Download Using SQL Databases from Dyalog APL (v11+)

Using SQL Databases from
APL (Dyalog & other)
J. Merrill
Analytical Software Corp.
[email protected]
Overview
• About my APL and SQL experience
• This talk emphasizes information about SQL
– Concepts you must know
– Weaknesses you need to understand
– Ideas about SQL database design
• Why so much about SQL?
– Could a few people learn the details about SQL, and
build tools to shield other developers from the issues?
• Why you should not hide SQL from APLers
– The very successful project that was a failure
• APL talking to SQL is not hard to do (any more)
My Background
• I started working with APL professionally in
1975, as a user of STSC timesharing.
• While working at STSC, I took a course in
relational database technology and SQL
from a representative of a small company
called Relational Software Inc. Later they
changed their name to Oracle – I’ve
worked with SQL for longer than Oracle
Corporation has existed!
SQL Work at STSC
• I automated connection of an Oracle database of
microcomputer software sales data to the
existing (APL-based) accounting system.
• I designed a portable interface between STSC’s
APL*Plus and SQL on these platforms:
– VMS talking to RDB
– VMS talking to Oracle
– Unix talking to Oracle
and implemented the APL in those interfaces.
STSC, Manugistics, and Later
• I was a software development consultant
for many years with both STSC and (after
the name change) Manugistics.
• Client projects included migrating an APLbased database of foreign exchange
trading data to Microsoft SQL Server.
• Since founding Analytical Software Corp.
I’ve worked extensively with clients using
SQL Server from APL+Win.
SQL Concepts You Must Know
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SQL has only four data manipulation verbs
RDBMSs use Client / Server techniques
SQL has the concept of null
All data is in a table (like an APL matrix)
SQL columns are named, not numbered
SQL columns are “strongly typed”
Relationships are data not structure
DML: select insert update delete
• SQL’s select is extraordinarily versatile
• SQL’s insert and update normally operate
on a single row at a time and therefore are
quite primitive
• If insert and update work on multiple rows,
not a single row, they become very
powerful (as powerful as select)
• SQL’s delete is usually simple but can use
some powerful select techniques
SQL Has DML and DDL
• DML means data manipulation language
• SQL’s four DML verbs are select, insert,
update, delete
• For defining the database structure
(schema), SQL has DDL
• DDL means data definition language
• DDL is less standardized between
products
SQL select is Very Powerful
• select supports extremely complex statements
that can access data from multiple tables,
potentially joining them using every available
join technique in a single query
• Much of select’s power is based on the ability to
define derived tables within a statement, and
use those tables as if they held real data – but
no data is actually stored to run the query
– The syntax is intended let you express intent without
specifying a particular processing sequence
• Powerful does not mean simple or intuitive
insert & update: SQL’s Weak Spot
• When data is in memory (in an application)
– insert statements allow you to add one row,
giving values for each column in the new row
– update statements typically reference a single
row by its primary key (though it can change
many columns in one update)
• Applying a group of changes based on
user input typically involves issuing many
one-row-each insert or update statements
Multi-Row insert and update
• insert supports adding the result of any
select to an existing table
• update similarly can update many rows of
one table from the contents of another
table, providing the tables can be joined
(usually on the primary key)
– The syntax for this is not completely standard
and some products may not support this
• Get the data on the server if possible!
