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Posted to commits@asterixdb.apache.org by im...@apache.org on 2018/10/12 21:47:57 UTC
[03/17] asterixdb-site git commit: fix missing docs and url
http://git-wip-us.apache.org/repos/asf/asterixdb-site/blob/b2f1d3e6/docs/0.9.4/sqlpp/filters.html
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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 Services"><span class="none"></span>Option 3: using Amazon Web Services</a></li>
+ <li class="nav-header">AsterixDB Primer</li>
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+ <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 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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+<!--
+ ! 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
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+ ! "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.
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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’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 – 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 – 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’s index types. To enable the use of filters, the user must specify the filter’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’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’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’ 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>
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http://git-wip-us.apache.org/repos/asf/asterixdb-site/blob/b2f1d3e6/docs/0.9.4/sqlpp/fulltext.html
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+<!DOCTYPE html>
+<!--
+ | Generated by Apache Maven Doxia Site Renderer 1.8.1 from src/site/markdown/sqlpp/fulltext.md at 2018-10-12
+ | Rendered using Apache Maven Fluido Skin 1.7
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+ <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><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>
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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="../sqlpp/filters.html" title="Filter-Based LSM Index Acceleration"><span class="none"></span>Filter-Based LSM Index Acceleration</a></li>
+ <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 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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+ <div id="bodyColumn" class="span10" >
+<!--
+ ! Licensed to the Apache Software Foundation (ASF) under one
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+ ! to you under the Apache License, Version 2.0 (the
+ ! "License"); you may not use this file except in compliance
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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 “rain” correctly returns a document when it contains “rain” as a word. However, a substring-matching query returns a document whenever it contains “rain” as a substring, for instance, a document with “brain” or “training” 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 “voice” as a word. Please note that an FTS search is case-insensitive. Thus, “Voice” or “voice” 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, "voice", {"mode":"any"})
+ return {"id": $msg.id}
+</pre></div></div>
+
+<p>The DDL and DML of TinySocial can be found in <a href="primer.html#ADM:_Modeling_Semistructed_Data_in_AsterixDB">ADM: Modeling Semistructed Data in AsterixDB</a>.</p>
+<p>The same query can be also expressed in the SQL++.</p>
+
+<div>
+<div>
+<pre class="source"> use TinySocial;
+
+ select element {"id":msg.id}
+ from TweetMessages as msg
+ where TinySocial.ftcontains(msg.`message-text`, "voice", {"mode":"any"})
+</pre></div></div>
+
+<p>The <tt>Expression1</tt> is an expression that should be evaluable as a string at runtime as in the above example where <tt>$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($msg.message-text, "sound")
+ ... where ftcontains($msg.message-text, "sound", {"mode":"any"})
+ ... where ftcontains($msg.message-text, ["sound", "system"], {"mode":"any"})
+ ... where ftcontains($msg.message-text, {{"speed", "stand", "customization"}}, {"mode":"all"})
+ ... where ftcontains($msg.message-text, let $keyword_list := ["voice", "system"] return $keyword_list, {"mode":"all"})
+ ... where ftcontains($msg.message-text, $keyword_list, {"mode":"any"})
+</pre></div></div>
+
+<p>In the last example above, <tt>$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>“any”</tt> or <tt>“all”</tt>. The default value for <tt>mode</tt> is <tt>“all”</tt>. If one specifies <tt>“any”</tt>, a disjunctive search will be conducted. For example, the following query will find documents whose <tt>message-text</tt> field contains “sound” or “system”, so a document will be returned if it contains eit
her “sound”, “system”, or both of the keywords.</p>
+
+<div>
+<div>
+<pre class="source"> ... where ftcontains($msg.message-text, ["sound", "system"], {"mode":"any"})
+</pre></div></div>
+
+<p>The other option parameter,<tt>“all”</tt>, specifies a conjunctive search. The following examples will find the documents whose <tt>message-text</tt> field contains both “sound” and “system”. If a document contains only “sound” or “system” but not both, it will not be returned.</p>
+
+<div>
+<div>
+<pre class="source"> ... where ftcontains($msg.message-text, ["sound", "system"], {"mode":"all"})
+ ... where ftcontains($msg.message-text, ["sound", "system"])
+</pre></div></div>
+
+<p>Currently AsterixDB doesn’t (yet) support phrase searches, so the following query will not work.</p>
+
+<div>
+<div>
+<pre class="source"> ... where ftcontains($msg.message-text, "sound system", {"mode":"any"})
+</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>$msg.message-text</tt> contains both “sound” and “system”, but the order and adjacency of “sound” and “system” are not checked, unlike in a phrase search. As a result, the query below would also return documents with “sound system can be installed.”, “system sound is perfect.”, or “sound is not clear. You may need to install a new system.”</p>
+
+<div>
+<div>
+<pre class="source"> ... where ftcontains($msg.message-text, ["sound", "system"], {"mode":"all"})
+ ... where ftcontains($msg.message-text, ["sound", "system"])
+</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>
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