Crate holmes [−] [src]
Holmes
Holmes is a Datalog inspired system for binding codependent analyses together.
Tutorial
Basic Datalog
If you are already familiar with logic languages, this section will likely be straightforwards for you, but it may still be useful to provide an overview of basic functions and syntax.
Datalog is a forward-chaining logic language. This means that a program written in Datalog consists of a set of rules which "fire" whenever their requirements are met which operate on a database of facts.
Predicates
A predicate represents a property on a list of typed values. For example, to express the distance between two cities in miles, we might write
predicate!(distance(string, string, uint64))
N.B. while this code is being built via doctests, there are a few lines of support code above and below being hidden for clarity. See the complete example at the end of the section for a template.
Facts
Facts are formed by the application of predicates to values. Continuing with the example from before, we can add a fact to the database for the predicate we defined
predicate!(distance(string, string, uint64)); fact!(distance("New York", "Albuquerque", 1810))
Rules
Rules are formed from a body clause and a head clause. When the rule body matches, variable assignments from the match are substituted into the head clause, which is then added to the database. Here, we might want to add the symmetry property to our previous example, e.g. "If the distance from A to B is N, then the distance from B to A is also N".
predicate!(distance(string, string, uint64)); fact!(distance("New York", "Albuquerque", 1810)); rule!(distance(B, A, N) <= distance(A, B, N))
In a rule or a query (in the next section), the possible restrictions on each slot are:
- Unbound:
[_]
- Constant Equality:
(value)
- Variable unification
var
Queries
Now that the database has more facts in it than we started with, it makes sense to be able to query the database and see what is inside.
holmes_exec!(holmes, { predicate!(distance(string, string, uint64)); fact!(distance("New York", "Albuquerque", 1810)); rule!(distance(B, A, N) <= distance(A, B, N)) }); let mut res = try!(query!(holmes, distance(A, [_], [_]))); assert_eq!(res, vec![vec!["Albuquerque".to_value()], vec!["New York".to_value()]]);
Recursive Rules
Let's go one step further, and use a rule to check connectivity between cities, based on the facts in the database. We want to express "If A connects to B, and B connects to C, then A connects to C".
holmes_exec!(holmes, { predicate!(distance(string, string, uint64)); fact!(distance("New York", "Albuquerque", 1810)); fact!(distance("New York", "Las Vegas", 2225)); fact!(distance("Las Vegas", "Palo Alto", 542)); fact!(distance("Rome", "Florence", 173)); rule!(distance(B, A, N) <= distance(A, B, N)); predicate!(connected(string, string)); rule!(connected(A, B) <= distance(A, B, [_])); rule!(connected(A, C) <= connected(A, B) & connected(B, C)) }); assert_eq!(try!(query!(holmes, connected(("Rome"), ("Las Vegas")))).len(), 0); let mut res = try!(query!(holmes, connected(("Palo Alto"), x))); assert_eq!(res, vec![vec!["Albuquerque".to_value()], vec!["Las Vegas".to_value()], vec!["New York".to_value()], vec!["Palo Alto".to_value()]]);
Complete Example
Finally, just for reference (so you can actually write your own program using this) here's the unredacted version of that last example:
#[macro_use] extern crate holmes; extern crate tokio_core; use holmes::{Engine, MemDB, Result}; use tokio_core::reactor::Core; use holmes::pg::dyn::values::ToValue; fn f () -> Result<()> { // Holmes uses the `tokio_core` event loop in order to schedule work // amongst various rules and enable asynchronous processing. // Unless you specifically want to do something async, this just // means you need to pass in a handle as shown here, and call // `core.run(holmes.quiesce())` before any queries to wait for // the engine to finish running. let mut core = Core::new().unwrap(); let mut holmes_own = Engine::new(MemDB::new(), core.handle()); // For the moment, the `holmes_exec` macro needs a &mut ident. I'll // try to make this more flexible in the future. let holmes = &mut holmes_own; holmes_exec!(holmes, { predicate!(distance(string, string, uint64)); fact!(distance("New York", "Albuquerque", 1810)); fact!(distance("New York", "Las Vegas", 2225)); fact!(distance("Las Vegas", "Palo Alto", 542)); fact!(distance("Rome", "Florence", 173)); rule!(distance(B, A, N) <= distance(A, B, N)); predicate!(connected(string, string)); rule!(connected(A, B) <= distance(A, B, [_])); rule!(connected(A, C) <= connected(A, B) & connected(B, C)) }); // Now we force the database to compute until quiescence. Otherwise, // any query results may be partial. core.run(holmes.quiesce()).unwrap(); assert_eq!(try!(query!(holmes, connected(("Rome"), ("Las Vegas")))).len(), 0); let mut res = try!(query!(holmes, connected(("Palo Alto"), x))); // Order is not gauranteed when it comes back from the query, so I // sort it in the example to get the doctest to pass. `Value` only has // `PartialOrd` implemented for it, since there isn't a clean comparison // between `Value`s of different types, so I just default to `Greater`. res.sort_by(|x, y| x.partial_cmp(y).unwrap_or( ::std::cmp::Ordering::Greater)); assert_eq!(res, vec![vec!["Albuquerque".to_value()], vec!["Las Vegas".to_value()], vec!["New York".to_value()], vec!["Palo Alto".to_value()]]); Ok(()) } fn main () {f().unwrap()}
Extensions
While Datalog itself is interesting, writing yet-another-datalog engine is not the goal of this project. Next, we'll go over some of the new features of this system.
