# Alf - Classy data-manipulation dressed in a DSL (+ commandline)
% [sudo] gem install alf
% alf --help
## Links
* {http://rubydoc.info/github/blambeau/alf/master/frames} (read this file there!)
* {http://github.com/blambeau/alf} (source code)
* {http://revision-zero.org} (author's blog)
## Description
Alf is a commandline tool and Ruby library to manipulate data with all the power
of a truly relational algebra approach. Objectives behind Alf are manifold:
* Pragmatically, Alf aims at being a useful commandline executable for
manipulating csv files, database records, or whatever looks like a (physical
representation of a) relation. See 'alf --help' for the list of available
commands and implemented relational operators.
% alf restrict suppliers -- "city == 'London'" | alf join cities
* Alf is also a 100% Ruby relational algebra implementation shipped with a simple
to use, powerful, functional DSL for compiling and evaluating relational queries.
Alf is not limited to simple scalar values, but admit values of arbitrary
complexity (under a few requirements about their implementation, see next
section). See 'alf --help' as well as .alf files in the examples directory
for syntactic examples.
Alf.lispy.compile{
(join (restrict :suppliers, lambda{ city == 'London' }), :cities)
}
* Alf is also an educational tool, that I've written to draw people's attention
about the ill-known relational theory (and ill-represented by SQL). The tool
is largely inspired from TUTORIAL D, the tutorial language of Chris Date and
Hugh Darwen in their books, more specifically in
{http://www.thethirdmanifesto.com/ The Third Manifesto (TTM)}.
However, Alf only provides an overview of the relational _algebra_ defined
there (Alf is neither a relational _database_, nor a relational _language_).
I hope that people (especially talented developers) will be sufficiently
enticed by features shown here to open that book, read it more deeply, and
implement new stuff around Date & Darwen vision. Have a look at the result of
the following query for things that you'll never ever have in SQL (see also
'alf help quota', 'alf help nest', 'alf help group', ...):
% alf --text summarize supplies --by=sid -- total "sum(:qty)" -- which "group(:pid)"
* Last, but not least, Alf is an attempt to help me test some research ideas and
communicate about them with people that already know (all or part) of the TTM
vision of relational theory. These people include members of the TTM mailing
list as well as other people implementing some of the TTM ideas (see
{https://github.com/dkubb/veritas Dan Kubb's Veritas project} for example). For
this reason, specific features and/or operators are mine, should be considered
'research work in progress', and used with care because not necessarily in
conformance with the TTM.
% alf --text quota supplies --by=sid --order=qty -- pos "count()"
## Overview of relational theory
We quickly recall relational theory in this section, as described in the TTM
book. Readers not familiar with Date and Darwen's vision of relational theory
should probably read this section, even if fluent in SQL. Others may probably
skip this section. A quick test?
> _A relation is a value, precisely a set of tuples, which are themselves values.
Therefore, a relation is immutable, not ordered, does not contain duplicates,
and does not have null/nil attributes._
Familiar? Skip. Otherwise, read on.
### The example database
This README file shows a lot of examples built on top of the following suppliers
& parts database (almost identical to the original version in C.J. Date database
books). By default, the alf command line is wired to this embedded example. All
examples shown here should therefore work immediately, if you want to reproduce
them!