SQL delete Does What It Says
• delete can use the full power of select to
determine what row or rows of a table
should be deleted
• When deleting rows based on a list of
primary key values, implementations that
limit the length of a SQL statement (or an
individual clause within the statement) can
require multiple delete statements to
delete a large number of rows
SQL Concepts You Must Know
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SQL has only four data manipulation verbs
RDBMSs use Client / Server techniques
SQL has the concept of null
All data is in a table (like an APL matrix)
SQL columns are named, not numbered
SQL columns are “strongly typed”
Relationships are data not structure
Client / Server Concepts
• Clients send requests to the server and it
responds with the results
• Only the DBMS server touches the data
• It’s good to have the server do things like
compute totals so that results are smaller
• The number of back-and-forth interactions
is potentially more significant than the
amount of data being transferred
SQL is the Language for Server
Requests and Responses
• Your program sends SQL statements (only) to
the server and handles the responses
• Some tools hide the fact that everything is done
with SQL, by generating SQL for you
• SQL is very powerful for reading data (select)
and deleting data (delete)
• SQL is quite stupidly simple for writing data in
memory into a table (insert, update)
• The over-the-wire protocol is complex, but is
hidden by (most) client libraries
One Program Touches the Data
• A single coordinating program, the DBMS
server, performs all access (reading and
writing) to data files holding the SQL data
• Access to data is controlled by the server
• The server process can be run on a very
powerful (expensive) computer
• Conflicting requests from different users
can be handled more easily
Let the Server Do the Work
• The server should be a better computer
than the one running your program
• It is optimal if the server can get the job
done without sending a lot of data over the
wire to your program
• Downside: the more you get the server to
do the work (by sending SQL), the more
you have to worry that things will need to
change if you switch to another DBMS
Avoid “Chatty” Programs
• Each interaction with the server takes time for
the back-and-forth protocol, in addition to
whatever actual data is passed
• Rather than sending a series of steps to the
server one after the other, it’s better to send one
larger request
• SQL’s simplistic insert & update is an issue
• Downside: the more you “bunch up” your
requests, the more difficult it is to handle errors
SQL Concepts You Must Know
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SQL has only four data manipulation verbs
RDBMSs use Client / Server techniques
SQL has the concept of null
All data is in a table (like an APL matrix)
SQL columns are named, not numbered
SQL columns are “strongly typed”
Relationships are data not structure
SQL’s Concept Called null
• Intended to represent a “missing” or “unknown”
value, rather than an “empty” value
• If a value is null, it is not equal to any particular
value, nor is it not-equal to any particular value.
Suppose A is an arbitrary SQL expression:
int_column = 5
char_column = ‘hello’
One would think that the result of
A or (not A)
has to be true but in SQL that’s not the case (!).
null Causes “three valued logic”
• APLers think we understand booleans, but then
along comes SQL’s notion of null that changes
the rules we’ve learned
• When nulls are involved, the result of a boolean
expression is one of true, false, or null
• The result of any expression is null if any value
involved in the expression is null
• The SQL function coalesce returns the first notnull parameter value passed:
coalesce(column, value_if_null)
Use Null Values Sparingly
• Allowing nulls sometimes makes sense
– Definitely makes sense for date or time
– Makes sense for numeric data when 0 is not
an appropriate “no information” replacement
• Don’t use a “missing value code” in the database,
even if you use one in your APL data
– Any distinction between NULL and ‘’ (an
empty string) is hard to see, justify, or explain
to non-programmers (or programmers!).
• Sometimes it’s not your decision
SQL Concepts You Must Know
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SQL has only four data manipulation verbs
RDBMSs use Client / Server techniques
SQL has the concept of null
All data is in a table (like an APL matrix)
SQL columns are named, not numbered
SQL columns are “strongly typed”
Relationships are data not structure
All SQL Data is in Tables
• There is no other persistent data structure
• SQL table values are conceptual scalars
– SQL can hold strings (enclosed char vectors)
– Otherwise, no nested data (in standard SQL)
• If APL had scalar strings, a SQL table
would be just like a simple APL matrix
• SQL locates rows using data values only
– No notion like row number in an APL matrix
SQL Columns
• Columns are named, not numbered
• Each column has a (scalar) data type
• Most data types can support the special
SQL value null
– Handling null values correctly can be difficult
Column Names
• One difference between products is in
what column names they allow
• Some allow spaces in names, but that
makes it necessary to “quote” those
column names (so SQL can parse
statements using them)
• Standard quoting techniques vary (!)
