Index: lucene/contrib/facet/src/java/org/apache/lucene/facet/package.html =================================================================== --- lucene/contrib/facet/src/java/org/apache/lucene/facet/package.html (revision 1226218) +++ lucene/contrib/facet/src/java/org/apache/lucene/facet/package.html (working copy) @@ -3,6 +3,7 @@ Faceted Indexing and Search - Provides faceted indexing and search capabilities. The userguide is recommended for a start. + + Provides faceted indexing and search capabilities. The userguide is recommended for a start. - \ No newline at end of file + Index: lucene/contrib/facet/src/java/overview.html =================================================================== --- lucene/contrib/facet/src/java/overview.html (revision 1226218) +++ lucene/contrib/facet/src/java/overview.html (working copy) @@ -21,6 +21,7 @@ - Provides faceted indexing and search capabilities (checkout the userguide). + + Provides faceted indexing and search capabilities (checkout the userguide). - \ No newline at end of file + Index: lucene/contrib/facet/docs/userguide.html =================================================================== --- lucene/contrib/facet/docs/userguide.html (revision 1226218) +++ lucene/contrib/facet/docs/userguide.html (working copy) @@ -1,786 +0,0 @@ - - - -Facet Userguide - - - - - - - - - - -

- Apache Lucene
- Faceted Search
- User's Guide

- -
-

Table of Contents

-
    -
  1. Introduction
    2. -
  2. Facet Features
    3. -
  3. Indexing Categories Illustrated
    4. -
  4. Accumulating Facets Illustrated
    5. -
  5. Indexed Facet Information
    6. -
  6. Taxonomy Index
    7. -
  7. Facet Parameters
    8. -
  8. Advanced Faceted Examples
    9. -
  9. Optimizations
    10. -
  10. Concurrent Indexing and Search
    11. -
- -

Introduction

-

-A category is an aspect of indexed documents which can be used to classify the -documents. For example, in a collection of books at an online bookstore, categories of -a book can be its price, author, publication date, binding type, and so on. -

-In faceted search, in addition to the standard set of search results, we also get facet -results, which are lists of subcategories for certain categories. For example, for the -price facet, we get a list of relevant price ranges; for the author facet, we get a list of -relevant authors; and so on. In most UIs, when users click one of these subcategories, -the search is narrowed, or drilled down, and a new search limited to this subcategory -(e.g., to a specific price range or author) is performed. -

-Note that faceted search is more than just the ordinary fielded search. In fielded -search, users can add search keywords like price:10 or author:"Mark -Twain" to the query to narrow the search, but this requires knowledge of which -fields are available, and which values are worth trying. This is where faceted search -comes in: it provides a list of useful subcategories, which ensures that the user only -drills down into useful subcategories and never into a category for which there are no -results. In essence, faceted search makes it easy to navigate through the search results. -The list of subcategories provided for each facet is also useful to the user in itself, -even when the user never drills down. This list allows the user to see at one glance -some statistics on the search results, e.g., what price ranges and which authors are -most relevant to the given query. -

-In recent years, faceted search has become a very common UI feature in search -engines, especially in e-commerce websites. Faceted search makes it easy for -untrained users to find the specific item they are interested in, whereas manually -adding search keywords (as in the examples above) proved too cumbersome for -ordinary users, and required too much guesswork, trial-and-error, or the reading of -lengthy help pages. -

-See http://en.wikipedia.org/wiki/Faceted_search for more information on faceted -search. - -

Facet Features

-First and main faceted search capability that comes to mind is counting, but in fact -faceted search is more than facet counting. We now briefly discuss the available -faceted search features. - -

Facet Counting

-

-Which of the available subcategories of a facet should a UI display? A query in a -book store might yield books by a hundred different authors, but normally we'd want -do display only, say, ten of those. -

-Most available faceted search implementations use counts to determine the -importance of each subcategory. These implementations go over all search results for -the given query, and count how many results are in each subcategory. Finally, the -subcategories with the most results can be displayed. So the user sees the price ranges, -authors, and so on, for which there are most results. Often, the count is displayed next -to the subcategory name, in parentheses, telling the user how many results he can -expect to see if he drills down into this subcategory. -

-The main API for obtaining facet counting is CountFacetRequest, as in the -following code snippet: -

-new CountFacetRequest(new CategoryPath("author"), 10));
-
-A detailed code example using count facet requests is shown below - see -Accumulating Facets. - -

Facet Associations

-

-So far we've discussed categories as binary features, where a document either belongs -to a category, or not. -

-While counts are useful in most situations, they are sometimes not sufficiently -informative for the user, with respect to deciding which subcategory is more -important to display. -

