Stopping Leading Observations in Oracle Based on Time Threshold

Stopping Leading Observations Once Certain Threshold Met in Oracle

Introduction

In this article, we’ll explore a common problem when working with temporal data in Oracle databases. Specifically, we’ll discuss how to stop leading observations once a certain threshold is met. We’ll provide an example query that demonstrates the solution and offer explanations and variations for different use cases.

Background

Temporal data can be challenging to work with, especially when it comes to filtering or aggregating data based on specific conditions. In this case, we’re dealing with a scenario where we have multiple encounters for a patient, each with its own timestamp. We want to include all observations from the first encounter and subsequent ones that meet a certain threshold (in this case, 4 hours).

The Problem

Let’s dive into the query provided in the Stack Overflow post. The goal is to output rows 1 and 2, but exclude row 4.

With Base as 
(select 123 as ID, 12345 as enc_id, TO_DATE('2019-07-01 13:27:18', 'YYYY-MM-DD HH24:MI:SS') as dt from dual union
 select 123 as ID, 12346 as enc_id, TO_DATE('2019-07-01 16:27:18', 'YYYY-MM-DD HH24:MI:SS') as dt from dual union
 select 123 as ID, 12347 as enc_id, TO_DATE('2019-07-02 16:27:18', 'YYYY-MM-DD HH24:MI:SS') as dt from dual union
  select 123 as ID, 12348 as enc_id, TO_DATE('2019-07-02 18:27:18', 'YYYY-MM-DD HH24:MI:SS') as dt from dual)

select * from (select ID,ENC_ID,dt,row_number() over (partition by ID order by DT) RK,
lag(dt) over (partition by ID order by dt) prev_dt,
(DT-lag(dt) over (partition by ID order by dt))*24 as time_dif_hrs from base) where RK=1 or TIME_DIF_HRS&lt;4

Understanding the Query

The query uses several Oracle-specific functions and features:

row_number(): assigns a unique row number to each row within a partition.
lag(): returns the value of a specified column from a previous row within a partition.

However, this query has a flaw: it stops including observations after the first one that doesn’t meet the threshold. This is because once we encounter an observation that doesn’t meet the condition, the TIME_DIF_HRS calculation becomes invalid for subsequent rows.

Solving the Problem

To fix this issue, we can use another analytical function called sum. We’ll create a new column called cond_met_running that keeps track of whether each row meets the threshold. If it does, we increment the count; otherwise, we reset it to 0.

With Base as 
(select 123 as ID, 12345 as enc_id, TO_DATE('2019-07-01 13:27:18', 'YYYY-MM-DD HH24:MI:SS') as dt from dual union
 select 123 as ID, 12346 as enc_id, TO_DATE('2019-07-01 16:27:18', 'YYYY-MM-DD HH24:MI:SS') as dt from dual union
 select 123 as ID, 12347 as enc_id, TO_DATE('2019-07-02 16:27:18', 'YYYY-MM-DD HH24:MI:SS') as dt from dual union
  select 123 as ID, 12348 as enc_id, TO_DATE('2019-07-02 18:27:18', 'YYYY-MM-DD HH24:MI:SS') as dt from dual)

select * from (select ID,ENC_ID,dt,row_number() over (partition by ID order by DT) RK,
lag(dt) over (partition by ID order by dt) prev_dt,
(DT-lag(dt) over (partition by ID order by dt))*24 as time_dif_hrs,
sum(case when (date - prev_date)* 24 &lt; 4 then 0 else 1 end) 
                over( partition by ID order by DT) as cond_met_running 
  from (select ID,ENC_ID,dt,
               row_number() over (partition by ID order by DT) RK,
               lag(dt) over (partition by ID order by dt) prev_dt 
         from base)
       )
) Where rk = 1 or cond_met_running = 0

Explanation

The sum function is used to create a running total of observations that meet the threshold. The case statement inside the sum function checks whether the time difference between the current row and the previous row is less than 4 hours. If it is, the expression evaluates to 0; otherwise, it evaluates to 1.

The cond_met_running column keeps track of this total. When we encounter an observation that doesn’t meet the threshold, the count resets to 0, effectively stopping further observations.

Conclusion

In conclusion, using another analytical function like sum can help solve problems like this one where leading observations need to be stopped once a certain threshold is met. By understanding how these functions work and applying them correctly, we can write more efficient and effective queries that meet our specific requirements.

Additional Variations

While the solution provided above should work for most use cases, there are some additional variations you might encounter:

Different conditions: Depending on your specific requirement, you might need to modify the condition inside the case statement. For example, if you want to stop observations after a certain number of rows that don’t meet the threshold, you can change the comparison operator or add more complex logic.
More complex calculations: In some cases, you might need to perform more complex calculations before determining whether an observation meets the threshold. You can do this by adding additional columns or using different analytical functions.

By understanding how these variations work and adapting your approach accordingly, you can write queries that meet even the most specific requirements.

Last modified on 2024-05-02