Friday, August 24, 2018

Adventures in Mapping Multivalues - Control Files

What Shape is Your Data?

In a SQL world, a specific column in a specific table has exactly one data type and it cannot change.  In a pinch, you can represent everything with a varchar, but that's not very useful. Arithmetic gets tricky for numbers, and dates become impossible to sort!

Pick, on the other hand, much like XML, is essentially free-form.  You can define dictionaries for your data, but there is nothing that forces you to follow the dictionary layout, or even warn you if you depart, and in many Pick dictionaries, you don't have to look far to find two dictionary records that point at the same attribute, but have completely different definitions.

Just about every Pick system I ever worked on had a control file. About 75 to 80% of the time, it was called "CF".  The item-id of this file would normally have a structure like this:

Control-record-type "*" Identifier

The asterix was probably the most common delimiter in early Pick systems and carries through to many applications.  So some examples might be:


The thing with this file is that each record type would have a unique structure. For SIZE the first attribute might be an integer, which is the number of inches.  COLOUR might have 3 integers for red, green and blue, and INDUSTRY.CODE might have a text name of the industry, followed by some tax rates.

In order to process this and a whole bunch of other problems, we came up with a strategy called a "Select Method" or "Filter".  This was a subroutine that you would associate with a table that you defined.  This filter was a Pick BASIC subroutine with this signature:


As soon as our SQL engine read a record for processing, it called this subroutine, passing the item-id in ID, and the full dynamic array text of the item body in ITEM.  You got to set PROCEED to 1 or 0 (true or false). If you set it to false, we'd skip the record.

So, for our example above, we'd define 3 table views on the CF file. The first might be called CF_SIZE and the filter method would look like this:


It would only process records that started with "SIZE*".

You'd then create another view called CF_COLOUR with this filter:


This would only process records that started with "COLOUR*".

And you'd wind up with one called CF_INDUSTRY_CODE with this filter method:


This would only process records that started with "INDUSTRY.CODE".

We would then define a different mapping for each record type.

Note that we avoided periods in table names as periods were significant separators in the SQL world. If you used them you'd have to quote your table names. In the early days, many ODBC clients wouldn't do this, so you were asking for trouble if you used them!

In addition to Row Select Methods, there were Row Delete Methods, Row Insert Methods and Row Update Methods that you could use to tweak how you wrote data back.  We'll deal with this in another post. Our next few posts will look at some more magic that we did with Row Select Methods, otherwise known as Filter Methods.

Thursday, August 16, 2018

Adventures in Mapping Multivalues - Two Common Approaches

The Problem

In a previous post, I talked about what a Pick database was.  Pick had a way of representing complex data that was intuitive and matched the structure of common documents that it was modeling very closely. It was a very human-friendly way to represent that data. Note that this is much the same argument for XML data (that's for another blog post...)

In this post, I'm going to talk about how this might look in a relational database, then I'll talk about two different approaches that Pick uses to represent this kind of data:

  • Correlated Multivalues
  • Subfiles

Our Document Data

So, let's consider the example we gave of our Invoice. Here is how it might look in a printed document, with no consideration of how it actually is stored in the database:

Invoice 0001 Date 11 Aug 2018 Customer ABC Company

Product Options Quantity   Price............ Extended...
WIDGET  GOLD           2            $115.95    $231.90
GIDGET  RED            1            $220.75    $220.75
Subtotal:                                      $452.65

Note that you have an invoice number, which should be a unique key, a date, and customer, that are all fields that have a single value.  Then you have a bunch of fields that have more than one value: Product, Options, Quantity, Price. We also have two calculated fields, one being a subtotal, and the other being the extended price (Quantity times Price), which has multiple values, one for each product line.  Note also that the Options value can have multiple values for each product value, so this is a sub-multivalued field, or subvalued for short.

Relational (SQL) Model

Here is one possible relational representation of that data:

Invoice_Id Invoice_date Customer...........................
      0001  11 Aug 2018 ABC Company

Invoice_Id Line_Id Product Quantity Price....
      0001       1 WIDGET         2    115.95
      0001       2 GIDGET         1    220.75

Invoice_Id Line_Id Option_Id Option
      0001       1         1 GOLD
      0002       1         1 RED
      0002       2         1 SMALL

Let's talk about some points here.  In the INVOICE_LINES table, we've added a Line_Id field, which combined with the Invoice_Id would be the primary key (unique identifier). If you knew for sure that you would never repeat a product in another line of your invoice, that any product would be unique within any invoice, you could skip that Line_Id field and use the Product value with the Invoice_Id as the primary key. That assumption would probably bite you. (In a multivalued database, you never have to create artificial keys like this.)  The same thing happens twice over in the INVOICE_OPTIONS table.  This mess of decisions, keys, and additional values creates code entropy, as programmers are required to do multiple reads, then navigate multiple data sets to process the data. One is generally safer to create a new key in these cases, but then you have a task of managing these keys when you write the data back, or insert (or delete) new invoice lines or options.

Correlated Multivalues

Here is the most common representation of the above in Pick:


Notice that in this structure, you have minimal wasted space. It was developed when memory and disk were both expensive, so keeping things small was beneficial.  The up-arrow character '^' is the attribute, or field delimiter. The square bracket ']' is a multivalue delimiter, and the backslash '\' is the subvalue delimiter. The actual delimiters are high-ASCII characters that were not commonly used in data in typical ASCII systems.

The first thing you see is what Pick calls the Item-Id. In Relational terms, this is a single field that is always the entire primary key. It is a unique identifier that is used to determine which bucket of the hash-file to put the data  in, or to find it in, if you are looking data up.

The next two fields are the invoice date (number of days since Dec 31, 1967) and the customer name. They both have just one value in them.

The next 4 fields contain multivalues. The first value of each of these fields is the data for line 1 of the invoice. The second value of each of these fields is the data for line 2 of the invoice.  For the Options field, the first value contains just that value. The second value contains two subvalues. The first one is the first option line for the second invoice line, the second is the second option line for the second invoice line.

For a Pick BASIC programmer who has read this record into a variable called X, he would access the first invoice line's product with this code:


The second line's product value would be this:


The Item-Id is not part of what comes back from a read (you need it to do the read), so it's often referred to as attribute 0.

To get the second option of the second invoice line, you would use this:


You basically had to know the attribute numbers for your fields and you could get at anything. One read statement got you everything for your document. Your record really was your document.


A less common, but still often used approach to representing this data is called a subfile. In a subfile, all the detail line data is contained in a single field. Here is how it would be represented:

0001^18486^ABC Company^WIDGET\C*GOLD\2\11595]GIDGET\C*RED,

All of your detail lines are contained in attribute 3. The first multivalue is all of invoice line 1's data. The second multivalue is invoice line 2's data.  The first subvalue of each multivalue is always the product, the second subvalue is the options, the third is the quantity and the fourth subvalue of each multivalue is the price.

Note that we ran out of delimiters, so we had to use a comma separator two delimit the second line's two option values.

So here is what a Pick BASIC programmer would do to get the first invoice line's product:


You need to specify that you only want that first subvalue, or you'll get the whole invoice line. That may be handy in itself, but then you'd have to do a second line to get that data out.

The second line's product value would be this:


To get the second option of the second invoice line, you would use this:


This line gets a bit more involved. The angle brackets only work with system delimiters, so it retrieves both option values, with the comma separator. The FIELD statement says to pull the 2nd comma delimited value out.

Again, your record is really the entire document.  Note also, that the BASIC implementation has data access and handling functionality built in. You don't have to call out to an API as it's all there!

Liberty Mapping Terminology

Liberty ODBC's SQL Mapping layer handled both of these structures very simply.

The Correlated Multivalues were termed a "Group Floating" table.  If you were dealing with Correlated Subvalues, you had a "Group Floating within Group Floating" table.

The subfiles were called a "Group Positional" table.  By using these structures, you would get the equivalent to the above.  You could define SQL columns that were the multivalue or subvalue offsets, effectively giving you the Line_Id and Option_Id fields.

When joining two tables where one was actually contained inside the other, our engine would optimize and read the Pick record only once for efficiency sake.


In some of my upcoming posts, I'll talk about some of the interesting and odd problems we had to deal with in order to efficiently map and process multivalued data in our SQL engine.

Saturday, August 11, 2018

What is a PICK MultiValue Database?

Why I'm Blogging This

In some upcoming posts, I'm going to be talking about how we implemented an ANSI/92 compliant SQL engine and ODBC driver for this, and some of the interesting challenges we encountered. In some cases, I'll be talking about the relative merits of these systems vs. relational databases.  They all have their strengths.

In order to do this, I thought it would be good to have a bit of a primer about how Pick data is stored and processed. I'll probably reference this little blog post quite a bit.

It's Still Here

The first thing I want to say is that systems running on Pick based multivalue are ubiquitous and they are EVERYWHERE! In some cases they are legacy solutions that have been in place and working for ever.  Probably every fortune 500 company has at least one system, running in some department, that runs on it.  It's also still being sold as part of applications around the world.

Examples of industries and solutions that continue to run and/or sell best-of-breed solutions running on PICK include (but are not limited to) Automotive Retail, Libraries, Banking, Healthcare, Insurance, Manufacturing, Distribution, EMS, Hospitality, Municipal, Government, and many more.

Ignore them at your peril!

Where Did it Come From?

In about 1965, the US department of defense awarded a contract to TRW to create a system to manage Cheyenne helicopter parts.  Two engineers, Don Nelson and Dick Pick were assigned to the task, and started work on an IBM System/360 computer. They named their system GIRLS, which was an acronym for Government Inventory Relational Language System.

This was before commodity hardware was a thing. Somehow they got the idea to create a system based on a virtual machine that they could implement on any hardware.  There was some really good architecture and design that went into this system.

How Does PICK Data Work?

The database was a very simple concept.  Whatever document you needed to represent, you would have a data structure that contained all the information in one place.  With a single read, a programmer could pull back all the key data that was needed for an invoice.  In real practice, your invoice would reference a customer by an id, and the customer name would remain in the customer master file. Similar things would happen with part numbers and the part names.  The invoice would include both the header information and the detail lines for the parts being ordered.

The data is stored in a hashed file structure and each record is stored as a sparse string with delimiters separating fields, values and subvalues.  Let's look at an example.

Still working with the invoice concept, lets consider a manufacturing organization that sells three key products, widgets, gidgets and gadgets.  The invoice file has an invoice date, customer id, then it has 4 fields that contain multiple values (multivalues) in them.  These are product id, options, quantity and price.  When you display an invoice it might look like this:

Invoice 0001 Date 11 Aug 2018 Customer ABC - ABC Company

Product Options Quantity   Price............ Extended...
WIDGET  GOLD           2            $115.95    $231.90
GIDGET  RED            1            $220.75    $220.75
Subtotal:                                      $452.65

When looking at the document, you can see that you have header data that occurs just once per document, including the invoice number, date, and customer information. You would look up the customer's name from the customer master file.  You also have computed or calculated fields, like extended costs, subtotals, taxes and final totals.

The interesting thing to note is the invoice lines data. In a real invoice all the lines, an set of arbitrary size, is part and parcel of, and contained in, the invoice document.  For a single invoice you could have an arbitrary number of products being purchased. for each you would have a quantity and a price.  In our case, we further complicated it by allowing different products to have different options. You might choose a colour and a size as with the gidget we had on the second invoice line.  At this point we not only have multiple values, but one of the values (the OPTIONS value for the GIDGET line) has two subvalues, one for the colour and one for the size.

Don and Dick came up with a database structure that allowed that.  Your fields were delimited by field or attribute mark delimiters. Your multiple values were delimited by value mark delimiters and your subvalues were delimited by subvalue mark delimiters. The data for the above INVOICE file might look like this:

Note that the blocks are the field delimiters. the superscripted '2' is a value mark, and the superscripted 'n' is a subvalue mark. These are high-ASCII delimiters. Going left to right, we have the first field, the item-id (similar to a primary key) which is the invoice number '00001'. Then you have a date. This is represented as number of days since Dec 31, 1967.  Next we have the customer id 'ABC' for ABC Company. Then we get a field that has two values. This field and all the subsequent fields have two values, the first one is line 1 of the detail lines and the second is line 2 of the detail lines. This first multivalued field has the product ids of the products being bought.The next one has the options selected for each line. You can see that for detail line 1 there is only one option, but for the 2nd detail line (the GIDGET), we have two options: The colour RED and the size SMALL. Then we have the quantities of each of these and the unit price for each line. The price has an implied 2 decimals (it is storing the value as cents, not decimal dollars).

For a programmer, the invoice number gave you the whole document. You might need to do ancilliary lookups in the CUSTOMER, PRODUCTS and OPTIONS tables, but a single read and a single write gets you your data and saves it to the database.

