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Term frequency is often used as part of a larger text processing pipeline, which may include operations such as stemming, stop word removal and topic modelling.</p> +<p><a class="anchor" id="function_syntax"></a></p><dl class="section user"><dt>Function Syntax</dt><dd></dd></dl> +<pre class="syntax"> + term_frequency(input_table, + doc_id_col, + word_col, + output_table, + compute_vocab) +</pre><p><b>Arguments:</b> </p><dl class="arglist"> +<dt>input_table </dt> +<dd><p class="startdd">TEXT. The name of the table containing the documents, with one document per row. Each row is in the form <doc_id, word_vector> where <code>doc_id</code> is an id unique to each document, and <code>word_vector</code> is a text array containing the words in the document. The <code>word_vector</code> should contain multiple entries of a word if the document contains multiple occurrence of that word. </p> +<p class="enddd"></p> +</dd> +<dt>doc_id_col </dt> +<dd><p class="startdd">TEXT. The name of the column containing the document id. </p> +<p class="enddd"></p> +</dd> +<dt>word_col </dt> +<dd><p class="startdd">TEXT. The name of the column containing the vector of words/terms in the document. This column should be of type that can be cast to TEXT[].</p> +<p class="enddd"></p> +</dd> +<dt>output_table </dt> +<dd><p class="startdd">TEXT. The name of the table to store the term frequency output. The output table contains the following columns:</p><ul> +<li><code>doc_id_col:</code> This the document id column (name will be same as the one provided as input).</li> +<li><code>word:</code> Word/term present in a document. Depending on the value of <code>compute_vocab</code> below, this is either the original word as it appears in <code>word_col</code>, or an id representing the word. Note that word id's start from 0 not 1.</li> +<li><code>count:</code> The number of times this word is found in the document. </li> +</ul> +<p class="enddd"></p> +</dd> +<dt>compute_vocab </dt> +<dd>BOOLEAN. (Optional, Default=FALSE) Flag to indicate if a vocabulary table is to be created. If TRUE, an additional output table is created containing the vocabulary of all words, with an id assigned to each word in alphabetical order. The table is called <em>output_table</em>_vocabulary (i.e., suffix added to the <em>output_table</em> name) and contains the following columns:<ul> +<li><code>wordid:</code> An id for each word in alphabetical order.</li> +<li><code>word:</code> The word/term corresponding to the id. </li> +</ul> +</dd> +</dl> +<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl> +<ol type="1"> +<li>First we create a document table with one document per row: <pre class="example"> +DROP TABLE IF EXISTS documents; +CREATE TABLE documents(docid INT4, contents TEXT); +INSERT INTO documents VALUES +(0, 'I like to eat broccoli and bananas. I ate a banana and spinach smoothie for breakfast.'), +(1, 'Chinchillas and kittens are cute.'), +(2, 'My sister adopted two kittens yesterday.'), +(3, 'Look at this cute hamster munching on a piece of broccoli.'); +</pre> You can apply stemming, stop word removal and tokenization at this point in order to prepare the documents for text processing. Depending upon your database version, various tools are available. Databases based on more recent versions of PostgreSQL may do something like: <pre class="example"> +SELECT tsvector_to_array(to_tsvector('english',contents)) from documents; +</pre> <pre class="result"> + tsvector_to_array ++---------------------------------------------------------- + {ate,banana,breakfast,broccoli,eat,like,smoothi,spinach} + {chinchilla,cute,kitten} + {adopt,kitten,sister,two,yesterday} + {broccoli,cute,hamster,look,munch,piec} +(4 rows) +</pre> In this example, we assume a database based on an older version of PostgreSQL and just perform basic punctuation removal and tokenization. The array of words is added as a new column to the documents table: <pre class="example"> +ALTER TABLE documents ADD COLUMN words TEXT[]; +UPDATE documents SET words = + regexp_split_to_array(lower( + regexp_replace(contents, E'[,.;\']','', 'g') + ), E'[\\s+]'); +\x on +SELECT * FROM documents ORDER BY docid; +</pre> <pre class="result"> +-[ RECORD 1 ]------------------------------------------------------------------------------------ +docid | 0 +contents | I like to eat broccoli and bananas. I ate a banana and spinach smoothie for breakfast. +words | {i,like,to,eat,broccoli,and,bananas,i,ate,a,banana,and,spinach,smoothie,for,breakfast} +-[ RECORD 2 ]------------------------------------------------------------------------------------ +docid | 1 +contents | Chinchillas and kittens are cute. +words | {chinchillas,and,kittens,are,cute} +-[ RECORD 3 ]------------------------------------------------------------------------------------ +docid | 2 +contents | My sister adopted two kittens yesterday. +words | {my,sister,adopted,two,kittens,yesterday} +-[ RECORD 4 ]------------------------------------------------------------------------------------ +docid | 3 +contents | Look at this cute hamster munching on a piece of broccoli. +words | {look,at,this,cute,hamster,munching,on,a,piece,of,broccoli} +</pre></li> +<li>Compute the frequency of each word in each document: <pre class="example"> +DROP TABLE IF EXISTS documents_tf, documents_tf_vocabulary; +SELECT madlib.term_frequency('documents', -- input table + 'docid', -- document id column + 'words', -- vector of words in document + 'documents_tf' -- output table + ); +\x off +SELECT * FROM documents_tf ORDER BY docid; +</pre> <pre class="result"> + docid | word | count +-------+-------------+------- + 0 | a | 1 + 0 | breakfast | 1 + 0 | banana | 1 + 0 | and | 2 + 0 | eat | 1 + 0 | smoothie | 1 + 0 | to | 1 + 0 | like | 1 + 0 | broccoli | 1 + 0 | bananas | 1 + 0 | spinach | 1 + 0 | i | 2 + 0 | ate | 1 + 0 | for | 1 + 1 | are | 1 + 1 | cute | 1 + 1 | kittens | 1 + 1 | chinchillas | 1 + 1 | and | 1 + 2 | two | 1 + 2 | yesterday | 1 + 2 | kittens | 1 + 2 | sister | 1 + 2 | my | 1 + 2 | adopted | 1 + 3 | this | 1 + 3 | at | 1 + 3 | a | 1 + 3 | broccoli | 1 + 3 | of | 1 + 3 | look | 1 + 3 | hamster | 1 + 3 | on | 1 + 3 | piece | 1 + 3 | cute | 1 + 3 | munching | 1 +(36 rows) +</pre></li> +<li>Next we create a vocabulary of the words and store a wordid in the output table instead of the actual word: <pre class="example"> +DROP TABLE IF EXISTS documents_tf, documents_tf_vocabulary; +SELECT madlib.term_frequency('documents', -- input table + 'docid', -- document id column + 'words', -- vector of words in document + 'documents_tf',-- output table + TRUE + ); +SELECT * FROM documents_tf ORDER BY docid; +</pre> <pre class="result"> + docid | wordid | count +-------+--------+------- + 0 | 17 | 1 + 0 | 9 | 1 + 0 | 25 | 1 + 0 | 12 | 1 + 0 | 13 | 1 + 0 | 15 | 2 + 0 | 0 | 1 + 0 | 2 | 2 + 0 | 28 | 1 + 0 | 5 | 1 + 0 | 6 | 1 + 0 | 7 | 1 + 0 | 8 | 1 + 0 | 26 | 1 + 1 | 16 | 1 + 1 | 11 | 1 + 1 | 10 | 1 + 1 | 2 | 1 + 1 | 3 | 1 + 2 | 30 | 1 + 2 | 1 | 1 + 2 | 16 | 1 + 2 | 20 | 1 + 2 | 24 | 1 + 2 | 29 | 1 + 3 | 4 | 1 + 3 | 21 | 1 + 3 | 22 | 1 + 3 | 23 | 1 + 3 | 0 | 1 + 3 | 11 | 1 + 3 | 9 | 1 + 3 | 27 | 1 + 3 | 14 | 1 + 3 | 18 | 1 + 3 | 19 | 1 +(36 rows) +</pre> Note above that wordid's start at 0 not 1. The vocabulary table maps wordid to the actual word: <pre class="example"> +SELECT * FROM documents_tf_vocabulary ORDER BY wordid; +</pre> <pre class="result"> + wordid | word +--------+------------- + 0 | a + 1 | adopted + 2 | and + 3 | are + 4 | at + 5 | ate + 6 | banana + 7 | bananas + 8 | breakfast + 9 | broccoli + 10 | chinchillas + 11 | cute + 12 | eat + 13 | for + 14 | hamster + 15 | i + 16 | kittens + 17 | like + 18 | look + 19 | munching + 20 | my + 21 | of + 22 | on + 23 | piece + 24 | sister + 25 | smoothie + 26 | spinach + 27 | this + 28 | to + 29 | two + 30 | yesterday +(31 rows) +</pre></li> +</ol> +<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl> +<p>See <a class="el" href="text__utilities_8sql__in.html" title="SQL functions for carrying out routine text operations. ">text_utilities.sql_in</a> for the term frequency SQL function definition and <a class="el" href="porter__stemmer_8sql__in.html" title="implementation of porter stemmer operations in SQL ">porter_stemmer.sql_in</a> for the stemmer function. </p> +</div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="footer">Generated on Mon Oct 15 2018 11:24:30 for MADlib by + <a href="http://www.doxygen.org/index.html";> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.14 </li> + </ul> +</div> +</body> +</html>
