site/markdown/aql/js-sdk.md at 2018-10-02
+ | Generated by Apache Maven Doxia Site Renderer 1.8.1 from 
src/site/markdown/aql/js-sdk.md at 2018-10-11
  | Rendered using Apache Maven Fluido Skin 1.7
 -->
 <html xmlns="http://www.w3.org/1999/xhtml"; xml:lang="en" lang="en">
   <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <meta name="Date-Revision-yyyymmdd" content="20181002" />
+    <meta name="Date-Revision-yyyymmdd" content="20181011" />
     <meta http-equiv="Content-Language" content="en" />
     <title>AsterixDB &#x2013; AsterixDB Javascript SDK</title>
     <link rel="stylesheet" href="../css/apache-maven-fluido-1.7.min.css" />
@@ -26,8 +26,8 @@
 
       <div id="breadcrumbs">
         <ul class="breadcrumb">
-        <li id="publishDate">Last Published: 2018-10-02</li>
-      <li id="projectVersion" class="pull-right">Version: 0.9.4</li>
+        <li id="publishDate">Last Published: 2018-10-11</li>
+      <li id="projectVersion" class="pull-right">Version: 0.9.5-SNAPSHOT</li>
       <li class="pull-right"><a href="../index.html" title="Documentation 
Home">Documentation Home</a></li>
         </ul>
       </div>
@@ -40,26 +40,26 @@
     <li><a href="../ansible.html" title="Option 2: using Ansible"><span 
class="none"></span>Option 2: using Ansible</a></li>
     <li><a href="../aws.html" title="Option 3: using Amazon Web 
Services"><span class="none"></span>Option 3: using Amazon Web Services</a></li>
       <li class="nav-header">AsterixDB Primer</li>
-    <li><a href="../sqlpp/primer-sqlpp.html" title="Option 1: using 
SQL++"><span class="none"></span>Option 1: using SQL++</a></li>
-    <li><a href="../aql/primer.html" title="Option 2: using AQL"><span 
class="none"></span>Option 2: using AQL</a></li>
+    <li><a href="../sqlpp/primer-sqlpp.html" title="Using SQL++"><span 
class="none"></span>Using SQL++</a></li>
       <li class="nav-header">Data Model</li>
     <li><a href="../datamodel.html" title="The Asterix Data Model"><span 
class="none"></span>The Asterix Data Model</a></li>
-      <li class="nav-header">Queries - SQL++</li>
+      <li class="nav-header">Queries</li>
     <li><a href="../sqlpp/manual.html" title="The SQL++ Query Language"><span 
class="none"></span>The SQL++ Query Language</a></li>
     <li><a href="../sqlpp/builtins.html" title="Builtin Functions"><span 
class="none"></span>Builtin Functions</a></li>
-      <li class="nav-header">Queries - AQL</li>
-    <li><a href="../aql/manual.html" title="The Asterix Query Language 
(AQL)"><span class="none"></span>The Asterix Query Language (AQL)</a></li>
-    <li><a href="../aql/builtins.html" title="Builtin Functions"><span 
class="none"></span>Builtin Functions</a></li>
       <li class="nav-header">API/SDK</li>
     <li><a href="../api.html" title="HTTP API"><span class="none"></span>HTTP 
API</a></li>
     <li><a href="../csv.html" title="CSV Output"><span class="none"></span>CSV 
Output</a></li>
       <li class="nav-header">Advanced Features</li>
-    <li><a href="../aql/fulltext.html" title="Support of Full-text 
Queries"><span class="none"></span>Support of Full-text Queries</a></li>
     <li><a href="../aql/externaldata.html" title="Accessing External 
Data"><span class="none"></span>Accessing External Data</a></li>
-    <li><a href="../feeds/tutorial.html" title="Support for Data 
Ingestion"><span class="none"></span>Support for Data Ingestion</a></li>
+    <li><a href="../feeds.html" title="Data Ingestion with Feeds"><span 
class="none"></span>Data Ingestion with Feeds</a></li>
     <li><a href="../udf.html" title="User Defined Functions"><span 
class="none"></span>User Defined Functions</a></li>
-    <li><a href="../aql/filters.html" title="Filter-Based LSM Index 
Acceleration"><span class="none"></span>Filter-Based LSM Index 
Acceleration</a></li>
-    <li><a href="../aql/similarity.html" title="Support of Similarity 
Queries"><span class="none"></span>Support of Similarity Queries</a></li>
+    <li><a href="../sqlpp/filters.html" title="Filter-Based LSM Index 
Acceleration"><span class="none"></span>Filter-Based LSM Index 
Acceleration</a></li>
+    <li><a href="../sqlpp/fulltext.html" title="Support of Full-text 
Queries"><span class="none"></span>Support of Full-text Queries</a></li>
+    <li><a href="../sqlpp/similarity.html" title="Support of Similarity 
Queries"><span class="none"></span>Support of Similarity Queries</a></li>
+      <li class="nav-header">Deprecated</li>
+    <li><a href="../aql/primer.html" title="AsterixDB Primer: Using AQL"><span 
class="none"></span>AsterixDB Primer: Using AQL</a></li>
