SQL server data backup/warehouse - sql

Question

I've been asked to do a snapshots of certain tables from the database, so in the future we can have a clear view of the situation for any given day in the past. lets say that one of such tables looks like this:
GKEY Time_in Time_out Category Commodity
1001 2014-05-01 10:50 NULL EXPORT Apples
1002 2014-05-02 11:23 2014-05-20 12:05 IMPORT Bananas
1003 2014-05-05 11:23 NULL STORAGE Null
The simples way to do a snapshot would be creating copy of the table with another column SNAPSHOT_TAKEN (Datetime) and populate it with an INSERT statement
INSERT INTO UNITS_snapshot (SNAPSHOT_TAKEN, GKEY,Time_in, Time_out, Category, Commodity)
SELECT getdate() as SNAPSHOT_TAKEN, * FROM UNITS
OK, it works fine, but it would make the destination table quite big pretty soon, especially if I'd like to run this query often. Better solution would be checking for changes between current live table and the latest snapshot and write them down, omitting everything that hasn't been changed.
Is there a simply way to write such query?
EDIT: Possible solution for the "Forward delta" (assuming no deletes from original table)
INSERT INTO UNITS_snapshot
SELECT getdate() as SNAP_DATE,
r.* -- Here goes all data from from the original table
CASE when b.gkey is null then 'I' else 'U' END AS change_type
FROM UNITS r left outer join UNITS_snapshot b
WHERE (r.time_in <>b.time_in or r.time_out<>b.time_out or r.category<>b.category or r.commodity<>b.commodity or b.gkey is null)
and (b.snap_date =
(SELECT max (b.snap_date) from UNITS_snapshot b right outer join UNITS r
on r.gkey=b.gkey) or b.snap_date is null)
Assumptions: no value from original table is deleted. Probably also every field in WHERE should be COALESCE (xxx,'') to avoid comparing null values with set ones.

Answer 1

Both Dan Bracuk and ITroubs have made very good comments.
Solution 1 - Daily snapshop
The first solution you proposed is very simple. You can build the snapshot with a simple query and you can also consult it and rebuild any day's snapshot with a very simple query, by just filtering on the SNAPSHOT_TAKEN column.
If you have just some thousands of records, I'd go with this one, without worrying too much about its growing size.
Solution 2 - Daily snapshop with rolling history
This is basically the same as solution 1, but you keep only some of the snapshots over time... to avoid having the snapshot DB growing indefinitely over time.
The simplest approach is just to save the snapshots of the last N days... maybe a month or two of data. A more sophisticated approach is to keep snapshot with a density that depends on age... so, for example, you could have every day of the last month, plus every sunday of the last 3 months, plus every end-of-month of the last year, etc...
This solution requires you develop a procedure to handle deletion of the snapshots that are not required any more. It's not as simple as using getdate() within a query. But you obtain a good balance between space and historic information. You just need to balance out a good snapshot retainment strategy to suit your needs.
Solution 3 - Forward row delta
Building any type of delta is a much more complex procedure.
A forward delta is built by storing the initial snapshot (as if all rows had been inserted on that day) and then, on the following snapshots, just storing information about the difference between snapshot(N) and snapshot(N-1). This is done by analyzing each row and just storing the data if the row is new or updated or deleted. If the main table does not change much over time, you can save quite a lot of space, as no info is stored for unchanged rows.
Obviously, to handle deltas, you now need 2 extra columns, not just one:
delta id (you snapshot_taken is good, if you only want 1 delta per
day)
row change type (could be D=deleted, I=inserted, U=updated... or
something similar)
The main complexity derives from the necessity to identify rows (usually by primary key) so as to calculate if between 2 snapshots any individual row has been inserted, updated, deleted... or none of the above.
The other complexity comes from reading the snapshot DB and building the latest (or any other) snapshot. This is necessary because, having only row differences in the table, you cannot simply select a day's snapshot by filtering on snapshot_taken.
This is not easy in SQL. For each row you must take into account just the final version... the one with MAX snapshot_taken that is <= the date of the snapshot you want to build. If it is an insert or update, then keep the data for that row, else (if it is a delete) then ignore it.
To build a delta of snapshot(N), you must first build the latest snapshot (N-1) from the snapshot DB. Then you must compare the two snapshots by primary key or row identity and calculate the change type (I/U/D) and insert the changes in the snapshot DB.
Beware that you cannot delete old snapshot data without consolidating it first. That is because all snapshots are calculated from the oldest initial one and all the subsequent difference data. If you want to remove a year's of old snapshots, you'll have to consolidate the old initial snapshot and all the year's variations in a new initial snapshot.
Solution 4 - Backward row delta
This is very similar to solution 3, but a bit more complex.
A backward delta is built by storing the final snapshot and then, on the following snapshots, just storing information about the difference between snapshot(N-1) and snapshot(N).
The advantage is that the latest snapshot is always readily available through a simple select on the snapshot DB. You only need to merge the difference data when you want to retrieve an older snapshot. Compare this to the forward delta, where you always need to rebuild the snapshot from the difference data unless you are actually interested in the very first snapshot.
Another advantage (compared to solution 3) is that you can remove older snapshots by just deleting the difference data older than a particular snapshot. You can do this easily because snapshots are calculated from the final snapshot and not from the initial one.
The disadvantage is in the obscure logic. Difference data is calculated backwards. Values must be stored on the (U)pdate and (D)elete variations, but are unnecessary on the I variations. Going backwards, rows must be ignored if the first variation you find is an (I)nsert. Doable, but a bit trickier.
Solution 5 - Forward and backward column delta
If the main table has many columns, or many long text or varchar columns, and only a bunch of these are updated, then it could make sense to store only column variations instead of row variations.
This is done by using a table with this structure:
delta id (you snapshot_taken is good, if you only want 1 delta per
day)
change type (could be D=deleted, I=inserted, U=updated... or
something similar)
column name
value
The difference can be calculated forward or backward, as per row deltas.
I've seen this done, but I really advise against it. There are just too many disadvantages and added complexity.
Value is a text or varchar, and there are typecasting issues to handle if you have numeric, boolean or date/time values... and, if you have a lot of these, it could very well be you won't be saving as much space as you think you are.
Rebuilding any snapshot is hell. Altogether... any operation on this type of table really requires a lot of knowledge of the main table's structure.