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Name of the table containing the input data.</p> +<p class="enddd"></p> +</dd> +<dt>output_table </dt> +<dd><p class="startdd">Name of output table. A new INTEGER column on the right called 'split' will identify 1 for train set and 0 for test set, unless the 'separate_output_tables' parameter below is TRUE, in which case two output tables will be created using the 'output_table' name with the suffixes '_train' and '_test'. The output table contains all the columns present in the source table unless otherwise specified in the 'target_cols' parameter below. </p> +<p class="enddd"></p> +</dd> +<dt>train_proportion </dt> +<dd><p class="startdd">FLOAT8 in the range (0,1). Proportion of the dataset to include in the train split. If the 'grouping_col' parameter is specified below, each group will be sampled independently using the train proportion, i.e., in a stratified fashion.</p> +<p class="enddd"></p> +</dd> +<dt>test_proportion (optional) </dt> +<dd><p class="startdd">FLOAT8 in the range (0,1). Proportion of the dataset to include in the test split. Default is the complement to the train proportion (1-'train_proportion'). If the 'grouping_col' parameter is specified below, each group will be sampled independently using the train proportion, i.e., in a stratified fashion.</p> +<p class="enddd"></p> +</dd> +<dt>grouping_cols (optional) </dt> +<dd><p class="startdd">TEXT, default: NULL. A single column or a list of comma-separated columns that defines how to stratify. When this parameter is NULL, the train-test split is not stratified.</p> +<p class="enddd"></p> +</dd> +<dt>target_cols (optional) </dt> +<dd><p class="startdd">TEXT, default NULL. A comma-separated list of columns to appear in the 'output_table'. If NULL or '*', all columns from the 'source_table' will appear in the 'output_table'.</p> +<p class="enddd"><a class="anchor" id="note"></a></p><dl class="section note"><dt>Note</dt><dd>Do not include 'grouping_cols' in the parameter 'target_cols', because they are always included in the 'output_table'.</dd></dl> +</dd> +<dt>with_replacement (optional) </dt> +<dd><p class="startdd">BOOLEAN, default FALSE. Determines whether to sample with replacement or without replacement (default). With replacement means that it is possible that the same row may appear in the sample set more than once. Without replacement means a given row can be selected only once.</p> +<p class="enddd"></p> +</dd> +<dt>separate_output_tables (optional) </dt> +<dd>BOOLEAN, default FALSE. If TRUE, two output tables will be created using the 'output_table' name with the suffixes '_train' and '_test'. </dd> +</dl> +<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl> +<p>Please note that due to the random nature of sampling, your results may look different from those below.</p> +<ol type="1"> +<li>Create an input table: <pre class="syntax"> +DROP TABLE IF EXISTS test; +CREATE TABLE test( + id1 INTEGER, + id2 INTEGER, + gr1 INTEGER, + gr2 INTEGER +); +INSERT INTO test VALUES +(1,0,1,1), +(2,0,1,1), +(3,0,1,1), +(4,0,1,1), +(5,0,1,1), +(6,0,1,1), +(7,0,1,1), +(8,0,1,1), +(9,0,1,1), +(9,0,1,1), +(9,0,1,1), +(9,0,1,1), +(0,1,1,2), +(0,2,1,2), +(0,3,1,2), +(0,4,1,2), +(0,5,1,2), +(0,6,1,2), +(10,10,2,2), +(20,20,2,2), +(30,30,2,2), +(40,40,2,2), +(50,50,2,2), +(60,60,2,2), +(70,70,2,2); +</pre></li> +<li>Sample without replacement: <pre class="syntax"> +DROP TABLE IF EXISTS out; +SELECT madlib.train_test_split( + 'test', -- Source table + 'out', -- Output table + 0.5, -- Sample proportion + 0.5, -- Sample proportion + 'gr1,gr2', -- Strata definition + 'id1,id2', -- Columns to output + FALSE, -- Sample without replacement + FALSE); -- Do not separate output tables +SELECT * FROM out ORDER BY split,gr1,gr2,id1,id2; +</pre> <pre class="result"> + gr1 | gr2 | id1 | id2 | split +-----+-----+-----+-----+------- + 1 | 1 | 1 | 0 | 0 + 1 | 1 | 4 | 0 | 0 + 1 | 1 | 6 | 0 | 0 + 1 | 1 | 9 | 0 | 0 + 1 | 1 | 9 | 0 | 0 + 1 | 1 | 9 | 0 | 0 + 1 | 2 | 0 | 3 | 0 + 1 | 2 | 0 | 4 | 0 + 1 | 2 | 0 | 5 | 0 + 2 | 2 | 10 | 10 | 0 + 2 | 2 | 30 | 30 | 0 + 2 | 2 | 40 | 40 | 0 + 2 | 2 | 60 | 60 | 0 + 1 | 1 | 2 | 0 | 1 + 1 | 1 | 3 | 0 | 1 + 1 | 1 | 5 | 0 | 1 + 1 | 1 | 7 | 0 | 1 + 1 | 1 | 8 | 0 | 1 + 1 | 1 | 9 | 0 | 1 + 1 | 2 | 0 | 1 | 1 + 1 | 2 | 0 | 2 | 1 + 1 | 2 | 0 | 6 | 1 + 2 | 2 | 20 | 20 | 1 + 2 | 2 | 50 | 50 | 1 + 2 | 2 | 70 | 70 | 1 +(25 rows) +</pre></li> +<li>Sample with replacement and create separate train and test tables: <pre class="syntax"> +DROP TABLE IF EXISTS out_train, out_test; +SELECT madlib.train_test_split( + 'test', -- Source table + 'out', -- Output table + 0.5, -- train_proportion + NULL, -- Default = 1 - train_proportion = 0.5 + 'gr1,gr2', -- Strata definition + 'id1,id2', -- Columns to output + TRUE, -- Sample with replacement + TRUE); -- Separate output tables +SELECT * FROM out_train ORDER BY gr1,gr2,id1,id2; +</pre> <pre class="result"> + gr1 | gr2 | id1 | id2 +-----+-----+-----+----- + 1 | 1 | 1 | 0 + 1 | 1 | 2 | 0 + 1 | 1 | 4 | 0 + 1 | 1 | 7 | 0 + 1 | 1 | 8 | 0 + 1 | 1 | 9 | 0 + 1 | 2 | 0 | 4 + 1 | 2 | 0 | 5 + 1 | 2 | 0 | 6 + 2 | 2 | 40 | 40 + 2 | 2 | 50 | 50 + 2 | 2 | 50 | 50 +(12 rows) +</pre> <pre class="syntax"> +SELECT * FROM out_test ORDER BY gr1,gr2,id1,id2; +</pre> <pre class="result"> + gr1 | gr2 | id1 | id2 +--—+--—+--—+--— + 1 | 1 | 1 | 0 + 1 | 1 | 1 | 0 + 1 | 1 | 3 | 0 + 1 | 1 | 4 | 0 + 1 | 1 | 5 | 0 + 1 | 1 | 9 | 0 + 1 | 2 | 0 | 1 + 1 | 2 | 0 | 5 + 1 | 2 | 0 | 6 + 2 | 2 | 20 | 20 + 2 | 2 | 20 | 20 + 2 | 2 | 20 | 20 + 2 | 2 | 70 | 70 +(13 rows) +</pre> </li> +</ol> +</div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="footer">Generated on Mon Oct 15 2018 11:24:30 for MADlib by + <a href="http://www.doxygen.org/index.html";> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.14 </li> + </ul> +</div> +</body> +</html> http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__tree.html ---------------------------------------------------------------------- diff --git a/docs/v1.15.1/group__grp__tree.html b/docs/v1.15.1/group__grp__tree.html new file mode 100644 index 0000000..5ad4e9c --- /dev/null +++ b/docs/v1.15.1/group__grp__tree.html @@ -0,0 +1,149 @@ +<!-- HTML header for doxygen 1.8.4--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd";> +<html xmlns="http://www.w3.org/1999/xhtml";> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.14"/> +<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/> +<title>MADlib: Tree Methods</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ + $(document).ready(initResizable); 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\ No newline at end of file http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__utilities.html ---------------------------------------------------------------------- diff --git a/docs/v1.15.1/group__grp__utilities.html b/docs/v1.15.1/group__grp__utilities.html new file mode 100644 index 0000000..1052ff1 --- /dev/null +++ b/docs/v1.15.1/group__grp__utilities.html @@ -0,0 +1,184 @@ +<!-- HTML header for doxygen 1.8.4--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd";> +<html xmlns="http://www.w3.org/1999/xhtml";> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.14"/> +<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/> +<title>MADlib: Database Functions</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ + $(document).ready(initResizable); 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+/* @license-end */ +</script> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ +$(document).ready(function(){initNavTree('group__grp__utilities.html','');}); +/* @license-end */ +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="headertitle"> +<div class="title">Database Functions<div class="ingroups"><a class="el" href="group__grp__other__functions.html">Utilities</a></div></div> </div> +</div><!