+    <li><a href="../aql/manual.html" title="Queries: The Asterix Query 
Language (AQL)"><span class="none"></span>Queries: The Asterix Query Language 
(AQL)</a></li>
+    <li><a href="../aql/builtins.html" title="Queries: Builtin Functions 
(AQL)"><span class="none"></span>Queries: Builtin Functions (AQL)</a></li>
 </ul>
           <hr />
           <div id="poweredBy">

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b/content/docs/0.9.4/aql/manual.html
index 7a45391..ce8e842 100644
--- a/content/docs/0.9.4/aql/manual.html
+++ b/content/docs/0.9.4/aql/manual.html
@@ -1,13 +1,13 @@
 <!DOCTYPE html>
 <!--
- | Generated by Apache Maven Doxia Site Renderer 1.8.1 from 
src/site/markdown/aql/manual.md at 2018-10-02
+ | Generated by Apache Maven Doxia Site Renderer 1.8.1 from 
src/site/markdown/aql/manual.md at 2018-10-11
  | Rendered using Apache Maven Fluido Skin 1.7
 -->
 <html xmlns="http://www.w3.org/1999/xhtml"; xml:lang="en" lang="en">
   <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <meta name="Date-Revision-yyyymmdd" content="20181002" />
+    <meta name="Date-Revision-yyyymmdd" content="20181011" />
     <meta http-equiv="Content-Language" content="en" />
     <title>AsterixDB &#x2013; The Asterix Query Language, Version 1.0</title>
     <link rel="stylesheet" href="../css/apache-maven-fluido-1.7.min.css" />
@@ -26,8 +26,8 @@
 
       <div id="breadcrumbs">
         <ul class="breadcrumb">
-        <li id="publishDate">Last Published: 2018-10-02</li>
-      <li id="projectVersion" class="pull-right">Version: 0.9.4</li>
+        <li id="publishDate">Last Published: 2018-10-11</li>
+      <li id="projectVersion" class="pull-right">Version: 0.9.5-SNAPSHOT</li>
       <li class="pull-right"><a href="../index.html" title="Documentation 
Home">Documentation Home</a></li>
         </ul>
       </div>
@@ -40,26 +40,26 @@
     <li><a href="../ansible.html" title="Option 2: using Ansible"><span 
class="none"></span>Option 2: using Ansible</a></li>
     <li><a href="../aws.html" title="Option 3: using Amazon Web 
Services"><span class="none"></span>Option 3: using Amazon Web Services</a></li>
       <li class="nav-header">AsterixDB Primer</li>
-    <li><a href="../sqlpp/primer-sqlpp.html" title="Option 1: using 
SQL++"><span class="none"></span>Option 1: using SQL++</a></li>
-    <li><a href="../aql/primer.html" title="Option 2: using AQL"><span 
class="none"></span>Option 2: using AQL</a></li>
+    <li><a href="../sqlpp/primer-sqlpp.html" title="Using SQL++"><span 
class="none"></span>Using SQL++</a></li>
       <li class="nav-header">Data Model</li>
     <li><a href="../datamodel.html" title="The Asterix Data Model"><span 
class="none"></span>The Asterix Data Model</a></li>
-      <li class="nav-header">Queries - SQL++</li>
+      <li class="nav-header">Queries</li>
     <li><a href="../sqlpp/manual.html" title="The SQL++ Query Language"><span 
class="none"></span>The SQL++ Query Language</a></li>
     <li><a href="../sqlpp/builtins.html" title="Builtin Functions"><span 
class="none"></span>Builtin Functions</a></li>
-      <li class="nav-header">Queries - AQL</li>
-    <li class="active"><a href="#"><span class="none"></span>The Asterix Query 
Language (AQL)</a></li>
-    <li><a href="../aql/builtins.html" title="Builtin Functions"><span 
class="none"></span>Builtin Functions</a></li>
       <li class="nav-header">API/SDK</li>
     <li><a href="../api.html" title="HTTP API"><span class="none"></span>HTTP 
API</a></li>
     <li><a href="../csv.html" title="CSV Output"><span class="none"></span>CSV 
Output</a></li>
       <li class="nav-header">Advanced Features</li>
-    <li><a href="../aql/fulltext.html" title="Support of Full-text 
Queries"><span class="none"></span>Support of Full-text Queries</a></li>
     <li><a href="../aql/externaldata.html" title="Accessing External 
Data"><span class="none"></span>Accessing External Data</a></li>
-    <li><a href="../feeds/tutorial.html" title="Support for Data 
Ingestion"><span class="none"></span>Support for Data Ingestion</a></li>
+    <li><a href="../feeds.html" title="Data Ingestion with Feeds"><span 
class="none"></span>Data Ingestion with Feeds</a></li>
     <li><a href="../udf.html" title="User Defined Functions"><span 
class="none"></span>User Defined Functions</a></li>
-    <li><a href="../aql/filters.html" title="Filter-Based LSM Index 
Acceleration"><span class="none"></span>Filter-Based LSM Index 
Acceleration</a></li>
-    <li><a href="../aql/similarity.html" title="Support of Similarity 
Queries"><span class="none"></span>Support of Similarity Queries</a></li>
