Oak Queries and Indexing

Introduction

Unlike Jackrabbit 2, Oak does not index content by default. Custom indexes need to be created when necessary, much like with traditional relational databases. If there is no index for a specific query, possibly many nodes will be traversed. The query may still work but probably be very slow.

If Oak encounters a query without an index, a WARN level log message is printed:

*WARN* Traversed 1000 nodes with filter Filter(query=select ...) consider creating an index or changing the query

Supported query languages

The Oak query engine supports the following languages:

• XPath (recommended)
• SQL-2
• SQL (deprecated)
• JQOM

Indexer types and cost calculation

The Apache Oak based backend allows different indexers to be plugged into the repository.

One indexer is the Property Index, for which the index definition is stored in the repository itself.

Implementations for Apache Lucene and Solr are also available by default, which both support fulltext indexing.

The Traversal Index is used if no other indexer is available. This means that the content is not indexed and content nodes are traversed to find matches to the query.

If multiple indexers are available for a query, each available indexer estimates the cost of executing the query. Oak then chooses the indexer with the lowest estimated cost.

The above diagram is a high level representation of the query execution mechanism of Apache Oak.

First, the query is parsed into an Abstract Syntax Tree. Then, the query is checked and transformed into SQL-2 which is the native language for Oak queries.

Next, each index is consulted to estimate the cost for the query. Once that is completed, the results from the cheapest index are retrieved. Finally, the results are filtered, both to ensure that the current user has read access to the result and that the result matches the complete query.

Configuring the indexes

If re-indexing is needed in very large repositories, specially when using MongoDB and for full-text indexes, consider text pre-extraction, and using oak-run to build the initial index and to re-index.

Indexes are configured as nodes in the repository under the oak:index node.

The type of the index node must be oak:QueryIndexDefinition. Several configuration options are available for each indexer as node properties. For more information, see the configuration details for each indexer type below.

The Property Index

The Property Index is generally useful for queries that have property constraints but are not full-text. It can be configured by following the below procedure:

1. Open CRXDE by going to http://localhost:4502/crx/de/index.jsp

2. Create a new node under oak:index

3. Name the node PropertyIndex, and set the node type to oak:QueryIndexDefinition

4. Set the following properties for the new node:

   • type: property (of type String)
   • propertyNames: jcr:uuid (of type Name)

   This particular example will index the jcr:uuid property, whose job is to expose the universally unique idetifier (UUID) of the node it is attached to.

5. Save the changes.

The Property Index has the following configuration options:

• The type property will specify the type of index, and in this case it must be set to property

• The propertyNames property indicates the list of the properties that will be stored in the index. In case it is missing, the node name will be used as a property name reference value. In this example, the jcr:uuid property whose job is to expose the unique identifier (UUID) of its node is added to the index.

• The unique flag which, if set to true adds a uniqueness constraint on the property index.

• The declaringNodeTypes propery allows you to specify a certain node type that the index will only apply to.

• The reindex flag which if set to true, will trigger a full content re-index.

The Ordered Index

The Ordered index is an extension of the Property index. However, it has been deprecated. Indexes of this type need to be replaced with the Lucene Property Index.

The Lucene Full Text Index

A full text indexer based on Apache Lucene is available in AEM 6.

If a full-text index is configured, then all queries that have a full-text condition use the full-text index, no matter if there are other conditions that are indexed, and no matter if there is a path restriction.

If no full-text index is configured, then queries with full-text conditions will not work as expected.

Because the index is updated via an asynchronous background thread, some full-text searches will be unavailable for a small window of time until the background processes are finished.

You can configure a Lucene full-text index, by following the below procedure:

1. Open CRXDE and create a new node under oak:index.

2. Name the node LuceneIndex and set the node type to oak:QueryIndexDefinition

3. Add the following properties to the node:

   • type: lucene (of type String)
   • async: async (of type String)

4. Save the changes.

The Lucene Index has the following configuration options:

• The type property which will specify the type of index must be set to lucene
• The async property which must be set to async. This will send the index update process to a background thread.
• The includePropertyTypes property, which will define what subset of property types will be included in the index.
• The excludePropertyNames property which will define a list of property names - properties that should be excluded from the index.
• The reindex flag which when set to true, triggers a full content re-index.

The Lucene Property Index

Since Oak 1.0.8, Lucene can be used to create indexes which involve property constraints that are not full-text.

In order to achieve a Lucene Property Index the fulltextEnabled property must always be set to false.

Take the following example query:

select * from [nt:base] where [alias] = '/admin'

In order to define a Lucene Property Index for the above query, you can add the following definition by creating a new node under oak:index:

• Name: LucenePropertyIndex
• Type: oak:QueryIndexDefinition

Once the node has been created, add the following properties:

• type:

  lucene (of type String)
  
  

• async:

  async (of type String)
  
  

• fulltextEnabled:

  false (of type Boolean)
  
  

• includePropertyNames: ["alias"] (of type String)

Lucene Analyzers

Since version 1.2.0, Oak supports Lucene analyzers.