--header--> +<div class="contents"> +<div class="toc"><b>Contents</b> <ul> +<li> +<a href="#utilities">Utility Functions</a> </li> +<li> +<a href="#rel;ated">Related Topics</a> </li> +</ul> +</div><p>Database functions are a collection of lower level utilities to assist data scientists and others in using MADlib.</p> +<p><a class="anchor" id="utilities"></a></p><dl class="section user"><dt>Utility Functions</dt><dd></dd></dl> +<table class="output"> +<tr> +<th><a class="el" href="utilities_8sql__in.html#a9ce7ffa76acc8a0638e9bc48be99f36a" title="Return MADlib build information. ">version()</a> </th><td><p class="starttd">Return MADlib build information. </p> +<p class="endtd"></p> +</td></tr> +<tr> +<th><a class="el" href="utilities_8sql__in.html#a76cfdbed8a6782f37f50710d855434bd" title="Raise an exception if the given condition is not satisfied. ">assert()</a> </th><td><p class="starttd">Raise an exception if the given condition is not satisfied. </p> +<p class="endtd"></p> +</td></tr> +<tr> +<th><a class="el" href="utilities_8sql__in.html#aa90ffaebb43a5cbcb5c48281cbadd3b0" title="Check if a SQL statement raises an error. ">check_if_raises_error()</a> </th><td><p class="starttd">Check if a SQL statement raises an error. </p> +<p class="endtd"></p> +</td></tr> +<tr> +<th><a class="el" href="utilities_8sql__in.html#ac1f509bd94c6309b59eb4d479fb2a470" title="Check if a column exists in a table. ">check_if_col_exists()</a> </th><td><p class="starttd">Check if a column exists in a table. </p> +<p class="endtd"></p> +</td></tr> +<tr> +<th><a class="el" href="utilities_8sql__in.html#a48bed63cab78a5446fa4c122eaae3a40" title="Check if a floating-point number is NaN (not a number) ">isnan()</a> </th><td><p class="starttd">Check if a floating-point number is NaN (not a number) </p> +<p class="endtd"></p> +</td></tr> +<tr> +<th><a class="el" href="utilities_8sql__in.html#a56501b6f9fabe65d7a6a6beb70a0e000" title="Create the temporary schema if it does not exist yet. ">create_schema_pg_temp()</a> </th><td><p class="starttd">Create the temporary schema if it does not exist yet. </p> +<p class="endtd"></p> +</td></tr> +<tr> +<th><a class="el" href="utilities_8sql__in.html#ab194d6a1222672c314ea39b78aad8fd0" title="Create volatile noop function. ">noop()</a> </th><td><p class="starttd">Create volatile noop function. </p> +<p class="endtd"></p> +</td></tr> +<tr> +<th><a class="el" href="utilities_8sql__in.html#aab5ea2cd6201e38e24ca62b0fb91fb7a" title="Drop all tables matching pattern 'madlib_temp' in a given schema. ">cleanup_madlib_temp_tables()</a> </th><td><p class="starttd">Drop all tables matching pattern 'madlib_temp' in a given schema. </p> +<p class="endtd"></p> +</td></tr> +<tr> +<th><a class="el" href="utilities_8sql__in.html#a244f77f87f2612800b1683033a21dcca" title="Creates a new table with a subset of columns dropped from another source table. ">dropcols()</a> </th><td><p class="starttd">Create a new table with a subset of the columns dropped from a source table. </p> +<p class="endtd"></p> +</td></tr> +</table> +<p>Note: If the function <a class="el" href="utilities_8sql__in.html#aab5ea2cd6201e38e24ca62b0fb91fb7a" title="Drop all tables matching pattern 'madlib_temp' in a given schema. ">cleanup_madlib_temp_tables()</a> gives an Out-of-memory error, then the number of tables to be dropped is too high to execute in one transaction. In such a case, please follow the instructions provided with the error to execute the command in multiple transactions.</p> +<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl> +<p>File <a class="el" href="utilities_8sql__in.html" title="SQL functions for carrying out routine tasks. ">utilities.sql_in</a> documenting the SQL functions. </p> +</div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="footer">Generated on Mon Oct 15 2018 11:24:30 for MADlib by + <a href="http://www.doxygen.org/index.html";> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.14 </li> + </ul> +</div> +</body> +</html>
