See: Description
| Interface | Description |
|---|---|
| BufferManager.Buffer |
A basic buffer interface.
|
| BufferManager.DistinctBuffer<T> |
A buffer used to determine distinct values.
|
| BufferManager.DistinctBufferMaker<T> |
An object use to create a new
BufferManager.DistinctBuffer. |
| BufferManager.Predicate<T> | |
| BufferManager.QueueBuffer<T> |
A buffer that maintains the insertion order of a series of values.
|
| BufferManager.QueueBufferMaker<T> |
An object use to create a new
BufferManager.DistinctBuffer. |
| BufferManager.SortingBuffer<SortType,RecordType> |
A buffer used to sort values into ascending or descending order.
|
| BufferManager.SortingBufferMaker<SortType,RecordType> |
An object use to create a new
BufferManager.SortingBuffer. |
| CancellableQuery |
A simple interface that allows tracking an executing query that can be cancelled.
|
| JcrQueryContext |
The context in which queries are executed.
|
| NodeSequence.Batch |
A batch of rows containing nodes and scores.
|
| NodeSequence.Restartable | |
| NodeSequence.RowAccessor |
An interface that abstracts accessing the node(s) and score(s) in a row.
|
| NodeSequence.RowFilter |
A filter of rows.
|
| QueryBuilder.DynamicOperandBuilder |
Interface that defines a dynamic operand portion of a criteria.
|
| QueryBuilder.OrderByOperandBuilder | |
| QueryEngine | |
| QueryResults |
The resulting output of a query.
|
| QueryResults.Columns |
Definition of the columns that are available in the results.
|
| RowExtractors.ExtractFromRow |
An operation that extracts a single value from the current row in a given
NodeSequence.Batch. |
| Tuples.TupleFactory<T> |
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.
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 QueryParser
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.
One of the QueryParser implementation provided out of the box is
the BasicSqlQueryParser, which understands a subset of SQL.
The 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
query results.
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 PlanNode
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 CanonicalPlanner
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
of the Optimizer implementations. A library of existing
OptimizerRule classes is provided, though it's very easy to
add more optimizer rules.
The 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
a Schemata interface that provides access to the immutable
Schemata.Table definitions (that then contain the
Schemata.Column and Schemata.Key definitions).
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 ImmutableSchemata
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 a NodeSequence
that abstractly represents the nodes that satisfy the query. When the sequence is accessed, the processing logic of the
query engine dynamically performs the necessary operations to return the correct nodes.
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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