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| <div id="projectname">MADlib |
|  <span id="projectnumber">0.7</span> <span style="font-size:10pt; font-style:italic"><a href="../latest/./group__grp__profile.html"> A newer version is available</a></span> |
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| <div class="title">Profile</div> </div> |
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| <dl class="user"><dt><b>About:</b></dt><dd>This module computes a "profile" of a table or view: a predefined set of aggregates to be run on each column of a table.</dd></dl> |
| <p>The following aggregates will be called on every integer column:</p> |
| <ul> |
| <li>min(), max(), <a class="el" href="linalg_8sql__in.html#a1aa37f73fb1cd8d7d106aa518dd8c0b4" title="Compute the average of vectors.">avg()</a></li> |
| <li>madlib.cmsketch_median()</li> |
| <li>madlib.cmsketch_depth_histogram()</li> |
| <li>madlib.cmsketch_width_histogram()</li> |
| </ul> |
| <p>And these on non-integer columns:</p> |
| <ul> |
| <li>madlib.fmsketch_dcount()</li> |
| <li>madlib.mfvsketch_quick_histogram()</li> |
| <li>madlib.mfvsketch_top_histogram()</li> |
| </ul> |
| <p>Because the input schema of the table or view is unknown, we need to synthesize SQL to suit. This is done either via the <code>profile</code> or <code>profile_full</code> user defined function.</p> |
| <dl class="user"><dt><b>Usage:</b></dt><dd></dd></dl> |
| <ul> |
| <li>Generate a basic profile information (subset of predefined aggregate functions) for all columns of the input table. <pre>SELECT * FROM <a class="el" href="profile_8sql__in.html#a22f0136bb3d7740262b13030a2925a80">profile</a>( '<em>input_table</em>');</pre></li> |
| <li>Generate a full profile information (all predefined aggregate functions) for all columns of the input table. <pre>SELECT * FROM <a class="el" href="profile_8sql__in.html#a879e445d74412b093d411ed9a7180a1e">profile_full</a>( '<em>input_table</em>', <em>buckets</em>);</pre></li> |
| </ul> |
| <dl class="user"><dt><b>Examples:</b></dt><dd></dd></dl> |
| <ul> |
| <li>For basic profile run: <div class="fragment"><pre class="fragment"> |
| sql> SELECT * FROM profile( 'pg_catalog.pg_tables'); |
| |
| schema_name | table_name | column_name | function | value |
| -------------+------------+-------------+-------------------+------- |
| pg_catalog | pg_tables | * | COUNT() | 105 |
| pg_catalog | pg_tables | schemaname | fmsketch_dcount() | 6 |
| pg_catalog | pg_tables | tablename | fmsketch_dcount() | 104 |
| pg_catalog | pg_tables | tableowner | fmsketch_dcount() | 2 |
| pg_catalog | pg_tables | tablespace | fmsketch_dcount() | 1 |
| pg_catalog | pg_tables | hasindexes | fmsketch_dcount() | 2 |
| pg_catalog | pg_tables | hasrules | fmsketch_dcount() | 1 |
| pg_catalog | pg_tables | hastriggers | fmsketch_dcount() | 2 |
| (8 rows) |
| </pre></div></li> |
| </ul> |
| <ul> |
| <li>For full profile run: <div class="fragment"><pre class="fragment"> |
| sql> SELECT * FROM profile_full( 'pg_catalog.pg_tables', 5); |
| |
| schema_name | table_name | column_name | function | value |
| -------------+------------+-------------+-------------------------------------------------+---------------------------------------------------------------------------------------------------- |
| pg_catalog | pg_tables | * | COUNT() | 105 |
| pg_catalog | pg_tables | schemaname | fmsketch_dcount() | 6 |
| pg_catalog | pg_tables | schemaname | array_collapse(mfvsketch_quick_histogram((),5)) | [0:4]={pg_catalog:68,public:19,information_schema:7,gp_toolkit:5,maddy:5} |
| pg_catalog | pg_tables | schemaname | array_collapse(mfvsketch_top_histogram((),5)) | [0:4]={pg_catalog:68,public:19,information_schema:7,gp_toolkit:5,maddy:5} |
| pg_catalog | pg_tables | tablename | fmsketch_dcount() | 104 |
| pg_catalog | pg_tables | tablename | array_collapse(mfvsketch_quick_histogram((),5)) | [0:4]={migrationhistory:2,pg_statistic:1,sql_features:1,sql_implementation_info:1,sql_languages:1} |
| pg_catalog | pg_tables | tablename | array_collapse(mfvsketch_top_histogram((),5)) | [0:4]={migrationhistory:2,pg_statistic:1,sql_features:1,sql_implementation_info:1,sql_languages:1} |
| pg_catalog | pg_tables | tableowner | fmsketch_dcount() | 2 |
| pg_catalog | pg_tables | tableowner | array_collapse(mfvsketch_quick_histogram((),5)) | [0:1]={agorajek:104,alex:1} |
| pg_catalog | pg_tables | tableowner | array_collapse(mfvsketch_top_histogram((),5)) | [0:1]={agorajek:104,alex:1} |
| pg_catalog | pg_tables | tablespace | fmsketch_dcount() | 1 |
| pg_catalog | pg_tables | tablespace | array_collapse(mfvsketch_quick_histogram((),5)) | [0:0]={pg_global:28} |
| pg_catalog | pg_tables | tablespace | array_collapse(mfvsketch_top_histogram((),5)) | [0:0]={pg_global:28} |
| pg_catalog | pg_tables | hasindexes | fmsketch_dcount() | 2 |
| pg_catalog | pg_tables | hasindexes | array_collapse(mfvsketch_quick_histogram((),5)) | [0:1]={t:59,f:46} |
| pg_catalog | pg_tables | hasindexes | array_collapse(mfvsketch_top_histogram((),5)) | [0:1]={t:59,f:46} |
| pg_catalog | pg_tables | hasrules | fmsketch_dcount() | 1 |
| pg_catalog | pg_tables | hasrules | array_collapse(mfvsketch_quick_histogram((),5)) | [0:0]={f:105} |
| pg_catalog | pg_tables | hasrules | array_collapse(mfvsketch_top_histogram((),5)) | [0:0]={f:105} |
| pg_catalog | pg_tables | hastriggers | fmsketch_dcount() | 2 |
| pg_catalog | pg_tables | hastriggers | array_collapse(mfvsketch_quick_histogram((),5)) | [0:1]={f:102,t:3} |
| pg_catalog | pg_tables | hastriggers | array_collapse(mfvsketch_top_histogram((),5)) | [0:1]={f:102,t:3} |
| (22 rows) |
| </pre></div></li> |
| </ul> |
| <dl class="user"><dt><b>Implementation Notes:</b></dt><dd></dd></dl> |
| <p>Because some of the aggregate functions used in profile return multi-dimensional arrays, which are not easily handled in pl/python, we are using <code>array_collapse</code> function to collaps the n-dim arrays to 1-dim arrays. All values of 2 and upper dimensions are separated with ':' character.</p> |
| <dl class="see"><dt><b>See also:</b></dt><dd>File <a class="el" href="profile_8sql__in.html" title="SQL function for single-pass table profiles.">profile.sql_in</a> documenting SQL functions. </dd></dl> |
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