com.bigdata.bop.joinGraph.rto.JGraph Maven / Gradle / Ivy
/**
Copyright (C) SYSTAP, LLC DBA Blazegraph 2006-2016. All rights reserved.
Contact:
SYSTAP, LLC DBA Blazegraph
2501 Calvert ST NW #106
Washington, DC 20008
[email protected]
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; version 2 of the License.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
/*
* Created on Feb 22, 2011
*/
package com.bigdata.bop.joinGraph.rto;
import java.util.Arrays;
import java.util.Collections;
import java.util.Formatter;
import java.util.Iterator;
import java.util.LinkedHashMap;
import java.util.LinkedHashSet;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.Future;
import java.util.concurrent.atomic.AtomicInteger;
import org.apache.log4j.Logger;
import com.bigdata.bop.BOp;
import com.bigdata.bop.BOpUtility;
import com.bigdata.bop.IConstraint;
import com.bigdata.bop.IPredicate;
import com.bigdata.bop.ap.SampleIndex.SampleType;
import com.bigdata.bop.engine.QueryEngine;
import com.bigdata.bop.joinGraph.NoSolutionsException;
import com.bigdata.bop.joinGraph.PartitionedJoinGroup;
import com.bigdata.bop.rdf.join.DataSetJoin;
import com.bigdata.rdf.sparql.ast.eval.AST2BOpRTO;
import com.bigdata.util.concurrent.ExecutionExceptions;
/**
* A runtime optimizer for a join graph. The {@link JoinGraph} bears some
* similarity to ROX (Runtime Optimizer for XQuery), but has several significant
* differences:
*
* -
* 1. ROX starts from the minimum cardinality edge of the minimum cardinality
* vertex. The {@link JoinGraph} starts with one or more low cardinality
* vertices.
* -
* 2. ROX always extends the last vertex added to a given join path. The
* {@link JoinGraph} extends all vertices having unexplored edges in each
* breadth first expansion.
* -
* 3. ROX is designed to interleave operator-at-once evaluation of join path
* segments which dominate other join path segments. The {@link JoinGraph} is
* designed to prune all join paths which are known to be dominated by other
* join paths for the same set of vertices in each round and iterates until a
* join path is identified which uses all vertices and has the minimum expected
* cumulative estimated cardinality. Join paths which survive pruning are
* re-sampled as necessary in order to obtain better information about edges in
* join paths which have a low estimated cardinality in order to address a
* problem with underflow of the cardinality estimates.
*
*
* TODO For join graphs with a large number of vertices we may need to constrain
* the #of vertices which are explored in parallel. This could be done by only
* branching the N lowest cardinality vertices from the already connected edges.
* Since fewer vertices are being explored in parallel, paths are more likely to
* converge onto the same set of vertices at which point we can prune the
* dominated paths.
*
* TODO Compare the cumulative expected cardinality of a join path with the
* tuples read on a join path. The latter allows us to also explore alternative
* join strategies, such as the parallel subquery versus scan and filter
* decision for named graph and default graph SPARQL queries.
*
* TODO Coalescing duplicate access paths can dramatically reduce the work
* performed by a pipelined nested index subquery. (A hash join eliminates all
* duplicate access paths using a scan and filter approach.) If we will run a
* pipeline nested index subquery join, then should the runtime query optimizer
* prefer paths with duplicate access paths?
*
* @todo Examine the overhead of the runtime optimizer. Look at ways to prune
* its costs. For example, by pruning the search, by recognizing when the
* query is simple enough to execute directly, by recognizing when we have
* already materialized the answer to the query, etc.
*
* @todo Cumulative estimated cardinality is an estimate of the work to be done.
* However, the actual cost of a join depends on whether we will use
* nested index subquery or a hash join and the cost of that operation on
* the database. There could be counter examples where the cost of the
* hash join with a range scan using the unbound variable is LT the nested
* index subquery. For those cases, we will do the same amount of IO on
* the hash join but there will still be a lower cardinality to the join
* path since we are feeding in fewer solutions to be joined.
*
* @todo Look at the integration with the SAIL. We need decorate to joins with some
* annotations. Those will have to be correctly propagated to the "edges"
* in order for edge sampling and incremental evaluation (or final
* evaluation) to work. The {@link DataSetJoin} essentially inlines one of
* its access paths. That should really be changed into an inline access
* path and a normal join operator so we can defer some of the details
* concerning the join operator annotations until we decide on the join
* path to be executed. An inline AP really implies an inline relation,
* which in turn implies that the query is a searchable context for
* query-local resources.
*
* For s/o, when the AP is remote, the join evaluation context must be ANY
* and otherwise (for s/o) it must be SHARDED.
*
* Since the join graph is fed the vertices (APs), it does not have access
* to the annotated joins so we need to generated appropriately annotated
* joins when sampling an edge and when evaluation a subquery.
*
* One solution would be to always use the unpartitioned views of the
* indices for the runtime query optimizer, which is how we are estimating
* the range counts of the access paths right now. [Note that the static
* query optimizer ignores named and default graphs, while the runtime
* query optimizer SHOULD pay attention to these things and exploit their
* conditional selectivity for the query plan.]
*
* @todo Handle optional join graphs by first applying the runtime optimizer to
* the main join graph and obtaining a sample for the selected join path.
* That sample will then be feed into the the optional join graph in order
* to optimize the join order within the optional join graph (a join order
* which is selective in the optional join graph is better since it build
* up the #of intermediate results more slowly and hence do less work).
*
* This is very much related to accepting a collection of non-empty
* binding sets when running the join graph. However, optional join graph
* should be presented in combination with the original join graph and the
* starting paths must be constrained to have the selected join path for
* the original join graph as a prefix. With this setup, the original join
* graph has been locked in to a specific join path and the sampling of
* edges and vertices for the optional join graph can proceed normally.
*
* True optionals will always be appended as part of the "tail plan" for
* any join graph and can not be optimized as each optional join must run
* regardless (as long as the intermediate solution survives the
* non-optional joins).
*
* @todo There are two cases where a join graph must be optimized against a
* specific set of inputs. In one case, it is a sample (this is how
* optimization of an optional join group proceeds per above). In the
* other case, the set of inputs is fixed and is provided instead of a
* single empty binding set as the starting condition. This second case is
* actually a bit more complicated since we can not use a random sample of
* vertices unless they do not share any variables with the initial binding
* sets. When there is a shared variable, we need to do a cutoff join of
* the edge with the initial binding sets. When there is not a shared
* variable, we can sample the vertex and then do a cutoff join.
*
* @todo When we run into a cardinality estimation underflow (the expected
* cardinality goes to zero) we could double the sample size for just
* those join paths which hit a zero estimated cardinality and re-run them
* within the round. This would imply that we keep per join path limits.
* The vertex and edge samples are already aware of the limit at which
* they were last sampled so this should not cause any problems there.
*
* A related option would be to deepen the samples only when we are in
* danger of cardinality estimation underflow. E.g., a per-path limit.
* Resampling vertices may only make sense when we increase the limit
* since otherwise we may find a different correlation with the new sample
* but the comparison of paths using one sample base with paths using a
* different sample base in a different round does not carry forward the
* cardinality estimates from the prior round (unless we do something like
* a weighted moving average).
*
* @todo When comparing choices among join paths having fully bound tails where
* the estimated cardinality has also gone to zero, we should prefer to
* evaluate vertices in the tail with better index locality first. For
* example, if one vertex had one variable in the original plan while
* another had two variables, then solutions which reach the 2-var vertex
* could be spread out over a much wider range of the selected index than
* those which reach the 1-var vertex. [In order to support this, we would
* need a means to indicate that a fully bound access path should use an
* index specified by the query optimizer rather than the primary index
* for the relation. In addition, this suggests that we should keep bloom
* filters for more than just the SPO(C) index in scale-out.]