% alf show database
+-------------------------------------+-------------------------------------------------+-------------------------+------------------------+
| :suppliers | :parts | :cities | :supplies |
+-------------------------------------+-------------------------------------------------+-------------------------+------------------------+
| +------+-------+---------+--------+ | +------+-------+--------+------------+--------+ | +----------+----------+ | +------+------+------+ |
| | :sid | :name | :status | :city | | | :pid | :name | :color | :weight | :city | | | :city | :country | | | :sid | :pid | :qty | |
| +------+-------+---------+--------+ | +------+-------+--------+------------+--------+ | +----------+----------+ | +------+------+------+ |
| | S1 | Smith | 20 | London | | | P1 | Nut | Red | 12.0000000 | London | | | London | England | | | S1 | P1 | 300 | |
| | S2 | Jones | 10 | Paris | | | P2 | Bolt | Green | 17.0000000 | Paris | | | Paris | France | | | S1 | P2 | 200 | |
| | S3 | Blake | 30 | Paris | | | P3 | Screw | Blue | 17.0000000 | Oslo | | | Athens | Greece | | | S1 | P3 | 400 | |
| | S4 | Clark | 20 | London | | | P4 | Screw | Red | 14.0000000 | London | | | Brussels | Belgium | | | S1 | P4 | 200 | |
| | S5 | Adams | 30 | Athens | | | P5 | Cam | Blue | 12.0000000 | Paris | | +----------+----------+ | | S1 | P5 | 100 | |
| +------+-------+---------+--------+ | | P6 | Cog | Red | 19.0000000 | London | | | | S1 | P6 | 100 | |
| | +------+-------+--------+------------+--------+ | | | S2 | P1 | 300 | |
| | | | | S2 | P2 | 400 | |
| | | | | S3 | P2 | 200 | |
| | | | | S4 | P2 | 200 | |
| | | | | S4 | P4 | 300 | |
| | | | | S4 | P5 | 400 | |
| | | | +------+------+------+ |
+-------------------------------------+-------------------------------------------------+-------------------------+------------------------+
Many people think that relational databases are necessary 'flat', that they are
necessarily limited to simply scalar values in two dimension tables. This is
wrong; most SQL databases are indeed 'flat', but _relations_ (in the mathematical
sense of the relational theory) are not! Look, **the example above is a relation!**;
that 'contains' other relations as particular values, which, in turn, could
'contain' relations or any other 'simple' or more 'complex' value... This is not
"flat" at all, after all :-)
### Types and Values
To understand what is a relation exactly, one needs to remember elementary
notions of set theory and the concepts of _type_ and _value_.
* A _type_ is a finite set of values; it is non particularly ordered and, being
a set, it does never contains two values which are considered equal.
* A _value_ is **immutable** (you cannot 'change' a value, in any way), has no
localization in time and space, and is always typed (that is, it is always
accompanied by some identification of the type it belongs to).
As you can see, _type_ and _value_ are not the same concepts as _class_ and
_object_, with which you are probably familiar with. Alf considers that the
latter are _implementations_ of the former. Alf assumes _valid_ implementations
(equality and hash methods must be correct) and _valid_ usage (objects used for
representing values are kept immutable in practice). Alf _assumes_ this, but
does not _enforces_ it: it is your responsibility to use Alf in conformance with
these preconditions. That being said, if you want **arrays, colors, ranges, or
whatever in your relations**, just do it! You can even join on them, restrict on
them, summarize on them, and so on:
% alf extend suppliers -- chars "name.chars.to_a" | alf --text restrict -- "chars.last == 's'"
+------+-------+---------+--------+-----------------+
| :sid | :name | :status | :city | :chars |
+------+-------+---------+--------+-----------------+
| S2 | Jones | 10 | Paris | [J, o, n, e, s] |
| S5 | Adams | 30 | Athens | [A, d, a, m, s] |
+------+-------+---------+--------+-----------------+
A last, very important word about values. **Null/nil is not a value**. Strictly
speaking therefore, you may not use null/nil inside your data files or datasources
representing relations. That being said, Alf provides specific support for handling
them, because they appear in today's databases in practice and that Alf aims at
being a tool that helps you tackling _practical_ problems. See the section with
title "Why is Alf Exactly?" later.
### Tuples and Relations
Tuples (aka records) and relations are values as well, which explains why you
can have them inside relations!
* Logically speaking, a tuple is a set of (attribute name, attribute value)
pairs. Moreover, it does not contain two attributes with the same name and is
**not particularly ordered**. Also, **a tuple is a _value_, and is therefore
immutable**. Last, but not least, a tuple **does not admit nulls/nils**. Tuples
in Alf are simply implemented with ruby hashes, taken as tuples implementations.
Not all hashes are valid tuple implementations, of course (those containing nil
are not, for example). Alf _assumes_ valid tuples, but does not _enforce_ this
precondition. It's up to you to use Alf the right way! No support is or will
ever be provided for ordering tuple attributes. Howeber, as hashes are ordered
in Ruby 1.9, Alf implements a best effort strategy to keep a friendly ordering
when rendering tuples and relations. This is a very good practical reason for
migrating to ruby 1.9 if not already done!