+    <li><a href="../sqlpp/filters.html" title="Filter-Based LSM Index 
Acceleration"><span class="none"></span>Filter-Based LSM Index 
Acceleration</a></li>
+    <li><a href="../sqlpp/fulltext.html" title="Support of Full-text 
Queries"><span class="none"></span>Support of Full-text Queries</a></li>
+    <li><a href="../sqlpp/similarity.html" title="Support of Similarity 
Queries"><span class="none"></span>Support of Similarity Queries</a></li>
+      <li class="nav-header">Deprecated</li>
+    <li><a href="../aql/primer.html" title="AsterixDB Primer: Using AQL"><span 
class="none"></span>AsterixDB Primer: Using AQL</a></li>
+    <li class="active"><a href="#"><span class="none"></span>Queries: The 
Asterix Query Language (AQL)</a></li>
+    <li><a href="../aql/builtins.html" title="Queries: Builtin Functions 
(AQL)"><span class="none"></span>Queries: Builtin Functions (AQL)</a></li>
 </ul>
           <hr />
           <div id="poweredBy">

http://git-wip-us.apache.org/repos/asf/asterixdb-site/blob/b2f1d3e6/content/docs/0.9.4/aql/primer.html
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diff --git a/content/docs/0.9.4/aql/primer.html 
b/content/docs/0.9.4/aql/primer.html
index 46918a7..36d59dd 100644
--- a/content/docs/0.9.4/aql/primer.html
+++ b/content/docs/0.9.4/aql/primer.html
@@ -1,13 +1,13 @@
 <!DOCTYPE html>
 <!--
- | Generated by Apache Maven Doxia Site Renderer 1.8.1 from 
src/site/markdown/aql/primer.md at 2018-10-02
+ | Generated by Apache Maven Doxia Site Renderer 1.8.1 from 
src/site/markdown/aql/primer.md at 2018-10-11
  | Rendered using Apache Maven Fluido Skin 1.7
 -->
 <html xmlns="http://www.w3.org/1999/xhtml"; xml:lang="en" lang="en">
   <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <meta name="Date-Revision-yyyymmdd" content="20181002" />
+    <meta name="Date-Revision-yyyymmdd" content="20181011" />
     <meta http-equiv="Content-Language" content="en" />
     <title>AsterixDB &#x2013; AsterixDB 101: An ADM and AQL Primer</title>
     <link rel="stylesheet" href="../css/apache-maven-fluido-1.7.min.css" />
@@ -26,8 +26,8 @@
 
       <div id="breadcrumbs">
         <ul class="breadcrumb">
-        <li id="publishDate">Last Published: 2018-10-02</li>
-      <li id="projectVersion" class="pull-right">Version: 0.9.4</li>
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-<h1>AsterixDB  Support of Similarity Queries</h1>
-<div class="section">
-<h2><a name="Table_of_Contents"></a><a name="toc" id="toc">Table of 
Contents</a></h2>
-<ul>
-
-<li><a href="#Motivation">Motivation</a></li>
-<li><a href="#DataTypesAndSimilarityFunctions">Data Types and Similarity 
Functions</a></li>
-<li><a href="#SimilaritySelectionQueries">Similarity Selection Queries</a></li>
-<li><a href="#SimilarityJoinQueries">Similarity Join Queries</a></li>
-<li><a href="#UsingIndexesToSupportSimilarityQueries">Using Indexes to Support 
Similarity Queries</a></li>
-</ul></div>
-<div class="section">
-<h2><a name="Motivation_.5BBack_to_TOC.5D"></a><a name="Motivation" 
id="Motivation">Motivation</a> <font size="4"><a href="#toc">[Back to 
TOC]</a></font></h2>
-<p>Similarity queries are widely used in applications where users need to find 
objects that satisfy a similarity predicate, while exact matching is not 
sufficient. These queries are especially important for social and Web 
applications, where errors, abbreviations, and inconsistencies are common.  As 
an example, we may want to find all the movies starring Schwarzenegger, while 
we don&#x2019;t know the exact spelling of his last name (despite his 
popularity in both the movie industry and politics :-)). As another example, we 
want to find all the Facebook users who have similar friends. To meet this type 
of needs, AsterixDB supports similarity queries using efficient indexes and 
algorithms.</p></div>
-<div class="section">
-<h2><a name="Data_Types_and_Similarity_Functions_.5BBack_to_TOC.5D"></a><a 
name="DataTypesAndSimilarityFunctions" 
id="DataTypesAndSimilarityFunctions">Data Types and Similarity Functions</a> 
<font size="4"><a href="#toc">[Back to TOC]</a></font></h2>
-<p>AsterixDB supports <a class="externalLink" 
href="http://en.wikipedia.org/wiki/Levenshtein_distance";>edit distance</a> (on 
strings) and <a class="externalLink" 
href="http://en.wikipedia.org/wiki/Jaccard_index";>Jaccard</a> (on sets).  For 
instance, in our <a 
href="primer.html#ADM:_Modeling_Semistructed_Data_in_AsterixDB">TinySocial</a> 
example, the <tt>friend-ids</tt> of a Facebook user forms a set of friends, and 
