Oracle LAG Function for each unique row

Introduction

The Oracle LAG function is a powerful tool used to retrieve data from a previous row in a result set. It allows you to access columns from rows that are earlier in the result set, and can be used to create complex calculations and comparisons.

In this article, we will explore how to use the LAG function with partitioning to achieve the desired behavior of lagging for each group of unique empl_ids.

Understanding the Problem

The problem at hand is that we have a table with multiple rows for each EEEV_EMPL_ID, and we want to lag the EEEV_CCTR_ID column for each group of unique empl_ids. This means that if it’s the last row of the group, the lag value should be NULL.

The current implementation uses the OVER clause without partitioning, which causes the function to lag to the next row, even if it’s a different empl_id. We need to find a way to partition the data by EEEV_EMPL_ID so that we can lag for each group of unique empl_ids.

Partitioning with LAG

The solution lies in using the PARTITION BY clause. By specifying a column or expression that defines the partitions, Oracle will divide the result set into groups based on that value.

To achieve our desired behavior, we need to partition by EEEV_EMPL_ID, and then order by EEEV_END_DT within each partition.

SELECT
    EEEV_EMPL_ID, 
    EEEV_DT, 
    EEEV_CCTR_ID, 
    LAG(EEEV_CCTR_ID, 1, ' ') OVER (PARTITION BY EEEV_EMPL_ID ORDER BY EEEV_END_DT ASC) AS prev
FROM 
    CDAS.VDWHEEEV1 eeev1
ORDER BY 
    EEEV_EMPL_ID

In this query, the PARTITION BY clause divides the result set into groups based on the value of EEEV_EMPL_ID. Within each partition (i.e., for each group of unique empl_ids), the ORDER BY clause sorts the rows by EEEV_END_DT.

By doing so, we ensure that we’re lagging for each group of unique empl_ids, and that if it’s the last row of the group, the lag value will be NULL.

How Partitioning Works

When using partitioning with LAG, Oracle uses a process called “partition pruning” to optimize the execution plan.

Here’s how it works:

Partition identification: Oracle identifies the partitions based on the specified column or expression.
Row retrieval: For each row in the result set, Oracle determines which partition it belongs to.
LAG calculation: Within each partition, Oracle calculates the value of LAG(EEEV_CCTR_ID, 1, ' ').
Partition pruning: If a partition is empty or has no rows that match the query conditions, Oracle can eliminate that partition from consideration.

By using partitioning with LAG, we can take advantage of these optimizations and improve the performance of our queries.

Example Use Cases

Partitioning with LAG is useful in a variety of scenarios, including:

Data analysis: When working with time-series data or other types of data that have a natural ordering.
Reporting and dashboards: To create custom reports or dashboards that require specific aggregations or comparisons.
Predictive analytics: To build predictive models that rely on historical data.

Conclusion

In conclusion, using partitioning with LAG allows us to achieve the desired behavior of lagging for each group of unique empl_ids. By specifying a column or expression that defines the partitions, we can divide the result set into groups based on that value and then order by another column within each partition.

This technique is particularly useful in data analysis, reporting, and predictive analytics scenarios where custom aggregations and comparisons are required.

By mastering the art of partitioning with LAG, you’ll be able to write more efficient and effective queries that meet the needs of your organization.

## Further Reading

For more information on Oracle's `LAG` function, see:

*   [Oracle Documentation: LAG Function](https://docs.oracle.com/en/database/oracle/oracle-database/21/sql/functions.html#SQLPLFGF1021)
*   [PlsqlGuide.com: LAG Function](https://plsqlguide.com/pls/oracabm/plsql-programming-guide/lag-function/)

Last modified on 2025-02-17