Count distinct case when teradata

COUNT and COUNT (Asterik) in Teradata. It will return the number of rows in a group. Applicable only for the unique values. SELECT DISTINCT applies to all the columns added in SELECT clause and not only to the first column.

To view the original result set click here. In the case of DISTINCT , the rows are redistributed immediately without any preaggregation taking place, while in the case of GROUP BY, in a first step a preaggregation is done and only then are the unique values redistributed across the AMPs. First, although there are three freshman, two sophomores, two juniors, two seniors and one row without a class code, only one output row is returned for each of these values. Lastly, the NULL is considered a unique value whether there is one row or multiple rows containing it. So, it is displayed one time.

Usually I do this to set a condition, usually time based. Count (1) was showing total number of non NULL values in the first column of the table. Teradata mode uses INTEGER to avoid regression problems. Similarly count (n) was showing non NULL values in nth column of the table. In principle one can say, DISTINCT means that data is distributed across the responsible AMPs immediately, rows will be sorted (to remove duplicates).

Using the DISTINCT Function with Aggregates Teradata. For example, one billion (0000000) is a valid value for an integer column because it is less than 1448647. However, if three rows each have one billion as their value and a SUM operation is performe it fails on the third row. We can count during aggregation using GROUP BY to make distinct when needed after the select statement to show the data with counts. They can be used with the SELECT statement.

SUM − Sums up the values of the specified column(s) MAX − Returns the large value of the specified column. MIN − Returns the minimum value of the specified column. AVG − Returns the average value of the specified column.

DealerCD) over (partition by storeid) from Mytable. The SELECT query called as inner query executed first and the outer query uses the result from the subquery. Below are the some of the features of the subquery. A query can have multiple subquery or subquery can have another subquery. Subqueries does not return duplicate records.

Back to the basics: Using DISTINCT with CASE expression. Here is the query to achieve required result along with the output. Note the usage of ‘distinct’ clause outside of case statement.

COUNT(DISTINCT oh.order_no) Total_Orders, COUNT(DISTINCT (CASE WHEN oh.stat_code = THEN oh.order_no ELSE NULL END) ). Wrong answer – Using distinct clause. Correct – Group by, having clause. Select pi p code, PI From party group by 3. It is effectively a replication count for the value x in the percentile set. Each element of the array must be between zero and one, and the array must be constant for all input rows.

If rows-per-value (RPV) is large, then GROUP BY is faster, because duplicates are eliminated locally before aggregation. The key thing is to see if LOCAL AGGREGATION can be fully utilized. The optimizer may choose not to use DISTINCT processing by replacing it with aggregate processing. Distinct Values: This is one of the simplest metho one can adopt.

Identify a column which is the most unique. This can be done by running the following query. SELECT Count ( DISTINCT ( column_name )) FROM tablename Larger the figure and closer to the Table count , better candidate for Primary Index.

This will always be approximate value. Option 2: If you have current stats on the table, then you could use HASHFunctions and decode the hidden value. GROUP BY does a AMP local grouping and distributes the grouped rows to the responsible AMPs afterwards, DISTINCT immediately does a redistribution. This means: use DISTINCT in case the grouping columns are unique or almost unique, use GROUP BY in case of a few distinct values in the grouping colums.

Count distinct is the bane of SQL analysts, so it was an obvious choice for our first blog post. First things first: If you have a huge dataset and can tolerate some imprecision, a probabilistic counter like HyperLogLog can be your best bet. If this is your first visit, be sure to check out the FAQ by clicking the link above.

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