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About Geoffrey De Smet

Geoffrey De Smet
Geoffrey De Smet (Red Hat) is the lead and founder of OptaPlanner. Before joining Red Hat in 2010, he was formerly employed as a Java consultant, an A.I. researcher and an enterprise application project lead. He has contributed to many open source projects (such as drools, jbpm, pressgang, spring-richclient, several maven plugins, weld, arquillian, ...). Since he started OptaPlanner in 2006, he’s been passionately addicted to planning optimization.

Drools : PHREAK Stack Based Evaluations and Backward Chaining

A while back I wrote a blog on our new algorithm: http://blog.athico.com/2013/11/rip-rete-time-to-get-phreaky.html

Someone asked me about the new stack based system, and how backward chaining works. I replied to them in an email, but I thought others might find it useful, so have pasted it below. It’s written straight from my brain onto the page, so it’ a bit raw in places.; but I hope some find it useful, regardless.


When a rule is evaluated, it evaluates from root to tip.

For each node it evaluates all possible joins and produces a tuple set. That child tuple set is then passed to the child node. The tuple set that is passed in is refered as the srcTupleSet  (for variable name) then all the children are placed into the trgTupleSet. The trgTupleSet is passed to the child node, where it becomes the srcTupleSet.

Line 245 shows this loop: https://github.com/droolsjbpm/drools/blob/master/drools-core/src/main/java/org/drools/core/phreak/RuleNetworkEvaluator.java#L245

srcTuples = trgTuples; // previous target, is now the source

When a node is entered it has a number of variables that are necessary to evaluate the node. The node id, the node memory, the segment memory, the srcTupleSet the trgTupleSet.  Any node can be paused and resumed (evaluate at a later point in time) by creating a StackEntry that references these values. The StackEntry is placed onto the stack. Here is the StackEntry class: https://github.com/droolsjbpm/drools/blob/master/drools-core/src/main/java/org/drools/core/phreak/StackEntry.java

This is needed for 2 reasons, backward chaining and sub networks. Backward chaining is done via the query node.

When the propagation reaches a query node it needs to suspend the evaluation of the current rule – so it creates a StackEntry and places it on the stack.

line 459 : https://github.com/droolsjbpm/drools/blob/master/drools-core/src/main/java/org/drools/core/phreak/RuleNetworkEvaluator.java#L459

A query is just a rule with no RHS, no consequence. It collects all the results that reach the terminal node and returns them to the caller. The query node allows a rule to invoke a query, passing in arguments. Invoking a query is done by inserting a DroolsQuery object, which matches the root pattern and triggers a propagation:

see line 67: https://github.com/droolsjbpm/drools/blob/master/drools-core/src/main/java/org/drools/core/phreak/PhreakQueryNode.java

LeftInputAdapterNode.doInsertObject(handle, pCtx, lian, wm, lm, false, dquery.isOpen());

Like prolog, arguments can be bound or unbound. A bound argument is an in-variable, and unbound argument is an out-variable. Implementation wise we do not apply constraints to unbound arguments. This allows for the classic prolog “transitive closure” type query. And while a rule can call a query, a query can also call a query (we don’t have tabling to detect infinite cycles).

query isContainedIn( String x, String y )
  Location( x, y; )
  ( Location( z, y; ) and isContainedIn( x, z; ) )

Note drools supports positional and slotted arguments in patterns. This is done by mapping all positions to a slot.

A step by step tutorial showing the above query in action, for reactive and non-reactive transitive closures, can be found here: https://www.youtube.com/watch?v=fCjIRVSRFvA

For the evaluating query, when the trgTulupleSet reaches the terminal node, it iterates and adds each tuple to the “collector”.

see line 65: https://github.com/droolsjbpm/drools/blob/master/drools-core/src/main/java/org/drools/core/phreak/PhreakQueryTerminalNode.java#L65

The collector creates  a special child tuple, that can be added into the calling parent.

see line 343: https://github.com/droolsjbpm/drools/blob/master/drools-core/src/main/java/org/drools/core/reteoo/QueryElementNode.java#L343

Once the query has finished evaluating, it returns. The process of retuning then allows the execution to visit the stack, where it pops the StackEntry and resumes the evaluation – but now the query results are available.

see lines 166 and 173: https://github.com/droolsjbpm/drools/blob/master/drools-core/src/main/java/org/drools/core/phreak/RuleNetworkEvaluator.java

A query can be invoked reactively and non-reactively. Non reactively means there is no left memory and the query is not left open. Reactively means there is left memory and the query is left open. The reactive query is fully incremental and supports updates and deletes:

see line 143 and 169: https://github.com/droolsjbpm/drools/blob/master/drools-core/src/main/java/org/drools/core/phreak/PhreakQueryNode.java#L143

The data structures we use for tuples and “nested” (query result) tuples is efficient and “copy free”  and “search free” – it’s all double linked lists. This was necessary to make incremental queries efficient.

Sub networks use a similar technique. At the point a subnetwork is reached, the outer rule is suspended (placed on the stack) and the inner network evaluation is created.

see lines 593 and 604: https://github.com/droolsjbpm/drools/blob/master/drools-core/src/main/java/org/drools/core/phreak/RuleNetworkEvaluator.java

Once the subnetwork is finished, the outer rule resumes and places the results into the right input of the outer child node:

line 662: https://github.com/droolsjbpm/drools/blob/master/drools-core/src/main/java/org/drools/core/phreak/RuleNetworkEvaluator.java#L662

As previous mentioned currently we provide lazy rule evaluation, but not incremental rule evaluation. Once a rule evaluation starts, all tuples are produced. However as a stack entry can paused and resumed in any node, it could be used to provide incremental rule evaluation too – although we don’t do this now. In effect you “take” X number of objects on the right input – which could be 1 or 5 or 25 or 100. The number allows you to tune latency vs throughput. After a take if there are still un-evaluated right inputs, you create a StackEntry that forces re-evaluation of this node after the current propagation has finished.

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