Window functions in Oracle Pl/Sql and Hive explained and compared with examples

by | Jun 6, 2024 | SQL | 0 comments

Window functions, also known as analytic functions, perform calculations across a set of table rows that are somehow related to the current row. This is different from regular aggregate functions, which aggregate results for the entire set of rows. Both Oracle PL/SQL and Apache Hive support window functions, but there are some differences in their implementation and usage.

Window Functions in Oracle PL/SQL

Oracle provides a comprehensive set of window functions that can be used to perform complex calculations.

Syntax:

function_name(expression) OVER (
[PARTITION BY partition_expression]
[ORDER BY order_expression]

)

Common Oracle Window Functions:

FunctionDescriptionExample Usage
ROW_NUMBERAssigns a unique number to each row within the partition.SELECT employee_id, ROW_NUMBER() OVER (ORDER BY salary DESC) AS row_num FROM employees;
RANKAssigns a rank to each row within the partition of a result set.SELECT employee_id, RANK() OVER (ORDER BY salary DESC) AS rank FROM employees;
DENSE_RANKSimilar to RANK but without gaps in ranking.SELECT employee_id, DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rank FROM employees;
NTILEDivides rows into a specified number of approximately equal groups.SELECT employee_id, NTILE(4) OVER (ORDER BY salary DESC) AS quartile FROM employees;
LAGProvides access to a row at a specified physical offset before that row.SELECT employee_id, LAG(salary, 1) OVER (ORDER BY hire_date) AS prev_salary FROM employees;
LEADProvides access to a row at a specified physical offset after that row.SELECT employee_id, LEAD(salary, 1) OVER (ORDER BY hire_date) AS next_salary FROM employees;
FIRST_VALUEReturns the first value in an ordered set of values.SELECT employee_id, FIRST_VALUE(salary) OVER (ORDER BY hire_date) AS first_salary FROM employees;
LAST_VALUEReturns the last value in an ordered set of values.SELECT employee_id, LAST_VALUE(salary) OVER (ORDER BY hire_date RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS last_salary FROM employees;
SUMCalculates the sum of a set of values.SELECT department_id, SUM(salary) OVER (PARTITION BY department_id) AS dept_total_salary FROM employees;
AVGCalculates the average of a set of values.SELECT department_id, AVG(salary) OVER (PARTITION BY department_id) AS dept_avg_salary FROM employees;

Window Functions in Apache Hive

Hive also supports window functions, although its implementation may have slight differences compared to Oracle.

Syntax:

function_name(expression) OVER (
[PARTITION BY partition_expression]
[ORDER BY order_expression]
[ROWS|RANGE BETWEEN start_expression AND end_expression]
)

Common Hive Window Functions:

FunctionDescriptionExample Usage
ROW_NUMBERAssigns a unique number to each row within the partition.SELECT employee_id, ROW_NUMBER() OVER (ORDER BY salary DESC) AS row_num FROM employees;
RANKAssigns a rank to each row within the partition of a result set.SELECT employee_id, RANK() OVER (ORDER BY salary DESC) AS rank FROM employees;
DENSE_RANKSimilar to RANK but without gaps in ranking.SELECT employee_id, DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rank FROM employees;
NTILEDivides rows into a specified number of approximately equal groups.SELECT employee_id, NTILE(4) OVER (ORDER BY salary DESC) AS quartile FROM employees;
LAGProvides access to a row at a specified physical offset before that row.SELECT employee_id, LAG(salary, 1) OVER (ORDER BY hire_date) AS prev_salary FROM employees;
LEADProvides access to a row at a specified physical offset after that row.SELECT employee_id, LEAD(salary, 1) OVER (ORDER BY hire_date) AS next_salary FROM employees;
FIRST_VALUEReturns the first value in an ordered set of values.SELECT employee_id, FIRST_VALUE(salary) OVER (ORDER BY hire_date) AS first_salary FROM employees;
LAST_VALUEReturns the last value in an ordered set of values.SELECT employee_id, LAST_VALUE(salary) OVER (ORDER BY hire_date RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS last_salary FROM employees;
SUMCalculates the sum of a set of values.SELECT department_id, SUM(salary) OVER (PARTITION BY department_id) AS dept_total_salary FROM employees;
AVGCalculates the average of a set of values.SELECT department_id, AVG(salary) OVER (PARTITION BY department_id) AS dept_avg_salary FROM employees;
Comparison of Window Functions in Oracle PL/SQL and Hive
FeatureOracle PL/SQLHive
Syntax for Window Functionsfunction_name(expression) OVER ([PARTITION BY] [ORDER BY] [window_clause])`function_name(expression) OVER ([PARTITION BY] [ORDER BY] [ROWS
ROW_NUMBERROW_NUMBER() OVER (ORDER BY column)ROW_NUMBER() OVER (ORDER BY column)
RANKRANK() OVER (ORDER BY column)RANK() OVER (ORDER BY column)
DENSE_RANKDENSE_RANK() OVER (ORDER BY column)DENSE_RANK() OVER (ORDER BY column)
NTILENTILE(n) OVER (ORDER BY column)NTILE(n) OVER (ORDER BY column)
LAGLAG(column, offset, default) OVER (ORDER BY column)LAG(column, offset, default) OVER (ORDER BY column)
LEADLEAD(column, offset, default) OVER (ORDER BY column)LEAD(column, offset, default) OVER (ORDER BY column)
FIRST_VALUEFIRST_VALUE(column) OVER (ORDER BY column)FIRST_VALUE(column) OVER (ORDER BY column)
LAST_VALUELAST_VALUE(column) OVER (ORDER BY column)LAST_VALUE(column) OVER (ORDER BY column)
SUMSUM(column) OVER (PARTITION BY partition_column ORDER BY column)SUM(column) OVER (PARTITION BY partition_column ORDER BY column)
AVGAVG(column) OVER (PARTITION BY partition_column ORDER BY column)AVG(column) OVER (PARTITION BY partition_column ORDER BY column)
Window ClauseSupports ROWS BETWEEN and RANGE BETWEENSupports ROWS BETWEEN and RANGE BETWEEN
Recursion in CTEsSupportedSupported (from Hive 3.1.0)