*
* @todo Examine behavior when we do not have perfect covering indices. This
* will mean that some vertices can not be sampled using an index and that
* estimation of their cardinality will have to await the estimation of
* the cardinality of the edge(s) leading to that vertex. Still, the
* approach should be able to handle queries without perfect / covering
* automatically. Then experiment with carrying fewer statement indices
* for quads.
*
* @todo Unit test when there are no solutions to the query. In this case there
* will be no paths identified by the optimizer and the final path length
* becomes zero.
*
* @see Runtime
* Query Optimization
* @see Integrate
* RTO into SAIL
* @see
* ROX
*/
public class JGraph {
private static final String NA = "N/A";
private static final transient Logger log = Logger.getLogger(JGraph.class);
/**
* The pipeline operator for executing the RTO. This provides additional
* context from the AST model that is necessary to handle some kinds of
* FILTERs (e.g., those which require conditional routing patterns for
* chunked materialization).
*/
private final JoinGraph joinGraph;
/**
* Vertices of the join graph.
*/
private final Vertex[] V;
/**
* Constraints on the join graph. A constraint is applied once all
* variables referenced by a constraint are known to be bound.
*/
private final IConstraint[] C;
/**
* The kind of samples that will be taken when we sample a {@link Vertex}.
*/
private final SampleType sampleType;
public List getVertices() {
return Collections.unmodifiableList(Arrays.asList(V));
}
@Override
public String toString() {
final StringBuilder sb = new StringBuilder();
sb.append("JoinGraph");
sb.append("{V=[");
for (Vertex v : V) {
sb.append("\nV[" + v.pred.getId() + "]=" + v);
}
sb.append("{C=[");
for (IConstraint c : C) {
sb.append("\nC[" + c.getId() + "]=" + c);
}
sb.append("\n]}");
return sb.toString();
}
/**
* @param joinGraph
* The pipeline operator that is executing the RTO. This defines
* the join graph (vertices, edges, and constraints) and also
* provides access to the AST and related metadata required to
* execute the join graph.
*
* @throws IllegalArgumentException
* if the joinGraph is null.
* @throws IllegalArgumentException
* if the {@link JoinGraph#getVertices()} is null.
* @throws IllegalArgumentException
* if the {@link JoinGraph#getVertices()} is an empty array.
* @throws IllegalArgumentException
* if any element of the {@link JoinGraph#getVertices()}is
* null.
* @throws IllegalArgumentException
* if any constraint uses a variable which is never bound by the
* given predicates.
* @throws IllegalArgumentException
* if sampleType is null.
*
* @todo unit test for a constraint using a variable which is never bound.
* the constraint should be attached at the last vertex in the join
* path. this will cause the query to fail unless the variable was
* already bound, e.g., by a parent query or in the solutions pumped
* into the {@link JoinGraph} operator.
*
* @todo unit test when the join graph has a single vertex (we never invoke
* the RTO for less than 3 vertices since with one vertex you just run
* it and with two vertices you run the lower cardinality vertex first
* (though there might be cases where filters require materialization
* where running for two vertices could make sense)).
*/
public JGraph(final JoinGraph joinGraph) {
if (joinGraph == null)
throw new IllegalArgumentException();
this.joinGraph = joinGraph;
final IPredicate[] v = joinGraph.getVertices();
if (v == null)
throw new IllegalArgumentException();
if (v.length < 2)
throw new IllegalArgumentException();
V = new Vertex[v.length];
for (int i = 0; i < v.length; i++) {
if (v[i] == null)
throw new IllegalArgumentException();
V[i] = new Vertex(v[i]);
}
final IConstraint[] constraints = joinGraph.getConstraints();
if (constraints != null) {
C = new IConstraint[constraints.length];
for (int i = 0; i < constraints.length; i++) {
if (constraints[i] == null)
throw new IllegalArgumentException();
C[i] = constraints[i];
}
} else {
// No constraints.
C = null;
}
final SampleType sampleType = joinGraph.getSampleType();
if (sampleType == null)
throw new IllegalArgumentException();
this.sampleType = sampleType;
}
// /**
// * Find a good join path in the data given the join graph. The join path is
// * not guaranteed to be the best join path (the search performed by the
// * runtime optimizer is not exhaustive) but it should always be a "good"
// * join path and may often be the "best" join path.
// *
// * @param queryEngine
// * The query engine.
// * @param limit
// * The limit for sampling a vertex and the initial limit for
// * cutoff join evaluation.
// * @param nedges
// * The edges in the join graph are sorted in order of increasing
// * cardinality and up to nedges of the edges having the
// * lowest cardinality are used to form the initial set of join
// * paths. For each edge selected to form a join path, the
// * starting vertex will be the vertex of that edge having the
// * lower cardinality.
// * @param sampleType
// * Type safe enumeration indicating the algorithm which will be
// * used to sample the initial vertices.
// *
// * @return The join path identified by the runtime query optimizer as the
// * best path given the join graph and the data.
// *
// * @throws NoSolutionsException
// * If there are no solutions for the join graph in the data (the
// * query does not have any results).
// * @throws IllegalArgumentException
// * if queryEngine is null.
// * @throws IllegalArgumentException
// * if limit is non-positive.
// * @throws IllegalArgumentException
// * if nedges is non-positive.
// * @throws Exception
// */
// public Path runtimeOptimizer(final QueryEngine queryEngine,
// final int limit, final int nedges) throws NoSolutionsException,
// Exception {
//
// final Map edgeSamples = new LinkedHashMap();
//
// return runtimeOptimizer(queryEngine, limit, nedges, edgeSamples);
//
// }
/**
* Find a good join path in the data given the join graph. The join path is
* not guaranteed to be the best join path (the search performed by the
* runtime optimizer is not exhaustive) but it should always be a "good"
* join path and may often be the "best" join path.
*
* @param queryEngine
* The query engine.
* @param edgeSamples
* A map that will be populated with the samples associated with
* each non-pruned join path. This map is used to associate join
* path segments (expressed as an ordered array of bopIds) with
* edge sample to avoid redundant effort.
*
* @return The join path identified by the runtime query optimizer as the
* best path given the join graph and the data.
*
* @throws NoSolutionsException
* If there are no solutions for the join graph in the data (the
* query does not have any results).
* @throws IllegalArgumentException
* if queryEngine is null.
* @throws IllegalArgumentException
* if limit is non-positive.
* @throws IllegalArgumentException
* if nedges is non-positive.
* @throws Exception
*
* TODO It is possible that this could throw a
* {@link NoSolutionsException} if the cutoff joins do not use a
* large enough sample to find a join path which produces at
* least one solution (except that no solutions for an optional
* join do not cause the total to fail, nor do no solutions for
* some part of a UNION).
*
* TODO We need to automatically increase the depth of search
* for queries where we have cardinality estimation underflows
* or punt to another method to decide the join order.
*
* TODO RTO: HEAP MANAGMENT : The edgeSamples map holds
* references to the cutoff join samples. To ensure that the map
* has the minimum heap footprint, it must be scanned each time
* we prune the set of active paths and any entry which is not a
* prefix of an active path should be removed.
*
* TODO RTO: MEMORY MANAGER : When an entry is cleared from this
* map, the corresponding allocation in the memory manager (if
* any) must be released. The life cycle of the map needs to be
* bracketed by a try/finally in order to ensure that all
* allocations associated with the map are released no later
* than when we leave the lexicon scope of that clause.
*/
public Path runtimeOptimizer(//
final QueryEngine queryEngine,//
final Map edgeSamples//
) throws Exception, NoSolutionsException {
if (queryEngine == null)
throw new IllegalArgumentException();
/*
* The limit for sampling a vertex and the initial limit for cutoff join
* evaluation.