In a relational database, the INVOICE file would need to be a minimum of 3 tables. The record represented above would take a minimum of 6 disk reads to get the same data. As you increase the number of detail lines and the number of product options, the complexity grows rapidly.  A programmer in this case needs to retrieve the data from the database and make sure he gets it all. Then he needs to properly relate the data sets in his program. The complexity is huge compared with the multivalued approach. 

So, in relational terms, you would probably have these tables:  INVOICE_HEADER, INVOICE_LINES, INVOICE_LINES, INVOICE_LINES_OPTIONS.

Think of how this would work if you stored your physical invoices like this. You'd go to the filing cabinet with header information and look up the header part of the invoice. Then you'd go to another filing cabinet where you'd have to find a separate page for each invoice line. You'd use the invoice number and line number. You'd keep looking until you couldn't find another line number for that invoice. Then you'd go to yet another filing cabinet, and for each invoice line for that invoice, you'd look for one or more pages that had information for the options (possibly multiple) for those invoice lines. I've seen invoices that had hundreds of lines in them. Can you imagine then having to organize and manage those hundreds of pages?  Instead of one invoice that was maybe 3 pages long?

There is also a performance component to this.  I remember working with a customer who had moved from a mainframe to a minicomputer platform, and had determined that they could not afford the disk head movement of a relational database.  They were a big customer of our ODBC technology as we enabled them to have a relation view of the data and use it with tools that required this, while still giving them the reduced disk head movement for their core application.

How Did You Access That Data?

Pick had a concept called a dictionary. You would have a file that contained field definition records associated with every file. It was completely optional, and once the BASIC language came out for Pick, it didn't enforce anything in it. In fact, you could have two dictionaries that defined the same attribute completely differently. Generally, one would be wrong and the other right, but I've seen where a single file has multiple different record types, based on a prefix in the key. Which dictionary you wanted to use depended on the record key!

You could create a dictionary definition item that actually used a value from the current record to read a value from another file. This was called a translate, and was very powerful.  For the example index above, you would take the customer id (let's say it's attribute 2) and use it to pull attribute 1 (customer name) from the CUSTOMERS file. That translate correlative would look like this:

001 A
002 2

Similarly, you could use this to pull information from a PRODUCTS file.

When these systems were first commercially sold, salespeople would walk a machine in from a panel van (they were the size of a fridge, so this was quite a feat.)  Then they'd use it to create a couple of tables, use the editor to create some dictionary definitions. Create a script program using PROC and BATCH to enter data. Then they'd use a LIST command to print out the data.

So the command "LIST INVOICES" would be automatically expanded out to:

which would give us this output:

It was not uncommon for this to be so impressive for the business users watching the demo that a cheque would be written and the computer left there. Then they had to get programmers in and write a system... but it was also not uncommon for a business person to dictate what the system had to do and create his own reports.  Compared with the options available at that time, this was a huge step forward, and many commercial systems that are still in use today had their start with a business owner looking over a programmer's shoulders. This was the first instance of agile and pair programming!

Later Add-ons

The first systems were written using a combination of PROC (a scripting language), BATCH (which was horrible, and impossible to make pretty - totally character based), and when you got stuck, Assembler.

Later, a BASIC implementation was added with built-in support for the multivalued database, and that allowed you to use extended features of an ASCII terminal.  These databases still exist and go by a number of names:

  • D3
  • Universe
  • Unidata
  • mvBase
  • mvEnterprise
  • jBase
  • OpenInsight
  • QM and OpenQM
  • RealityX
  • And others

Relational Value and Pushback

An interesting thing to note is that many applications are being written today to use what are called NoSQL databases. The rationale for these databases sounds like an ad for Pick.  While relational databases have their uses, there are clearly applications where they are just not the right solution.  

That said, a huge amount of investment has been made by companies like Oracle, IBM, Microsoft, Cognos, Business Objects and others into enhancements and tools that leverage relational databases. This has not been the case with Pick, so there are many applications where it is imperative that the regardless where your data originates, you need to get it into relational in order to leverage these tools and technologies.

The industry is clearly seeing a divide form between SQL and NoSQL databases, yet there is also growing clarity about when you should use each.  What's not as clear is that there is a NoSQL option that has already existed for a long time and that is widely in use, that's another option.