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In my test data, ID 0 has no review date. When expanding the first row, I may find a start date that is earlier than the review date (see 4A above).įor days earlier than the review date, I output a row with a null review date and a null group id.Īllen’s Interval Algebra defines 13 possible relations between date ranges.The end is the earlier of end date and next review date.In that case this is the first row for an ID, so the beginning is the start date. The beginning is the later of start date and review date – except if the previous review date is null.Now I have to create a new date range that represents just the intersection.It’s a LEFT JOIN since I produce rows whether the ID is in TABLE_2 or not. When the review date is earlier than the end date,Īnd the next review date is either null or later than the start date. To identify the first row for each ID, I use the LAG() analytic function to get the previous review date (which will be NULL on the first row).I use the LEAD() analytic function to get the next review date, which is the end of the range.TABLE_2 has a review date, which starts a range.TABLE_1 already has a start date and an end date.So let’s try to join the two tables directly, based on intersecting date ranges: When I tell you that each ID can have a date range of several months, then you see that the extra work and memory required are not negligible. If you look at the pretty table just above, you might think that I need to generate the green rows, then join each table to them. Note: the most recent review is used even if that review took place before the start date in table 1. Review_date date not null check (review_date = trunc(review_date)), In the output, each row should contain the data from the most recent review – or null if there was no prior review.
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The second table contains the ID and a “review date” that assigns a “group ID”. Start_date date not null check (start_date = trunc(start_date)),Įnd_date date not null check (end_date = trunc(end_date)), (I recommend to always use exclusive end dates.) The final output should have one row for every date within the date range. The first table contains an ID, a start date and an end date. I am going to simplify a bit from the OTN setup. I think it’s more efficient to join on date range, then expand. One solution was to expand the data from each table, creating one row per date, then join on date. A recent question on the OTN SQL forum asked how best to join two tables related by ID and date range, in order to insert one row per date into a data warehouse.