I have a requirement to create a Fact table which stores granted_share_qty awarded to employees. There are surrounding Dimensions like SPS Grant_dim which stores info about each grant, SPS Plan Dim which stores info about the Plan, SPS Client Dim which stores info about the Employer and SPS Customer Dim which stores info about the customer. The DimKeys (Surrogate Key) and DurableKeys(Supernatural Keys) from each Dimension is added to the Fact.
Reporting need is "as-of" ie on any given date, one should be able to see the granted_share_qty as of that date (similar to account balance as of that date) along with point-in-time values of few attributes from the Grant,Plan, Client, Customer dimensions.
First, we thought of creating a daily snapshot table where the data is repeated everyday in the fact (unless source sends any changes). However since there could be more than 100 million grant records , repeating this everyday was almost impossible, moreover the granted_share_qty doesnt change that often so why copy this everyday?.
So instead of a daily snapshot we thought of adding an EFFECTIVE_DT and EXPIRATION_DT on the Fact table (like a TIMESPAN PERIODIC SNAPSHOT table if such a thing exists)
This reduces the volume and perfectly satisfies a reporting need like "get me the granted_qty and grant details,client, plan, customer details as of 10/01/2022 " will translate to "select granted_qty from fact where 10/01/2022 between EFFECTIVE_DT and EXPIRATION_DT and Fact.DimKeys=Dim.DimKeys"
The challenge however is to keep the Dim Keys of the Fact in sync with Dim Keys of the Dimensions. Even if the Fact doesn't change, any DimKey changes due to versioning in any of the Dimension need to be tracked and versioned in the Fact. This has become an implementation nightmare
(To worsen the things, the Dims could undergo multiple intraday changes , so these are to be tracked near-real-time :-( )
Any thoughts how to handle such situations will be highly appreciated (Database: Snowflake)
P:S: We could remove the DimKeys from the Fact and use DurableKeys+Date to join between the Facts and Type 2 Dims, but that proposal is not favored/approved as of now
Thanks
Sunil
First, we thought of creating a daily snapshot table where the data is repeated everyday in the fact (unless source sends any changes). However
Stop right there. Whenever you know the right model but think it's un-workable for some reason, try harder. At a minimum test your assumption that it would be "too much data", and consider not materializing the snapshot but leaving it as a view and computing it at query time.
... moreover the granted_share_qty doesnt change that often so why copy this everyday?.
And there's your answer. Use a monthly snapshot instead of a daily snapshot, and you've divided the data by 30.
(my reason for asking this question is based on having read this answer, which made me rethink my current setup)
I currently am developing a ruby on rails application in which there are many languages, each of which has a dictionary of base words attached to it, as well as a list of the words that map to each base word. The way I currently have it set up, there is a base_words table that contains the base_word as a string, along with the language_id as a foreign key. There is also a words table, each row of which contains a word string, along with the base_word_id as a foreign key. There is also a language_id indexed on each column, although I'm almost positive that this is superfluous due to the language_id on base_word, so I'm planning to take it off (although this could be a bad assumption on my part).
In sum, on the contrary to the answer I mentioned in the beginning, the tables are not separated by language, because I've reasoned that I can simply pull out the language words programmatically when the time comes. However, my application will also have translation(s) associated with each base word (as did the answer I referenced), and so I'm doubting my structure due to the realization that each translation will actually be a base_word in the same table as itself, which would mean that the translation would actually be just an id of another base word in said table. This may be completely fine, or it may not be - I have no clue (this is my first ever programming project).
Is this ok? Do I need to separate my base_words into separate tables for each language, or can I leave it all in one table?
Another example: I also need to store many phrases for each language, along with their translations. Should I have one table where each row has the appropriate translation of the phrase, or one table where each row contains simply one phrase and a language_id, or multiple tables (one for each language)?
Un saludo,
Michael
As in the other scenario, you'll have a translations table. There is no technical reason it couldn't have multiple foreign keys to base_words (a source_word_id and target_word_id, perhaps). So yes, you can absolutely store all your words in one table. There are some minor side effects involved with translations being directional relationships: it becomes possible to have translations which only work one way, and there will be many pairs of entries with opposite source and target. Neither of these is much of a worry: the first is even potentially desirable in order to represent words with double meanings in one language but not the other, and as for the second, space is cheap and indexing is easy.
You are correct that you do not need words.language_id, so long as you always join base_words when you're querying words and the language matters. This obviously changes if you have a use case where it makes sense to leave base_words out, but that scenario sounds unlikely based on what you describe.
As for phrases: why should they be handled any differently than base_words?
I'm trying to create a datamart for the healthcare application. The facts in the datamart are basically going to be measurements and findings related to heart, and we have 100s of them. Starting from 1000 and can go to as big as 20000 per exam type.
