Presentation on theme: "SQLBits 8, 9 th April 2011, Brighton Vincent Rainardi"— Presentation transcript:
SQLBits 8, 9 th April 2011, Brighton Vincent Rainardi firstname.lastname@example.org
Advanced Dimensional Modelling 1. Dimensions - Structure SCD Type 6 1 or 2 Dimensions When To Snowflake A Dimension with Only 1 Attribute Transaction Level Dimension 2. Fact Tables Fact Table Primary Key Snapshotting Transaction Fact Tables Aggregate Fact Tables Vertical Fact Tables 3. Dimensions - Behavior Rapidly Changing Dimension Very Large Dimensions Banding Dimension Rows Stamping Dimension Rows Dimensions with Multi Valued Attributes 4. Combinations Real Time Fact Table Dealing with Currency Rates Dealing with Status 4 sections, lots of material, may not able to finish. Some pages we may have to touch lightly. Questions between sections, available after.
SCD Type 6 SCD Type 6 is a combination of Type 1, 2 & 3 e.g. type 2 + type 1 : (telco example) 6 = 1 + 2 + 3 (Ref: Ross/Kimball, Wikipedia) http://www.rkimball.com/html/articles_search/articles%202005/0503IE.html http://en.wikipedia.org/wiki/Slowly_changing_dimension
SCD Type 6 Used for “As Was” reporting e.g. balances by tariff (price plan) at the end of last year, had the customers were on today’s tariff.
1 or 2 dimensions Simplicity, customer attributes in dim account Hierarchy from customer attribute &account attribute Use when we don’t have fact tables requiring customer grain. We can get the customer attributes without knowing the account key Disadvantage: can’t go from account to customer without going through the fact table - performance
1 or 2 dimensions Modular: we have 2 separate dim tables but we can combine them easily to create a bigger dimension To get the breakdown of a measure by a customer attribute is a bit more complicated than a) select c.cust_attribute, sum(f.measure1) from fact1 f inner join dim_account a on f.account_key = a.account_key inner join dim_customer c on a.customer_key = c.customer_key group by c.cust_attribute
1 or 2 dimensions Try to fix weakness on b and c: We can “go” from account dim to customer dim We can access dim customer directly from the fact table. Weakness: maintain customer key in 2 places: fact table and dim account. a.k.a. “Star with a Back Door”
1 or 2 dimensions Try to fix weakness of a: unable to build a fact table with grain = customer. Add a column in dim account: customer key Not as popular as c) and d) in solving DimCustomer issue. It is “undecisive” : trying to create DimCustomer but doesn’t want to create DimCustomer. DimCustomer is hidden inside dim_account, making it: a) more difficult to maintain (especially for a type 2), and b) less modular/flexible which are the main disadvantages of this approach.
When to Snowflake 1. When the sub dim is used by several dims City-Country-Region columns exist in DimBroker, DimPolicy, DimOffice and DimInsured Replaced by Location/GeoKey pointing to DimLocation / DimGeography Advantage: consistent hierarchy, i.e. relationship between City, Country & Region. Weakness: we would lose flexibility. City to Country are more or less fixed, but the grouping of countries might be different between dimensions.
When to Snowflake 2. When the sub dim is used by both the main dim and the fact table(s) DimCustomer is used in DimAccount, and is also used in the fact table. DimManufacturer is used in DimProduct, and is also used in the fact table. DimProductGroup is used in DimProduct, and is also used in some fact table. The alternative is maintaining two full dimensions.
4. To enrich a date attribute When to Snowflake 3. To make “base dim” and “detail dim” Insurance classes, account types (banking), product lines, diagnosis, treatment (health care) Policies for marine, aviation & property classes have different attributes. Pull common attributes into 1 dim: DimBasePolicy Put class-specific attributes into DimMarine, DimProperty, DimAviation Month, Quarter, Year, etc. Like #1, a sub dim used by several dims. Ref: Kimball DW Toolkit 2 nd edition page 213
A dimension with only 1 attribute Reasons for putting single attribute in its own dim: Keep fact table slim (4 bytes int not 100 bytes varchar) When the value changes, we don’t have to update the big fact table – ETL performance Grain is much lower than fact table – small dim Yes it’s only 1 attribute today, but in the future there could be another attribute Should we put the attribute in the fact table? (like DD = Degenerate Dim) Probably, if the grain = fact table, and it’s short or it’s a number.