{:sid => "S1", :name => "Smith", :status => 20, :city => "London"}
* A _relation_ is a set of tuples. Being a set, a relation does **never contain
duplicates** (unlike SQL that works on bags, not on sets) and is **not
particularly ordered**. Moreover, all tuples of a relation must have the same
_heading_, that is, the same set of attribute (name, type) pairs. Also, **a
relation is a _value_, is therefore immutable** and **does not admit null/nil**.
Alf being mainly an implementation of relational algebra (see section below)
it loosely considers any Iterator of tuples as a potentially valid relation
implementation (see later).
### Relational Algebra
In classical algebra, you can do computations like (5 + 2) - 3. In
relational algebra, you can do similar things on relations. Alf uses an infix,
functional programming-oriented syntax for algebra expressions:
(minus (union :suppliers, xxx), yyy)
All relational operators take relation operands in input and return a relation
as output. We say that the relational algebra is _closed_ under its operators.
In practice, it means that operands may always be sub-expressions, **always**.
(minus (union (restrict :suppliers, lambda{ zzz }), xxx), yyy)
In shell, the closure property means that you can pipe alf invocations the way
you want! The same query, in shell:
alf restrict suppliers -- "zzz" | alf union xxx | alf minus yyy
## What is Alf exactly?
The Third Manifesto defines a series of prescriptions, proscriptions and very
strong suggestions for designing a truly relational _language_, called a _D_,
as an alternative to SQL for managing relational databases. This is far behind
our objective with Alf, as we don't look at database aspects at all (persistence,
transactions, and so on.) and don't actually define a programming language either
(only a small functional ruby DSL).
Alf must simply be interpreted as a ruby library implementing (a variant of)
Date's and Darwen relational algebra. This library is designed as a set of operator
implementations, that work as tuple iterators taking other tuple iterators as
input. Under the pre-condition that you provide them _valid_ tuple iterators as
input (no duplicates, no nil, + other preconditions on an operator basis), the
result is a valid iterator as well. Unless explicitely stated otherwise, any
behavior observed when not respecting these preconditions, even an interesting
behavior, is not guaranteed and can change with tiny version changes (see section
about versioning policy at the end of this file).
### In ruby
#
# Provided that :suppliers and :cities are valid relation representations
# (under the responsibility shared by you and the Reader and Environment
# subclasses you use -- see later), then,
#
op = Alf.lispy.compile{
(join (restrict :suppliers, lambda{ city == 'London' }), :cities)
}
# op is a thread-safe Enumerable of tuples, that can be taken as a valid
# relation representation. It can therefore be used as the input operand
# of any other expression. This is under Alf's responsibility, and any
# failure must be considered a bug!
### In shell
#
# Provided that suppliers and cities are valid relation representations
# [something similar]
#
% alf restrict suppliers -- "city == 'London'" | alf join cities
# the resulting stream is a valid relation representation in the output
# stream format that you have selected (.rash by default). It can therefore
# be piped to another alf shell invocation, or saved to a file and re-read
# later (under the assumption that input and output data formats match, or
# course). [Something similar about responsibility and bug].
### Coping with non-relational data sources (nil, duplicates, etc.)
Alf aims at being a tool that helps you tackling practical problems, and
denormalized and/or noisy data is one of them. Missing values occur. Duplicates
abound in SQL databases lacking primary keys, and so on. Using Alf's relational
operators on such inputs is not a good idea, because it is a strong precondition
violation. This is not because relational theory is weak, but because extending
it to handle null/nil and duplicates correctly has been proven at best a nightmare,
and at worse a mess. As a practical exercice, try to extend classical algebra
with versions of +, - * and / that handle nil in such a way that the resulting
theory is sound and still looks intuitive! Then do it on boolean algebra with
_and_, _or_ and _not_. Then, add null/nil to classical set theory. Classical
algebra, boolean algebra, and set theory are important building blocks behind
relational algebra because almost all of its operators are defined on top of
them...
So what? The approach choosen in Alf to handle this conflict is very pragmatic.