*/
final int limit = joinGraph.getLimit();
if (limit <= 0)
throw new IllegalArgumentException();
/*
* The edges in the join graph are sorted in order of increasing
* cardinality and up to nedges of the edges having the lowest
* cardinality are used to form the initial set of join paths. For each
* edge selected to form a join path, the starting vertex will be the
* vertex of that edge having the lower cardinality.
*/
final int nedges = joinGraph.getNEdges();
if (nedges <= 0)
throw new IllegalArgumentException();
if (edgeSamples == null)
throw new IllegalArgumentException();
// Setup the join graph.
Path[] paths = round0(queryEngine, limit, nedges);
/*
* The initial paths all have one edge, and hence two vertices. Each
* round adds one more vertex to each path. We are done once we have
* generated paths which include all vertices.
*
* This occurs at round := nvertices - 1
*
* Note: There are a few edge cases, such as when sampling can not
* find any solutions, even with an increased sampling limit.
* Eventually we wind up proving that there are no solutions for the
* query.
*/
final int nvertices = V.length;
int round = 1; // #of rounds.
int nunderflow = 0; // #of paths with card. underflow (no solutions).
while (paths.length > 0 && round < nvertices - 1) {
/*
* Resample the paths.
*
* Note: Since the vertex samples are random, it is possible for the
* #of paths with cardinality estimate underflow to jump up and down
* due to the sample which is making its way through each path in
* each round.
*
* Note: The RTO needs an escape hatch here. Otherwise, it is
* possible for it to spin in a loop while resampling.
*
* TODO For example, if the sum of the expected IOs for some path(s)
* strongly dominates all other paths sharing the same vertices,
* then we should prune those paths even if there is a cardinality
* estimate underflow in those paths. This will allow us to focus
* our efforts on those paths having less IO cost while we seek
* cardinality estimates which do not underflow.
*
* TODO We should be examining the actual sampling limit that is
* currently in place on each vertex and for each path. This is
* available by inspection of the VertexSamples and EdgeSamples, but
* it is not passed back out of the resamplePaths() method as a
* side-effect. We should limit how much we are willing to raise the
* limit, e.g., by specifying a MAX_LIMIT annotation on the
* JoinGraph operator.
*/
for (int i = 0; i < 3; i++) {
nunderflow = resamplePaths(queryEngine, limit, round, paths,
edgeSamples);
if (nunderflow == 0) {
// No paths have cardinality estimate underflow.
break;
}
/*
* Show information about the paths and the paths that are
* experiencing cardinality underflow.
*/
log.warn("Cardinality estimate underflow - resampling: round="
+ round + ", npaths=" + paths.length + ", nunderflow="
+ nunderflow + ", limit=" + limit + "\n"
+ showTable(paths, null/* pruned */, edgeSamples));
}
if (nunderflow > 0) {
log.warn("Continuing: some paths have cardinality underflow!");
}
/*
* Extend the paths by one vertex.
*/
paths = expand(queryEngine, limit, round++, paths, edgeSamples);
}
if (paths.length == 0) {
// There are no solutions for the join graph in the data.
throw new NoSolutionsException();
}
/*
* In general, there should be one winner. However, if there are paths
* with cardinality estimate underflow (that is, paths that do not have
* any solutions when sampling the path) then there can be multiple
* solutions.
*
* When this occurs we have a choice. We can either the path with the
* minimum cost (min sumEstCard) or we can take a path that does not
* have a cardinality estimate underflow because we actually have an
* estimate for that path. In principle, the path with the minimum
* estimated cost should be the better choice, but we do not have any
* information about the order of the joins beyond the point where the
* cardinality underflow begins on that path. If we take a path that
* does not have a cardinality estimate underflow, then at least we know
* that the join order has been optimized for the entire path.
*/
final Path selectedPath;
if (paths.length != 1) {
log.warn("Multiple paths exist: npaths=" + paths.length
+ ", nunderflow=" + nunderflow + "\n"
+ showTable(paths, null/* pruned */, edgeSamples));
Path t = null;
for (Path p : paths) {
if (p.edgeSample.isUnderflow()) {
/*
* Skip paths with cardinality estimate underflow. They are
* not fully tested in the data since no solutions have made
* it through all of the joins.
*/
continue;
}
if (t == null || p.sumEstCard < t.sumEstCard) {
// Accept path with the least cost.
t = p;
}
}
if (t == null) {
/* Arbitrary choice if all paths underflow.
*
* TODO Or throw out NoSolutionsException?
*/
t = paths[0];
}
selectedPath = t;
} else {
selectedPath = paths[0];
}
if (log.isInfoEnabled()) {
log.info("\n*** Selected join path: "
+ Arrays.toString(selectedPath.getVertexIds()) + "\n"
+ showPath(selectedPath, edgeSamples));
}
return selectedPath;
}
/**
* Return a permutation vector which may be used to reorder the given
* {@link IPredicate}[] into the evaluation order selected by the
* runtime query optimizer.
*
* @throws IllegalArgumentException
* if the argument is null.
* @throws IllegalArgumentException
* if the given {@link Path} does not cover all vertices in
* the join graph.
*/
public int[] getOrder(final Path p) {
if(p == null)
throw new IllegalArgumentException();
final IPredicate[] path = p.getPredicates();
if (path.length != V.length) {
throw new IllegalArgumentException(
"Wrong path length: #vertices=" + V.length
+ ", but path.length=" + path.length);
}
final int[] order = new int[V.length];
for (int i = 0; i < order.length; i++) {
boolean found = false;
for (int j = 0; j < order.length; j++) {
if (path[i].getId() == V[j].pred.getId()) {
order[i] = j;
found = true;
break;
}
}
if (!found)
throw new RuntimeException("No such vertex: id="
+ path[i].getId());
}
return order;
}
/**
* Choose the starting vertices.
*
* @param nedges
* The maximum #of edges to choose.
* @param paths
* The set of possible initial paths to choose from.
*
* @return Up to nedges minimum cardinality paths.
*/
public Path[] chooseStartingPaths(final int nedges, final Path[] paths) {
final List tmp = new LinkedList();
// Sort them by ascending expected cardinality.
Arrays.sort(paths, 0, paths.length,
EstimatedCardinalityComparator.INSTANCE);
// Choose the top-N edges (those with the least cardinality).
for (int i = 0; i < paths.length && i < nedges; i++) {
tmp.add(paths[i]);
}
return tmp.toArray(new Path[tmp.size()]);
}
/**
* Choose up to nedges edges to be the starting point. For each of
* the nedges lowest cardinality edges, the starting vertex will be
* the vertex with the lowest cardinality for that edge.
*
* Note: An edge can not serve as a starting point for exploration if it
* uses variables (for example, in a CONSTRAINT) which are not bound by
* either vertex (since the variable(s) are not bound, the constraint would
* always fail).
*
* @param queryEngine
* The query engine.
* @param limit
* The cutoff used when sampling the vertices and when sampling
* the edges.
* @param nedges
* The maximum #of edges to choose. Those having the smallest
* expected cardinality will be chosen.
*
* @return An initial set of paths starting from at most nedges.
*
* @throws Exception
*
* @todo UNIT TEST : with runFirst predicates in the join graph. [If we
* allow "runFirst" predicates into the join graph, then an initial
* non-empty join path needs to be constructed from those vertices.
* The operators for those APs would also have to be modified to
* support cutoff evaluation or to be fully materialized.]
*/
public Path[] round0(final QueryEngine queryEngine, final int limit,
final int nedges) throws Exception {
/*
* Sample the vertices.
*/
sampleAllVertices(queryEngine, limit);
if (log.isInfoEnabled()) {
final StringBuilder sb = new StringBuilder();
sb.append("limit=" + limit + ", nedges=" + nedges);
sb.append(", sampled vertices::\n");
for (Vertex v : V) {
if (v.sample != null) {
sb.append("id=" + v.pred.getId() + " : ");
sb.append(v.sample.toString());
sb.append("\n");
}
}
log.info(sb.toString());
}
/*
* Estimate the cardinality for each edge.