Analyzers are used both when a document is indexed, and at query time. An analyzer examines the text of fields and generates a token stream. Lucene analyzers are composed of a series of tokenizer and filter classes.

The analyzers can be configured via the analyzers node (of type nt:unstructured) inside the oak:index definition.

The default analyzer for an index is configured in the default child of the analyzers node.

Specifying the Analyzer Class Directly

If you wish to use any out of the box analyzer, you can configure it following the below procedure:

1. Locate the index you wish to use the analyzer with under the oak:index node.

2. Under the index, create a child node called default of type nt:unstructured.

3. Add a property to the default node with the following properties:

   • Name: class
   • Type: String
   • Value: org.apache.lucene.analysis.standard.StandardAnalyzer

   The value is the name of the analyzer class you wish to use.

   You can also set the analyzer to be used with a specific lucene version by using the optional luceneMatchVersion string propery. A valid synthax for using it with Lucene 4.7 would be:

   • Name: luceneMatchVersion
   • Type: String
   • Value: LUCENE_47

   If luceneMatchVersion is not provided, Oak will use the version of Lucene it is shipped with.

4. If you wish to add a stopwords file to the analyzer configurations, you can create a new node under the default one with the following properties:

   • Name: stopwords
   • Type: nt:file

Creating Analyzers via Composition

Analyzers can also be composed based on Tokenizers, TokenFilters and CharFilters. You can do this by specifying an analyzer and creating children nodes of its optional tokenizers and filters which will be applied in listed order. See also https://wiki.apache.org/solr/AnalyzersTokenizersTokenFilters#Specifying_an_Analyzer_in_the_schema

Consider this node structure as an example:

• Name: analyzers

  • Name: default

    • Name: charFilters

    • Type: nt:unstructured

      • Name: HTMLStrip
      • Name: Mapping

    • Name: tokenizer

      • Property Name: name

        • Type: String
        • Value: Standard

    • Name: filters

    • Type: nt:unstructured

      • Name: LowerCase

      • Name: Stop

        • Property name: words

          • Type: String
          • Value: stop1.txt, stop2.txt

        • Name: stop1.txt

          • Type: nt:file

        • Name: stop2.txt

          • Type: nt:file

The name of the filters, charFilters and tokenizers are formed by removing the factory suffixes. Thus:

• org.apache.lucene.analysis.standard.StandardTokenizerFactory becomes standard

• org.apache.lucene.analysis.charfilter.MappingCharFilterFactory becomes Mapping

• org.apache.lucene.analysis.core.StopFilterFactory becomes Stop

Any configuration parameter required for the factory is specified as property ofthenode in question.

For cases such as loading stop words where content from external files needs to be loaded, the content can be provided by creating a child node of nt:file type for the file in question.

The Solr Index

The purpose of the Solr index is mainly full-text search but it can also be used to index search by path, property restrictions and primary type restrictions. This means the Solr index in Oak can be used for any type of JCR query.

The integration in AEM happens at the repository level so that Solr is one of the possible indexes that can be used in Oak, the new repository implementation shipped with AEM.

It can be configured to work as a remote server with the AEM instance.

Configuring AEM with a single remote Solr server

AEM can also be configured to work with a remote Solr server instance:

1. Download and extract the latest version of Solr. For more info on how to do this, please consult the Apache Solr Installation documentation.

2. Now, create two Solr shards. You can do this by creating folders for each shard in the folder where Solr has been unpacked:

   • For the first shard, create the folder:

   <solrunpackdirectory>\aemsolr1\node1

   • For the second shard, create the folder:

   <solrunpackdirectory>\aemsolr2\node2

3. Locate the example instance in the Solr package. It is usually located in a folder called " example" in the root of the package.

4. Copy the following folders from the example instance to the two shard folders ( aemsolr1\node1 and aemsolr2\node2):

   • contexts
   • etc
   • lib
   • resources
   • scripts
   • solr-webapp
   • webapps
   • start.jar

5. Create a new folder called " cfg" in each of the two shard folders.

6. Place your Solr and Zookeeper configuration files in the newly created cfg folders.

   NOTE

   For more info on Solr and ZooKeeper configuration, consult the Solr Configuration documentation and the ZooKeeper Getting Started Guide.

7. Start the first shard with ZooKeeper support by going to aemsolr1\node1 and running the following command:

   java -Xmx2g -Dbootstrap_confdir=./cfg/oak/conf -Dcollection.configName=myconf -DzkRun -DnumShards=2 -jar start.jar
   

8. Start the second shard by going to aemsolr2\node2 and running the following command:

   java -Xmx2g -Djetty.port=7574 -DzkHost=localhost:9983 -jar start.jar
   

9. After both shards have been started, test that everything is up and running by connecting to the Solr interface at http://localhost:8983/solr/#/

10. Start AEM and go to the Web Console at http://localhost:4502/system/console/configMgr

11. Set the following configuration under Oak Solr remote server configuration:

    • Solr HTTP URL: http://localhost:8983/solr/

12. Choose Remote Solr in the drop down list under Oak Solr server provider.

13. Go to CRXDE and login as Admin.

14. Create a new node called solrIndex under oak:index, and set the following properties:

    • type: solr (of type String)
    • async: async (of type String)
    • reindex: true (of type Boolean)

15. Save the changes.

Recommended configuration for Solr

Below is an example of a base configuration that can be used with all three Solr deployments described in this article. It accommodates the dedicated property indexes that are already present in AEM and should not be used with other applications.