First of all, Alf implements a best effort strategy -- where possible -- to
remain friendly in presence of null/nil on attributes that have no influence on
an operator's job. For example, the query below will certainly fail if _status_
is null/nil, but it won't probably fail if any other attribute is nil.
% alf restrict suppliers -- "status > 10"
This best-effort strategy is not enough, and striclty speaking, must be considered
unsound (for example, it strongly hurts optimization possibilities). Therefore,
we strongly encourage you to go a step further: **if relational operators want
true relations as input, please, give them!**. For this, Alf also provides a few
non-relational operators in addition to relational ones. Those operators must be
interpreted as "pre-relational" operators, in the sense that they help obtaining
valid relation representations from invalid ones. Provided that you use them
correctly, their output can safely be used as input of a relational operator.
You'll find,
* alf autonum -- ensure no duplicates by generating a unique attribute
* alf compact -- brute-force duplicates removal
* alf defaults -- replace nulls/nil by valid values, on an attribute
basis
Play the game, it's easy!
- _Give id, name and status of suppliers whose status is greater that 10_
- Hey man, we don't know supplier's status for all of them! What about the others?
- _Ignore them_
- No problem dude!
% alf defaults --strict suppliers -- sid '' name '' status 0 | alf restrict -- "status > 10"
### Alf is duck-typed
The relational theory is often considered under a statically-typed point
of view. When considering tuples and relations, for example, the notion of
_heading_, a set of (name,type) pairs, is central. For example, a heading for
a supplier tuple/relation could be:
{:sid => String, :name => Name, :status => Integer, :city => String}
Most relational operators have preconditions in terms of the headings of their
operands. For example, _minus_ and _union_ require their operands to have same
heading, while _rename_ requires renamed attributes to exist in operand's
heading, and so on. Given an expression in relational algebra, it is always
possible to compute the heading of the resulting relation, by statically
analyzing the whole query expression in the light of a catalog of typed
operators. This way, a tool can check that a query is statically valid, i.e.
that it respects operator preconditions. While this approach has the major
advantage of allowing strong optimizations, it also has a few drawbacks (as
knowing the heading of used datasources in advance) and is difficult to mary
with dynamically-typed languages like Ruby. Therefore, Alf takes another approach,
which is similar to duck-typing. In essence, this approach can be summarized as
follows:
- _You have the responsibility of ensuring that the evaluation of your query
will succeed and will return valid results_
- No problem dude!
## Getting started in shell
% alf --help
The help command will display the list of available operators. Each of them is
completely described with 'alf help OPERATOR'. They all have a similar invocation
syntax in shell:
% alf operator operands... -- args...
For example, try the following:
# display suppliers that live in Paris
% alf restrict suppliers -- "city == 'Paris'"
# join suppliers and cities (no args here)
% alf join suppliers cities
### Recognized data streams/files (.rash files)
For educational purposes, 'suppliers' and 'cities' inputs are magically resolved
as denoting the files examples/suppliers.rash and examples/cities.rash,
respectively. You'll find other data files: parts.rash, supplies.rash that are
resolved magically as well and with which you can play. For non-educational
purposes, operands may always be explicit files, or you can force the folder in
which datasource files have to be found:
# The following invocations are equivalent
% alf restrict /tmp/foo.rash -- "..."
% alf --env=/tmp restrict foo -- "..."
A .rash file is simply a file in which each line is a ruby Hash, intended to
represent a tuple. Under theory-driven preconditions, a .rash file can be seen
as a valid (straightforward but useful) physical representation of a relation!
When used in shell, alf dumps query results in the .rash format by default,
which opens the ability of piping invocations! Indeed, unary operators read their
operand on standard input if not specific as command argument. For example, the
invocation below is equivalent to the one given above.
# display suppliers that live in Paris
% cat examples/suppliers.rash | alf restrict -- "city == 'Paris'"
Similarly, when only one operand is present in invocations of binary operators,
they read their left operand from standard input. Therefore, the join given in
previous section can also be written as follows:
% cat examples/suppliers.rash | alf join cities
The relational algebra is _closed_ under its operators, which means that these
operators take relations as operands and return a relation. Therefore operator
invocations can be nested, that is, operands can be other relational expressions.
