Some Geode users have data models containing nested and complex objects. Lucene text search in Geode 1.2.0 supports indexing and querying only the top-level fields in the data object. The objective of this feature is to support indexing and querying an arbitrary depth of nested objects.
User can specify fields in nested object to be indexed.
User can query on these nested fields and collections of nested fields as well as top level fields.
Expose a LuceneSerializer interface and let the user write code to convert their objects to lucene docs.
Provide a default LuceneSerializer when creating the index that will flatten nested objects (eg, person.address.zip just gets stored as a list of zipcodes on the top level document).
- Provide a query syntax to search nested, flattened, fields.
Out of Scope
- Extending core Apache Lucene functionality to define a syntax for creating multiple documents and performing automatic joins in the StandardQueryParser for handling parent/child relationships with collections of nested fields.
- Add a new method to create a lucene index that takes a callback. The callback gives the user explicit control of how their value is converted to lucene documents and stored in the index.
<region name="region" refid="PARTITION">
<lucene:field name="a" analyzer="org.apache.lucene.analysis.core.KeywordAnalyzer"/>
<lucene:field name="b" analyzer="org.apache.lucene.analysis.core.SimpleAnalyzer"/>
<lucene:field name="c" analyzer="org.apache.lucene.analysis.standard.ClassicAnalyzer"/>
We will also provide a built-in implementation for LuceneSerializer called FlatFormatSerializer(). With this example serializer users can specify nested fields using the syntax fieldnameAtLevel1.fieldnameAtLevel2 for both indexing and querying.
For example, in the following data model Customer object contains both a Person object and a collection of Page objects. The Person object also contains a Page object.
The example below demonstrates how to index the nested fields: contacts.name, contacts.email, contacts.address, contacts.homepage.title.
Note: each segment is a field name, not a field type, because Customer class could have more than one field of type Person; e.g. Person contacts and Person deliveryman. The field name is used to identify the parent field.
gfsh command line:
The syntax for querying the nested field is the same as for a top level field, but with the additional qualifying parent field name, such as "contacts.name:tzhou11*". This distinguishes which "name" field when there can potentially be more than one 'name' field at different hierarchical levels in the object.
We will provide an out-of-box implementation for the LuceneSerializer: FlatFormatSerializer.
It will still create one document for each parent object adding the nested object as embedded fields of the document. The field name will use the qualified name. Collections will be flattened and treated as tokens in the single field.
For example, the FlatFormatSerializer will convert a Customer object into a document as
(name:John11),(contacts.name:tzhou11), (contacts.email:firstname.lastname@example.org), (contacts.address:15220 Wall St), (contacts.homepage.id:11), (contacts.homepage.title: Mr. tzhou11), (contacts.homepage.content: xxx)
Risks and Mitigations
With this solution, collections (lists and maps) will be treated as a single flattened field, with the risk that queries into a collection may produce the wrong results. For example, a person entry with 2 address fields, one containing "main street" and the other containing zipcode "90210", a query such as:
person.address.zip=90210 and person.address.street=main
would incorrectly return this person entry. Because Apache Lucene does not define a standard approach for this, we are providing the LuceneSerializer interface to allow a user to write code to convert their objects to separate Lucene documents and to use Lucene ParentBlockJoinQuery to produce the desired results. For example, using the above query, the user could write the following code to produce the desired results: