I frequently have to "import" data from one database to another in order to get on with more interesting work, a process sometimes ingloriously referred to as "ETL: Extract, Transform, Load". Usually this must be done in some programming language already established at an organization, such as Python or Scala. However, in developing a library to do this quickly and fairly declaratively, I found it useful to write a mostly-undefined prototype in Haskell, to "type-check" my thoughts. This essay is very much about databases as they exist in the wild, but draws quite a bit of inspiration from the philosophical aspects of Rich Hickey’s talks on programming and Datomic / the database as a value. There is undoubtedly extensive database theory literature that would also be helpful; please do point me towards it in the comments.
Databases, datums, and imports
It is useful to step back to basic principles to model this scenario sufficiently abstractly. In particular, I want to have no particular notion of a database, except that it is a place that one stores things one learns. So I will define abstract types
DB for storage and
Datum for the input.
> import Control.Monad.Reader -- Ignore for now, imports are just required up top > > data Datum -- Essentially a row of the source database, if row-based > data DB -- The target database
Import is a function that takes just one additional datum and incorporates all the learned knowledge into the database.
> type Import = Datum -> DB -> DB
Not just any function is suitable to be an
Import. Learning from the same datum twice – in this setting – should not result in more new information. In order words for
f :: Import we require that
f be idempotent.
f datum == f datum . f datum
Moreover, if we treat the analogy of database contents with knowledge very strictly, we should be unable to ever "know" a contradiction. We can add information, but never remove or change it. This idea is usuall discussed as the "information ordering" but I will just call it
<= where no information (aka
NULL or ⊥) is less than any value, and all other values are related only to each other.
db <= f datum db
Note that this is stronger than monotonicity, as a constant function is always monotonic. The best word for this property in the context of databases is that
f is consistent.
Knowing these properties of an import, I can be assured that it is safe to run the import on all the data available as many times as I like. The order may effect how many runs it takes, since we do not require commutativity:
f datum1 . f datum2 =?= f datum2 . f datum1
However, we can be assured that re-running will eventually hit a fixed point. In practice, it is usually very easy to order an import so that a single run suffices, two in more complex scenarios.
There are two equivalent yet legitimately interesting ways to think about importing a list of data. The first is the obvious one: For each piece of data, transform the database and pass on the result.
> importAll :: Import -> [Datum] -> DB -> DB > importAll importOne datums db = foldr importOne db datums
The second considers functions
DB -> DB to be more central, and does not even bind the variable
db in its definition: It first composes all the single transformations into one mondo transformation, which is then applied to the input database.
> importAll' :: Import -> [Datum] -> DB -> DB > importAll' importOne datums = foldr (.) id (map importOne datums)
Conclusions, Deductions, and Translations
The above definition is complete and flexible, but there is more structure to most databases, hence most imports. To model the extremely likely scenario that the database has an atomic element, such as rows for SQL or documents for various flavors of NoSQL, call these things
Conclusions with a single fundamental operation
> data Conclusion > > save :: Conclusion -> DB -> DB > save = undefined
With the expected idempotency condition that
save con == save con . save con.
Now a typical import will consist of drawing some set of
Conclusions for each
Datum encountered, possibly by combining the datum with information already stored in the database. For lack of a better name, I will call this a
Deduction, and transform it into an
Import with the help of
> type Deduction = Datum -> DB -> [Conclusion] > > deduce :: Deduction -> Import > deduce upsertion datum db = foldr save db (upsertion datum db)
However, it may be that things are even simpler and that each
Datum results in a single new
Conclusion. This is more the usual notion of a "data import" and means – to stretch an already thin analogy – that each
Datum is sort of already a
Conclusion but with respect to the wrong context, so I’ll call this a
> type Translation = Datum -> DB -> Conclusion > > translate :: Translation -> Import > translate trans datum db = save (trans datum db) db
However, there is a major problem: Neither
deduce result in consistent imports, because multiple
Datums may result in the a
Conclusion with the same primary key but different attributes. This is almost never desirable; when two translations or deductions emit a conclusion with the same primary key, it is intended to be consistent with the database, i.e. they should only emit conclusions
con such that
save con is consistent
db <= save con db
In normal database parlance, this is like an "upsert" except on all attributes as well. At the level of rows or documents, we must first fetch the document that would be created, and then modify it according to the new conclusion. Any conflicting attributes is an error (hacks excepted, of course). I will break these apart into
Augment, which are then recombined in the brilliantly named
> type Lookup = Datum -> DB -> Conclusion > type Augment = Datum -> DB -> Conclusion -> Conclusion > > lookupAndAugment :: Lookup -> Augment -> Translation > lookupAndAugment lookup augment datum db = augment datum db (lookup datum db)
One could implement
lookupAndAugment to enforce that the output conclusion is consistent with the input. 1
Making it imperative
To get a step closer to the imperative scripting that this prototype targets, this section adjusts the definitions above to stop passing the database around quite so much.
A first step is to note that the database is "always there" as part of the environment, which is exactly what the
Reader monad represents. Here are all of the above definitions rewritten without ever taking the database as input.
> type Import' = Datum -> Reader DB DB > type Translation' = Datum -> Reader DB Conclusion > type Lookup' = Translation' > type Augment' = Datum -> Reader DB (Conclusion -> Conclusion) > > save' :: Conclusion -> Reader DB DB > save' = undefined > > translate' :: Translation' -> Import' > translate' trans datum = do db <- ask > conclusion <- trans datum > save' conclusion > > lookupAndAugment' :: Lookup' -> Augment' -> Translation' > lookupAndAugment' lookup augment datum = do current <- lookup datum > improvements <- augment datum > return (improvements current)
The next and final step is to stop returning the database, but mutate it in place, by replacing
Reader DB with
> type Import'' = Datum -> IO () > type Translation'' = Datum -> IO Conclusion > type Lookup'' = Translation'' > type Augment'' = Datum -> IO (Conclusion -> Conclusion) > > save'' :: Conclusion -> IO () > save'' = undefined > > translate'' :: Translation'' -> Import'' > translate'' trans datum = do conclusion <- trans datum > save'' conclusion > > lookupAndAugment'' :: Lookup'' -> Augment'' -> Translation'' > lookupAndAugment'' lookup augment datum = do current <- lookup datum > improvements <- augment datum > return (improvements current)
And it is nice to see that moving from
Reader DB to
IO does not change the text of
lookupAndAugment, so I have some confidence that it is a canonical definition.
That’s it! Just a bit of how I do typed functional prototyping before committing to the task of implementing in a lower-level scripting language.
In a test-edit-debug cycle, you’ll need a way to turn off consistency checking, unless you snapshot and reset the target database with each run. A good idea, but slow, and it is usually fine to operate on a test database and just mutate it repeatedly.↩