we can define a similarity between the sets of friends of two users. We can 
also convert a string to a set of grams of a length &#x201c;n&#x201d; (called 
&#x201c;n-grams&#x201d;) and define the Jaccard similarity between the two gram 
sets of the two strings. Formally, the &#x201c;n-grams&#x201d; of a string are 
its substrings of length &#x201c;n&#x201d;. For instance, the 3-grams of the 
string <tt>schwarzenegger</tt> are <tt>sch</tt>, <tt>chw</tt>, <tt>hwa</tt>, 
&#x2026;, <tt>ger</tt>.</p>
-<p>AsterixDB provides <a 
href="functions.html#Tokenizing_Functions">tokenization functions</a> to 
convert strings to sets, and the <a 
href="functions.html#Similarity_Functions">similarity functions</a>.</p></div>
-<div class="section">
-<h2><a name="Similarity_Selection_Queries_.5BBack_to_TOC.5D"></a><a 
name="SimilaritySelectionQueries" id="SimilaritySelectionQueries">Similarity 
Selection Queries</a> <font size="4"><a href="#toc">[Back to 
TOC]</a></font></h2>
-<p>The following query asks for all the Facebook users whose name is similar 
to <tt>Suzanna Tilson</tt>, i.e., their edit distance is at most 2.</p>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    for $user in dataset('FacebookUsers')
-    let $ed := edit-distance($user.name, &quot;Suzanna Tilson&quot;)
-    where $ed &lt;= 2
-    return $user
-</pre></div></div>
-
-<p>The following query asks for all the Facebook users whose set of friend ids 
is similar to <tt>[1,5,9,10]</tt>, i.e., their Jaccard similarity is at least 
0.6.</p>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    for $user in dataset('FacebookUsers')
-    let $sim := similarity-jaccard($user.friend-ids, [1,5,9,10])
-    where $sim &gt;= 0.6f
-    return $user
-</pre></div></div>
-
-<p>AsterixDB allows a user to use a similarity operator <tt>~=</tt> to express 
a condition by defining the similarity function and threshold using 
&#x201c;set&#x201d; statements earlier. For instance, the above query can be 
equivalently written as:</p>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    set simfunction &quot;jaccard&quot;;
-    set simthreshold &quot;0.6f&quot;;
-
-    for $user in dataset('FacebookUsers')
-    where $user.friend-ids ~= [1,5,9,10]
-    return $user
-</pre></div></div>
-
-<p>In this query, we first declare Jaccard as the similarity function using 
<tt>simfunction</tt> and then specify the threshold <tt>0.6f</tt> using 
<tt>simthreshold</tt>.</p></div>
-<div class="section">
-<h2><a name="Similarity_Join_Queries_.5BBack_to_TOC.5D"></a><a 
name="SimilarityJoinQueries" id="SimilarityJoinQueries">Similarity Join 
Queries</a> <font size="4"><a href="#toc">[Back to TOC]</a></font></h2>
-<p>AsterixDB supports fuzzy joins between two sets. The following <a 
href="primer.html#Query_5_-_Fuzzy_Join">query</a> finds, for each Facebook 
user, all Twitter users with names similar to their name based on the edit 
distance.</p>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    set simfunction &quot;edit-distance&quot;;
-    set simthreshold &quot;3&quot;;
-
-    for $fbu in dataset FacebookUsers
-    return {
-        &quot;id&quot;: $fbu.id,
-        &quot;name&quot;: $fbu.name,
-        &quot;similar-users&quot;: for $t in dataset TweetMessages
-                            let $tu := $t.user
-                            where $tu.name ~= $fbu.name
-                            return {
-                            &quot;twitter-screenname&quot;: $tu.screen-name,
-                            &quot;twitter-name&quot;: $tu.name
-                            }
-    };
-</pre></div></div>
-</div>
-<div class="section">
-<h2><a 
name="Using_Indexes_to_Support_Similarity_Queries_.5BBack_to_TOC.5D"></a><a 
name="UsingIndexesToSupportSimilarityQueries" 
id="UsingIndexesToSupportSimilarityQueries">Using Indexes to Support Similarity 
Queries</a> <font size="4"><a href="#toc">[Back to TOC]</a></font></h2>
-<p>AsterixDB uses two types of indexes to support similarity queries, namely 
&#x201c;ngram index&#x201d; and &#x201c;keyword index&#x201d;.</p>
-<div class="section">
-<h3><a name="NGram_Index"></a>NGram Index</h3>
-<p>An &#x201c;ngram index&#x201d; is constructed on a set of strings.  We 
generate n-grams for each string, and build an inverted list for each n-gram 
that includes the ids of the strings with this gram.  A similarity query can be 
answered efficiently by accessing the inverted lists of the grams in the query 
and counting the number of occurrences of the string ids on these inverted 
lists.  The similar idea can be used to answer queries with Jaccard similarity. 