• Advice: use simple, not-very-long names
• It’s not always your choice
SQL Concepts You Must Know
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SQL has only four data manipulation verbs
RDBMSs use Client / Server techniques
SQL has the concept of null
All data is in a table (like an APL matrix)
SQL columns are named, not numbered
SQL columns are “strongly typed”
Relationships are data not structure
Column Type Catgeories
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Numbers
Strings
Dates and Times
Binary
Time Intervals
Boolean
Numbers
• Integers of various sizes (1, 2, 4, 8)
– Usually no “unsigned integer”
• 4- or 8-byte floating point
• Fixed width and number of decimals
• Money sometimes available
– 64-bit integer with 4 implied decimals
Strings
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Fixed length with maximum size
Variable length with maximum size
“Unlimited size” often available
Multi-byte data (Unicode) often available
Dates and Times
• One or more of date, time, datetime
• Concept of NULL is very useful here
• Dates are stored as Julian date values
– Many support negative Julian dates
• Time values are stored as fraction of a day
– 0.25 is 6am, 0.5 is noon, 0.75 is 6pm
• Datetime values stored as (day# + timefraction)
• If only datetime is available,
– time can are represented as being “on day 0”
– date can be represented as being “at midnight”
Time Intervals
• Supposedly standard
• Not universally available (SQL Server)
• Can be represented with a date or
datetime value holding the result of
subtracting one date or datetime value
from another
Binary
• Fixed length with a maximum size
• Variable length with a maximum size
• “Unlimited size” often available
Boolean
• A single bit (!)
– Not “fixed or variable length with a maximum
size” like string and binary data
• Sometimes boolean columns support null
(so there are 3 states)
• My advice: use a one-wide character
column instead
Time for a Break ?
SQL Concepts You Must Know
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SQL has only four data manipulation verbs
RDBMSs use Client / Server techniques
SQL has the concept of null
All data is in a table (like an APL matrix)
SQL columns are named, not numbered
SQL columns are “strongly typed”
Relationships are data not structure
How Relationships Work in SQL
• In APL, you can choose to represent
related structures by nesting
• In SQL, there are only tables and
there is no possibility of nesting
• Relationships are stored by having a
column in one table store a value
identifying a related row in another
table (or null if there is no related row)
SQL Table Design
• All columns in a table should represent
information about one entity (or concept)
– row = data about one instance
– column = same data re many instances
• The term “field” is deprecated (but widely used)
• Each table needs a “primary key” column whose
value can be stored in any other table that has a
relationship with rows in this table
• SQL-generated primary key values allow
relationships to be represented easily and can
prevent issues that arise when users think they
want to change key values
SQL Relationships
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One to many (parent - child)
Many to one (lookup)
Hierarchy
Many to many
Relationships – one to many
• Each (one) invoice can have any number
(many) of line items
– LineItem table has an InvoiceID column
holding Invoice table primary key (PK) value
– “No line items for invoice N” is easily
represented by there being no rows in the
LineItem table with InvoiceID = N
• An APL application might use nesting,
storing a vector or matrix of lineitem data
within each Invoice data structure
One to Many = Parent - Child
• The “one” table (Invoice) is parent
• The “many” table (LineItem) is child
• Column holding primary key (PK) of
another table is called a foreign key
(FK) to (or into) that table
• Foreign keys are critical to competent
SQL database design
Relationships – many to one
• There can be any number (many) of employees
in each (one) department
– Employee table has a column holding Department
table PK value (read as “FK to Department”)
– “No employees in department N” is again easy
• Not thought of as parent / child; you don’t think
of departments as “owning” employees the way
invoices “own” line items
• An APL application could (but wouldn’t) use
nesting, storing a vector of EmployeeID values
(but not Employee data) for each department
Relationships – hierarchy
• Each employee has a manager; the
manager is also an employee
• Employee table has a ManagerID
column holding FK to the same
(Employee) table
• Top of hierarchy is represented by
null ManagerID value (no manager)
• APL could use nesting, but I haven’t
Relationships – many to many
• Requires a third table to hold two many-toone relationships to the other two tables
• ClassStudent table records that
– each class can have any number of students
enrolled in it
– each student enrolls in any number of classes
• You can’t use nesting for this (without
double-storing the information), so APL
applications usually do it the same way
More SQL Design Thoughts
• Advice: use your product’s “identity” or
“sequence” feature to create PK values
• When in doubt, define another table
• Examples of extra tables that add flexibility
• Storing historical data (not just the current
value, but changes over time)
Advice: Use DBMS-assigned
Values as Primary Keys
• Most implementations support having the DBMS
assign the value of a particular column to be 1+
the previous value, during insert of a new row
• Columns defined this way make excellent PKs
that are efficient and easily used as FKs
• Columns that are user-visible “primary keys”
(e.g. Product Code, Region Code, Department
Code) become attributes
– Changing them no longer causes pain and trouble
When In (Any) Doubt, Define
Another Table
• When you see a numeric suffix on a
column name, that’s an indication that
another table should have been built
• FK columns in an Operation table named
Doctor1 Doctor2 Doctor3 Doctor4 would
be a poor design
– What if more than 4 doctors involved?