Example Comparison

Oracle PL/SQL Example:

SELECT employee_id,
department_id,
salary,
SUM(salary) OVER (PARTITION BY department_id ORDER BY salary DESC) AS running_total
FROM employees;

Hive Example:

SELECT employee_id,
department_id,
salary,
SUM(salary) OVER (PARTITION BY department_id ORDER BY salary DESC) AS running_total
FROM employees;

Both Oracle PL/SQL and Hive support window functions with similar syntax and capabilities. However, Oracle generally offers more comprehensive and robust support for advanced SQL features due to its long history and broad usage in enterprise environments. Hive, while powerful, is tailored more towards big data processing in Hadoop ecosystems, which may affect performance and feature sets depending on the version and underlying infrastructure.

Complex Use Cases for Window Functions

Window functions go beyond basic ranking and aggregations, enabling powerful data manipulations within a result set. Here are some complex use cases that showcase their capabilities:

1. Identifying Customer Churn:

  • Scenario: You want to predict customer churn by analyzing purchase behavior.
  • Process:
    • Use ROW_NUMBER() to assign a sequential number to each customer purchase record, ordered by purchase date (most recent first).
    • Calculate the difference between the current purchase date and the previous purchase date using LAG() with an offset of 1. This gives the time gap between purchases.
    • Identify customers with a significant increase in time gap between purchases compared to their historical buying pattern. This could indicate potential churn.

2. Flagging Stock Price Volatility:

  • Scenario: You want to identify periods of unusual stock price volatility.
  • Process:
    • Calculate the daily rolling standard deviation of the stock price using STDDEV() over a window of the past X days (e.g., 30 days).
    • Compare the daily standard deviation with a historical average or a threshold. Flag days where the standard deviation is significantly higher, indicating potential volatility.

3. Analyzing User Engagement in Web Applications:

  • Scenario: You want to understand user engagement patterns in a web application.
  • Process:
    • Use DENSE_RANK() to assign a rank to each user session based on the total time spent on the site within a specific timeframe (e.g., day or week). This identifies the most engaged users.
    • Calculate the cumulative number of page views per user session using SUM() over a window defined by the session ID. This helps analyze browsing depth.

4. Comparing Sales Performance Across Different Locations:

  • Scenario: You want to compare sales performance between stores in different regions while accounting for overall trends.
  • Process:
    • Calculate the percentage change in daily sales for each store using PERCENT_CHANGE() over a window of the previous N days. This removes the influence of seasonal trends.
    • Use RANK() to compare the percentage change for each store within a specific region, identifying top and bottom performers relative to their regional peers.

5. Identifying Data Anomalies (Outliers):

  • Scenario: You want to detect outliers in a dataset that might indicate errors or suspicious activity.
  • Process:
    • Calculate the interquartile range (IQR) for a specific column using custom logic or dedicated functions (if available). The IQR represents the middle 50% of the data distribution.
    • Identify rows where the value falls outside the range defined by Q1 – 1.5 * IQR and Q3 + 1.5 * IQR. These could be potential outliers.

Additional Considerations:

  • When dealing with complex calculations, ensure proper handling of null values within window functions to avoid unexpected results.
  • Frame clauses (ROWS BETWEEN or CURRENT ROW) can be used to define the specific window size or offset for calculations within the window.
  • Explore advanced window functions like LEAD() and LAG() with custom offsets for more intricate data manipulations.

Written by HintsToday Team

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