Functions
Normally, logic languages expect the computation to be encoded as rules only (or in special cases, as external predicates). In order to allow the user to write things which make more sense as traditional code, we allow the binding of functions:
func!(let f : uint64 -> uint64 = |x : &u64| { x * 3 })
In this case, we have declared a function called f
, said that it takes
as input a uint64
, and should output a uint64
.
The type of the input to the function should be the output of the .get()
call of the relevant value, which will usually be a reference to the rust
equivalent of the type.
The output should be a value which .to_value()
will convert to a
correctly typed Value
.
Additionally, the type system allows for tuples and lists. Tuple types
are denoted (t1, t2)
, and list types are denoted [t]
. Lists and tuples
will be unpacked through by the func!
macro, so a function with a
[uint64]
input would expect to take a Vec<&u64>
, and a function taking
(string, uint64)
would expect to take a (&String, &u64).
For example:
func!(let replicate : (string, uint64) -> [string] = |(s, n) : (&String, &u64)| { let mut vec : Vec<String> = Vec::new(); for i in 0..*n { vec.push(s.clone()); }; vec } )
Where Clauses
Telling Holmes about functions isn't useful without a way to use them. Where clauses are a way to perform a transformation on the data after the map, but before the head clause is produced and sent to the database.
Extending the example from earlier, we might want to generate a distances for the connection paths we found.
holmes_exec!(holmes, { predicate!(distance(string, string, uint64)); fact!(distance("New York", "Albuquerque", 1810)); fact!(distance("New York", "Las Vegas", 2225)); fact!(distance("Las Vegas", "Palo Alto", 542)); fact!(distance("Rome", "Florence", 173)); //rule!(distance(B, A, N) <= distance(A, B, N)); predicate!(path(string, string, uint64)); rule!(path(A, B, N) <= distance(A, B, N)); func!(let add : (uint64, uint64) -> uint64 = |(x, y) : (&u64, &u64)| { x + y }); rule!(path(A, C, NSum) <= path(A, B, N1) & path(B, C, N2), { let NSum = {add([N1], [N2])} }) });
The astute reader will notice there is something wrong with this example.
It builds, and it runs, I'm not trying to mess with you while teaching.
However, the last rule we added (which does the sum of the distances) will
loop forever if there is any cycle in the distance
predicate.
This is why I commented out the rule flipping the distance direction
around, as this would cause this example to run infinitely.
Normally in Datalog, we have a termination property - no matter what
rules or facts you add, the database will always eventually stop growing.
This proof follows from the inability of a rule firing to introduce a new
value, which means there are only a finite number of derivable facts. With
the addition of where clauses, we lose this property, because new values
can appear, as per the add
function above.
However, we also add other kinds of binds to the where clause that can help the programmer control this kind of situation.
N.B. the postgres backend doesn't currently support list persistence, so
if you wanted to use a list in a predicate, you'd actually need to make a
custom Path
type and value that knew how to store itself.
Binds
Variable binding
This is as in the inital example. They are written let x = expression
,
and simply bind the expression to the variable.
Destructuring
This kind of bind is basically just shorthand to prevent the need for
functions like access_tuple_field_1
, access_tuple_field_2
.
It is written let (x, y, z) = expression
Value binding
This is the first unusual kind of binding, and the one we can use to fix
up the previous example. Value binds are written let (expr) = expr2
.
If expr
and expr2
evaluate to the same value, this expression has no
effect. However, if expr
and expr2
differ, the variable assignment
currently generated by the where clause will stop.
To fix the previous example, we can track the path we've gone through thus
far, and store it in an additional slot in the path
predicate.