A dimension with only 1 attribute Exception: snapshot month Snapshot month is used in periodic snapshot fact table. Snapshot month is in the form of an integer (201104 for April 2011). Doesn’t violate the 3 points above. It is an integer, not char(6). The value never changes, April 2011 will be April 2011 forever There will not be other attributes in the dim
Transaction Level Dimension A dim with grain = the transaction fact table Transaction, not accumulative or periodic snapshot Examples: IT Helpdesk DW: Dim Ticket Telco DW: Dim Call Banking/Asset Mgt DW: Dim Trade Insurance DW: Dim Premium Transaction Level Dim
Transaction Level Dimension 1.Query Performance DD columns are moved to a dim, away from the heavy traffic in fact tables. DW queries don’t touch those DD columns unless they need to – performance. DD attributes totalling 30 bytes, replaced by 4 bytes int column. Slimmer fact table, better for queries. 2.Periodic Snapshot Fact Table For periodic snapshot fact table, saving is even greater. Monthly snapshot fact, 10 years / 120 months. Rather than specifying the DDs repeatedly 120x, they are specified once in the transaction dim. All that is left on the fact table is a slim 1 int col: the transaction key. Advantages:
3.Some fact tables have grains greater than the transaction A payment from a customer is posted into 4 accounts in the GL fact table. That single financial transaction becomes 4 fact rows but only has 1 row in the trans dim. Fact table with 10m rows, trans dim only 3 million rows. 4.Related Transactions Some transactions are related, e.g. in retail, a purchase of a kitchen might need to be created as 2 related orders, because the worktop is made-to- order. Rather than creating a ‘related order’ column on the fact tables, it might be better (depends on how it’s used) to create it on the trans dim because: a) an order can consist of many fact rows (1 row per item) so the “related order number” will be duplicated across these fact rows b) slimmer fact table c) the transaction could be on many fact tables, not only one. Transaction Level Dimension
Transaction fact table and the grain of the trans dim = grain of the fact table, and only 1 DD column: perhaps better leave the DD in the fact table. Not a lot of space/speed gain by putting it on trans dim. Mart/DW only used for SSAS: there is little point of having trans dim physically. In SSAS we can create the transaction dimension “on the fly” from the fact table (“fact dimension”). Disadvantages/not suitable: Using trans dim to put attributes as opposed to put them in the main dimensions, with the argument of: that’s the value of the attribute when the transaction happened – this is not right, use type 2 SCD for this. Main Trans Acct type Location
Transaction Level Dimension Any dim with grain = fact table (like trans dim) is questionable Do we really need this dim at this grain? Perhaps it should be divided into several dims instead? A dim with grain = fact table - potential performance issue (unless the fact table is small). e.g. fact table = 10m rows, trans dim = 10m rows. Joining 10m to 10m potentially slow, especially if the physical ordering of the trans dim is not the joining column. Disadvantages/not suitable:
1. Dimensions - Structure SCD Type 6 1 or 2 Dimensions When To Snowflake A Dimension with Only 1 Attribute Transaction Level Dimension 2. Fact Tables Fact Table Primary Key Snapshotting Transaction Fact Tables Aggregate Fact Tables Vertical Fact Tables 3. Dimensions - Behavior Rapidly Changing Dimension Very Large Dimensions Banding Dimension Rows Stamping Dimension Rows Dimensions with Multi Valued Attributes 4. Combinations Real Time Fact Table Dealing with Currency Rates Dealing with Status 25% Time, Questions
Fact Table Primary Key Should we have a PK? Yes if we need to be able to identify each fact row 1. Need to refer to a fact row from another fact row e.g. chain of events 2. Many identical fact rows and we need to update/delete only one 3. To link the fact table to another fact table Example of not having a PK If duplicate fact rows are allowed. e.g. retail DW: Store Key, Date Key, Product Key, Customer Key Same customer buying the same milk in the same shop on the same day twice --- Order Line ID as DD to make it unique (not all EPOS has it)
Fact Table Primary Key Single or Multi Column? Single Column: Generated Identity Multi Column: Dimension Keys Single-column PK is better than multi-column PK because : 1) A multi-column PK may not be unique. A single-column PK guarantees that the PK is unique, because it is an identity column. 2) A single-column PK is slimmer than a multi-column PK, better query performance. To do a self join in the fact table (e.g to link the current fact row to the previous fact row), we join on a single integer column.
Fact Table Primary Key Advantage: Prevent duplicate rows Disadvantage: performance Indexing the PK: cluster or not? Cluster the PK if: the PK is an identity column Don’t cluster the PK if: the PK is a composite, or when you need the cluster index for query performance (with partitioning)
Snapshotting Transaction Fact Tables Potentially huge – billions rows Only take what you need Smart date key/month, e.g. 20110409 Monthly or daily Trunc-reload of current month/day Daily (4 wk), Weekly (1 yr), Monthly (10 yr) Purging & Archiving Load from staging (cached) Index/partition on snapshot date Trans Snapshot Staging
Aggregate Fact Tables What are they? High level aggregation of base fact tables A “select group by” query on a 2 billion rows fact table can take 30 mins if it joins with two big fact tables, even with indexes in place So we do this query in advance as part of the DW load and store it as an Aggregate Fact Table The report only takes 1 second to run. Aggregate Fact Table Base Fact Tables Report 30 mins 1 sec
Aggregate Fact Tables What For? For report performance (group by is costly) BO: aggregate aware Not SSAS: aggregate in cubes, not tables Loading & indexing: Best to load from staging (at the same time as loading the main fact table) not from the main fact table (this would be working 2x) Partition for data distribution or narrow query Indexing: by the main dim keys
Vertical Fact Tables Normalised 1 measure column The meaning of that measure column depends on “measure type” column Used for Finance/GL mart Advantage: flexibility: using accounts, balance, Dr Cr Disadvantage: non additive “Normal” Fact Table Vertical Fact Table many measures 1 measure
1. Dimensions - Structure SCD Type 6 1 or 2 Dimensions When To Snowflake A Dimension with Only 1 Attribute Transaction Level Dimension 2. Fact Tables Fact Table Primary Key Snapshotting Transaction Fact Tables Aggregate Fact Tables Vertical Fact Tables 3. Dimensions - Behavior Rapidly Changing Dimension Very Large Dimensions Banding Dimension Rows Stamping Dimension Rows Dimensions with Multi Valued Attributes 4. Combinations Real Time Fact Table Dealing with Currency Rates Dealing with Status 50% Time, Questions
Rapidly Changing Dimension Why is it a problem Large SCD2 dim – Attributes change every day Slow query when join with large fact tables What to do Put into a separate dim, link direct to fact table. Just store the latest, type 1 attributes Store in the fact table (for small attribute, e.g. indicator)
Very Large Dimension Why is it a problem SSAS: 4 GB string store limit for dimension SSAS: dim is select distinct on each attribute – long processing time “Valid date” join on SCD2 for as was Usually customer dim where the “quality stamp” keep changing, or number of distinct values Difficult to browse high cardinality attribute Join with fact tables – performance
What to do Split into 2 dims, same grain. Always cut vertically. Remove SCD2, or at least only certain columns. Most common: separate the attributes with high cardinality/change frequency Bucketing/banding, group values into ranges Very Large Dimension VLD
Banding Dimension Rows It is grouping numerical values (numerical attributes, not measure) into several bands, e.g. engine size, distance from station, amount purchased (last complete year). Benefits: easier for analysis & reporting, comparing between categories. Issue/problem: limit e.g. bucketing criteria 1 hour to implement, 3 months to argue
Stamping Dimension Row Calculate internally or buy data from outside Customer categories (loyalty programme) e.g. A, B, C of customer class. To reflect c0nsumer interest on the product (product categorisation based on customer interest level) Any other dates or measures summarized as stamped attribute, i.e. “new customer”, “big spender”, or results from recommendation analysis/algorithm e.g. customer behaviour based on previous purchases. Used for analysis / reporting “Stamped” Attributes