I'm wondering what my design choices for the fact tables are:
Grain: 1 row per patient per exam type.
Some of the choices that I can think of -
1) A big wide fact table with 1000 or more columns.
2) EAV based design - A separate Measure dimension table. This foreign key will go into the fact table and the measure value will be in fact table. So the grain of the fact table will be changed to 1 row per patient per exam type per measurement.
3) Create smaller multiple fact tables per exam type per some other criteria like subgroup. But the end user is going to query across subgroups for that exam type and fact-fact join is not recommended.
4) any other ideas?
Any inputs would be appreciated.
1. A big wide fact table with 1000 or more columns.
One very wide fact table gives end-user maximum flexibility if queries are executed directly in the data warehouse. However some considerations should be taken into account, as you might hit some limits depending on a platform.
SQL Server 2014 limits are as per below:
Bytes per row 8,060. A row-overflow storage might be a solution, however it supports only few column types typically not related to fact nature, i.e. varchar, nvarchar, varbinary, sql_variant. Also not supported in In-Memory OLTP. https://technet.microsoft.com/en-us/library/ms186981(v=sql.105).aspx
Columns per non-wide table 1024. Wide-tables and sparse columns are solution as columns per wide table limit is 30,000. However the same Bytes per row limit applies. https://technet.microsoft.com/en-us/library/cc280604(v=sql.120).aspx
Columns per SELECT/INSERT/UPDATE statement 4,096
Non-clustered indexes per table 999
https://technet.microsoft.com/en-us/library/ms143432(v=sql.120).aspx
2. EAV based design - A separate Measure dimension table. This foreign key will go into the fact table and the measure value will be in fact table. So the grain of the fact table will be changed to 1 row per patient per exam type per measurement.
According to Kimball, EAV design is called Fact Normalization. It may make sense when a number of measurements is extremely lengthy, but sparsely populated for a given fact and no computations are made between facts.
Because facts are normalized therefore:
Extensibility is very easy, i.e. it's easy to add new measurements without the need to amend the data structure.
It's good to extract all measurements for one exam and present measurements as rows on the screen.
It's hard to extract/aggregate/make computation between several measurements (e.g. average HDL to CHOL ration) and present measurements/aggregates/computations as columns, i.e. requires complex WHERE/PIVOTING or multi-joins. SQL makes it difficult to make computations between facts in different rows.
If primary end-user platform is an OLAP cube then Fact Normalization makes sense. The cubes allows to make computation across any dimension.
Data importing could be an issue if data format is in a flat style CSV.
This questions is also discussed here Should I use EAV model?.
3) Create smaller multiple fact tables per exam type per some other criteria like subgroup. But the end user is going to query across subgroups for that exam type and fact-fact join is not recommended.
In some scenarios multiple smaller fact tables perfectly makes sense. One of the reason is if you hit some physical limits set by platform, e.g. Bytes per row.
The facts could be grouped either by subject area, e.g. measurement group/subgroup, or by frequency of usage. Each table could be placed on a separate file group and drive to maximize I/O.
Further, you could duplicate measurements across different fact tables to reduce the need of fact tables join, i.e. put one measurement in a specific measurement subgroup fact table and in frequently used measurement fact table.
However some considerations should be taken into account if there are some specific requirements for data loading. For example, if a record errors out in your ETL to one fact table, you might want to make sure that the corresponding records in the other fact tables are deleted and staged to your error table so you don't end up with any bogus information. This is especially true if end users have their own calculations in the front end tool.
If you use OLAP cubes then multiple fact tables actually becomes a source of a measure group to a specific fact table.
In terms of fact-to-fact join, you (BI application) should never issue SQL that joins two fact tables together across the fact table’s foreign keys. Instead, the technique of Drilling Across two fact tables should be used, where the answer sets from two or more fact tables are separately created, and the results sort-merged on the common row header attribute values to produce the correct result.
More on this topic: http://www.kimballgroup.com/2003/04/the-soul-of-the-data-warehouse-part-two-drilling-across/
4) any other ideas?
SQL XML or some kind NoSQL could be an option, but the same querying / aggregation / computation / presentation issues exist.
I have a fact table that includes "wait times in hours" for certain services. I have a lot of dimensions that could describe the wait-times based on different slices; however, I am also interested in knowing how many people (counts) came for services through the filters of the same dimensions.
Given the dimensions for both the wait-times in hours and the number of people who got services are exactly the same, I think it's best practice to keep it in the same fact table. My question is:
Should there be a different fact table for the count measure mentioned?
How would I include this measure? Do I just put 1 in every single row? Because regardless of the wait-time, they've gotten the service only once (you cannot go above/below 1 in my scenario).
