+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. + +
+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.
+
++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. + +
+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. + +
+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).
+
+
+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: +
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. | +
+Facets accumulation reflects a set of documents over some facet requests: +
Document set - a subset of the index documents, usually documents
+matching a user query.Facet requests - facet accumulation specification, e.g. count a certain facet
+dimension.
+FacetRequest is a basic component in faceted search - it describes the facet
+information need. Every facet request is made of at least two fields:
+
CategoryPath - root category of the facet request. The categories that
+are returned as a result of the request will all be descendants of this rootNumber of Results - number of sub-categories to return (at most).
+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: +
+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. + +
+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 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 are consulted with during indexing. Among several +parameters it defines, the following two are likely to interest many applications: +
DefaultFacetIndexingParams maintains
+this information for all categories under the same special dedicated term.
+One case where it is needed to maintain two categories in separate category
+lists, is when it is known that at search time it would be required to use
+different types of accumulation logic for each, but at the same accumulation
+call.DefaultFacetIndexingParams maintains category lists in a single
+partition, hence it defines the partition size as Integer.MAX_VALUE. The
+importance of this parameter is on allowing to handle very large
+taxonomies without exhausting RAM resources. This is because at facet
+accumulation time, facet values arrays are maintained in the size of the
+partition. With a single partition, the size of these arrays is as the size of the
+taxonomy, which might be OK for most applications. Limited partition
+sizes allow to perform the accumulation with less RAM, but with some
+runtime overhead, as the matching documents are processed for each of the
+partitions.
+See the API Javadocs of FacetIndexingParams for additional configuration
+capabilities which were not discussed here.
+
+
+Facet Search Parameters, consulted at search time (during facets accumulation) are +rather plain, providing the following: +
+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".
+
+
+We now provide examples for more advanced facet indexing and search, such as +drilling-down on facet values and multiple category lists. + +
+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.
+
+
+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.
+
+
+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. + +
+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.
+
+
+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 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.
+
+
+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. (Old one can
+be closed as soon as it is no more used.)
+
+
+
+