Dimensions with Multi Valued Attributes What is a Multi Valued Attribute? An attribute which has more than 1 value per dimension row. MV Attribute or MV Dimension? MV Dim = For each fact row there could be more than 1 dimension row Why do I need to know this? To be able to model it If wrong, difficult at BI/report
Dimensions with Multi Valued Attributes Approaches to deal with MV Attributes 1. Lower the grain of the dim 2. Put the MV attributes in a separate dim, link direct to the fact table Before After Before After Fact table requires that the product dimension is at Product Code grain, e.g. no sales info per size, but only per product code. Often we don’t have the allocation information e.g. 50-50 or 30-70, we only know that product1 has 2 sizes
Dimensions with Multi Valued Attributes 3. Use a bridge table to link the 2 dims Fact Table Dim Product Bridge Table Dim Size 4. Have several columns in the dim for that attribute If the number of attributes is small and fixed, this is a popular approach. But if the number of attributes is large (e.g. >10) or if it’s variable (e.g. sometimes 2, sometimes 20), approach 2 and 3 above are more popular, and more appropriate.
5. Put the attribute in a snowflake sub dim Dimensions with Multi Valued Attributes We can’t really do this, as it is 1 to many (1 row in the main dim corresponds to many rows in the sub dim). So we need a bridge table, which brings us back to approach 3. 6. Keep in one column using delimiters e.g. “Small|Medium ″. A crazy idea. More flexible than having several columns (approach 4) and simpler than approach 3 or 2. If the purpose of the attribute is “display only” on a report (rather than analyse or slice & dice), there is an argument for using this approach, particularly if the number of attributes is small (e.g. 1 to 4).
Dimensions - Structure SCD Type 6 1 or 2 Dimensions When To Snowflake A Dimension with Only 1 Attribute Transaction Level Dimension Fact Tables Fact Table Primary Key Snapshotting Transaction Fact Tables Aggregate Fact Tables Vertical Fact Tables Dimensions - Behavior Rapidly Changing Dimension Very Large Dimensions Banding Dimension Rows Stamping Dimension Rows Dimensions with Multi Valued Attributes Combinations Real Time Fact Table Dealing with Currency Rates Dealing with Status 75% Time, Questions
Real Time Fact Table Reporting the transaction system in real time View to union with the normal fact table, or use partitions Freezing the dims for key lookup, -3 for unknown dim Key corrections next day Real time partition (intraday today) Dims as of yesterday Main partition (up to last night)
Dealing with Currency Rates What for/background/requirements Report in 3 reporting currencies, using today rates or past Analyse over time without the impact of currency rates (using fixed currency rates, e.g. 2010 EOY rates) Had the transactions happened today FX historical analysis Transaction Currency DW Currency Reporting Currency Transaction Rates Reporting Rates (many transaction dates) ( 1 reporting date) 100 countries 40 currencies 1 currency 3-4 currencies GBP, USD, EUR, Original
Dealing with Currency Rates Approaches Store in original currencies, convert to DW currency at runtime. Or convert at load, store in DW currency – inaccuracy. Or store in both original and DW currency Currency rate fact table (date, currency, rate) Or store rates in the fact table On report/cube: date input at run time (default = today) Fact Tables FX Fact Table
Dealing with Currency Rates Concept of FX Rate Type/Profile in original currency, DW currency or both
Dealing with Status What/background Workflow (policies, contracts, documents) Bottleneck analysis (no of days between stages) How many on each stage Status 1 Status 3 Status 4 Status 5 Status 6Status 2 date1 date4 date3 date2
Dealing with Status Approaches Accumulative Snapshot Fact, 1 row per application SCD2 on DimApp App Status fact table
Thanks Email: email@example.com Blog: http://dwbi1.wordpress.com Special thanks to Guang Ming Xing and Simon Jensen who helped reviewing this presentation and provided useful comments.