1) Think about the grain of your existing fact table. It sounds like it's probably "an occasion on which a person received a service." If that's the same thing you're trying to count, then yes - the waiting time and the count are the same grain.
However, while they may well be the same grain, there might be no need to add anything to the table. Read point 2 for an explanation.
2) You could put a 1 in a column on every row, but I'm not sure what you'd gain from it. You've not said what tools will be consuming this data, but you should be able to do a count/distinct count of some kind.
Working on the basis that you've tagged SSIS so are likely using Microsoft's BI stack:
TSQL has count(), and you can do count(distinct [column]).
SSAS has both counts and distinct counts as aggregation types.
MDX offers several different types of count.
SSRS has Count, CountDistinct, and CountRows.
Whether you do a normal count or a distinct count will depend on whether you're trying to ask "How many people used this service?" or "How many different people used this service?"
I have just read #PerformanceDBA's arguments re: 6NF and E-A-V. I am intrigued. I had previously been skeptical of 6NF as it was presented as "merely" sticking some timestamp columns on tables.
I have always worked with a data dictionary and do not need to be convinced to use one, or to generate SQL code. So I expect an answer that would require a dictionary (or catalog) that is used to generate code.
So I would like to know how 6NF would deal with an extremely simple example. A table of items, descriptions and prices. The prices change over time.
So anyway, what does the Items table look like when converted to 6NF? What is the "explosion of tables?" that happens here?
If the example does not work with a table this simple, feel free to add what is necessary to get the point across.
I actually started putting an answer together, but I ran into complications, because you (quite understandably) want a simple example. The problem is manifold.
First I don't have a good idea of your level of actual expertise re Relational Databases and 5NF; I don't have a starting point to take up and then discuss the specifics of 6NF,
Second, just like any of the other NFs, it is variegated. You can just barely step into it; you can implement 6NF for certan tables; you can go the full hog on every table, etc. Sure there is an explosion of tables, but then you Normalise that, and kill the explosion; that's an advanced or mature implementation of 6NF. No use providing the full or partial levels of 6NF, when you are asking for the simplest, most straight-forward example.
I trust you understand that some tables can be "in 5NF" while others are "in 6NF".
So I put one together for you. But even that needs explanation.
Now SQL barely supports 5NF, it does not support 6NF at all (I think dportas says the same thing in different words). Now I implement 6NF at a deep level, for performance reasons, simplified pivoting (of entire tables; any and all columns, not the silly PIVOT function in MS), columnar access, etc. For that you need a full catalogue, which is an extension to the SQL catalogue, to support the 6NF that SQL does not support, and maintain data Integrity and business Rules. So, you really do not want to implement 6NF for fun, you only do that if you have a need, because you have to implement a catalogue. (This is what the EAV crowd do not do, and this is why most EAV systems have data integrity problems. Most of them do not use the declarative Referential & Data Integrity that SQL does have.)
But most people who implement 6NF don't implement the deeper level, with a full catalogue. They have simpler needs, and thus implement a shallower level of 6NF. So, let's take that, to provide a simple example for you. Let's start with an ordinary Product table that is declared to be in 5NF (and let's not argue about what 5NF is). The company sells various different kinds of Products, half the columns are mandatory, and the other half are optional, meaning that, depending on the Product Type, certain columns may be Null. While they may have done a good job with the database, the Nulls are now a big problem: columns that should be Not Null for certain ProductTypes are Null, because the declaration states NULL, and their app code is only as good as the next guy's.
So they decide to go with 6NF to fix that problem, because the subtitle of 6NF states that it eliminates The Null Problem. Sixth Normal Form is the irreducible Normal Form, there will be no further NFs after this, because the data cannot be Normalised further. The rows have been Normalised to the utmost degree. The definition of 6NF is:
a table is in 6NF when the row contains the Primary Key, and at most one, attribute.
Notice that by that definition, millions of tables across the planet are already in 6NF, without having had that intent. Eg. typical Reference or Look-up tables, with just a PK and Description.
Right. Well, our friends look at their Product table, which has eight non-key attributes, so if they make the Product table 6NF, they will have eight sub-Product tables. Then there is the issue that some columns are Foreign Keys to other tables, and that leads to more complications. And they note the fact that SQL does not support what they are doing, and they have to build a small catalogue. Eight tables are correct, but not sensible. Their purpose was to get rid of Nulls, not to write a little subsytem around each table.
Simple 6NF Example
Readers who are unfamiliar with the Standard for Modelling Relational Databases may find IDEF1X Notation useful in order to interpret the symbols in the example.
So typically, the Product Table retains all the Mandatory columns, especially the FKs, and each Optional column, each Nullable column, is placed in a separate sub-Product table. That is the simplest form I have seen. Five tables instead of eight. In the Model, the four sub-Product tables are "in 6NF"; the main Product table is "in 5NF".
