A simple interface that allows tracking an executing query that can be cancelled.
A single rule that dictates how a single property should be indexed.
The context in which queries are executed.
Interface that defines a dynamic operand portion of a criteria.
Interface used to record in the indexes the changes to content.
The resulting output of a query.
Definition of the columns that are available in the results, which outline the structure of the
An interface used to walk through the results.
The set of rules that dictate how properties should be indexed.
A builder of immutable
Abstract implementation of JCR's
The results of a query.
A component that can be used to programmatically create
An immutable context in which queries are to be executed.
The Query API provides a mechanism for building and executing queries. The framework
provides a reusable and extensible query engine that is capable of planning, validating, optimizing, and executing queries
against a generic back-end system. Simply subclass the
that creates a
to operates against the back-end system, and assemble a
QueryEngine that can be used to provide a rich
At the heart of the entire query system is a single representation of what constitutes a query. The
abstract query model defines a language-independent vocabulary
for queries, and consists of a family of Java classes each represent the important semantic elements
needed to fully define a query.
There are two ways to construct abstract query models. The first is to programmatically construct a query
model using the
QueryBuilder, which provides a fluent API that makes it easy to create a query
with Java code. The second (and more common) approach is to use a
that parses a query represented in a specific language (like SQL or XPath) and then creates the query's equivalent abstract query model.
There's even a
QueryParsers class that can manage the parsers for multiple languages.
The abstract query model classes are immutable, making them very easily shared or reused if that is advantageous.
QueryEngine is the component that accepts and executes queries expressed as abstract query models.
Each submitted query is planned, validated, optimized, and then processed to compute and return the final
Note that the QueryEngine is thread-safe.
In the planning stage, a canonical plan is generated for each query. This plan is a tree of
objects that each represent a different aspect of the query, and is a form that is easily manipulated by subsequent stages.
Any implementation of
Planner can be used, though a
implementation is provided and will be sufficient for most cases. In fact, the subsequent execution steps often
require the plan to be in its canonical form, so for most situations it may be best to simply reuse the CanonicalPlanner
and in other simply extend it.
Note that query plans are mutable and not thread-safe, meaning that such plans are not intended to be shared or reused.
In the optimization stage, the canonical query plan is evaluated, validated, and manipulated to produce a more
a single optimized query processing plan. The query plan is often changed in situ, although this is not required
Optimizer implementations. A library of existing
OptimizerRule classes is provided, though it's very easy to
add more optimizer rules.
RuleBasedOptimizer is an implementation that optimizes a query
using a stack of rules. A new stack is created for each rule, though the rules are required to be immutable and thus
often shared and reused. And, the RuleBasedOptimizer is easily subclassed to define a custom stack of rules.
The canonical planner or the optimization rules have access to the table and column definitions that may be
queried. The query framework does not prescribe the semantics of a table or column, but instead provides
Schemata interface that provides access to the immutable
Schemata.Table definitions (that then contain the
The canonical planner and a number of the provided optimizer rules use the Schemata to verify that the
query is referencing an existing table and columns, whatever they are defined to be. Although any Schemata
implementaiton can be used, the query framework provides an
class with a
builder with a fluent API
that can create the corresponding immutable table, column and key definitions.
In the processing stage, the optimized query plan is used to construct and assemble the
ProcessingComponent that correspond to the various parts of the
quer plan. The resulting components form the basic processing engine for that query. At the bottom are
the "access" components that perform the low-level access of the tuples from the graph container.
Above these are the other components that implement various operations, such as limits, joins (using merge and
nested loop algorithms), unions, intersects, distinct, sorts, and even column projections. At the top is
a single component that produces tuples that represent the results of the query.
QueryProcessor creates the ProcessingComponent assembly,
the top-level component is executed. Execution involves requesting from the child processing component(s) the next batch of results,
processing each of the tuples according to the specific ProcessingComponent algorithm, and finally returning
the processed tuples.
A query over a graph of content will result in a set of nodes that matched the criteria specified in the query.
Each node contained in the results will be identified by its
NodeKey as well
as any values for the selected properties. Typically, queries will result in a single node per row, although
joins may result in multiple rows per row.
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