The SessionPseudoClock is invaluable for testing rule sets using temporal
operators and STREAM-related features based on Time.
While all Engine activity due to the artificial advancing of the clock is
taken care of, the other side of the coin becomes apparent as soon as
actions outside the Engine are based on System Time. Several methods must be
redirected to the Session(Pseudo)Clock, but this can be handled with some
helper classes. But there are several methods where System Time is used
implicitly. One of these is Lock.tryLock( int, TimeUnit ), another one is
Condition.awaitUntil( Date ). I think that these situations can be handled
with tolerable effort using the facilities of org.drools.time and the
PseudoClockScheduler (implementing SessionPseudoClock) methods scheduleJob
But all of the aforementioned types are "unstable", including
SessionPseudoClock with the essential method advanceTime(long), even though
the Fusion manual claims that "Drools ... offers out of the box ... a clock
that is controlled by the application" (sic!).
What this amounts to is me crabbing again of being lured into the unstable
parts of the API by the "official" announcement of some feature. On Madison
Avenue they call this a "teaser".
I have tried
@role( event )
x : Void
@role( event )
x : Void
rule "mock-A" # only rule for EventA, EventB
$a: EventA( this after[0,10s] $b )
org.drools.rule.TypeDeclaration tda =
(org.drools.rule.TypeDeclaration)rpkg.pkg.getTypeDeclaration( "EventA" );
System.out.println( "EventA expires: " + tda.getExpirationOffset() + "
role:" + tda.getRole());
org.drools.rule.TypeDeclaration tdb =
(org.drools.rule.TypeDeclaration)rpkg.pkg.getTypeDeclaration( "EventB" );
System.out.println( "EventB expires: " + tdb.getExpirationOffset() + "
role:" + tdb.getRole());
EventA expires: -1 role:EVENT
EventB expires: -1 role:EVENT
Since the DRL code is equivalent to example 2.19 ("correlate orders") in
Fusion, this raises the question whether the inferred expiration offset is
actually computed as described.
we found a bug in Drools that might be critical:
Various rules stop firing when adding more, totally unrelated rules to
a knowledge base.
We think this is critical for everyone who uses Drools in production
and uses "declared" functions.
Our workaround was to convert all declared functions into Java
methods. Now every rule is executed again.
My question: Should I post about it and collect votes? I don't what to
blame anybody :)
I'm using Drools in a system of alarms. I have defined timed events and
interval events. My idea is that an external timer inserts some interval
events time to time. Well, the thing is that I need update an interval event
if another event appears.
I have read somewhere in documentation that updating an event is equal to
remove it and insert a new event with the changes made. If I update an
interval event, will its timestamp change? That is, if I update an interval
event, the moment that this event finishes will be delayed? The problem will
be solved if I assign its timestamp to one property of the event?
Thanks in advance.
View this message in context: http://drools-java-rules-engine.46999.n3.nabble.com/Updating-interval-eve...
Sent from the Drools - Dev mailing list archive at Nabble.com.
This simple declare for a new (not imported) class isn't permitted by
the compiler, although it makes sense.
Of course, it's possible to work around by adding a dummy field.
But is this restriction intentional by design, to alert users when
they misspell a class name, or forget to import a class?
If not, I'll make a request to permit this.
On Wed, Oct 27, 2010 at 5:21 PM, tizo <tizone(a)gmail.com> wrote:
> Hi there,
> I have seen in chapter 6 of the documentation, that Drools Flow can be
> configured to use JPA and transactions to persist the running states.
> However, I am in a EJB where I obtain entity managers with a
> @PersistenceContext annotation, and the transactions are managed by the
> container, whereas in the example an EntityManagerFactory and a
> TransactionManager are used.
> Could I configure Flow in a direct way to be used by my EJB?
> Thanks very much,
Ok, looking at the code, I guess that Drools flow is not ready to work with
standards EJBs. The reasons are the following:
* The JPA annotations are not standard JPA annotations, but hibernate ones.
For example "CollectionOfElements" in ProcessInstanceInfo class.
* Transactions are managed by Drools, as opposed to some EJBs where
transactions are managed by the container.
As for that, I will probably modify the codes, so Flow could be used in our
EJBs. I would like to know if someone could guide me on what should I
I will post the modifications in case they are of interest to someone.
Thanks very much,
I've been using the Rete DSL a lot these days and was thinking that it could
be a good idea to provide some Eclipse infra-structure (syntax highlighting,
code completion, validation) to it using Xtext (
http://www.eclipse.org/Xtext/). It's built on top of antlr, so maybe we
could even reuse the grammar that we already have. I know that, by default,
you should right you grammar using their simplified version.
Maybe the guys there would also be interested in providing something to
Drools. It could be a showcase for them.
Not sure if this also applies to the Eclipse DRL things that we have.