Now we really do not need every code segment that SELECTs from Product to have to figure out what columns it should construct, based on the ProductType, etc, so we supply a View, which essentially provides the 5NF "view" of the Product table cluster.
The next thing we need is the basic rudiments of an extension to the SQL catalog, so that we can ensure that the rules (data integrity) for the various ProductTypes are maintained in one place, in the database, and not dependent on app code. The simplest catalogue you can get away with. That is driven off ProductType, so ProductType now forms part of that Metadata. You can implement that simple structure without a catalogue, but I would not recommend it.
Update
It is important to note that I implement all Business Rules in the database. Otherwise it is not a database (the notion of implementing rules "in application code" is hilarious in the extreme, especially nowadays, when we have florists working as "developers"). Therefore all rules, etc are first and foremost implemented as SQL declarations, CHECK constraints, functions, etc. That preserves all Declarative Referential Integrity, and declarative Data Integrity. The extension to the SQL catalog covers the area that SQL does not have declarations for, and they are then implemented as SQL. Being a good data dictionary, it does much more. Eg. I do not write Views every time I change the tables or add or change columns or their characteristics, they are created directly from the catalog+extension using a simple code generator.
One more very important note. You cannot implement 6NF (or EAV properly, for that matter), without completing a full and faithful Normalisation exercise, to 5NF. The problem I see at every site is, they don't have a genuine 5NF state, they have a mish-mash of partial normalisation or no normalisation at all, but they are very attached to that. Creating either 6NF or EAV from that is a disaster. Creating EAV or 6NF from that without all business rules implemented in declarative SQL is a nuclear disaster, burning for years. You get what you pay for.
End update.
Finally, yes, there are at least four further levels of Normalisation (Normalisation is a Principle, not a mere reference to a Normal Form), that can be applied to that simple 6NF Product cluster, providing more control, less tables, etc. The deeper we go, the more extensive the catalogue. And higher levels of performance. When you are ready, just ask, I have already erected the models and posted details in other answers.
In a nutshell, 6NF means that every relation consists of a candidate key plus no more than one other (key or non-key) attribute. To take up your example, if an "item" is identified by a ProductCode and the other attributes are Description and Price then a 6NF schema would consist of two relations (* denotes the key in each):
ItemDesc {ProductCode*, Description}
ItemPrice {ProductCode*, Price}
This is potentially a very flexible approach because it minimises the dependencies. That's also its main disadvantage however, especially in a SQL database. SQL makes it hard or impossible to enforce many multi-table constraints. Using the above schema, in most cases it will not be possible to enforce a business rule that every product must always have a description AND a price. Similarly, you may not be able to enforce some compound keys that ought to apply (because their attributes could be split over multiple tables).
So in considering 6NF you have to weigh up which dependencies and integrity rules are important to you. In many cases you may find it more practical and useful to stick to 5NF and normalize no further than that.
I had previously been skeptical of 6NF
as it was presented as "merely"
sticking some timestamp columns on
tables.
I'm not quite sure where this apparent misconception comes from. Perhaps the fact that 6NF was introduced for the book "Temporal Data and The Relational Mode" by Date, Darwen and Lorentzos? Anyhow, I hope the other answers here have clarified that 6NF is not limited to temporal databases.
The point I wanted to make is, although 6NF is "academically respectable" and always achievable, it may not necessarily lead to the optimal design in every case (and not just when considering implementation using SQL either). Even the aforementioned discoverers and proponents of 6NF seem to agree e.g.
Chris Date: "For practical purposes, stick to 5NF (and 6NF)."
Hugh Darwen: "the 6NF decomposition around Date [not the person!] would be overkill... an optimal design for the soccer club is... 5-and-a-bit-NF!"
Hugh Darwen: "we are in 5NF but not in 6NF, and again 5NF is sufficient" (several similar examples).
Then again, I can also find evidence to the contrary:
Chris Date: "Darwen and I have both felt for some time that all base relvars should be in 6NF".
On a practical note, I recently extended the SQL schema of one of our products to add a minor feature. I adopted a 6NF to avoid nullable columns and ended up with six new tables where most (all?) of my colleagues would have used one table (or perhaps extended an existing table) with nullable columns. Despite me proving several 'helper' stored procs and a 'denormalized' VIEW with a INSTEAD OF triggers, every coder that has had to work with this feature at the SQL level has gone out of their way to curse me :)
These guys have it down: Anchor Modeling. Great academic papers on the subject, combined with practical examples. Their writings have finally pushed me over the edge to consider building a DW in 6nf on an upcoming project. The POC work I have done has validated (for me, at least) that the enormous benefits of 6nf don't outweigh the costs.