 A detailed description of these techniques is available at this <a 
class="externalLink" 
href="http://www.ics.uci.edu/~chenli/pub/icde2009-memreducer.pdf";>paper</a>.</p>
-<p>For instance, the following DDL statements create an ngram index on the 
<tt>FacebookUsers.name</tt> attribute using an inverted index of 3-grams.</p>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    create index fbUserIdx on FacebookUsers(name) type ngram(3);
-</pre></div></div>
-
-<p>The number &#x201c;3&#x201d; in &#x201c;ngram(3)&#x201d; is the length 
&#x201c;n&#x201d; in the grams. This index can be used to optimize similarity 
queries on this attribute using <a 
href="functions.html#edit-distance">edit-distance</a>, <a 
href="functions.html#edit-distance-check">edit-distance-check</a>, <a 
href="functions.html#similarity-jaccard">similarity-jaccard</a>, or <a 
href="functions.html#similarity-jaccard-check">similarity-jaccard-check</a> 
queries on this attribute where the similarity is defined on sets of 3-grams.  
This index can also be used to optimize queries with the &#x201c;<a 
href="(functions.html#contains">contains()</a>&#x201d; predicate (i.e., 
substring matching) since it can be also be solved by counting on the inverted 
lists of the grams in the query string.</p>
-<div class="section">
-<h4><a name="NGram_Index_usage_case_-_edit-distance"></a>NGram Index usage 
case - <a href="functions.html#edit-distance">edit-distance</a></h4>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    for $user in dataset('FacebookUsers')
-    let $ed := edit-distance($user.name, &quot;Suzanna Tilson&quot;)
-    where $ed &lt;= 2
-    return $user
-</pre></div></div>
-</div>
-<div class="section">
-<h4><a name="NGram_Index_usage_case_-_edit-distance-check"></a>NGram Index 
usage case - <a 
href="functions.html#edit-distance-check">edit-distance-check</a></h4>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    for $user in dataset('FacebookUsers')
-    let $ed := edit-distance-check($user.name, &quot;Suzanna Tilson&quot;, 2)
-    where $ed[0]
-    return $ed[1]
-</pre></div></div>
-</div>
-<div class="section">
-<h4><a name="NGram_Index_usage_case_-_similarity-jaccard"></a>NGram Index 
usage case - <a 
href="functions.html#similarity-jaccard">similarity-jaccard</a></h4>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    for $user in dataset('FacebookUsers')
-    let $sim := similarity-jaccard($user.friend-ids, [1,5,9,10])
-    where $sim &gt;= 0.6f
-    return $user
-</pre></div></div>
-</div>
-<div class="section">
-<h4><a name="NGram_Index_usage_case_-_similarity-jaccard-check"></a>NGram 
Index usage case - <a 
href="functions.html#similarity-jaccard-check">similarity-jaccard-check</a></h4>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    for $user in dataset('FacebookUsers')
-    let $sim := similarity-jaccard-check($user.friend-ids, [1,5,9,10], 0.6f)
-    where $sim[0]
-    return $user
-</pre></div></div>
-</div>
-<div class="section">
-<h4><a name="NGram_Index_usage_case_-_contains.28.29"></a>NGram Index usage 
case - <a href="(functions.html#contains">contains()</a></h4>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    for $i in dataset('FacebookMessages')
-    where contains($i.message, &quot;phone&quot;)
-    return {&quot;mid&quot;: $i.message-id, &quot;message&quot;: $i.message}
-</pre></div></div>
-</div></div>
-<div class="section">
-<h3><a name="Keyword_Index"></a>Keyword Index</h3>
-<p>A &#x201c;keyword index&#x201d; is constructed on a set of strings or sets 
(e.g., OrderedList, UnorderedList). Instead of generating grams as in an ngram 
index, we generate tokens (e.g., words) and for each token, construct an 
inverted list that includes the ids of the objects with this token.  The 
following two examples show how to create keyword index on two different 
types:</p>
-<div class="section">
-<h4><a name="Keyword_Index_on_String_Type"></a>Keyword Index on String 
Type</h4>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    drop index FacebookMessages.fbMessageIdx if exists;
-    create index fbMessageIdx on FacebookMessages(message) type keyword;
-
-    for $o in dataset('FacebookMessages')
-    let $jacc := similarity-jaccard-check(word-tokens($o.message), 
word-tokens(&quot;love like ccast&quot;), 0.2f)
-    where $jacc[0]
-    return $o
-</pre></div></div>
-</div>
-<div class="section">
-<h4><a name="Keyword_Index_on_UnorderedList_Type"></a>Keyword Index on 
UnorderedList Type</h4>
-
-<div>
-<div>
-<pre class="source">    use dataverse TinySocial;
-
-    create index fbUserIdx_fids on FacebookUsers(friend-ids) type keyword;
-
-    for $c in dataset('FacebookUsers')
-    let $jacc := similarity-jaccard-check($c.friend-ids, {{3,10}}, 0.5f)
-    where $jacc[0]
-    return $c
-</pre></div></div>
-
-<p>As shown above, keyword index can be used to optimize queries with 
token-based similarity predicates, including <a 
href="functions.html#similarity-jaccard">similarity-jaccard</a> and <a 
href="functions.html#similarity-jaccard-check">similarity-jaccard-check</a>.</p></div></div></div>
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+<!DOCTYPE html>
+<!--
+ | Generated by Apache Maven Doxia Site Renderer 1.8.1 from 