*/
final Path[] a = estimateInitialEdgeWeights(queryEngine, limit);
// if (log.isDebugEnabled()) {
// final StringBuilder sb = new StringBuilder();
// sb.append("All possible initial paths:\n");
// for (Path x : a) {
// sb.append(x.toString());
// sb.append("\n");
// }
// log.debug(sb.toString());
// }
if (log.isInfoEnabled())
log.info("\n*** Initial Paths\n" + JGraph.showTable(a));
/*
* Choose the initial set of paths.
*/
final Path[] paths_t0 = chooseStartingPaths(nedges, a);
if (log.isInfoEnabled())
log.info("\n*** Paths @ t0\n" + JGraph.showTable(paths_t0));
/*
* Discard samples for vertices which were not chosen as starting points
* for join paths.
*
* Note: We do not need the samples of the other vertices once we decide
* on the initial vertices from which the join paths will be grown so
* they could even be discarded at this point.
*/
{
final Set initialVertexSet = new LinkedHashSet();
for (Path x : paths_t0) {
initialVertexSet.add(x.vertices[0]);
}
for (Vertex v : V) {
if (!initialVertexSet.contains(v)) {
// Discard sample.
v.sample = null;
}
}
}
return paths_t0;
}
/**
* Resample the initial vertices for the specified join paths and then
* resample the cutoff join for each given join path in path order.
*
* @param queryEngine
* The query engine.
* @param limitIn
* The original limit.
* @param round
* The round number in [1:n].
* @param a
* The set of paths from the previous round. For the first round,
* this is formed from the initial set of edges to consider.
* @param edgeSamples
* A map used to associate join path segments (expressed as an
* ordered array of bopIds) with {@link EdgeSample}s to avoid
* redundant effort.
*
* @return The number of join paths which are experiencing cardinality
* estimate underflow.
*
* @throws Exception
*/
protected int resamplePaths(final QueryEngine queryEngine,
final int limitIn, final int round, final Path[] a,
final Map edgeSamples) throws Exception {
if (queryEngine == null)
throw new IllegalArgumentException();
if (limitIn <= 0)
throw new IllegalArgumentException();
if (round <= 0)
throw new IllegalArgumentException();
if (a == null)
throw new IllegalArgumentException();
if (a.length == 0)
throw new IllegalArgumentException();
/*
* Re-sample the vertices which are the initial vertex of any of the
* existing paths.
*
* Note: We do not need to resample vertices unless they are the first
* vertex in some path. E.g., the initial vertices from which we start.
* The inputs to an EdgeSample are always either the sample of an
* initial vertex or the sample of a prior cutoff join in the join
* path's own history.
*
* Note: A request to re-sample a vertex is a NOP unless the limit has
* been increased since the last time the vertex was sampled. It is also
* a NOP if the vertex has been fully materialized.
*
* Note: Before resampling the vertices, decide what the maximum limit
* will be for each vertex by examining the paths using that vertex,
* their current sample limit (TODO there should be a distinct limit for
* the vertex and for each cutoff join), and whether or not each path
* experiences a cardinality estimate underflow.
*/
{
if (log.isDebugEnabled())
log.debug("Re-sampling in-use vertices.");
final Map();
for (Path x : a) {
final int limit = x.getNewLimit(limitIn);
final Vertex v = x.vertices[0];
AtomicInteger theLimit = vertexLimit.get(v);
if (theLimit == null) {
vertexLimit.put(v, theLimit = new AtomicInteger());
}
theLimit.set(limit);
}
// re-sample vertices (this is done in parallel).
sampleVertices(queryEngine, vertexLimit);
}
/*
* Re-sample the cutoff join for each edge in each of the existing paths
* using the newly re-sampled vertices.
*
* Note: The only way to increase the accuracy of our estimates for
* edges as we extend the join paths is to re-sample each edge in the
* join path in path order.
*
* Note: An edge must be sampled for each distinct join path prefix in
* which it appears within each round. However, it is common for
* surviving paths to share a join path prefix, so do not re-sample a
* given path prefix more than once per round.
*
* FIXME PARALLELIZE: Parallelize the re-sampling for the active paths.
* This step is responsible for deepening the samples on the non-pruned
* paths. There is a data race that can occur since the [edgeSamples]
* map can contain samples for the same sequence of edges in different
* paths. This is because two paths can shared a common prefix sequence
* of edges, e.g., [2, 4, 6, 7] and [2, 4, 6, 9] share the path prefix
* [2, 4, 6]. Therefore both inspection and update of the [edgeSamples]
* map MUST be synchronized. This code is single threaded since that
* synchronization mechanism has not yet been put into place. The
* obvious way to handle this is to use a memoization pattern for the
* [ids] key for the [edgeSamples] map. This will ensure that the
* threads that need to resample a given edge will coordinate with the
* first such thread doing the resampling and the other thread(s)
* blocking until the resampled edge is available.
*/
if (log.isDebugEnabled())
log.debug("Re-sampling in-use path segments.");
// #of paths with cardinality estimate underflow.
int nunderflow = 0;
final List> tasks = new LinkedList>();
for (Path x : a) {
tasks.add(new ResamplePathTask(queryEngine, x, limitIn, edgeSamples));
} // next Path [x].
// Execute in the caller's thread.
for (Callable task : tasks) {
if (task.call()) {
nunderflow++;
}
}
return nunderflow;
}
/**
* Resample the edges along a join path. Edges are resampled based on the
* desired cutoff limit and only as necessary.
*
* @author Bryan
* Thompson
*/
private class ResamplePathTask implements Callable {
private final QueryEngine queryEngine;
private final Path x;
private final int limitIn;
private final Map edgeSamples;
public ResamplePathTask(final QueryEngine queryEngine, final Path x,
final int limitIn, final Map edgeSamples) {
this.queryEngine = queryEngine;
this.x = x;
this.limitIn = limitIn;
this.edgeSamples = edgeSamples;
}
@Override
public Boolean call() throws Exception {
/*
* Get the new sample limit for the path.
*
* TODO We only need to increase the sample limit starting at the
* vertex where we have a cardinality underflow or variability in
* the cardinality estimate. This is increasing the limit in each
* round of expansion, which means that we are reading more data
* than we really need to read.
*/
final int limit = x.getNewLimit(limitIn);
// The cutoff join sample of the one step shorter path segment.
EdgeSample priorEdgeSample = null;
for (int segmentLength = 2; segmentLength <= x.vertices.length; segmentLength++) {
// Generate unique key for this join path segment.
final PathIds ids = new PathIds(BOpUtility.getPredIds(x
.getPathSegment(segmentLength)));
// Look for sample for this path in our cache.
EdgeSample edgeSample = edgeSamples.get(ids);
if (edgeSample != null && edgeSample.limit < limit
&& !edgeSample.isExact()) {
if (log.isTraceEnabled())
log.trace("Will resample at higher limit: " + ids);
// Time to resample this edge.
edgeSamples.remove(ids);
edgeSample = null;
}
if (priorEdgeSample == null) {
/*
* This is the first edge in the path.
*
* Test our local table of join path segment estimates to
* see if we have already re-sampled that edge. If not, then
* re-sample it now.
*/
assert segmentLength == 2;
if (edgeSample == null) {
/*
* Re-sample the 1st edge in the join path, updating the
* sample on the edge as a side-effect. The cutoff
* sample is based on the vertex sample for the minimum
* cardinality vertex.
*/
edgeSample = AST2BOpRTO.cutoffJoin(//
queryEngine, //
joinGraph, //
limit,//
x.getPathSegment(2),// 1st edge.
C,// constraints
V.length == 2,// pathIsComplete
x.vertices[0].sample// source sample.
);
// Cache the sample.
if (edgeSamples.put(ids, edgeSample) != null)
throw new AssertionError();
}
// Save sample. It will be used to re-sample the next edge.
priorEdgeSample = edgeSample;
} else {
/*
* The path segment is at least 3 vertices long.
*/
assert ids.length() >= 3;
if (edgeSample == null) {
/*
* This is some N-step edge in the path, where N is
* greater than ONE (1). The source vertex is the vertex
* which already appears in the prior edges of this join
* path. The target vertex is the next vertex which is
* visited by the join path. The sample passed in is the
* prior edge sample -- that is, the sample from the
* path segment without the target vertex. This is the
* sample that we just updated when we visited the prior
* edge of the path.
*/
edgeSample = AST2BOpRTO.cutoffJoin(//
queryEngine,//
joinGraph,//
limit,//
x.getPathSegment(ids.length()),//
C, // constraints
V.length == ids.length(), // pathIsComplete
priorEdgeSample//
);
if (log.isTraceEnabled())
log.trace("Resampled: " + ids + " : " + edgeSample);
if (edgeSamples.put(ids, edgeSample) != null)
throw new AssertionError();
}
// Save sample. It will be used to re-sample the next edge.
priorEdgeSample = edgeSample;
}
} // next path prefix in Path [x]
if (priorEdgeSample == null)
throw new AssertionError();
// Save the result on the path.
x.edgeSample = priorEdgeSample;
final boolean underflow = x.edgeSample.estimateEnum == EstimateEnum.Underflow;
if (underflow) {
if (log.isDebugEnabled())
log.debug("Cardinality underflow: " + x);
}
// Done.
return underflow;
}
}
/**
* Do one breadth first expansion. In each breadth first expansion we extend
* each of the active join paths by one vertex for each remaining vertex
* which enjoys a constrained join with that join path. In the event that
* there are no remaining constrained joins, we will extend the join path
* using an unconstrained join if one exists. In all, there are three
* classes of joins to be considered:
*
* - The target predicate directly shares a variable with the source join
* path. Such joins are always constrained since the source predicate will
* have bound that variable.
* - The target predicate indirectly shares a variable with the source
* join path via a constraint can run for the target predicate and which
* shares a variable with the source join path. These joins are indirectly
* constrained by the shared variable in the constraint. BSBM Q5 is an
* example of this case.
* - Any predicates may always be join to an existing join path. However,
* joins which do not share variables either directly or indirectly will be
* full cross products. Therefore such joins are added to the join path only
* after all constrained joins have been consumed.
*
*
* @param queryEngine
* The query engine.
* @param limitIn
* The original limit.
* @param round
* The round number in [1:n].
* @param a
* The set of paths from the previous round. For the first round,
* this is formed from the initial set of edges to consider.
* @param edgeSamples
* A map used to associate join path segments (expressed as an
* ordered array of bopIds) with {@link EdgeSample}s to avoid
* redundant effort.
*
* @return The set of paths which survived pruning in this round.
*
* @throws Exception
*/
public Path[] expand(final QueryEngine queryEngine, int limitIn,
final int round, final Path[] a,
Map edgeSamples) throws Exception {
if (queryEngine == null)
throw new IllegalArgumentException();
if (limitIn <= 0)
throw new IllegalArgumentException();
if (round <= 0)
throw new IllegalArgumentException();
if (a == null)
throw new IllegalArgumentException();
if (a.length == 0)
throw new IllegalArgumentException();
/*
* Ensure that we use a synchronized view of this collection since we
* will write on it from parallel threads when we expand the join paths
* in parallel.
*/
edgeSamples = Collections.synchronizedMap(edgeSamples);
// // increment the limit by itself in each round.
// final int limit = (round + 1) * limitIn;
if (log.isDebugEnabled())
log.debug("round=" + round + ", #paths(in)=" + a.length);
/*
* Expand each path one step from each vertex which branches to an
* unused vertex.
*/
if (log.isDebugEnabled())
log.debug("Expanding paths: #paths(in)=" + a.length);
// The new set of paths to be explored.
final List tmpAll = new LinkedList();
// Setup tasks to expand the current join paths.
final List>> tasks = new LinkedList>>();
for (Path x : a) {
tasks.add(new ExpandPathTask(queryEngine, x, edgeSamples));
}
// Expand paths in parallel.
final List>> futures = queryEngine.getIndexManager().getExecutorService()
.invokeAll(tasks);
// Check future, collecting new paths from each task.
for(Future> f : futures) {
tmpAll.addAll(f.get());
}
/*
* Now examine the set of generated and sampled join paths. If any paths
* span the same vertices then they are alternatives and we can pick the
* best alternative now and prune the other alternatives for those
* vertices.
*/
final Path[] paths_tp1 = tmpAll.toArray(new Path[tmpAll.size()]);
final Path[] paths_tp1_pruned = pruneJoinPaths(paths_tp1, edgeSamples);
if (log.isDebugEnabled()) // shows which paths were pruned.
log.info("\n*** round=" + round + ": paths{in=" + a.length
+ ",considered=" + paths_tp1.length + ",out="
+ paths_tp1_pruned.length + "}\n"
+ JGraph.showTable(paths_tp1, paths_tp1_pruned));
if (log.isInfoEnabled()) // only shows the surviving paths.
log.info("\n*** round=" + round
+ ": paths{in=" + a.length + ",considered="
+ paths_tp1.length + ",out=" + paths_tp1_pruned.length
+ "}\n" + JGraph.showTable(paths_tp1_pruned));
return paths_tp1_pruned;
}
/**
* Task expands a path by one edge into one or more new paths.
*
* @author Bryan Thompson
*/
private class ExpandPathTask implements Callable> {
private final QueryEngine queryEngine;
private final Path x;
/**
* Note: The collection provided by the caller MUST be thread-safe since
* this task will be run by parallel threads over the different join
* paths from the last round. There will not be any conflict over writes
* on this map since each {@link PathIds} instance resulting from the
* expansion will be unique, but we still need to use a thread-safe
* collection since there will be concurrent modifications to this map.
*/
private final Map edgeSamples;
public ExpandPathTask(final QueryEngine queryEngine, final Path x,
final Map edgeSamples) {
this.queryEngine = queryEngine;
this.x = x;
this.edgeSamples = edgeSamples;
}
@Override
public List call() throws Exception {
/*
* We already increased the sample limit for the path in the loop
* above.
*/
final int limit = x.edgeSample.limit;
/*
* The set of vertices used to expand this path in this round.
*/
final Set used = new LinkedHashSet();
/*
* Any vertex which (a) does not appear in the path to be
* extended; (b) has not already been used to extend the path;
* and (c) does not share any variables indirectly via
* constraints is added to this collection.
*
* If we are not able to extend the path at least once using a
* constrained join then we will use this collection as the
* source of unconnected edges which need to be used to extend
* the path.
*/
final Set nothingShared = new LinkedHashSet();
// The new set of paths to be explored as extensions to this path.
final List tmp = new LinkedList();
// Consider all vertices.
for (Vertex tVertex : V) {
// Figure out which vertices are already part of this path.
final boolean vFound = x.contains(tVertex);
if (vFound) {
// Vertex is already part of this path.
if (log.isTraceEnabled())
log.trace("Vertex: " + tVertex
+ " - already part of this path.");
continue;
}
if (used.contains(tVertex)) {
// Vertex already used to extend this path.
if (log.isTraceEnabled())
log
.trace("Vertex: "
+ tVertex
+ " - already used to extend this path.");
continue;
}
// FIXME RTO: Replace with StaticAnalysis.
if (!PartitionedJoinGroup.canJoinUsingConstraints(//
x.getPredicates(),// path
tVertex.pred,// vertex
C// constraints
)) {
/*
* Vertex does not share variables either directly
* or indirectly.
*/
if (log.isTraceEnabled())
log
.trace("Vertex: "
+ tVertex
+ " - unconstrained join for this path.");
nothingShared.add(tVertex);
continue;
}
// add the new vertex to the set of used vertices.
used.add(tVertex);
// Extend the path to the new vertex.
final Path p = x.addEdge(//
queryEngine, //
joinGraph, //
limit,//
tVertex,//
C, //
x.getVertexCount() + 1 == V.length// pathIsComplete
);
// Add to the set of paths for this round.
tmp.add(p);
// Record the sample for the new path.
if (edgeSamples.put(new PathIds(p.getVertexIds()),
p.edgeSample) != null)
throw new AssertionError();
if (log.isTraceEnabled())
log.trace("Extended path with dynamic edge: vnew="
+ tVertex.pred.getId() + ", new path=" + p);
} // next target vertex.
if (tmp.isEmpty()) {
/*
* No constrained joins were identified as extensions of this
* join path, so we must consider edges which represent fully
* unconstrained joins.
*/
assert !nothingShared.isEmpty();
/*
* Choose any vertex from the set of those which do
* not share any variables with the join path. Since
* all of these are fully unconstrained joins we do
* not want to expand the join path along multiple
* edges in this iterator, just along a single
* unconstrained edge.
*/
final Vertex tVertex = nothingShared.iterator().next();
// Extend the path to the new vertex.
final Path p = x.addEdge(//
queryEngine, //
joinGraph,//
limit, //
tVertex, //
C,//
x.getVertexCount() + 1 == V.length// pathIsComplete
);
// Add to the set of paths for this round.
tmp.add(p);
if (log.isTraceEnabled())
log.trace("Extended path with dynamic edge: vnew="
+ tVertex.pred.getId() + ", new path=" + p);
} // if(tmp.isEmpty())
return tmp;
}
}
/**
* Return the {@link Vertex} whose {@link IPredicate} is associated with
* the given {@link BOp.Annotations#BOP_ID}.
*
* @param bopId
* The bop identifier.
* @return The {@link Vertex} -or- null if there is no such
* vertex in the join graph.
*/
public Vertex getVertex(final int bopId) {
for (Vertex v : V) {
if (v.pred.getId() == bopId)
return v;
}
return null;
}
/**
* Obtain a sample and estimated cardinality (fast range count) for each
* vertex.
*
* @param queryEngine
* The query engine.
* @param limit
* The sample size.
*
* TODO Only sample vertices with an index.
*
* TODO If any required join has a vertex with a proven exact
* cardinality of zero, then there are no solutions for the join
* group. Throw a {@link NoSolutionsException} and have the
* caller handle it?
*
* TODO Consider other cases where we can avoid sampling a vertex
* or an initial edge.
*
* Be careful about rejecting high cardinality vertices here as
* they can lead to good solutions (see the "bar" data set
* example).
*
* BSBM Q5 provides a counter example where (unless we translate
* it into a key-range constraint on an index) some vertices do
* not share a variable directly and hence will materialize the
* full cross product before filtering which is *really*
* expensive.
*/
private void sampleAllVertices(final QueryEngine queryEngine,
final int limit) {
final Map vertexLimit = new LinkedHashMap();
for (Vertex v : V) {
vertexLimit.put(v, new AtomicInteger(limit));
}
sampleVertices(queryEngine, vertexLimit);
}
/**
* (Re-)sample a set of vertices. Sampling is done in parallel.
*
* Note: A request to re-sample a vertex is a NOP unless the limit has been
* increased since the last time the vertex was sampled. It is also a NOP if
* the vertex has been fully materialized.
*
* @param queryEngine
* @param vertexLimit
* A map whose keys are the {@link Vertex vertices} to be
* (re-)samples and whose values are the limit to be
* used when sampling the associated vertex. This map is
* read-only so it only needs to be thread-safe for concurrent
* readers.
*/
private void sampleVertices(final QueryEngine queryEngine,
final Map vertexLimit) {
// Setup tasks to sample vertices.
final List> tasks = new LinkedList>();
for (Map.Entry e : vertexLimit.entrySet()) {
final Vertex v = e.getKey();
final int limit = e.getValue().get();
tasks.add(new SampleVertexTask(queryEngine, v, limit, sampleType));
}
// Sample vertices in parallel.
final List> futures;
try {
futures = queryEngine.getIndexManager().getExecutorService()
.invokeAll(tasks);
} catch (InterruptedException e) {
// propagate interrupt.
Thread.currentThread().interrupt();
return;
}
// Check futures for errors.
final List causes = new LinkedList();
for (Future f : futures) {
try {
f.get();
} catch (InterruptedException e) {
log.error(e);
causes.add(e);
} catch (ExecutionException e) {
log.error(e);
causes.add(e);
}
}
/*
* If there were any errors, then throw an exception listing them.
*/
if (!causes.isEmpty()) {
// Throw exception back to the leader.
if (causes.size() == 1)
throw new RuntimeException(causes.get(0));
throw new RuntimeException("nerrors=" + causes.size(),
new ExecutionExceptions(causes));
}
}
/**
* Task to sample a vertex.
*
* @author Bryan
* Thompson
*/
static private class SampleVertexTask implements Callable {
private final QueryEngine queryEngine;
private final Vertex v;
private final int limit;
private final SampleType sampleType;
public SampleVertexTask(final QueryEngine queryEngine, final Vertex v,
final int limit, final SampleType sampleType) {
this.queryEngine = queryEngine;
this.v = v;
this.limit = limit;
this.sampleType = sampleType;
}
@Override
public Void call() throws Exception {
v.sample(queryEngine, limit, sampleType);
return null;
}
}
/**
* Estimate the cardinality of each edge. This is only invoked by
* {@link #round0(QueryEngine, int, int)} when it is trying to select the
* minimum cardinality edges which it will use to create the initial set of
* join paths from which the exploration will begin.
*
* @param queryEngine
* The query engine.
* @param limit
* The sample size.
*
* @throws Exception
*
* TODO It would be very interesting to see the variety and/or
* distribution of the values bound when the edge is sampled.
* This can be easily done using a hash map with a counter. That
* could tell us a lot about the cardinality of the next join
* path (sampling the join path also tells us a lot, but it does
* not explain it as much as seeing the histogram of the bound
* values). I believe that there are some interesting online
* algorithms for computing the N most frequent observations and
* the like which could be used here.
*/
private Path[] estimateInitialEdgeWeights(final QueryEngine queryEngine,
final int limit) throws Exception {
final List> tasks = new LinkedList>();
/*
* Examine all unordered vertex pairs (v1,v2) once. If any vertex has
* not been sampled, then it is ignored. For each unordered vertex pair,
* determine which vertex has the minimum cardinality and create an
* ordered vertex pair (v,vp) such that [v] is the vertex with the
* lesser cardinality. Finally, perform a cutoff join of (v,vp) and
* create a join path with a single edge (v,vp) using the sample
* obtained from the cutoff join.
*/
final boolean pathIsComplete = 2 == V.length;
for (int i = 0; i < V.length; i++) {
final Vertex v1 = V[i];
if (v1.sample == null) {
/*
* We can only estimate the cardinality of edges connecting
* vertices for which samples were obtained.
*/
continue;
}
for (int j = i + 1; j < V.length; j++) {
final Vertex v2 = V[j];
if (v2.sample == null) {
/*
* We can only estimate the cardinality of edges connecting
* vertices for which samples were obtained.
*/
continue;
}
/*
* Figure out which vertex has the smaller cardinality. The sample
* of that vertex is used since it is more representative than the
* sample of the other vertex.
*/
final Vertex v, vp;
if (v1.sample.estCard < v2.sample.estCard) {
v = v1;
vp = v2;
} else {
v = v2;
vp = v1;
}
if (!PartitionedJoinGroup.canJoinUsingConstraints(
new IPredicate[] { v.pred }, vp.pred, C)) {
/*
* If there are no shared variables, either directly or
* indirectly via the constraints, then we can not use this
* as an initial edge.
*
* TODO It may be possible to execute the join in one
* direction or the other but not both. This seems to be
* true for RangeBOp.
*
* @todo UNIT TEST : correct rejection of initial paths for
* vertices which are unconstrained joins.
*
* @todo UNIT TEST : correct acceptance of initial paths for
* vertices which are unconstrained joins IFF there are no
* constrained joins in the join graph.
*/
continue;
}
tasks.add(new CutoffJoinTask(queryEngine, limit, v, vp,
pathIsComplete));
} // next other vertex.
} // next vertex
/*
* Now sample those paths in parallel.
*/
final List paths = new LinkedList();
// // Sample the paths in the caller's thread.
// for(Callable task : tasks) {
//
// paths.add(task.call());
//
// }
// Sample the paths in parallel.
final List> futures = queryEngine.getIndexManager()
.getExecutorService().invokeAll(tasks);
// Check future, collecting new paths from each task.
for (Future f : futures) {
paths.add(f.get());
}
return paths.toArray(new Path[paths.size()]);
}
/**
* Cutoff sample an initial join path consisting of two vertices.
*
* @author Bryan Thompson
*/
private class CutoffJoinTask implements Callable {
private final QueryEngine queryEngine;
private final int limit;
private final Vertex v;
private final Vertex vp;
private final boolean pathIsComplete;
public CutoffJoinTask(final QueryEngine queryEngine, final int limit,
final Vertex v, final Vertex vp, final boolean pathIsComplete) {
this.queryEngine = queryEngine;
this.limit = limit;
this.v = v;
this.vp = vp;
this.pathIsComplete = pathIsComplete;
}
@Override
public Path call() throws Exception {
// The path segment
final IPredicate[] preds = new IPredicate[] { v.pred, vp.pred };
// cutoff join of the edge (v,vp)
final EdgeSample edgeSample = AST2BOpRTO.cutoffJoin(//
queryEngine,//
joinGraph,//
limit, // sample limit
preds, // ordered path segment.
C, // constraints
pathIsComplete,//
v.sample // sourceSample
);
final Path p = new Path(v, vp, edgeSample);
return p;
}
}
/**
* Prune paths which are dominated by other paths. Paths are extended in
* each round. Paths from previous rounds are always pruned. Of the new
* paths in each round, the following rule is applied to prune the search to
* just those paths which are known to dominate the other paths covering the
* same set of vertices:
*
* If there is a path, [p] != [p1], where [p] is an unordered variant of
* [p1] (that is the vertices of p are the same as the vertices of p1), and
* the cumulative cost of [p] is LTE the cumulative cost of [p1], then [p]
* dominates (or is equivalent to) [p1] and p1 should be pruned.
*
* @param a
* A set of paths.
* @param edgeSamples
* The set of samples for path segments. Samples which are no
* longer in use after pruning will be cleared from the map and
* their materialized solution sets will be discarded.
*
* @return The set of paths with all dominated paths removed.
*/
public Path[] pruneJoinPaths(final Path[] a,
final Map edgeSamples) {
final boolean neverPruneUnderflow = true;
/*
* Find the length of the longest path(s). All shorter paths are
* dropped in each round.
*/
int maxPathLen = 0;
for (Path p : a) {
if (p.vertices.length > maxPathLen) {
maxPathLen = p.vertices.length;
}
}
final StringBuilder sb = new StringBuilder();
final Formatter f = new Formatter(sb);
final Set pruned = new LinkedHashSet();
for (int i = 0; i < a.length; i++) {
final Path Pi = a[i];
if (Pi.edgeSample == null)
throw new RuntimeException("Not sampled: " + Pi);
if (Pi.vertices.length < maxPathLen) {
/*
* Only the most recently generated set of paths survive to
* the next round.
*/
pruned.add(Pi);
continue;
}
if (pruned.contains(Pi)) {
// already pruned.
continue;
}
if (neverPruneUnderflow
&& Pi.edgeSample.estimateEnum == EstimateEnum.Underflow) {
// Do not use path to prune if path has cardinality underflow.
continue;
}
for (int j = 0; j < a.length; j++) {
if (i == j)
continue;
final Path Pj = a[j];
if (Pj.edgeSample == null)
throw new RuntimeException("Not sampled: " + Pj);
if (pruned.contains(Pj)) {
// already pruned.
continue;
}
final boolean isPiSuperSet = Pi.isUnorderedVariant(Pj);
if (!isPiSuperSet) {
// Can not directly compare these join paths.
continue;
}
final long costPi = Pi.sumEstCost;
final long costPj = Pj.sumEstCost;
final boolean lte = costPi <= costPj;
List prunedByThisPath = null;
if (lte) {
prunedByThisPath = new LinkedList();
if (pruned.add(Pj))
prunedByThisPath.add(j);
for (int k = 0; k < a.length; k++) {
final Path x = a[k];
if (x.beginsWith(Pj)) {
if (pruned.add(x))
prunedByThisPath.add(k);
}
}
}
if (log.isDebugEnabled()) {
f
.format(
"Comparing: P[%2d] with P[%2d] : %10d %2s %10d %s",
i, j, costPi, (lte ? "<=" : ">"),
costPj, lte ? " *** pruned "
+ prunedByThisPath : "");
log.debug(sb);
sb.setLength(0);
}
} // Pj
} // Pi
/*
* Generate a set of paths which will be retained.
*/
final Path[] b;
{
final Set keep = new LinkedHashSet();
for (Path p : a) {
if (pruned.contains(p))
continue;
keep.add(p);
}
// convert the retained paths to an array.
b = keep.toArray(new Path[keep.size()]);
}
/*
* Clear any entries from the edgeSamples map which are not prefixes of
* the retained join paths.
*/
{
final Iterator> itr = edgeSamples
.entrySet().iterator();
int ncleared = 0;
while (itr.hasNext()) {
final Map.Entry e = itr.next();
final PathIds ids = e.getKey();
// Consider the retained paths.
boolean found = false;
for (Path p : b) {
if (p.beginsWith(ids.ids)) {
// This sample is still in use.
found = true;
break;
}
}
if (!found) {
/*
* Clear sample no longer in use.
*
* Note: In fact, holding onto the sample metadata is
* relatively cheap if there was a reason to do so (it only
* effects the size of the [edgeSamples] map). It is holding
* onto the materialized solution set which puts pressure on
* the heap.
*/
if (log.isTraceEnabled())
log.trace("Clearing sample: " + ids);
// release the sampled solution set.
e.getValue().releaseSample();
// clear the entry from the array.
itr.remove();
ncleared++;
}
}
if (ncleared > 0 && log.isDebugEnabled())
log.debug("Cleared " + ncleared + " samples");
}
// Return the set of retained paths.
return b;
}
/**
* Comma delimited table showing the estimated join hit ratio, the estimated
* cardinality, and the set of vertices for each of the specified join
* paths.
*
* @param a
* An array of join paths.
*
* @return A table with that data.
*/
static public String showTable(final Path[] a) {
return showTable(a, null/* pruned */);
}
/**
* Return a comma delimited table showing the estimated join hit ratio, the
* estimated cardinality, and the set of vertices for each of the specified
* join paths.
*
* @param a
* A set of paths (typically those before pruning).
* @param pruned
* The set of paths after pruning (those which were retained)
* (optional). When given, the paths which were pruned are marked
* in the table.
*
* @return A table with that data.
*/
static public String showTable(final Path[] a, final Path[] pruned) {
return showTable(a, pruned, null/* edgeSamples */);
}
/**
* Return a comma delimited table showing the estimated join hit ratio, the
* estimated cardinality, and the set of vertices for each of the specified
* join paths.