– Better to have a child table with any number
(even zero) of doctors for an operation
More Tables Are Often Better
• Some cases where using more tables
results in a more flexible system:
– Address table where both Customer and
Employee tables have AddressID column
• Address table has column AddressType (FK to
AddressType table with descriptions Billing,
Shipping, Home, Office…)
– Name table where Patient table has NameID
• Column Primary in Name table marks the current
(primary) name to use for this person
Historical Data
• Data that changes over time where history
needs to be maintained can be in a child table
with a ValidUntil column, and possibly also a
ValidSince column
– The currently active child table row is marked by a
null ValidUntil value
– Find the active child table row on date X:
(X >= coalesce( ValidSince, X )) ^
(X <= coalesce( ValidUntil, X ))
– Code needs to ensure that timespans don’t overlap
– Can store ID of active child table row in parent
SQL Joins
• Because data values are used to define
relationships, SQL needs to be able to combine
(join) tables based on the defined FK-to-table
relations – and it can do so quite flexibly
• Each SQL statement must re-specify the join
condition(s); tools to generate SQL code can
make this easier to get right
• SQL supports different kinds of joins:
– Natural join
– Left (or right) join
– Cross join
Time for Lunch ?
SQL Join is What in APL?
• Similar to:
A,B[B[;pk]A[;fkToB];]
• Natural join
– values of A[;fkToB] that would cause
INDEX ERROR in APL instead remove those
rows from A (!)
– if there are multiple rows that match in B, rows of A
are repeated to match
• Left join: adds a row of null values to B to avoid
losing rows of A with unmatched values:
A,(Bnull)[B[;pk]A[;fk
ToB];]
• Cross join: like APL outer product (usually this is
Relationships vs. Join Types
• Parent-child relationship = left join
• Lookup table = left join
• Natural join removes unmatched rows –
make sure that’s what you want
• Many-to-many should normally be two left
joins from the middle table, because it’s
two combined one-to-many relationships
• Hierarchies are non-trivial to handle
Given the Issues, Why Use SQL?
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Standard outside the APL world
Superb support for transactions
Security is built-in
Advanced database features would be
very difficult to provide otherwise
SQL Databases are Standard
• Wide availability of expertise
• Backup and recovery are solved problems
• Other parts of an application can be
developed by others or using other tools
• Many non-APL tools can work with them
– Logical and physical data modeling
– Reporting
– Performance analysis
Database Transactions
• ACID characteristics
– Atomicity, Consistency, Isolation, Durability
• All-or-nothing data updates, ensuring data
consistency even when there are errors
during updates
• Each user’s work is not affected by the
work of other users
• Server or application software crashes
leave data intact after “a restart
Security Built In
• DBMS server controls all access to data
– application can be the only way to data; or
– can use Windows identity as SQL identity
• Access can be removed from the
underlying tables, perhaps granting select
access via views that use user identity
• Corporations are accustomed to securing
their databases, but not their (APL) files
Advanced DBMS Features
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Flexible high-performance indexing
Views (including “materialized” views)
Sub-queries
Stored procedures
Triggers
XML data handling
Partitioned tables
Materialized views
Database Indexes
• Auxiliary data structures to speed access
to data, like the index of a book
• Can include multiple columns
• A “unique index” prevents duplicates
• A “clustered index” changes the physical
arrangement of rows
– Clustering a child table by the parent key
places all child rows adjacent on disk
Database Views
• Almost any SQL select statement can be
saved as a view
• Views act like virtual tables
– There is no data in a view, only in the table or
tables referenced by the view
– Views can be targeted by select statements
exactly as if they were physical tables
– In some limited cases, views can be updated
– Users can be granted access to a view rather
than to the table(s) named in the view
SQL Subqueries
• SQL statements can define derived tables
that exist only for that select
– Like a view that exists for one statement
• Who has not purchased product X?