When you use alf in a shell, it simply means that you can pipe operators as you
want:
% alf show --rash suppliers | alf join cities | alf restrict -- "status > 10"
### Obtaining a friendly output
The show command (which is **not** a relational operator) can be used to obtain
a more friendly output:
# it renders a text table by default
% alf show [--text] suppliers
+------+-------+---------+--------+
| :sid | :name | :status | :city |
+------+-------+---------+--------+
| S1 | Smith | 20 | London |
| S2 | Jones | 10 | Paris |
| S3 | Blake | 30 | Paris |
| S4 | Clark | 20 | London |
| S5 | Adams | 30 | Athens |
+------+-------+---------+--------+
# and reads from standard input without argument!
% alf restrict suppliers "city == 'Paris'" | alf show
+------+-------+---------+-------+
| :sid | :name | :status | :city |
+------+-------+---------+-------+
| S2 | Jones | 10 | Paris |
| S3 | Blake | 30 | Paris |
+------+-------+---------+-------+
Other formats can be obtained (see 'alf help show'). For example, you can generate
a .yaml file, as follows:
% alf restrict suppliers -- "city == 'Paris'" | alf show --yaml
### Executing .alf files
You'll also find .alf files in the examples folder, that contain more complex
examples in the Ruby functional syntax (see section below).
% cat examples/group.alf
#!/usr/bin/env alf
(group :supplies, [:pid, :qty], :supplying)
You can simply execute these files with alf directly as follows:
# the following works, as well as the shortcut 'alf show group'
% alf examples/group.alf | alf show
+------+-----------------+
| :sid | :supplying |
+------+-----------------+
| S1 | +------+------+ |
| | | :pid | :qty | |
| | +------+------+ |
| | | P1 | 300 | |
| | | P2 | 200 | |
...
Also, mimicing the ruby executable, the following invocation is also possible:
% alf -e "(restrict :suppliers, lambda{ city == 'Paris' })"
where the argument is a relational expression in Alf's Lispy dialect, which
is detailed in the next section.
## Lispy expressions
If you take a look at .alf example files, you'll find functional ruby expressions
like the following:
% cat examples/minus.alf
# Give all suppliers, except those living in Paris
(minus :suppliers,
(restrict :suppliers, lambda{ city == 'Paris' }))
# This is a contrived example for illustrating minus, as the
# following is equivalent
(restrict :suppliers, lambda{ city != 'Paris' })
You can simply execute such expressions with the alf command line itself (the
three following invocations return the same result):
% alf examples/minus.alf | alf show
% alf show minus
% alf -e "(restrict :suppliers, lambda{ city != 'Paris' })" | alf show
Symbols are magically resolved from the environment, which is wired to the
examples by default. See the dedicated sections below to update this behavior
to your needs.
### Algebra is closed under its operators!
Of course, from the closure property of a relational algebra (that states that
operators works on relations and return relations), you can use a sub expression
*everytime* a relational operand is expected, everytime:
# Compute the total qty supplied in each country together with the subset
# of products shipped there. Only consider suppliers that have a status
# greater than 10, however.
(summarize \
(join \
(join (restrict :suppliers, lambda{ status > 10 }),
:supplies),
:cities),
[:country],
:which => Agg::group(:pid),
:total => Agg::sum{ qty })
Of course, complex queries quickly become unreadable that way. But you can always
split complex tasks in more simple ones using _with_:
with( :kept_suppliers => (restrict :suppliers, lambda{ status > 10 }),
:with_countries => (join :kept_suppliers, :cities),
:supplying => (join :with_countries, :supplies) ) do
(summarize :supplying,
[:country],
:which => Agg::group(:pid),
:total => Agg::sum{ qty })
end
And here is the result !
+----------+----------+--------+
| :country | :which | :total |
+----------+----------+--------+
| England | +------+ | 2200 |
| | | :pid | | |
| | +------+ | |
| | | P1 | | |
| | | P2 | | |
| | | P3 | | |
| | | P4 | | |
| | | P5 | | |
| | | P6 | | |
| | +------+ | |
| France | +------+ | 200 |
| | | :pid | | |
| | +------+ | |
| | | P2 | | |
| | +------+ | |
+----------+----------+--------+
### Going further
For now, the Ruby API is documented in the commandline help itself (a cheatsheet
or something will be provided as soon as possible). For example, you'll find the
allowed syntaxes for RESTRICT as follows:
% alf help restrict
...