-For this, the facets package allows to associate a value with a category. The search -time interpretation of the associated value is application dependent. For example, a -possible interpretation is as a match level (e.g., confidence level). This value can -then be used so that a document that is very weakly associated with a certain category -will only contribute little to this category's aggregated weight. - -

Multiple Facet Requests

-

-A single faceted accumulation is capable of servicing multiple facet requests. -Programmatic, this is quite simple - wrap all the facet requests of interest into the -facet-search-parameters which are passed to a facets accumulator/collector (more on -these objects below). The results would be comprised of as many facet results as there -were facet requests. -

-However there is a delicate limitation: all facets maintained in the same location in -the index are required to be treated the same. See the section on Indexing Parameters -for an explanation on maintaining certain facets at certain locations. - -

Facet Labels at Search Time

-

-Facets results always contain the facet (internal) ID and (accumulated) value. Some of -the results also contain the facet label, AKA the category name. We mention this here -since computing the label is a time consuming task, and hence applications can -specify with a facet request to return top 1000 facets but to compute the label only for -the top 10 facets. In order to compute labels for more of the facet results it is not -required to perform accumulation again. -

-See FacetRequest.getNumResults(), FacetRequest.getNumLabel() and -FacetResultNode.getLabel(TaxonomyReader). - -

Indexing Categories Illustrated

-

-In order to find facets at search time they must first be added to the index at indexing -time. Recall that Lucene documents are made of fields for textual search. The addition -of categories is performed by an appropriate DocumentBuilder - or -CategoryDocumentBuilder in our case. -

-Indexing therefore usually goes like this: -

-Following is a code snippet for indexing categories. The complete example can be -found in package org.apache.lucene.facet.example.simple.SimpleIndexer. -
-IndexWriter writer = ...
-TaxonomyWriter taxo = new DirectoryTaxonomyWriter(taxoDir, OpenMode.CREATE);
-...
-Document doc = new Document();
-doc.add(new Field("title", titleText, Store.YES, Index.ANALYZED));
-...
-List<CategoryPath> categories = new ArrayList<CategoryPath>();
-categories.add(new CategoryPath("author", "Mark Twain"));
-categories.add(new CategoryPath("year", "2010"));
-...
-DocumentBuilder categoryDocBuilder = new CategoryDocumentBuilder(taxo);
-categoryDocBuilder.setCategoryPaths(categories);
-categoryDocBuilder.build(doc);
-writer.addDocument(doc);
-
-

-We now explain the steps above, following the code line numbers: - - - - - - - - - - - - - - - - - - - - - -
(4)Document contains not only text search fields but also facet search -information.
(7)Prepare a container for document categories.
(8)Categories that should be added to the document are accumulated in the -categories list.
(11)A CategoryDocumentBuilder is created, set with the appropriate list -of categories, and invoked to build - that is, to populate the document -with categories. It is in this step that the taxonomy is updated to contain the -newly added categories (if not already there) - see more on this in the -section about the Taxonomy Index below. This line could be made more -compact: one can create a single CategoryDocumentBuilder cBuilder and reuse it like this: -
-DocumentBuilder cBuilder = new CategoryDocumentBuilder(taxo);
-cBuilder.setCategoryPaths(categories).build(doc);
-
-
(14)Add the document to the index. As a result, category info is saved also in -the regular search index, for supporting facet aggregation at search time -(e.g. facet counting) as well as facet drill-down. For more information on -indexed facet information see below the section Indexed Facet Information.
- -

Accumulating Facets Illustrated

-

-Facets accumulation reflects a set of documents over some facet requests: -

-

-FacetRequest is a basic component in faceted search - it describes the facet -information need. Every facet request is made of at least two fields: -

-

-There are other parameters to a facet request, such as -how many facet results to -label-, -how deep to go from the request root when serving the facet request- and -more - see the API Javadocs for FacetRequest and its subclasses for more -information on these parameters. For labels in particular, see the section Facet Labels -at Search Time. -

-FacetRequest in an abstract class, open for extensions, and users may add their -own requests. The most often used request is CountFacetRequest - used for -counting facets. -

-Facets accumulation is - not surprisingly - driven by a FacetsAccumulator. The -most used one is StandardFacetsAccumulator, however there are also accumulators -that support sampling - to be used in huge collections, and there's an adaptive facets -accumulator which applies sampling conditionally on the statistics of the data. While -facets accumulators are very extendible and powerful, they might be too -overwhelming for beginners. For this reason, the code offers a higher level interface -for facets accumulating: the FacetsCollector. It extends Collector, and as such -can be passed to the search() method of Lucene's IndexSearcher. In case the -application also needs to collect documents (in addition to accumulating/collecting -facets), it can wrap multiple collectors with MultiCollector. Most code samples -below use FacetsCollector due to its simple interface. It is quite likely that -FacetsCollector should suffice the needs of most applications, therefore we -recommend to start with it, and only when needing more flexibility turn to directly -use facets accumulators. -

-Following is a code snippet from the example code - the complete example can be -found under org.apache.lucene.facet.example.simple.Searcher: -

-IndexReader indexReader = IndexReader.open(indexDir);
-Searcher searcher = new IndexSearcher(indexReader);
-TaxonomyReader taxo = new DirectoryTaxonomyReader(taxoDir);
-...
-Query q = new TermQuery(new Term(SimpleUtils.TEXT, "white"));
-TopScoreDocCollector tdc = TopScoreDocCollector.create(10, true);
-...
-FacetSearchParams facetSearchParams = new FacetSearchParams();
-facetSearchParams.addFacetRequest(new CountFacetRequest(
-    new CategoryPath("author"), 10));
-...
-FacetsCollector facetsCollector = new FacetsCollector(facetSearchParams, indexReader, taxo);
-searcher.search(q, MultiCollector.wrap(topDocsCollector, facetsCollector));
-List<FacetResult> res = facetsCollector.getFacetResults();
-
-

-We now explain the steps above, following the code line numbers: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
(1)Index reader and Searcher are initialized as usual.
(3)A taxonomy reader is opened - it provides access to the facet information -which was stored by the Taxonomy Writer at indexing time.
(5)Regular text query is created to find the documents matching user need, and -a collector for collecting the top matching documents is created.
(8)Facet-search-params is a container for facet requests.
(10)A single facet-request - namely a count facet request - is created and added -to the facet search params. The request should return top 10 Author -subcategory counts.
(12)Facets-Collector is the simplest interface for facets accumulation (counting -in this example).
(13)Lucene search takes both collectors - facets-collector and top-doccollector, -both wrapped by a multi-collector. This way, a single search -operation finds both top documents and top facets. Note however that facets -aggregation takes place not only over the top documents, but rather over all -documents matching the query.
(14)Once search completes, facet-results can be obtained from the facetscollector.
- -

-Returned facet results are organized in a list, conveniently ordered the same as the -facet-requests in the facet-search-params. Each result however contains the request -for which it was created. -

-Here is the (recursive) structure of the facet result: -

-

-Note that not always there would be sub result nodes - this depends on the -requested result mode: -

- -

Indexed Facet Information

-

-When indexing a document to which categories were added, information on these -categories is added to the search index, in two locations: -

-

-When a category is added to the index (that is, when a document containing a -category is indexed), all its parent categories are added as well. For example, indexing -a document with the category <"author", -"American-, "Mark Twain"> results in -creating three tokens: "/author", "/author/American", and -"/author/American/Mark Twain" (the character '/' here is just a human -readable separator - there's no such element in the actual index). This allows drilling down -and counting any category in the taxonomy, and not just leaf nodes, enabling a -UI application to show either how many books have authors, or how many books -have American authors, or how many books have Mark Twain as their (American) -author. -

-Similarly, Drill-down capabilities are this way possible also for node categories. -

-In order to keep the counting list compact, it is built using category ordinal - an -ordinal is an integer number attached to a category when it is added for the first time -into the taxonomy. -

-For ways to further alter facet index see the section below on Facet Indexing -Parameters. - -

Taxonomy Index

-

-The taxonomy is an auxiliary data-structure maintained side-by-side with the regular -index to support faceted search operations. It contains information about all the -categories that ever existed in any document in the index. Its API is open and allows -simple usage, or more advanced for the interested users. -

-When a category is added to a document, a corresponding node is added to the -taxonomy (unless already there). In fact, sometimes more than one node is added - -each parent category is added as well, so that the taxonomy is maintained as a Tree, -with a virtual root. -

-So, for the above example, adding the category the category <"author", -"American-, "Mark Twain"> -actually added three nodes: one for "/author", one for "/author/American" and one for -"/author/American/Mark Twain". -

-An integer number - called ordinal is attached to each category the first time the -category is added to the taxonomy. This allows for a compact representation of -category list tokens in the index, for facets accumulation. -