select … from Customer where CustomerID not in
(select distinct inv.CustomerID
from Invoice as inv join LineItem as li
on inv.InvoiceID = li.InvoiceID
where li.Product = X)
Stored Procedures and Triggers
• Programs that run within the database
– Stored Procedures (SPs) can be called from
client programs, or by other SPs
– Triggers are a special type of SP that fire (run)
on any of insert, update, delete
– Can be used to validate or audit
• Usually written a product-specific
proprietary language
– Writing in Java or .Net is becoming possible
Support for XML Data
• Products are vying for the best support
• XML data can of course be stored as
variable-length character data, but that
doesn’t require any special support
• Examples of XML support
– Load data or create virtual tables from XML
– Use XPath syntax to locate data within the
XML stored in a column, and use that data to
select rows
Partitioned Tables
• Dividing a logical table, one defined by a
union view, into separate physical tables
• The purpose is to divide the workload of
accessing (or updating) the table between
multiple servers
• Only appropriate for huge tables, or ones
that become a bottleneck in a transaction
processing system
Materialized Views
• Some views are better off being computed once
and stored, rather than being virtual (and thus
re-computed each time they’re accessed)
• If SQL can know how to update the view when
the underlying data changes, this can speed up
access to the view at the cost of storing the
materialized view and making needed updates
• The views that are eligible to be materialized are
fairly limited in most products today
A Quick Look at Implementation
• Usually multiple tables in each physical file
– flexible assignment of tables to files
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Each table’s data is a group of pages
A page of data contains multiple rows
Pages of a table are linked together
Index data may be in file with table data
Indexes reference rows by page and
position within the page
Which database to choose?
• It’s often not your choice
• If you can choose, what matters most to you?
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cost
reliability
availability of knowledgeable people
performance
• If performance is critical, test lesser-known
products; some are amazingly fast for simpler
usage scenarios yet quite robust and stable
Can You Choose?
• In corporate development, the data likely
already exists in a particular database
• Only if you are building a new system (or a
new product) are you given the opportunity
to select the database platform
• Many customers care only that it’s “one of
the big ones” (Oracle, SQL Server, DB2)
• To customers, the formula is often that the
unknown is risky, and risk is bad
What’s Most Important?
• Licensing costs vary dramatically
– Pure open source is “free” for a reason
• no support unless you pay
– The “big three” cost an arm and a leg
• but your company may already license it
– Lesser-known products can be a great value
• If it’s mission-critical, you want “big 3”
– but it’s still your problem (and will be said to
be your fault) if the database fails
What’s Most Important?
• Sometimes you’ll need to find expertise
– Oracle is notorious for needing a guru
– If any part of the application is built outside
APL, you’ll find people more easily if you go
with one of the big boys
• Performance is rarely known to be critical
– Starting out, you may have no idea if the
DBMS’s performance will be a problem
– Except in extreme cases, you can handle it
Lesser-known Products
• I’m somewhat biased towards them
– Sometimes support is unbelievably great
– They’re swimming against the tide – help
show the world that they deserve to exist
• Using APL is already “weird” and “risky”
– If your test results impress you as much as
they’ve impressed me, why not use what you
think is the best tool, despite the “risk” of an
unknown tool?
APL-based or APL-aware?
• If you find a system that seems to fit well,
and is either based on APL or has some
special support for APL callers, why would
you reject it automatically?
• But you might not really be “using a SQL
RDBMS” in all such those cases, and it
depends on why you’re leaning towards
SQL in the first place
Time for a Break ?
