Spark SQL - DENSE_RANK Window Function

Raymond Tang Raymond Tang 0 1253 0.77 index 1/6/2021

About DENSE\_RANK function

DENSE_RANK is similar as Spark SQL - RANK Window Function. It calculates the rank of a value in a group of values. It returns one plus the number of rows proceeding or equals to the current row in the ordering of a partition. The returned values are sequential in each window thus no gaps will be generated.

DENSE\_RANK without partition

The following sample SQL uses DENSE_RANK function without PARTITION BY clause:

SELECT TXN.*, DENSE_RANK() OVER (ORDER BY TXN_DT) AS ROW_RANK FROM VALUES 
(101,10.01, DATE'2021-01-01'),
(101,102.01, DATE'2021-01-01'),
(102,93., DATE'2021-01-01'),
(103,913.1, DATE'2021-01-02'),
(102,913.1, DATE'2021-01-02'),
(101,900.56, DATE'2021-01-03')
AS TXN(ACCT,AMT, TXN_DT);

Result:

ACCT    AMT     TXN_DT  ROW_RANK
101     10.01   2021-01-01      1
101     102.01  2021-01-01      1
102     93.00   2021-01-01      1
103     913.10  2021-01-02      2
102     913.10  2021-01-02      2
101     900.56  2021-01-03      3

warning The following warning message will show: WARN window.WindowExec: No Partition Defined for Window operation! Moving all data to a single partition, this can cause serious performance degradation. 

DENSE\_RANK with partition

The following sample SQL returns a rank number for each records in each window (defined by PARTITION BY):

SELECT TXN.*, 
DENSE_RANK() OVER (PARTITION BY TXN_DT ORDER BY AMT DESC) AS ROW_RANK 
FROM VALUES 
(101,10.01, DATE'2021-01-01'),
(101,102.01, DATE'2021-01-01'),
(102,93., DATE'2021-01-01'),
(103,913.1, DATE'2021-01-02'),
(102,913.1, DATE'2021-01-02'),
(101,900.56, DATE'2021-01-03')
AS TXN(ACCT,AMT, TXN_DT);

Result:

ACCT    AMT     TXN_DT  ROW_RANK
101     102.01  2021-01-01      1
102     93.00   2021-01-01      2
101     10.01   2021-01-01      3
101     900.56  2021-01-03      1
103     913.10  2021-01-02      1
102     913.10  2021-01-02      1

Records are allocated to windows based on TXN_DTcolumn and the rank is computed based on column AMT in each window.

infoBy default, records will be sorted in ascending order. Use ORDER BY .. DESC to sort records in descending order.

Example table

The virtual table/data frame is cited from SQL - Construct Table using Literals.

spark-sql spark-sql-function

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