Then, in the where clause for adding a new step to the path, we can check
for membership in the existing path. If it is present, we can use a value
binding to stop pursuing this avenue. If it is not present, then we can
proceed as before.
try!(holmes_exec!(holmes, { predicate!(distance(string, string, uint64)); fact!(distance("New York", "Albuquerque", 1810)); fact!(distance("New York", "Las Vegas", 2225)); fact!(distance("Las Vegas", "Palo Alto", 542)); fact!(distance("Rome", "Florence", 173)); rule!(distance(B, A, N) <= distance(A, B, N)); predicate!(path(string, string, [string], uint64)); func!(let two_vec : (string, string) -> [string] = |(x, y) : (&String, &String)| { vec![x.clone(), y.clone()] }); rule!(path(A, B, steps, N) <= distance(A, B, N), { let steps = {two_vec([A], [B])}}); func!(let add : (uint64, uint64) -> uint64 = |(x, y) : (&u64, &u64)| { x + y }); func!(let append : (string, [string]) -> [string] = |(x, y) : (&String, Vec<&String>)| { let mut out : Vec<String> = y.into_iter().cloned().collect(); out.push(x.clone()); out }); func!(let mem : (string, [string]) -> bool = |(needle, haystack) : (&String, Vec<&String>)| { haystack.contains(&needle) }); rule!(path(A, C, path2, NSum) <= path(A, B, path, N1) & distance(B, C, N2), { // If we've already walked over C, we aren't interested let (false) = {mem([C], [path])}; let path2 = {append([C], [path])}; let NSum = {add([N1], [N2])} }) })); let mut res = query!(holmes, path(("New York"), dest, [_], dist))?; assert_eq!(res, vec![ vec!["Albuquerque".to_value(), 1810.to_value()], vec!["Las Vegas".to_value(), 2225.to_value()], vec!["Palo Alto".to_value(), 2767.to_value()], ]);
Iteration
The last kind of bind is the iterative bind. This works similarly to the List monad in Haskell if you are familiar with it, but you don't need to know anything about that to proceed.
An iterative bind is written let [x] = expr
, where the expression should
evaluate to a list-typed value. When this bind is run, the set of possible
answers splits into a different instance for each value in the list. So, if
we had
rule!(q(x, y) <= p(y), {
let [x] = f(y)
})
it would first find all y
such that p(y)
, and then for each of them,
it would apply f
and get a list. Imagine that f
just returns a list of
y
and y + 1
, and that p
is only populated with p(1)
and p(2)
.
The match would produce the possible assignment sets y = 1
and y = 2
.
After running the where clause, the first one would become x = 1, y = 1
,
x = 2, y = 1
, and the secould would become x = 2, y = 2
, x = 3, y = 2
. This ends with the database containing q(1, 1), q(2, 1), q(2, 2), q(3, 2)
.
That example is somewhat abstract, but hopefully it illustrates the multiplicative effect of the iteration bind. The iteration bind can also be used to terminate early a rule, similar to the value bind, by iterating over an empty list. If an iteration bind is used multiple times in a where clause, it will operate on each of the new answer sets from the previous iteration bind individually.
As a more concrete example, say we wanted to define a predicate which contained all sities that might be used on a path from New York to Palo Alto. We can take the example from earlier and add:
predicate!(on_the_road(string, string, string)); rule!(on_the_road(A, B, stop) <= path(A, B, path, [_]), { let [stop] = [path] }) let mut res = query!(holmes, on_the_road(("New York"), ("Palo Alto"), stop))?; assert_eq!(res, vec![ vec!["Las Vegas".to_value()], vec!["New York".to_value()], vec!["Palo Alto".to_value()], ]);
Reexports
pub use engine::Engine; |
pub use pg::PgDB; |
pub use mem_db::MemDB; |
Modules
edsl |
Holmes EDSL |
engine |
Holmes/Datalog Execution Engine |
fact_db |
This module defines the interface which a fact database must present to be used as a backend by the Holmes engine. |
mem_db |
This is a memory mock for the fact database interface. |
pg |
Postgres-based Fact Database |
simple |
You likely don't want to use this module - its primary purpose is to make benchmarking and testing easier to do in practice. |
Macros
bind_match |
Constructs a bind match outer object. |
clause | |
clause_match |
Generates a |
db_expr | |
fact |
Stores a fact with the |
field | |
func |
Registers a native rust function with the |
hexpr |
Generates an expression structure |
holmes_exec |
Shorthand notation for performing many actions with the same holmes context
Analogous to a weaker version of the |
htype |
Converts an EDSL type specification into a Holmes type object Takes the name of a variable containing a holmes object as the first parameter, and a type description as the second. |
predicate |
Registers a predicate with the |
query |
Runs a datalog query against the |
rule |
Adds a Holmes rule to the system |
typed_unpack |
Given a value and a type it is believed to be, unpack it to the greatest extent possible (e.g. unpack through tupling and lists) |
typet_boiler | |
typet_inner | |
typet_inner_eq | |
valuet_boiler |
Structs
Error |
The Error type. |
Enums
ErrorKind |
The kind of an error. |
Type Definitions
Result |
Convenient wrapper around |