APL Can Speak to SQL Easily
• SQAPL for Dyalog APL (uses ODBC)
– Also available for Dyalog Unix (not free, nor are the
best ODBC drivers for Unix)
• DataDirect apparently has the best Unix ODBC drivers; they
are recommended by Dyalog (others work as well)
– Same API for APL+Win is called APL+Link
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ADO.Net – Windows database API #6
ADO (original), or OLE DB, via COM/OCX
Write directly to some API
Use a home-grown DLL, OCX, or AuxProc
SQAPL Wins
• It’s very hard to prove that statement in
every environment, with every workload
• Why it’s better
– It’s the only interface that understands how
APL works and inherently supports its arrays
– Other interfaces require major work to hide
one-row-at-a-time programming models
• Version 5 has a new mechanism that can
provide a significant performance
improvement (10x better has been
reported) with a relatively small change
Other Choices Are Inferior
• Not APL oriented
• Very “loopy” with “scalar thinking” APIs
• Only recently have APIs (in .Net) reached parity
with SQAPL techniques from 5+ years ago!
• Why bang your head against the wall when
SQAPL comes free with Dyalog APL?
• SQAPL is a proven, tested, robust solution
– Put in something about the stupidity of trying to reinvent a tough, well-worn, smooth, very round wheel
SQAPL Sets
• APL developers think of changes to data
happening all at once, when an array
value is stored (e.g. in a file component)
• Storing an APL array as SQL data
frequently involves multiple SQL
operations, because each SQL operation
changes only one row at a time
• SQAPL Sets are an abstraction to make
working with SQL data more APL-like
Basics of Using Sets
• If you plan to modify data, start by reading
data using one of the “set” tools; the result
is a “set handle” and a data matrix
• Modify the matrix by adding and/or
deleting rows, and/or updating values
• Call “set update” to store the data, passing
the set handle and the new matrix
How Do Sets Work?
• The retrieved data is stored, associated
with the “set handle” that is returned to you
• When you call “set update”, APL is used to
compare the old and new “tables” and
make the changes by generating the
needed SQL statements and running them
• Sets are an extraordinarily simple but
powerful idea; take advantage of them
SQAPL’s Current “Sets”
Implementation is Not Complete
• Should use SQL transactions
– So the new version of the in-memory array
will be successfully stored in the database, or
the database will be unchanged
• Could support DBMS-assigned “identity”
values
– As I’ve said, I think these are very useful
– Returning the identity values back to APL is
very important (to avoid the need to re-query)
More Possible Enhancements
• Fix a few bugs
– SQL handles are not closed ASAP
– Occasional inconsistent error handling (some
now fixed)
• Could easily support multi-column keys
(but I prefer to use identity values as keys)
• Could support optimistic concurrency
• Could support automatic updates to
related (child) tables
Database-Specific Tools
• SQAPL provides the tools to talk to various SQL
databases. (Remember that they only
understand SQL statements.)
• A desirable tool would be one to improve the
performance of Set Update by passing data to
be updated in fewer steps, perhaps as XML.
• With SQL Server in particular, sending XML data
for insert and update can let them operate in
bulk, rather than one row at a time. Impressive
performance gains would be quite likely.
Improving SQAPL’s API
Working with an APL+Win client, I did
major design and implementation work on
an object-oriented layer built on the
SQAPL code. This included making most
of the enhancements discussed above,
adding major features for calling stored
procedures, creating and modifying tables,
and much more. I hope to have a similar
opportunity in Dyalog APL.
Performance Issues
• Most of these are SQL / DBMS issues, not
issues of the APL interface to SQL
– LAN vs WAN makes a huge difference
(latency)
– Consider using a “middle tier” in WAN case
• The nature of these issues varies wildly
from one DBMS to another
– The same SQL statement may be very fast in
one implementation, very slow in another
Figuring Out What’s Wrong
• APL is the wrong tool for analyzing
performance problems
– APL can’t see what the DBMS is doing
– APL can’t see the system-level slowdowns
– Non-APL tools exist for no other purpose
• If the problem is “too much back and
forth”, can you find a way to “blast” the
data across in fewer steps?
Metadata Based Tools
• Most APL applications are based on, or at least
use, metadata – data about the data being
manipulated. APLers use these techniques
naturally. It surprises me how infrequently other
programmers in other languages seem to do so.