-One interesting fact about the taxonomy index is worth knowing: once a category -is added to the taxonomy, it is never removed, even if all related documents are -removed. This differs from a regular index, where if all documents containing a -certain term are removed, and their segments are merged, the term will also be -removed. This might cause a performance issue: large taxonomy means large ordinal -numbers for categories, and hence large categories values arrays would be maintained -during accumulation. It is probably not a real problem for most applications, but be -aware of this. If, for example, an application at a certain point in time removes an -index entirely in order to recreate it, or, if it removed all the documents from the index -in order to re-populate it, it also makes sense in this opportunity to remove the -taxonomy index and create a new, fresh one, without the unused categories. - -

Facet Parameters

-

-Facet parameters control how categories and facets are indexed and searched. Apart -from specifying facet requests within facet search parameters, under default settings it -is not required to provide any parameters, as there are ready to use working defaults -for everything. -

-However many aspects are configurable and can be modified by providing altered -facet parameters for either search or indexing. - -

Facet Indexing Parameters

-

-Facet Indexing Parameters are consulted with during indexing. Among several -parameters it defines, the following two are likely to interest many applications: -

-

-See the API Javadocs of FacetIndexingParams for additional configuration -capabilities which were not discussed here. - -

Facet Search Parameters

-

-Facet Search Parameters, consulted at search time (during facets accumulation) are -rather plain, providing the following: -

- -

Category Lists, Multiple Dimensions

-

-Category list parameters which are accessible through the facet indexing parameters -provide the information about: -

-

-For cases when certain categories should be maintained in different location than -others, use PerDimensionIndexingParams, which returns a different -CategoryListParams object for each dimension. This is a good opportunity to -explain about dimensions. This is just a notion: the top element - or first element - in -a category path is denoted as the dimension of that category. Indeed, the dimension -stands out as a top important part of the category path, such as "Location" for the -category "Location/Europe/France/Paris". - -

Advanced Faceted Examples

-

-We now provide examples for more advanced facet indexing and search, such as -drilling-down on facet values and multiple category lists. - -

Drill-Down with Regular Facets

-

-Drill-down allows users to focus on part of the results. Assume a commercial sport -equipment site where a user is searching for a tennis racquet. The user issues the -query tennis racquet and as result is shown a page with 10 tennis racquets, by -various providers, of various types and prices. In addition, the site UI shows to the -user a break down of all available racquets by price and make. The user now decides -to focus on racquets made by Head, and will now be shown a new page, with 10 -Head racquets, and new break down of the results into racquet types and prices. -Additionally, the application can choose to display a new breakdown, by racquet -weights. This step of moving from results (and facet statistics) of the entire (or larger) -data set into a portion of it by specifying a certain category, is what we call Drilldown. -We now show the required code lines for implementing such a drill-down. -

-Query baseQuery = queryParser.parse("tennis racquet");
-Query q2 = DrillDown.query(baseQuery, new CategoryPath("make", "head"), 10));
-
-

-In line 1 the original user query is created and then used to obtain information on -all tennis racquets. -

-In line 2, a specific category from within the facet results was selected by the user, -and is hence used for creating the drill-down query. -

-Please refer to SimpleSearcher.searchWithDrillDown() for a more detailed -code example performing drill-down. - -

Multiple Category Lists

-

-The default is to maintain all categories information in a single list. While this will -suit most applications, in some situations an application may wish to use multiple -category lists, for example, when the distribution of some category values is different -than that of other categories and calls for using a different encoding, more efficient -for the specific distribution. Another example is when most facets are rarely used -while some facets are used very heavily, so an application may opt to maintain the -latter in memory - and in order to keep memory footprint lower it is useful to -maintain only those heavily used facets in a separate category list. -

-First we define indexing parameters with multiple category lists: -

-PerDimensionIndexingParams iParams = new PerDimensionIndexingParams();
-iParams.addCategoryListParams(new CategoryPath("Author"), 
-    new CategoryListParams(new Term("$RarelyUsed", "Facets")));
-iParams.addCategoryListParams(new CategoryPath("Language"),
-    new CategoryListParams(new Term("$HeavilyUsed", "Ones")));
-
-

-This will cause the Language categories to be maintained in one category list, and -Author facets to be maintained in a another category list. Note that any other category, -if encountered, will still be maintained in the default category list. -

-These non-default indexing parameters should now be used both at indexing and -search time. As depicted below, at indexing time this is done when creating the -category document builder, while at search time this is done when creating the search -parameters. Other than that the faceted search code is unmodified. -

-DocumentBuilder categoryDocBuilder = new CategoryDocumentBuilder(taxo, iParams);
-...
-FacetSearchParams facetSearchParams = new FacetSearchParams(iParams);
-
-

-A complete simple example can be found in package org.apache.lucene.facet.example.multiCL -under the example code. - -