API & EXAMPLE
# Restrict to suppliers with status greater than 20
(restrict :suppliers, lambda{ status > 20 })
# Restrict to suppliers that live in London
(restrict :suppliers, lambda{ city == 'London' })
...
## Interfacing Alf in Ruby
### Calling commands 'ala' shell
For simple cases, the easiest way of using Alf in ruby is probably to mimic
what you have in shell:
% alf restrict suppliers -- "city == 'Paris'"
Then, in ruby
#
# 1. create an engine on an environment (see section about environments later)
# 2. run a command
# 3. op is a thread-safe enumerable of tuples, see the Lispy section below)
#
lispy = Alf.lispy(Alf::Environment.examples)
op = lispy.run(['restrict', 'suppliers', '--', "city == 'Paris'"])
If this kind of API is not sufficiently expressive for you, you'll have to learn
the APIs deeper, and use the Lispy functional style that Alf provides, which can
be compiled and used as explained in the next section.
### Compiling lispy expressions
If you want to use Alf in ruby directly (that is, not in shell or by executing
.alf files), you can simply compile expressions and use resulting operators as
follows:
#
# Expressions can simply be compiled as illustrated below. We use the
# examples environment here, see the dedicated section later about other
# available environments.
#
lispy = Alf.lispy(Alf::Environment.examples)
op = lispy.compile do
(restrict :suppliers, lambda{ city == 'London' })
end
#
# Returned _op_ is an enumerable of ruby hashes. Provided that datasets
# offered by the environment (:suppliers here) can be enumerated more than
# once, the operator may be used multiple times and is even thread safe!
#
op.each do |tuple|
# tuple is a ruby Hash
end
#
# Now, maybe you want to reuse op in a larger query, for example
# by projecting on the city attribute... Here is how with expressions
# can be handled in that case
#
projection = lispy.with(:kept_suppliers => op) do
(project :kept_suppliers, [:city])
end
## Going further
### Using/Implementing other Environments
An Environment instance if passed as first argument of Alf.lispy
and is responsible of resolving named datasets. A base class Environment::Folder
is provided with the Alf distribution, with a factory method on the Environment
class itself.
env = Alf::Environment.folder("path/to/a/folder")
An environment built that way will look for .rash and .alf files in the specified
folder and sub-folders. I'll of course strongly consider any contribution
implementing the Environment contract on top of SQL or NoSQL databases or anything
that can be useful to manipulate with relational algebra. Such contributions can
be added to the project directly, in the lib/alf/environment folder, for example.
A base template would look like:
class Foo < Alf::Environment
#
# You should at least implement the _dataset_ method that resolves a
# name (a Symbol instance) to an Enumerable of tuples (typically a
# Reader). See Alf::Environment for exact contract details.
#
def dataset(name)
end
end
### Adding file decoders, aka Readers
Environments should not be confused with Readers (see Reader class and its
subclasses). While the former resolve named datasets, the latter decode files
and/or other resources as tuple enumerables. Environments typically serve Reader
instances in response to dataset resolving.
Reader implementations decoding .rash and .alf files are provided in the main
alf.rb file. It's relatively easy to implement the Reader contract by extending
the Reader class and implementing an each method. Once again, contributions are
very welcome in lib/alf/reader (.csv files, .log files, and so on). A basic
template for this is as follows:
class Bar < Alf::Reader
#
# You should at least implement each, see Alf::Reader which provides a
# base implementation and a few tools
#
def each
# [...]
end
# By registering it, the Folder environment will automatically
# recognize and decode .bar files correctly!
Alf::Reader.register(:bar, [".bar"], self)
end
### Adding outputters, aka Renderers
Similarly, you can contribute renderers to output relations in html, or whatever
format you would consider interesting. See the Renderer class, and consider the
following template for contributions in lib/alf/renderer
class Glim < Alf::Renderer
#
# You should at least implement the execute method that renders tuples
# given in _input_ (an Enumerable of tuples) on the output buffer
# and returns the latter. See Alf::Renderer for the exact contract
# details.
#
def execute(output = $stdout)
# [...]
output
end
# By registering it, the output options of 'alf show' will
# automatically provide your --glim contribution
Alf::Renderer.register(:glim, "as a .glim file", self)
end
## Related Work & Tools
- You should certainly have a look at the Third Manifesto website: http://www.thethirdmanifesto.com/
- Why not reading the {http://www.dcs.warwick.ac.uk/~hugh/TTM/DBE-Chapter01.pdf
third manifesto paper} itself?
