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<!DOCTYPE concept PUBLIC "-//OASIS//DTD DITA Concept//EN" "concept.dtd">
<concept id="group_by">
<title>GROUP BY Clause</title>
<data name="Category" value="Impala"/>
<data name="Category" value="SQL"/>
<data name="Category" value="Querying"/>
<data name="Category" value="Aggregate Functions"/>
<data name="Category" value="Developers"/>
<data name="Category" value="Data Analysts"/>
Specify the <codeph>GROUP BY</codeph> clause in queries that use aggregation functions, such as
<codeph><xref href="impala_count.xml#count">COUNT()</xref></codeph>,
<codeph><xref href="impala_sum.xml#sum">SUM()</xref></codeph>,
<codeph><xref href="impala_avg.xml#avg">AVG()</xref></codeph>,
<codeph><xref href="impala_min.xml#min">MIN()</xref></codeph>, and
<codeph><xref href="impala_max.xml#max">MAX()</xref></codeph>. Specify in the
<codeph><xref href="impala_group_by.xml#group_by">GROUP BY</xref></codeph> clause the names of all the
columns that do not participate in the aggregation operation.
<!-- Good to show an example of cases where ORDER BY does and doesn't work with complex types. -->
<p conref="../shared/impala_common.xml#common/complex_types_blurb"/>
<p rev="2.3.0">
In <keyword keyref="impala23_full"/> and higher, the complex data types <codeph>STRUCT</codeph>,
<codeph>ARRAY</codeph>, and <codeph>MAP</codeph> are available. These columns cannot
be referenced directly in the <codeph>ORDER BY</codeph> clause.
When you query a complex type column, you use join notation to <q>unpack</q> the elements
of the complex type, and within the join query you can include an <codeph>ORDER BY</codeph>
clause to control the order in the result set of the scalar elements from the complex type.
See <xref href="impala_complex_types.xml#complex_types"/> for details about Impala support for complex types.
<p conref="../shared/impala_common.xml#common/zero_length_strings"/>
<p conref="../shared/impala_common.xml#common/example_blurb"/>
For example, the following query finds the 5 items that sold the highest total quantity (using the
<codeph>SUM()</codeph> function, and also counts the number of sales transactions for those items (using the
<codeph>COUNT()</codeph> function). Because the column representing the item IDs is not used in any
aggregation functions, we specify that column in the <codeph>GROUP BY</codeph> clause.
<b>ss_item_sk</b> as Item,
<b>count</b>(ss_item_sk) as Times_Purchased,
<b>sum</b>(ss_quantity) as Total_Quantity_Purchased
from store_sales
<b>group by ss_item_sk</b>
order by sum(ss_quantity) desc
limit 5;
| item | times_purchased | total_quantity_purchased |
| 9325 | 372 | 19072 |
| 4279 | 357 | 18501 |
| 7507 | 371 | 18475 |
| 5953 | 369 | 18451 |
| 16753 | 375 | 18446 |
The <codeph>HAVING</codeph> clause lets you filter the results of aggregate functions, because you cannot
refer to those expressions in the <codeph>WHERE</codeph> clause. For example, to find the 5 lowest-selling
items that were included in at least 100 sales transactions, we could use this query:
<b>ss_item_sk</b> as Item,
<b>count</b>(ss_item_sk) as Times_Purchased,
<b>sum</b>(ss_quantity) as Total_Quantity_Purchased
from store_sales
<b>group by ss_item_sk</b>
<b>having times_purchased &gt;= 100</b>
order by sum(ss_quantity)
limit 5;
| item | times_purchased | total_quantity_purchased |
| 13943 | 105 | 4087 |
| 2992 | 101 | 4176 |
| 4773 | 107 | 4204 |
| 14350 | 103 | 4260 |
| 11956 | 102 | 4275 |
When performing calculations involving scientific or financial data, remember that columns with type
<codeph>FLOAT</codeph> or <codeph>DOUBLE</codeph> are stored as true floating-point numbers, which cannot
precisely represent every possible fractional value. Thus, if you include a <codeph>FLOAT</codeph> or
<codeph>DOUBLE</codeph> column in a <codeph>GROUP BY</codeph> clause, the results might not precisely match
literal values in your query or from an original Text data file. Use rounding operations, the
<codeph>BETWEEN</codeph> operator, or another arithmetic technique to match floating-point values that are
<q>near</q> literal values you expect. For example, this query on the <codeph>ss_wholesale_cost</codeph>
column returns cost values that are close but not identical to the original figures that were entered as
decimal fractions.
<codeblock>select ss_wholesale_cost, avg(ss_quantity * ss_sales_price) as avg_revenue_per_sale
from sales
group by ss_wholesale_cost
order by avg_revenue_per_sale desc
limit 5;
| ss_wholesale_cost | avg_revenue_per_sale |
| 96.94000244140625 | 4454.351539300434 |
| 95.93000030517578 | 4423.119941283189 |
| 98.37999725341797 | 4332.516490316291 |
| 97.97000122070312 | 4330.480601655014 |
| 98.52999877929688 | 4291.316953108634 |
Notice how wholesale cost values originally entered as decimal fractions such as <codeph>96.94</codeph> and
<codeph>98.38</codeph> are slightly larger or smaller in the result set, due to precision limitations in the
hardware floating-point types. The imprecise representation of <codeph>FLOAT</codeph> and
<codeph>DOUBLE</codeph> values is why financial data processing systems often store currency using data types
that are less space-efficient but avoid these types of rounding errors.
<p conref="../shared/impala_common.xml#common/related_info"/>
<xref href="impala_select.xml#select"/>,
<xref href="impala_aggregate_functions.xml#aggregate_functions"/>