I hope I'm in the right list here. Wasn't sure whether to go to users or devs. So, I chose the second one as my problem is more dev related.
I'm having trouble building guvnor and would appreciate your help. I checked out all the drools trunk into eclipse and can run the guvnor test cases without problem. Yet, when I try to package and install, I get the ERROR 1 below. And when I try to build the complete drools project with its subprojects, I get the second error error message below. I have the exact trunk copy.
I understand I'm doing something wrong with maven, that's why I need your input.
[ERROR] Failed to execute goal on project drools-guvnor: Unable to get dependency information for org.drools:drools-repository:jar:5.2.0.SNAPSHOT: Failed to process POM for org.drools:drools-repository:jar:5.2.0.SNAPSHOT: Non-resolvable parent POM org.drools:drools:5.2.0.SNAPSHOT for org.drools:drools-repository:5.2.0.SNAPSHOT: Failed to resolve POM for org.drools:drools:5.2.0.SNAPSHOT due to Missing:
[ERROR] The project org.drools:drools-grid-local:5.2.0.SNAPSHOT (C:\Development\worskpace\trunk\drools-grid\drools-grid-local\pom.xml) has 1 error
[ERROR] 'dependencies.dependency.(groupId:artifactId:type:classifier)' must be unique: org.codehaus.btm:btm:jar -> duplicate declaration of version 1.3.3
[ERROR] The project org.drools:drools-grid-task:5.2.0.SNAPSHOT (C:\Development\worskpace\trunk\drools-grid\drools-grid-task\pom.xml) has 2 errors
[ERROR] 'dependencies.dependency.(groupId:artifactId:type:classifier)' must be unique: org.apache.mina:mina-core:jar -> duplicate declaration of version 2.0.0-M6
[ERROR] 'dependencies.dependency.(groupId:artifactId:type:classifier)' must be unique: com.h2database:h2:jar -> version (?) vs 1.2.128
[ERROR] The project org.drools:drools-bpmn2:5.2.0.SNAPSHOT (C:\Development\worskpace\trunk\drools-process\drools-bpmn2\pom.xml) has 1 error
[ERROR] 'dependencies.dependency.(groupId:artifactId:type:classifier)' must be unique: org.apache.felix:org.osgi.core:jar -> duplicate declaration of version (?)
[ERROR] For more information about the errors and possible solutions, please read the following articles:
[ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/ProjectBuildingException
GMX DSL Doppel-Flat ab 19,99 €/mtl.! Jetzt auch mit
gratis Notebook-Flat! http://portal.gmx.net/de/go/dsl
In an effort to help encourage those thinking of learning more about
the internals of rule engines. I have made a document on implementating
left and right unlinking. I describe the initial paper in terms relevant
to Drools users, and then how that can be implemented in Drools and a
series of enhancements over the original paper. The task is actually
surprisingly simple and you only need to learn a very small part of the
Drools implementation to do it, as such it's a great getting started
task. For really large stateful systems of hundreds or even thousands of
rules and hundreds of thousands of facts it should save significant
amounts of memory.
The following paper describes Left and Right unlinking enhancements for
Rete based networks:
A rete based rule engine consists of two parts of the network, the alpha
nodes and the beta nodes. When an object is first inserted into the
engine it is discriminated against by the object type Node, this is a
one input and one output node. From there it may be further
discriminated against by alpha nodes that constrain on literal values
before reaching the right input of a join node in the beta part of the
network. Join nodes have two inputs, left and right. The right input
receives propagations consisting of a single object from the alpha part
of the network. The left input receives propagations consisting of 1 or
more objects, from the parent beta node. We refer to these propagating
objects as LeftTuple and RightTuple, other engines also use the terms
tokens or partial matches. When a tuple propagation reaches a left or
right input it's stored in that inputs memory and it attempts to join
with all possible tuples on the opposite side. If there are no tuples on
the opposite side then no join can happen and the tuple just waits in
the node's memory until a propagation from the opposite side attempts to
join with it. If a given. It would be better if the engine could avoid
populating that node's memory until both sides have tuples. Left and
right unlinking are solutions to this problem.
The paper proposes that a node can either be left unlinked or right
unlinked, but not both, as then the rule would be completely
disconnected from the network. Unlinking an input means that it will not
receive any propagations and that the node's memory for that input is
not populated, saving memory space. When the opposite side, which is
still linked, receives a propagation the unlinked side is linked back in
and receives all the none propagated tuples. As both sides cannot be
unlinked, the paper describes a simple heuristic for choosing which side
to unlink. Which ever side becomes empty first, then unlink the other.
It says that on start up just arbitrarily chose to unlink one side as
default. The initial hit from choosing the wrong side will be
negligible, as the heuristic corrects this after the first set of
If the left input becomes empty the right input is unlink, thus clearing
the right input's memory too. The moment the left input receives a
propagation it re-attaches the right input fully populating it's memory.
