I am just trying to figure out what would be most optimal to arrange my data. I have a large data set (~1.7m rows)...analyzing data on 2,000+ 'stores' at a daily granularity...my fact table is set up something along the lines of:
Date | Store_ID | Manager_ID | Other_IDs... | TY_Items_Sold | TY_Tot_Rev | LY_Items_Sold | LY_Tot_Rev...
My question is:
In order to calculate YoY sales data, is it more efficient to have it set up this way, where I have just TY's Date (YTD) and have 'TY' and additional 'LY' columns, or would it be better to have both years' dates? This would allow me to remove the 'LY' columns, but would double the length of my data set (creating a longer & skinnier data set)? Doing that would also allow me to make use of the DAX time intelligence functions...
Or, alternatively (I don't think this would be the optimal way, but...), have a TY table, and a separate LY table (with TY dates) and set it up that way?
TY!
Related
I am supposed to do some changes to an enormous CSV file based on a different file. Therefore I chose to do it in SQL but after further consideration I am not sure how to proceed..
In the 1st table I have a list of contracts. Columns represent some segments the contract belongs to and some products that can be linked to the contract (example in the table below).
Here contract no. 1234 belongs to segments X1 and Y2. There is no product number 1 linked to it, but it has product number 2 linked to it. The product originaly ends on the 1st of January 2030.
cont_n|date|segment_1|segment_2|..|prod_1|date_prod_1|product_2|date_product_2|..
1234 |3011| X1 | Y2 |..| | |YES |01/01/2030 |..
The 2nd file is a list of combinations of segments and an indication how the "date" columns should be adjusted. The example shows following situation - if there is prod_2 linked to the contract which belongs to groups X1 and Y2, end the prod_2 this year. I need this result to alter table no. 1.
prod_no|segment_1|segment_2|result
prod_2 | X1 | Y2 | end the product on anniversary
Ergo I need to get to the result:
cont_n|date|segment_1|segment_2|..|prod_1|date_prod_1|product_2|date_product_2|..
1234 |3011| X1 | Y2 |..| | |YES |30/11/2019 |..
In the original files I have around 600k rows and more than 300 columns (meaning around 100 different products) in table 1 and around 800 possible combinations of segments in table 2.
The algorithm I need to implement (very generally):
for x=1 to 100
IF product_x = YES THEN date_product_x = date + "Seach for result in table2"
Is there a reasonable way how to change the "date_product_x" columns based on the 2nd table or would it be better to find a different solution?
Thanks a lot!
I can only give you a general approach, because the information in your question is general (for example, why does "end the product on anniversary" translate to "30/11/2019"? It's not explained in the question, so I assume you're going to be able to handle that part of the logic).
You can approach this by using an UNPIVOT on Table 1 to get a structure like:
cont_n | segment1 | segment2 | product_number | product_date
You will UNPIVOT..FOR date_product_1 thru date_product_100. You'll either have to type out all 100 column names, or use dynamic sql to build the whole thing.
You'll do some string manipulation to grab the "x" portion of "date_product_x", and turn it into "prod_x", and then you can join to the second table on the two segment columns and the "prod_x" column, get the result column value, and do whatever rules you're doing to get the value you want for date_product_x.
Finally, you take that result, and PIVOT it back to the one-row-per-contract form, and JOIN it to your original table to UPDATE the date_product_x columns.
I have a table in BQ which I refresh on daily basis. It's a full snapshot every day.
I have a business requirement to create deltas of that feed.
Table Details :
Table contains 10 columns
Out of 10 columns, 5 columns change on daily basis. How do I identify which columns changed and only create a snapshot for that?
For eg here are the columns in tableA: columns which will frequently change are in bold.
Custid - ABC
first_product - toy
first_product_purchase_date - 2015-01-01
last_product - ebook
last_product_purchase_date - 2018-05-01
second_product - Magazine
second_product_purchase_date - 2016-01-01
third_product - null
third_product_purchase_date - null
fourth_product - null
fourth_product_purchase_date - null
After more purchase Data will look like this:
Custid - ABC
first_product - toy
first_product_purchase_date - 2015-01-01
last_product - Hardbook
last_product_purchase_date - 2018-05-17
second_product - Magazine
second_product_purchase_date - 2016-01-01
third_product - CD
third_product_purchase_date - 2017-01-01
fourth_product - null
fourth_product_purchase_date - null
first_product = first product ever purchased
last_product = most recent product purchased
This is just one row of records for one customer. I have millions of customers with all these columns, and let's say half a million of the rows will be updated on daily basis.
In my delta, I just want the rows where any of the column value changed.
It seems like you have a column for each product bought and their repetition, perhaps this comes from a de-normalize dimensional models. To query the last "update" you would have to compare each columns the previous row by using the lead function. This would use a lot of computation and might not be optimal.
I recommend using repeated fields. The product and product_purchase_date would be repeated field and you could simply query using a where product_purchase_date = current_date() which would use much less computation.
De-normalize dimensional models are meant to use less computation on traditional data warehouses. Bigquery being fast, highly scalable, enterprise data warehouse has a lot of computing power.
To have a beter understanding on how BigQuery works under the hood I recommend reviewing this document.
I haven't been able to figure out exactly how to put together this SQL string. I'd really appreciate it if someone could help me out. I am using Access 2016, so please only provide answers that will work with Access. I have two queries that both have different fields except for one in common. I need to find the minimum absolute difference between the two similar columns. Then, I need to be able to pull the data from that corresponding record. For instance,
qry1.Col1 | qry1.Col2
-----------|-----------
10245.123 | Have
302044.31 | A
qry2.Col1 | qry2.Col2
----------------------
23451.321 | Great
345622.34 | Day
Find minimum absolute difference in a third query, qry3. For instance, Min(Abs(qry1!Col1 - qry2!Col1) I imagine it would produce one of these tables for each value in qry1.Col1. For the value 10245.123,
qry3.Col1
----------
13206.198
335377.217
Since 13206.198 is the minimum absolute difference, I want to pull the record corresponding to that from qry2 and associate it with the data from qry1 (I'm assuming this uses a JOIN). Resulting in a fourth query like this,
qry4.Col1 (qry1.Col1) | qry4.Col2 (qry1.Col2) | qry4.Col3 (qry2.Col2)
----------------------------------------------------------------------
10245.123 | Have | Great
302044.31 | A | Day
If this is all doable in one SQL string, that would be great. If a couple of steps are required, that's okay as well. I just would like to avoid having to time consumingly do this using loops and RecordSet.Findfirst in VBA.
You can use a correlated subquery:
select q1.*,
(select top 1 q2.col2
from qry2 as q2
order by abs(q2.col1 - q1.col1), q2.col2
) as qry2_col2
from qry1 as q1;
I realize that referring to these as dimension and fact tables is not exactly appropriate. I am at a lost for better terminology, so please excuse this categorization that I use in the post.
I am building an application for employee record keeping.
The database will contain organizational information. The information is mostly defined in three tables: Locations, Divisions, and Departments. However, there are others with similar problems. First, I need to store the available values for these tables. This will allow for available values in the application when managing an employee and for management of these values when adding/deleting departments and such. For instance, the Locations table may look like,
LocationId | LocationName | LocationStatus
1 | New York | Active
2 | Denver | Inactive
3 | New Orleans | Active
I then need to store these values for each employee and keep their history. My first thought was to create LocationHistory, DivisionHistory, and DepartmentHistory tables. I cannot pinpoint why, but this struck me as poor design. My next inclination was to create a DimLocation/FactLocation, DimDivision/FactDivision, DimDepartment/FactDepartment set of tables. I do not believe this makes sense either. I have also considered naming them as a combination of Employee, i.e. EmployeeLocations, EmployeeDivisions, etc. Regardless of the naming convention for these tables, I imagine that data would look similar to a simplified version I have below:
EmployeeId | LocationId | EffectiveDate | EndDate
1 | 3 | 2008-07-01 | NULL
1 | 2 | 2007-04-01 | 2008-06-30
I realize any of the imagined solutions I described above could work, but I am really looking to create a design that will be easy for others to maintain with an intuitive, familiar structure. I would like to receive this community's help, opinions, and experience with this matter. I am open to and would welcome any suggestion to consider. For instance, should I even store the available values for these three tables in the database? Should they be maintained in the application code/business logic layer? Do I just need to get over seeing the word History repeating three times?
Thanks!
Firstly, I see no issue in describing these as Dimension and Fact tables outside of a warehouse :)
In terms of conceptualising and understanding the relationships, I personally see the use of start/end dates perfectly easy for people to understand. Allowing Agent and Location fact tables, and then time dependant mapping tables such as Agent_At_Location, etc. They do, however, have issues worthy of taking note.
If EndDate is 2008-08-30, was the employee in that location UP TO 30th August, or UP TO and INCLUDING 30th August.
Dealing with overlapping date periods in queries can give messy queries, but more importantly, slow queries.
The first one seems simply a matter of convention, but it can have certain implications when dealign with other data. For example, consider that an EndDate of 2008-08-30 means that they ARE at that location UP TO and INCLUDING 30th August. Then you join on to their Daily Agent Data for that day (Such as when they Actually arrived at work, left for breaks, etc). You need to join ON AgentDailyData.EventTimeStamp < '2008-08-30' + 1 in order to include all the events that happened during that day.
This is because the data's EventTimeStamp isn't measured in days, but probably minutes or seconds.
If you consider that the EndDate of '2008-08-30' means that the Agent was at that Location UP TO but NOT INCLDUING 30th August, the join does not need the + 1. In fact you don't need to know if the date is DAY bound, or can include a time component or not. You just need TimeStamp < EndDate.
By using EXCLUSIVE End markers, all of your queries simplify and never need + 1 day, or + 1 hour to deal with edge conditions.
The second one is much harder to resolve. The simplest way of resolving an overlapping period is as follows:
SELECT
CASE WHEN TableA.InclusiveFrom > TableB.InclusiveFrom THEN TableA.InclusiveFrom ELSE TableB.InclusiveFrom END AS [NetInclusiveFrom],
CASE WHEN TableA.ExclusiveFrom < TableB.ExclusiveFrom THEN TableA.ExclusiveFrom ELSE TableB.ExclusiveFrom END AS [NetExclusiveFrom],
FROM
TableA
INNER JOIN
TableB
ON TableA.InclusiveFrom < TableB.ExclusiveFrom
AND TableA.ExclusiveFrom > TableB.InclusiveFrom
-- Where InclusiveFrom is the StartDate
-- And ExclusiveFrom is the EndDate, up to but NOT including that date
The problem with that query is one of indexing. The first condition TableA.InclusiveFrom < TableB.ExclusiveFrom could be be resolved using an index. But it could give a Massive range of dates. And then, for each of those records, the ExclusiveDates could all be just about anything, and certainly not in an order that could help quickly resolve TableA.ExclusiveFrom > TableB.InclusiveFrom
The solution I have previously used for that is to have a maximum allowed gap between InclusiveFrom and ExclusiveFrom. This allows something like...
ON TableA.InclusiveFrom < TableB.ExclusiveFrom
AND TableA.InclusiveFrom >= TableB.InclusiveFrom - 30
AND TableA.ExclusiveFrom > TableB.InclusiveFrom
The condition TableA.ExclusiveFrom > TableB.InclusiveFrom STILL can't benefit from indexes. But instead we've limitted the number of rows that can be returned by searching TableA.InclusiveFrom. It's at most only ever 30 days of data, because we know that we restricted the duration to a maximum of 30 days.
An example of this is to break up the associations by calendar month (max duration of 31 days).
EmployeeId | LocationId | EffectiveDate | EndDate
1 | 2 | 2007-04-01 | 2008-05-01
1 | 2 | 2007-05-01 | 2008-06-01
1 | 2 | 2007-06-01 | 2008-06-25
(Representing Employee 1 being in Location 2 from 1st April to (but not including) 25th June.)
It's effectively a trade off; using Disk Space to gain performance.
I've even seen this pushed to the extreme of not actually storing date Ranges, but storing the actual mapping for each and every day. Essentially, it's like restricting the maximum duration to 1 day...
EmployeeId | LocationId | EffectiveDate
1 | 2 | 2007-06-23
1 | 2 | 2007-06-24
1 | 3 | 2007-06-25
1 | 3 | 2007-06-26
Instinctively I initially rebelled against this. But in subsequent ETL, Warehousing, Reporting, etc, I actually found it Very powerful, adaptable, and maintainable. I actually saw people making fewer coding mistakes, writing code in less time, the code ending up running faster, and being much more able to adapt to clients' changing needs.
The only two down sides were:
1. More disk space taken (But trival compared to the size of fact tables)
2. Inserts and Updates to this mapping was slower
The actual slow down for Inserts and Updates only actually matter Once, where this model was being used to represent a constantly changing process net; where the app wanted to change the mapping about 30 times a second. Even then it worked, it just chomped up more CPU time than was ideal.
If you want to be efficient and keep a history, do these things. There are multiple solutions to this problem, but this is the one that I keep going back to:
Remember that each row represents a single entity, if you make corrections that entity, that's fine, but don't re-use and ID for a new Location. Set it up so that instead of deleting a Location, you mark it as deleted with a bit and hide it from the interface, that way when it's referenced historically, it's still there.
Create a history table that includes the current value, or no records if a value isn't currently set. Have the foreign key tie back to the employee and tie to the location.
Create a column in the employee table that points to the current active location in the history. When you need to get the employees location, you join in the history table based on this ID. When you need to get all of the history for an employee you join from the history table.
This structure keeps it all normalized, and gives you an easy way to find the current value without having to do any date comparisons.
As far as using the word history, think of it in different terms: since it contains the current item as well as historical items, it's really just a junction table that keeps around the old item. As such you can name it something like EmployeeLocations.
I've been beating my head on the desk trying to figure this one out. I have a table that stores job information, and reasons for a job not being completed. The reasons are numeric,01,02,03,etc. You can have two reasons for a pending job. If you select two reasons, they are stored in the same column, separated by a comma. This is an example from the JOBID table:
Job_Number User_Assigned PendingInfo
1 user1 01,02
There is another table named Pending, that stores what those values actually represent. 01=Not enough info, 02=Not enough time, 03=Waiting Review. Example:
Pending_Num PendingWord
01 Not Enough Info
02 Not Enough Time
What I'm trying to do is query the database to give me all the job numbers, users, pendinginfo, and pending reason. I can break out the first value, but can't figure out how to do the second. What my limited skills have so far:
select Job_number,user_assigned,SUBSTRING(pendinginfo,0,3),pendingword
from jobid,pending
where
SUBSTRING(pendinginfo,0,3)=pending.pending_num and
pendinginfo!='00,00' and
pendinginfo!='NULL'
What I would like to see for this example would be:
Job_Number User_Assigned PendingInfo PendingWord PendingInfo PendingWord
1 User1 01 Not Enough Info 02 Not Enough Time
Thanks in advance
You really shouldn't store multiple items in one column if your SQL is ever going to want to process them individually. The "SQL gymnastics" you have to perform in those cases are both ugly hacks and performance degraders.
The ideal solution is to split the individual items into separate columns and, for 3NF, move those columns to a separate table as rows if you really want to do it properly (but baby steps are probably okay if you're sure there will never be more than two reasons in the short-medium term).
Then your queries will be both simpler and faster.
However, if that's not an option, you can use the afore-mentioned SQL gymnastics to do something like:
where find ( ',' |fld| ',', ',02,' ) > 0
assuming your SQL dialect has a string search function (find in this case, but I think charindex for SQLServer).
This will ensure all sub-columns begin and start with a comma (comma plus field plus comma) and look for a specific desired value (with the commas on either side to ensure it's a full sub-column match).
If you can't control what the application puts in that column, I would opt for the DBA solution - DBA solutions are defined as those a DBA has to do to work around the inadequacies of their users :-).
Create two new columns in that table and make an insert/update trigger which will populate them with the two reasons that a user puts into the original column.
Then query those two new columns for specific values rather than trying to split apart the old column.
This means that the cost of splitting is only on row insert/update, not on _every single select`, amortising that cost efficiently.
Still, my answer is to re-do the schema. That will be the best way in the long term in terms of speed, readable queries and maintainability.
I hope you are just maintaining the code and it's not a brand new implementation.
Please consider to use a different approach using a support table like this:
JOBS TABLE
jobID | userID
--------------
1 | user13
2 | user32
3 | user44
--------------
PENDING TABLE
pendingID | pendingText
---------------------------
01 | Not Enough Info
02 | Not Enough Time
---------------------------
JOB_PENDING TABLE
jobID | pendingID
-----------------
1 | 01
1 | 02
2 | 01
3 | 03
3 | 01
-----------------
You can easily query this tables using JOIN or subqueries.
If you need retro-compatibility on your software you can add a view to reach this goal.
I have a tables like:
Events
---------
eventId int
eventTypeIds nvarchar(50)
...
EventTypes
--------------
eventTypeId
Description
...
Each Event can have multiple eventtypes specified.
All I do is write 2 procedures in my site code, not SQL code
One procedure converts the table field (eventTypeIds) value like "3,4,15,6" into a ViewState array, so I can use it any where in code.
This procedure does the opposite it collects any options your checked and converts it in
If changing the schema is an option (which it probably should be) shouldn't you implement a many-to-many relationship here so that you have a bridging table between the two items? That way, you would store the number and its wording in one table, jobs in another, and "failure reasons for jobs" in the bridging table...
Have a look at a similar question I answered here
;WITH Numbers AS
(
SELECT ROW_NUMBER() OVER(ORDER BY (SELECT 0)) AS N
FROM JobId
),
Split AS
(
SELECT JOB_NUMBER, USER_ASSIGNED, SUBSTRING(PENDING_INFO, Numbers.N, CHARINDEX(',', PENDING_INFO + ',', Numbers.N) - Numbers.N) AS PENDING_NUM
FROM JobId
JOIN Numbers ON Numbers.N <= DATALENGTH(PENDING_INFO) + 1
AND SUBSTRING(',' + PENDING_INFO, Numbers.N, 1) = ','
)
SELECT *
FROM Split JOIN Pending ON Split.PENDING_NUM = Pending.PENDING_NUM
The basic idea is that you have to multiply each row as many times as there are PENDING_NUMs. Then, extract the appropriate part of the string
While I agree with DBA perspective not to store multiple values in a single field it is doable, as bellow, practical for application logic and some performance issues. Let say you have 10000 user groups, each having average 1000 members. You may want to have a table user_groups with columns such as groupID and membersID. Your membersID column could be populated like this:
(',10,2001,20003,333,4520,') each number being a memberID, all separated with a comma. Add also a comma at the start and end of the data. Then your select would use like '%,someID,%'.
If you can not change your data ('01,02,03') or similar, let say you want rows containing 01 you still can use " select ... LIKE '01,%' OR '%,01' OR '%,01,%' " which will insure it match if at start, end or inside, while avoiding similar number (ie:101).