Optimizations

-

-Faceted search through a large collection of documents with large numbers of facets -altogether and/or large numbers of facets per document is challenging performance -wise, either in CPU, RAM, or both. A few ready to use optimizations exist to tackle -these challenges. - -

Sampling

-

-Facet sampling allows to accumulate facets over a sample of the matching -documents set. In many cases, once top facets are found over the sample set, exact -accumulations are computed for those facets only, this time over the entire matching -document set. -

-Two kinds of sampling exist: complete support and wrapping support. The -complete support is through SamplingAccumulator and is tied to an extension of the -StandardFacetsAccumulator and has the benefit of automatically applying other -optimizations, such as Complements. The wrapping support is through -SamplingWrapper and can wrap any accumulator, and as such, provides more -freedom for applications. - -

Complements

-

-When accumulating facets over a very large matching documents set, possibly -almost as large as the entire collection, it is possible to speed up accumulation by -looking at the complement set of documents, and then obtaining the actual results by -subtracting from the total results. It should be noted that this is available only for -count requests, and that the first invocation that involves this optimization might take -longer because the total counts have to be computed. -

-This optimization is applied automatically by StandardFacetsAccumulator. - -

Partitions

-

-Partitions are also discussed in the section about Facet Indexing parameters. -

-Facets are internally accumulated by first accumulating all facets and later on -extracting the results for the requested facets. During this process, accumulation -arrays are maintained in the size of the taxonomy. For a very large taxonomy, with -multiple simultaneous faceted search operations, this might lead to excessive memory -footprint. Partitioning the faceted information allows to relax the memory usage, by -maintaining the category lists in several partitions, and by processing one partition at -a time. This is automatically done by StandardFacetsAccumulator. However the -default partition size is Integer.MAX_VALUE, practically setting to a single partition, -i.e. no partitions at all. -

-Decision to override this behavior and use multiple partitions must be taken at -indexing time. Once the index is created and already contains category lists it is too -late to modify this. -

-See FacetIndexingParams.getPartitionSize() for API to alter this default -behavior. - -

Concurrent Indexing and Search

-

-Sometimes, indexing is done once, and when the index is fully prepared, searching -starts. However, in most real applications indexing is incremental (new data comes in -once in a while, and needs to be indexed), and indexing often needs to happen while -searching is continuing at full steam. -

-Luckily, Lucene supports multiprocessing - one process writing to an index while -another is reading from it. One of the key insights behind how Lucene allows multiprocessing -is Point In Time semantics. The idea is that when an IndexReader is opened, -it gets a view of the index at the point in time it was opened. If an IndexWriter -in a different process or thread modifies the index, the reader does not know about it until a new -IndexReader is opened (or the reopen() method of an existing IndexReader is called). -

-In faceted search, we complicate things somewhat by adding a second index - the -taxonomy index. The taxonomy API also follows point-in-time semantics, but this is -not quite enough. Some attention must be paid by the user to keep those two indexes -consistently in sync: -

-The main index refers to category numbers defined in the taxonomy index. -Therefore, it is important that we open the TaxonomyReader after opening the -IndexReader. Moreover, every time an IndexReader is reopen()ed, the -TaxonomyReader needs to be refresh()'ed as well. -

-But there is one extra caution: whenever the application deems it has written -enough information worthy a commit, it must first call commit() for the -TaxonomyWriter and only after that call commit() for the IndexWriter. -Closing the indices should also be done in this order - first close the taxonomy, and only after -that close the index. -

-To summarize, if you're writing a faceted search application where searching and -indexing happens concurrently, please follow these guidelines (in addition to the usual -guidelines on how to use Lucene correctly in the concurrent case): -

-

-Note that the above discussion assumes that the underlying file-system on which -the index and the taxonomy are stored respects ordering: if index A is written before -index B, then any reader finding a modified index B will also see a modified index A. -

-Note: TaxonomyReader's refresh() is simpler than IndexReader's reopen(). -While the latter keeps both the old and new reader open, the former keeps only the new reader. The reason -is that a new IndexReader might have modified old information (old documents deleted, for -example) so a thread which is in the middle of a search needs to continue using the old information. With -TaxonomyReader, however, we are guaranteed that existing categories are never deleted or modified - -the only thing that can happen is that new categories are added. Since search threads do not care if new categories -are added in the middle of a search, there is no reason to keep around the old object, and the new one suffices. -
However, if the taxonomy index was recreated since the TaxonomyReader was opened or -refreshed, this assumption (that categories are forevr) no longer holds, and refresh() will -throw an InconsistentTaxonomyException, guiding the application to open -a new TaxonomyReader for up-to-date taxonomy data. 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