*
* @param a
* A set of paths (typically those before pruning).
* @param pruned
* The set of paths after pruning (those which were retained)
* (optional). When given, the paths which were pruned are marked
* in the table.
* @param edgeSamples
* When non-null, the details will be shown (using
* {@link #showPath(Path, Map)}) for each path that is
* experiencing cardinality estimate underflow (no solutions in
* the data).
*
* @return A table with that data.
*/
static public String showTable(final Path[] a, final Path[] pruned,
final Map edgeSamples) {
final StringBuilder sb = new StringBuilder(128 * a.length);
final List underflowPaths = new LinkedList();
final Formatter f = new Formatter(sb);
f.format("%-4s %10s%1s * %10s (%8s %8s %8s %8s %8s %8s) = %10s %10s%1s : %10s %10s %10s %10s",
"path",//
"srcCard",//
"",// sourceSampleExact
"f",//
// (
"in",//
"sumRgCt",//sumRangeCount
"tplsRead",//
"out",//
"limit",//
"adjCard",//
// ) =
"estRead",//
"estCard",//
"",// estimateIs(Exact|LowerBound|UpperBound)
"sumEstRead",//
"sumEstCard",//
"sumEstCost",//
"joinPath\n"
);
for (int i = 0; i < a.length; i++) {
final Path x = a[i];
// true iff the path survived pruning.
Boolean prune = null;
if (pruned != null) {
prune = Boolean.TRUE;
for (Path y : pruned) {
if (y == x) {
prune = Boolean.FALSE;
break;
}
}
}
final EdgeSample edgeSample = x.edgeSample;
if (edgeSample == null) {
f.format("%4d %10s%1s * %10s (%8s %8s %8s %8s %8s %8s) = %10s %10s%1s : %10s %10s %10s",//
i, NA, "", NA, NA, NA, NA, NA, NA, NA, NA, NA, "", NA, NA,
NA);
} else {
f.format("%4d %10d%1s * % 10.2f (%8d %8d %8d %8d %8d %8d) = %10d % 10d%1s : % 10d % 10d % 10d", //
i,//
edgeSample.sourceSample.estCard,//
edgeSample.sourceSample.estimateEnum.getCode(),//
edgeSample.f,//
edgeSample.inputCount,//
edgeSample.sumRangeCount,//
edgeSample.tuplesRead,//
edgeSample.outputCount,//
edgeSample.limit,//
edgeSample.adjCard,//
// =
edgeSample.estRead,//
edgeSample.estCard,//
edgeSample.estimateEnum.getCode(),//
x.sumEstRead,//
x.sumEstCard,//
x.sumEstCost
);
}
sb.append(" [");
for (Vertex v : x.getVertices()) {
f.format("%2d ", v.pred.getId());
}
sb.append("]");
if (pruned != null) {
if (prune)
sb.append(" pruned");
}
// for (Edge e : x.edges)
// sb.append(" (" + e.v1.pred.getId() + " " + e.v2.pred.getId()
// + ")");
sb.append("\n");
if(x.edgeSample.isUnderflow()) {
underflowPaths.add(x);
}
}
if (edgeSamples != null && !underflowPaths.isEmpty()) {
// Show paths with cardinality estimate underflow.
sb.append("\nPaths with cardinality estimate underflow::\n");
for (Path p : underflowPaths) {
sb.append(showPath(p, edgeSamples));
sb.append("----\n");
}
}
return sb.toString();
}
/**
* Show the details of a join path, including the estimated cardinality and
* join hit ratio for each step in the path.
*
* @param p
* The join path (required).
* @param edgeSamples
* A map containing the samples utilized by the {@link Path}
* (required).
*/
static public String showPath(final Path x,
final Map edgeSamples) {
if (x == null)
throw new IllegalArgumentException();
if (edgeSamples == null)
throw new IllegalArgumentException();
final StringBuilder sb = new StringBuilder();
final Formatter f = new Formatter(sb);
{
/*
* @todo show limit on samples?
*/
f.format("%4s %10s%1s * %10s (%8s %8s %8s %8s %8s %8s) = %10s %10s%1s : %10s %10s",// %10s %10s",//
"vert",
"srcCard",//
"",// sourceSampleExact
"f",//
// (
"in",//
"sumRgCt",// sumRangeCount
"tplsRead",// tuplesRead
"out",//
"limit",//
"adjCard",//
// ) =
"estRead",//
"estCard",//
"",// estimateIs(Exact|LowerBound|UpperBound)
"sumEstRead",//
"sumEstCard"//
);
long sumEstRead = 0; // sum(estRead), where estRead := tuplesRead*f
long sumEstCard = 0; // sum(estCard)
// double sumEstCost = 0; // sum(f(estCard,estRead))
for (int i = 0; i < x.vertices.length; i++) {
final int[] ids = BOpUtility
.getPredIds(x.getPathSegment(i + 1));
final int predId = x.vertices[i].pred.getId();
final SampleBase sample;
if (i == 0) {
sample = x.vertices[i].sample;
if (sample != null) {
sumEstRead = sample.estCard; // dbls as estRead for vtx
sumEstCard = sample.estCard;
}
} else {
// edge sample from the caller's map.
sample = edgeSamples.get(new PathIds(ids));
if (sample != null) {
sumEstRead+= ((EdgeSample) sample).estRead;
sumEstCard += ((EdgeSample) sample).estCard;
}
}
sb.append("\n");
if (sample == null) {
f.format("% 4d %10s%1s * %10s (%8s %8s %8s %8s %8s %8s) = %10s %10s%1s : %10s %10s",// %10s %10s",//
predId,//
NA, "", NA, NA, NA, NA, NA, NA, NA, NA, NA, "", NA, NA);//,NA,NA);
} else if(sample instanceof VertexSample) {
/*
* Show the vertex sample for the initial vertex.
*
* Note: we do not store all fields for a vertex sample
* which are stored for an edge sample because so many of
* the values are redundant for a vertex sample. Therefore,
* this sets up local variables which are equivalent to the
* various edge sample columns that we will display.
*/
final long sumRangeCount = sample.estCard;
final long estRead = sample.estCard;
final long tuplesRead = Math.min(sample.estCard, sample.limit);
final long outputCount = Math.min(sample.estCard, sample.limit);
final long adjCard = Math.min(sample.estCard, sample.limit);
f.format("% 4d %10s%1s * %10s (%8s %8s %8s %8s %8s %8s) = % 10d % 10d%1s : %10d %10d",// %10d %10s",//
predId,//
" ",//srcSample.estCard
" ",//srcSample.estimateEnum
" ",//sample.f,//
" ",//sample.inputCount,
sumRangeCount,//
tuplesRead,//
outputCount,//
sample.limit,// limit
adjCard,// adjustedCard
estRead,// estRead
sample.estCard,// estCard
sample.estimateEnum.getCode(),//
sumEstRead,//
sumEstCard//
);
} else {
// Show the sample for a cutoff join with the 2nd+ vertex.
final EdgeSample edgeSample = (EdgeSample)sample;
f.format("% 4d %10d%1s * % 10.2f (%8d %8d %8d %8d %8d %8d) = % 10d % 10d%1s : %10d %10d",// %10d %10",//
predId,//
edgeSample.sourceSample.estCard,//
edgeSample.sourceSample.estimateEnum.getCode(),//
edgeSample.f,//
edgeSample.inputCount,//
edgeSample.sumRangeCount,//
edgeSample.tuplesRead,//
edgeSample.outputCount,//
edgeSample.limit,//
edgeSample.adjCard,//
edgeSample.estRead,//
edgeSample.estCard,//
edgeSample.estimateEnum.getCode(),//
sumEstRead,//
sumEstCard//
);
}
}
}
sb.append("\n");
return sb.toString();
}
} // class JGraph