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+    <title>AsterixDB &#x2013; Installation using Amazon Web Services</title>
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src="images/asterixlogo.png"  alt="AsterixDB"/></a></div>
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+        <ul class="breadcrumb">
+        <li id="publishDate">Last Published: 2018-10-12</li>
+      <li id="projectVersion" class="pull-right">Version: 0.9.4</li>
+      <li class="pull-right"><a href="index.html" title="Documentation 
Home">Documentation Home</a></li>
+        </ul>
+      </div>
+      <div class="row-fluid">
+        <div id="leftColumn" class="span2">
+          <div class="well sidebar-nav">
+    <ul class="nav nav-list">
+      <li class="nav-header">Get Started - Installation</li>
+    <li><a href="ncservice.html" title="Option 1: using NCService"><span 
class="none"></span>Option 1: using NCService</a></li>
+    <li><a href="ansible.html" title="Option 2: using Ansible"><span 
class="none"></span>Option 2: using Ansible</a></li>
+    <li class="active"><a href="#"><span class="none"></span>Option 3: using 
Amazon Web Services</a></li>
+      <li class="nav-header">AsterixDB Primer</li>
+    <li><a href="sqlpp/primer-sqlpp.html" title="Using SQL++"><span 
class="none"></span>Using SQL++</a></li>
+      <li class="nav-header">Data Model</li>
+    <li><a href="datamodel.html" title="The Asterix Data Model"><span 
class="none"></span>The Asterix Data Model</a></li>
+      <li class="nav-header">Queries</li>
+    <li><a href="sqlpp/manual.html" title="The SQL++ Query Language"><span 
class="none"></span>The SQL++ Query Language</a></li>
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+      <li class="nav-header">API/SDK</li>
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API</a></li>
+    <li><a href="csv.html" title="CSV Output"><span class="none"></span>CSV 
Output</a></li>
+      <li class="nav-header">Advanced Features</li>
+    <li><a href="aql/externaldata.html" title="Accessing External Data"><span 
class="none"></span>Accessing External Data</a></li>
+    <li><a href="feeds.html" title="Data Ingestion with Feeds"><span 
class="none"></span>Data Ingestion with Feeds</a></li>
+    <li><a href="udf.html" title="User Defined Functions"><span 
class="none"></span>User Defined Functions</a></li>
+    <li><a href="sqlpp/filters.html" title="Filter-Based LSM Index 
Acceleration"><span class="none"></span>Filter-Based LSM Index 
Acceleration</a></li>
+    <li><a href="sqlpp/fulltext.html" title="Support of Full-text 
Queries"><span class="none"></span>Support of Full-text Queries</a></li>
+    <li><a href="sqlpp/similarity.html" title="Support of Similarity 
Queries"><span class="none"></span>Support of Similarity Queries</a></li>
+      <li class="nav-header">Deprecated</li>
+    <li><a href="aql/primer.html" title="AsterixDB Primer: Using AQL"><span 
class="none"></span>AsterixDB Primer: Using AQL</a></li>
+    <li><a href="aql/manual.html" title="Queries: The Asterix Query Language 
(AQL)"><span class="none"></span>Queries: The Asterix Query Language 
(AQL)</a></li>
+    <li><a href="aql/builtins.html" title="Queries: Builtin Functions 
(AQL)"><span class="none"></span>Queries: Builtin Functions (AQL)</a></li>
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+<!--
+ ! Licensed to the Apache Software Foundation (ASF) under one
+ ! or more contributor license agreements.  See the NOTICE file
+ ! distributed with this work for additional information
+ ! regarding copyright ownership.  The ASF licenses this file
+ ! to you under the Apache License, Version 2.0 (the
+ ! "License"); you may not use this file except in compliance
+ ! with the License.  You may obtain a copy of the License at
+ !
+ !   http://www.apache.org/licenses/LICENSE-2.0
+ !
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+ ! software distributed under the License is distributed on an
+ ! "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ ! KIND, either express or implied.  See the License for the
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+ ! under the License.
+ !-->
+<h1>Installation using Amazon Web Services</h1>
+<div class="section">
+<h2><a name="Table_of_Contents"></a><a name="atoc" id="#toc">Table of 
Contents</a></h2>
+<ul>
+
+<li><a href="#Introduction">Introduction</a></li>
+<li><a href="#Prerequisites">Prerequisites</a></li>
+<li><a href="#config">Cluster Configuration</a></li>
+<li><a href="#lifecycle">Cluster Lifecycle Management</a></li>
+</ul><!--
+ ! Licensed to the Apache Software Foundation (ASF) under one
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+ ! "License"); you may not use this file except in compliance
+ ! with the License.  You may obtain a copy of the License at
+ !
+ !   http://www.apache.org/licenses/LICENSE-2.0
+ !
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+ ! software distributed under the License is distributed on an
+ ! "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ ! KIND, either express or implied.  See the License for the
+ ! specific language governing permissions and limitations
+ ! under the License.
