नोट
इस पृष्ठ तक पहुंच के लिए प्राधिकरण की आवश्यकता होती है। आप साइन इन करने या निर्देशिकाएँ बदलने का प्रयास कर सकते हैं।
इस पृष्ठ तक पहुंच के लिए प्राधिकरण की आवश्यकता होती है। आप निर्देशिकाएँ बदलने का प्रयास कर सकते हैं।
Applies to: 
Databricks SQL Databricks Runtime 16.2 and above
Azure Databricks supports SQL pipeline syntax which allows composing queries from combinations of chained operators.
- Any query can have zero or more pipe operators as a suffix, delineated by the pipe character
|>. - Each piped operation starts with one or more SQL keywords followed by its own grammar.
- Operators can apply in any order, any number of times.
- Typically FROM relation_name is used to start a pipeline, but any query can start a pipeline.
Syntax
{ FROM | TABLE } relation_name { |> piped_operation } [ ...]
Parameters
-
Identifies a table or view to be used as the input for the pipeline. If the table or view cannot be found, Azure Databricks raises a TABLE_OR_VIEW_NOT_FOUND error.
-
An operation consuming the preceding query or pipe operation. See piped_operation for details.
Example
This is query 13 from the TPC-H benchmark written in ANSI SQL:
> SELECT c_count, COUNT(*) AS custdist
FROM
(SELECT c_custkey, COUNT(o_orderkey) c_count
FROM customer
LEFT OUTER JOIN orders ON c_custkey = o_custkey
AND o_comment NOT LIKE '%unusual%packages%'
GROUP BY c_custkey
) AS c_orders
GROUP BY c_count
ORDER BY custdist DESC, c_count DESC;
To write the same logic using SQL pipe operators, you can express it like this:
> FROM customer
|> LEFT OUTER JOIN orders ON c_custkey = o_custkey
AND o_comment NOT LIKE '%unusual%packages%'
|> AGGREGATE COUNT(o_orderkey) c_count
GROUP BY c_custkey
|> AGGREGATE COUNT(*) AS custdist
GROUP BY c_count
|> ORDER BY custdist DESC, c_count DESC;