- Also have a look at {http://www.dcs.warwick.ac.uk/~hugh/TTM/Projects.html other
implementation projects}, especially {http://dbappbuilder.sourceforge.net/Rel.php Rel}
which provides an implementation of the TUTORIAL D language.
- {https://github.com/dkubb/veritas Dan Kubb's Veritas} project is worth considering
also in the Ruby community. While very similar to Alf in providing a pure ruby
algebra implementation, Veritas mostly provides a framework for manipulating
and statically analyzing algebra expressions so as to be able to
{https://github.com/dkubb/veritas-optimizer optimize them} and
{https://github.com/dkubb/veritas-sql-generator compile them to SQL}. We are
working together with Dan Kubb to see how Alf and Veritas could be closer from
each other in the future, if not in their codebase, at least in using the very
same terminology for the same concepts.
## Contributing
### Alf is open source
You know the rules:
* The code is on github https://github.com/blambeau/alf
* Please report any problem or bug in the issue tracker on github
* Don't hesitate to fork and send me a pull request for any contribution/idea!
Alf is distributed under a MIT licence. Please let me know if it does not fit
your needs and I'll see what I can do!
### Internals -- Tribute to Sinatra
Alf's code style is very inspired from what I've found in Sinatra when looking
at its internals a few month ago. Alf, as Sinatra, is mostly implemented in a
single file, lib/alf.rb. Everything is there except additional contributions
(in lib/alf/...). You'll need an editor or IDE that supports code folding/unfolding.
Then, follow the guide:
1. Fold everything but the Alf module.
2. Main concepts, first level of abstraction, should fit on the screen
3. Unfold the concept you're interested in, and return to the previous bullet
### Roadmap
Below is what I've imagined about Alf's future. However, this is to be interpreted
as my own wish list, while I would love hearing yours instead.
- Towards 1.0.0, I would like to stabilize and document Alf public APIs as well
as internals (a few concepts are still unstable there). Alf also has a certain
number of limitations that are worth overcoming for version 1.0.0. The latter
include the semantically wrong way of applying joins on sub-relations, the
impossibility to use Lispy expressions on sub-relations in extend, and the error
management which is unspecific and unfriendly so far.
- I also would like starting collecting Reader, Renderer and Environment
contributions for common data sources (SQL, NoSQL, CSV, LOGS) and output
formats (HTML, XML, JSON). Contributions could be either developped as different
gem projects or distributed with Alf's gem and source code, I still need to
decide the exact policy (suggestions are more than welcome here)
- Alf will remain a practical tool before everything else. In the middle term,
I would like to complete the set of available operators (relational and non-
relational ones). Some of them will be operators described in D & D books
while others will be new suggestions of mine.
- In the long term Alf should be able to avoid loading tuples in memory (under
a certain number of conditions on datasources) for almost all queries.
- Without targetting a fast tool at all, I also would like Alf to provide a basic
optimizer that would be able to push equality restrictions down and materialize
sub-expressions used more than once in with expressions.
### Versioning policy
Alf respects {http://semver.org/ semantic versioning}, which means that it has
a X.Y.Z version number and follows a few rules:
- The public API is made of both the commandline tool as well as the Lispy
dialect and will become stable with version 1.0.0 in a near future.
- Backward compatible bug fixes will increase Z.
- New features and enhancements that do not break backward compatibility of the
public API will increase the Y number.
- Non backward compatible changes of the public API will increase the X number.
All classes and modules but the Alf module itself and the Lispy DSL are part of
the private API and may change at any time. A best-effort strategy is followed
to avoid breaking internals on tiny (Z) version increases.
## Enjoy Alf!
- No problem dude!