+ !-->
+</div>
+<div class="section">
+<h2><a name="Introduction" id="Introduction">Introduction</a></h2>
+<p>Note that you can always manually launch a number of Amazon Web Services 
EC2 instances and then run the Ansible cluster installation scripts as 
described <a href="ansible.html">here</a> separately to manage the lifecycle of 
an AsterixDB cluster on those EC2 instances.</p>
+<p>However, via this installation option, we provide a combo solution for 
automating both AWS EC2 and AsterixDB, where you can run only one script to 
deploy, start, stop, and terminate an AsterixDB cluster on AWS.</p></div>
+<div class="section">
+<h2><a name="Prerequisites" id="Prerequisites">Prerequisites</a></h2>
+<ul>
+
+<li>
+
+<p>Supported operating systems for the client: <b>Linux</b> and 
<b>MacOS</b></p>
+</li>
+<li>
+
+<p>Supported operating systems for Amazon Web Services instances: 
<b>Linux</b></p>
+</li>
+<li>
+
+<p>Install pip on your client machine:</p>
+<p>CentOS</p>
+
+<div>
+<div>
+<pre class="source"> $ sudo yum install python-pip
+</pre></div></div>
+
+<p>Ubuntu</p>
+
+<div>
+<div>
+<pre class="source"> $ sudo apt-get install python-pip
+</pre></div></div>
+
+<p>macOS</p>
+
+<div>
+<div>
+<pre class="source"> $ brew install pip
+</pre></div></div>
+</li>
+<li>
+
+<p>Install Ansible, boto, and boto3 on your client machine:</p>
+
+<div>
+<div>
+<pre class="source"> $ pip install ansible
+ $ pip install boto
+ $ pip install boto3
+</pre></div></div>
+
+<p>Note that you might need <tt>sudo</tt> depending on your system 
configuration.</p>
+<p><b>Make sure that the version of Ansible is no less than 2.2.1.0</b>:</p>
+
+<div>
+<div>
+<pre class="source"> $ ansible --version
+ ansible 2.2.1.0
+</pre></div></div>
+
+<p><b>For users with macOS 10.11+</b>, please create a user-level Ansible 
configuration file at:</p>
+
+<div>
+<div>
+<pre class="source"> ~/.ansible.cfg
+</pre></div></div>
+
+<p>and add the following configuration:</p>
+
+<div>
+<div>
+<pre class="source"> [ssh_connection]
+ control_path = %(directory)s/%%C
+</pre></div></div>
+</li>
+<li>
+
+<p>Download the AsterixDB distribution package, unzip it, navigate to 
<tt>opt/aws/</tt></p>
+
+<div>
+<div>
+<pre class="source"> $ cd opt/aws
+</pre></div></div>
+
+<p>The following files and directories are in the directory 
<tt>opt/aws</tt>:</p>
+
+<div>
+<div>
+<pre class="source"> README  bin  conf  yaml
+</pre></div></div>
+
+<p><tt>bin</tt> contains scripts that start and terminate an AWS-based cluster 
instance, according to the configuration specified in files under 
<tt>conf</tt>, and <tt>yaml</tt> contains internal Ansible scripts that the 
shell scripts in <tt>bin</tt> use.</p>
+</li>
+<li>
+
+<p>Create an AWS account and an IAM user.</p>
+<p>Set up a security group that you&#x2019;d like to use for your AWS cluster. 
<b>The security group should at least allow all TCP connections from 
anywhere.</b> Provide the name of the security group as the value for the 
<tt>group</tt> field in <tt>conf/aws_settings.yml</tt>.</p>
+</li>
+<li>
+
+<p>Retrieve your AWS EC2 key pair name and use that as the <tt>keypair</tt> in 
<tt>conf/aws_settings.yml</tt>;</p>
+<p>retrieve your AWS IAM <tt>access key ID</tt> and use that as the 
<tt>access_key_id</tt> in <tt>conf/aws_settings.yml</tt>;</p>
+<p>retrieve your AWS IAM <tt>secret access key</tt> and use that as the 
<tt>secret_access_key</tt> in <tt>conf/aws_settings.yml</tt>.</p>
+<p>Note that you can only read or download <tt>access key ID</tt> and 
<tt>secret access key</tt> once from your AWS console. If you forget them, you 
have to create new keys and delete the old ones.</p>
+</li>
+<li>
+
+<p>Configure your ssh setting by editing <tt>~/.ssh/config</tt> and adding the 
following entry:</p>
+
+<div>
+<div>
+<pre class="source"> Host *.amazonaws.com
+      IdentityFile &lt;path_of_private_key&gt;
+</pre></div></div>
+
+<p>Note that &lt;path_of_private_key&gt; should be replaced by the path to the 
file that stores the private key for the key pair that you uploaded to AWS and 
used in <tt>conf/aws_settings</tt>. For example:</p>
+
+<div>
+<div>
+<pre class="source"> Host *.amazonaws.com
+      IdentityFile ~/.ssh/id_rsa
+</pre></div></div>
+</li>
+</ul></div>
+<div class="section">
+<h2><a name="Cluster_Configuration"></a><a name="config" id="config">Cluster 
Configuration</a></h2>
+<ul>
+
+<li>
+
+<p><b>AWS settings</b>.  Edit <tt>conf/instance_settings.yml</tt>. The meaning 
of each parameter is listed as follows:</p>
+
+<div>
+<div>
+<pre class="source"> # The OS image id for ec2 instances.
+ image: ami-76fa4116
+
+ # The data center region for ec2 instances.
+ region: us-west-2
+
+ # The tag for each ec2 machine. Use different tags for isolation.
+ tag: scale_test
+
+ # The name of a security group that appears in your AWS console.
+ group: default
+
+ # The name of a key pair that appears in your AWS console.
+ keypair: &lt;to be filled&gt;
+
+ # The AWS access key id for your IAM user.
+ access_key_id: &lt;to be filled&gt;
+
+ # The AWS secret key for your IAM user.
+ secret_access_key: &lt;to be filled&gt;
+
+ # The AWS instance type. A full list of available types are listed at:
+ # https://aws.amazon.com/ec2/instance-types/
+ instance_type: t2.micro
+
+ # The number of ec2 instances that construct a cluster.
+ count: 3
+
+ # The user name.
+ user: ec2-user
+
+ # Whether to reuse one slave machine to host the master process.
+ cc_on_nc: false
+</pre></div></div>
+
+<p><b>As described in <a href="#Prerequisites">prerequisites</a>, the 
following parameters must be customized:</b></p>
+
+<div>
+<div>
+<pre class="source"> # The tag for each ec2 machine. Use different tags for 
isolation.
+ tag: scale_test
+
+ # The name of a security group that appears in your AWS console.
+ group: default
+
+ # The name of a key pair that appears in your AWS console.
+ keypair: &lt;to be filled&gt;
+
+ # The AWS access key id for your IAM user.
+ access_key_id: &lt;to be filled&gt;
+
+ # The AWS secrety key for your IAM user.
+ secret_access_key: &lt;to be filled&gt;
+</pre></div></div>
+</li>
+<li>
+
+<p><b>Remote working directories</b>. Edit <tt>conf/instance_settings.yml</tt> 
to change the remote binary directory (the variable &#x201c;binarydir&#x201d;) 
when necessary. By default, the binary directory will be under the home 
directory (as the value of Ansible builtin variable ansible_env.HOME) of the 
ssh user account on each node.</p>
+</li>
+</ul></div>
+<div class="section">
+<h2><a name="Cluster_Lifecycle_Management"></a><a name="lifecycle" 
id="lifecycle">Cluster Lifecycle Management</a></h2>
+<ul>
+
+<li>
+
+<p>Allocate AWS EC2 nodes (the number of nodes is specified in 
<tt>conf/instance_settings.yml</tt>) and deploy the binary to all allocated EC2 
nodes:</p>
+
+<div>
+<div>
+<pre class="source"> bin/deploy.sh
+</pre></div></div>
+</li>
+<li>
+
+<p>Before starting the AsterixDB cluster, you the instance configuration file 
<tt>conf/instance/cc.conf</tt> can be modified with the exception of the IP 
addresses/DNS names which are are generated and cannot be changed. All 
available parameters and their usage can be found <a 
href="ncservice.html#Parameters">here</a>.</p>
+</li>
+<li>
+
+<p>Launch your AsterixDB cluster on EC2:</p>
+
+<div>
+<div>
+<pre class="source"> bin/start.sh
+</pre></div></div>
+
+<p>Now you can use the multi-node AsterixDB cluster on EC2 by by opening the 
master node listed in <tt>conf/instance/inventory</tt> at port <tt>19001</tt> 
(which can be customized in <tt>conf/instance/cc.conf</tt>) in your browser.</p>
+</li>
+<li>
+
+<p>If you want to stop the AWS-based AsterixDB cluster, run the following 
script:</p>
+
+<div>
+<div>
+<pre class="source"> bin/stop.sh
+</pre></div></div>
+
+<p>Note that this only stops AsterixDB but does not stop the EC2 nodes.</p>
+</li>
+<li>
+
+<p>If you want to terminate the EC2 nodes that run the AsterixDB cluster, run 
the following script:</p>
+
+<div>
+<div>
+<pre class="source"> bin/terminate.sh
+</pre></div></div>
+
+<p><b>Note that it will destroy everything in the AsterixDB cluster you 
installed and terminate all EC2 nodes for the cluster.</b></p>
+</li>
+</ul></div>
+        </div>
+      </div>
+    </div>
+    <hr/>
+    <footer>
+      <div class="container-fluid">
+        <div class="row-fluid">
+<div class="row-fluid">Apache AsterixDB, AsterixDB, Apache, the Apache
+        feather logo, and the Apache AsterixDB project logo are either
+        registered trademarks or trademarks of The Apache Software
+        Foundation in the United States and other countries.
+        All other marks mentioned may be trademarks or registered
+        trademarks of their respective owners.
+      </div>
+        </div>
+      </div>
+    </footer>
+  </body>
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