The node can then attempt joins as normal. Vice-versa if the right input
becomes empty it unlinks the left input. The moment the right input
receives a propagation it re-attaches the left input fully populating
it's memory so that the node can attempt to join as normal.
Implementing Left and Right Unlinking for shared Knowledge Bases
The description of unlinking in the paper won't work for Drools or for
other rule engines that share the knowledge base between multiple
sessions. In Drools the session data is decoupled from the main
knowledge base and multiple sessions can share the same knowledge base.
The paper above describes systems where the session data is tightly
coupled to the knowledge base and the knowledge base has only a single
session. In shared systems a node input that is empty for one session
might not be empty for another. Instead of physically unlinking the
nodes, as described in the paper, an integer value can be used on the
session's node memory that indicates if the node is unlinked for left,
right or both inputs. When the propagating node attempts to propagate
instead of just creating a left or right tuple and pushing it into the
node. It'll first retrieve the node's memory and only create the tuple
and propagate if it's linked.
This is great as it also avoids creating tuple objects that would just
be discarded afterwards as there would be nothing to join with, making
things lighter on the GC. However it means the engine looks up the node
memory twice, once before propagating to the node and also inside of the
node as it attempt to do joins. Instead the node memory should be looked
up once, prior to propagating and then passed as an argument, avoiding
the double lookup.
Traditional Rete has memory per alpha node, for each literal constraint,
in the network. Drools does not have alpha memory, instead facts are
pulled from the object type node. This means that facts may needlessly
evaluate in the alpha part of the network, only to be refused addition
to the node memory afterwards. Rete supports something called "node
sharing", where multiple rules with similar constructs use the same
nodes in the network. For this reason shared nodes cannot easily be
unlinked. As a compromise when the alpha node is no longer shared, the
network can do a node memory lookup, prior to doing the evaluation and
check if that section of the network is unlinked and avoid attempting
the evaluation if it is. This allows for left and right unlinking to be
used in a engine such as Drools.
Using Left and Right Unlinking at the Same Time
The original paper describes an implantation in which a node cannot have
both the left and right inputs unlinked for the same node. Building on
the extension above to allow unlinking to work with a shared knowledge
base the initial linking status value can be set to both left and right
being unlinked. However in this initial state, where both sides are
unlinked, the leaf node's right input isn't just waiting for a left
propagation so the right can re-link itself (which it can't as the left
is unlinked too). It's also waiting to receive it's first propagation,
when it does it will link the left input back in. This will then tell
it's parent node's right input to also do the same, i.e. wait for it's
first right input propagation and link in the left when it happens. If
it already has a right propagation it'll just link in the left anyway.
This will trickle up until the root is finally linked in and
propagations can happen as normally, and the rule's nodes return to the
above heuristics for when to link and unlink the nodes.
Avoid Unnecessary Eager Propagations
A rule always eagerly propagates all joins, regardless of whether the
child node can undertake joins too, for instance of there is no
propagates for the leaf node then no rules can fire, and the eager
propagations are wasted work. Unlinking can be extended to try to
prevent some level of eager propagations. Should the leaf node become
right unlinked and that right input also become empty it will unlink the
left too (so both sides are unlinked) and go back to waiting for the
first right propagation, at which point it'll re-link the left. If the
parent node also has it's right input unlinked at the point that it's
child node unlinks the left it will do this too. It will repeat this up
the chain until it reaches a node that has both left and right linked
in. This stops any further eager matching from occurring that we know
can't result in an activation until the leaf node has at least one right
Heuristics to Avoid Churn from Excessive and Unnecessary Unlinking
The only case where left and right linking would be a bad idea is in
situations that would cause a "churn". Churn is when a node with have a
large amount of right input memory is continually caused to be linked in
and linked out, forcing those nodes to be repeatedly populated which
causes a slow down. However heuristics can be used here too, to avoid
unnecessary unlinking. The first time an input becomes empty unlink the
opposite and store a time stamp (integer counter for fact handles from
the WM). Then have a minimum delta number, say 100. The next time it
attempts to unlink, calculate the delta of the current time stamp
(integer counter on fact handle) and the time stamp of the node which
last unlinked (which was recorded at the point of unlinking) if it's
less than 100 then do nothing and don't unlink until it's 100 or more.
If it's 100 or more then unlink and as well as storing the unlink time
stamp, then take the delta of 100 or more and apply a multiple (2, 3, 4
etc depending on how steep you want it to rise, 3 is a good starting
number) and store it. Such as if the delta is 100 then store 300. The
next time the node links and attempts to unlink it must be a delta of
300 or more, the time after that 900 the time after that 2700.