• Tools that know your database structure can be
used for many things. For example, if the table
hierarchy is available as metadata, a routine can
be written to produce the SQL where clause to
join an arbitrary list of tables.
Other Tools
• Knowing the datatype of database table
columns can simplify creating UI screens.
• Knowing the relationship between tables
can let (tools called by) model code
retrieve related data automatically.
• Automating changes to the database
schema given metadata is possible – I
know, I’ve done it. (It’s not trivial!)
Lessons Learned
• Always (except that one should never say
always) use identity values as PKs
– Unless you can prove that you have one of
the (very rare, in my opinion) cases where
there’s a real reason not to
– GUID PKs are an alternative to identity
values, but …
• they are comparatively “fat” vs. integers
• the problems with identity values are overstated
• the “any location” benefit is overstated
More Lessons
• Always write APL that writes SQL
– Build tools to help you do so
• Don’t hide SQL from APL developers
• Table names should be singular (my
opinion); not all people agree
• Try not to base your design on the ability
of your (current) DBMS to handle it well
Unproven (by me) Ideas
• These are thoughts that haven’t been fully
implemented (at least not by me in APL),
so be aware of that before basing your
development on them.
• They represents some potential “best
practices” that borrow significantly from
work done by others, mostly in other
languages.
Model Definition is Often Hazy
• The line between UI, data model, and
database can be unclear.
• Data manipulated by the UI of a form is
most easily stored in attributes of the form.
• Many programs have a “UI data model”
that’s separate from the model of the data
as stored persistently (whether or not it’s
stored in a SQL database).
Problems Caused by UI Models
• Changes must be coordinated
– When model data is stored in the UI, making changes
to either the UI or the data definition forces them to
change at the same time.
• UI validation code works from the UI model
– Code for event-handling in the UI naturally does BR
(business rule) validation using UI-stored data.
• Validation logic gets repeated
– The model must enforce BRs to prevent bad data
from being stored, so logic gets repeated.
Use Model Code in the UI
• When a model’s data is simple – and the power of APL
to manipulate data structures makes that common when
writing in APL – it can be tempting to do the manipulation
directly.
• User interface code should avoid that, instead invoking
methods of the model even though they may be trivial.
• Consider a web app – server-side code can’t rely on
data being correct when submitted, so it must do
validation.
• Code for browser-side validation either repeats the logic
or consults the server. Ideally, the latter is done
asynchronously (using an Ajax-style model).
UI / Data Model Separation
• It is clear that the user interface code is best
kept separate from (a client of) data models.
• This results in models that are easier to test, and
easier to re-use in other contexts.
• When the UI needs to implement a Save action,
it can be tempting to use your app’s data storage
APIs directly in the code that gathers the data
from where it’s stored in the UI.
• Better would be to call a model’s Save method.
UI Forms Reference a Model
• UI forms should have a reference to a
model object.
• The model should be able to hold data
about multiple data instances (database
table rows) – this is APL, after all.
• The model can have documented events
to which the UI can subscribe.
• UI data should be passed to the model as
soon as possible.
Unified Memory / Database Model
• The model should know about the state of the
data it holds relative to the database.
– Is it in the database (vs. new data)?
• If it is, has it changed since then?
– Is it currently valid (ready to be saved)?
• If not, what error message data is there?
• One good source of design ideas is Scott
Ambler’s ActiveRecord model. It is only slightly
array-oriented, so it is not ideal unchanged.
Writing Models
Write model methods on an as-needed
basis. APL programmers often work to
create tools that have much more
functionality than is needed today. When
writing models, put required functionality
into lower-level tools (creating them as
needed) and keep only high-level logic,
and calls to the lower-level tools, in the
models. Maximize the work not done.
A Big Thank You to Morten
I would not be here if Morten had not
asked me to deliver this talk. I had not
seen Dyalog APL in more than 20 years,
having used only APL*PLUS (now APL+)
for all that time. I appreciate seeing where
Dyalog APL is today, and I’m always
happy to visit Denmark. I’ve enjoyed
what’s felt like a warm welcome from this
community. I thank both you and Morten
for your generosity.