In order to properly use it, you need to place the contents of the archive directly in the Solr Home Directory. In the case of multi-node deployments, it should go directly under the root folder of each node.

Recommended Solr configuration files

Get File

AEM Indexing Tools

AEM 6.1 also integrates two indexing tools present in AEM 6.0 as part of the Adobe Consulting Services Commons toolset:

1. Explain Query, a tool designed to help administrators understand how queries are executed;
2. Oak Index Manager, a Web User Interface for maintaining existing indexes.

You can now reach them by going to Tools - Operations - Dashboard - Diagnosis from the AEM Welcome screen.

For more information on how to use them, see the Operations Dashboard documentation.

Creating Property Indexes via OSGi

The ACS Commons package also exposes OSGi configurations that can be used to create property indexes.

You can access it from the Web Console by searching for “Ensure Oak Property Index”.

Troubleshooting indexing issues

Situations may arise where queries take a long time to execute, and the general system response time is slow.

This section presents a set of recommendations on what needs to be done in order to track down the cause of such issues and advice on how to resolve them.

Preparing Debugging Info for Analysis

The easiest way to get required information for the query being executed is via the Explain Query tool. This will enable you to collect the precise information that is needed to debug a slow query without the need to consult the log level information. This is desirable if you know the query that is being debugged.

If this is not possible for any reason, you can gather the indexing logs in a single file and use it to troubleshoot your particular problem.

Enable Logging

To enable logging, you need to enable DEBUG level logs for the categories pertaining to Oak indexing and queries. These categories are:

• org.apache.jackrabbit.oak.plugins.index
• org.apache.jackrabbit.oak.query
• com.day.cq.search

The com.day.cq.search category is only applicable if you are using the AEM provided QueryBuilder utility.

You can enable logging by following this procedure:

1. Point your browser to https://serveraddress:port/system/console/slinglog
2. Click the Add new Logger button in the lower part of the console.
3. In the newly created row, add the categories mentioned above. You can use the + sign to add more than one category to a single logger.
4. Choose DEBUG from the Log level drop down list.
5. Set the output file to logs/queryDebug.log. This will correlate all the DEBUG events into a single log file.
6. Run the query or render the page that is using the query you wish to debug.
7. Once you have executed the query, go back to the logging console and change the log level of the newly created logger to INFO.

Index Configuration

The way the query gets evaluated is largely affected by the index configuration. It is important to get the index configuration in order to be analyzed or sent to support. You can either get the configuration as a content package or get a JSON rendition.

Since in most cases, the indexing configuration is stored under the /oak:index node in CRXDE, you can get the JSON version at:

https://serveraddress:port/oak:index.tidy.-1.json

If the index is configured at a different location, change the path accordingly.

MBean output

In some cases it is helpful to provide the output of index related MBeans for debugging. You can do this by:

1. Going to the JMX console at:
   https://serveraddress:port/system/console/jmx

2. Search for the following MBeans:

   • Lucene Index statistics
   • CopyOnRead support statistics
   • Oak Query Statistics
   • IndexStats

3. Click each of the MBeans to get the performance statistics. Create a screenshot or note them down in case submission to support is required.

You can also get the JSON variant of these statistics at the following URLs:

• https://serveraddress:port/system/sling/monitoring/mbeans/org/apache/jackrabbit/oak/%2522LuceneIndex%2522.tidy.-1.json
• https://serveraddress:port/system/sling/monitoring/mbeans/org/apache/jackrabbit/oak/%2522LuceneIndex%2522.tidy.-1.json
• https://serveraddress:port/system/sling/monitoring/mbeans/org/apache/jackrabbit/oak/%2522LuceneIndex%2522.tidy.-1.json
• https://serveraddress:port/system/sling/monitoring/mbeans/org/apache/jackrabbit/oak/%2522LuceneIndex%2522.tidy.-1.json

You can also provide consolidated JMX output via https://serveraddress:port/system/sling/monitoring/mbeans/org/apache/jackrabbit/oak.tidy.3.json. This would include all Oak related MBean details in JSON format.

Other Details

You can gather additional details in order to help troubleshoot the problem, such as:

1. The Oak version your instance is running on. You can see this by opening CRXDE and looking at the version in the lower right corner of the welcome page, or by checking the version of the org.apache.jackrabbit.oak-core bundle.
2. The QueryBuilder Debugger output of the troublesome query. The debugger can be accessed at: https://serveraddress:port/libs/cq/search/content/querydebug.html

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