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+    <li><a href="../udf.html" title="User Defined Functions"><span 
class="none"></span>User Defined Functions</a></li>
+    <li class="active"><a href="#"><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>
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Language (AQL)"><span class="none"></span>Queries: The Asterix Query Language 
(AQL)</a></li>
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(AQL)"><span class="none"></span>Queries: Builtin Functions (AQL)</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/>
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+    <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 
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+      <li class="nav-header">Advanced Features</li>
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class="none"></span>User Defined Functions</a></li>
+    <li><a href="../sqlpp/filters.html" title="Filter-Based LSM Index 
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+    <li class="active"><a href="#"><span class="none"></span>Support of 
Full-text Queries</a></li>
+    <li><a href="../sqlpp/similarity.html" title="Support of Similarity 
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+      <li class="nav-header">Deprecated</li>
+    <li><a href="../aql/primer.html" title="AsterixDB Primer: Using AQL"><span 
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+<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/>
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+        registered trademarks or trademarks of The Apache Software
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+        trademarks of their respective owners.
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