Lucene phrase query

Lucene phrase query

Lucene is a query language that can be used to filter messages in your PhishER inbox. A query written in Lucene can be broken down into three parts:. Note: This is a condensed list of operators and modifiers. Below is an example of how a Lucene query string is constructed:. The field referred to in your string must match a field acknowledged in the database you are running a query against.

Then, type your query string in the Search Query strings will vary depending on the intended goal of your search. Below are example query strings you may customize and run in your PhishER inbox.

lucene phrase query

This query will pull all messages tagged as a threat with "urgent" or "immediately" in the subject line:. Replace your-organization-domain. Then, this query will pull all messages that are NOT sent from your domain:.

Lucene - PhraseQuery

This query will pull all messages with words or phrases starting with "network" in the subject line and NOT tagged as spam:. This is a brief overview of Lucene query syntax to get you started with custom searches in your PhishER inbox. Visit here for full Lucene query syntax documentation.

What is Lucene Query Syntax? A query written in Lucene can be broken down into three parts: Field The ID or name of a specific container of information in a database. A term does not have to be enclosed in quotation marks. Wildcard that is a placeholder for a single character. This wildcard cannot be used as a placeholder for the first character of a string.

Apache Lucene - Query Parser Syntax

Note: This is a brief overview of Lucene query syntax to get you started with custom searches in your PhishER inbox. Have more questions? Submit a request.The tokenization process is therefore key to how a search engine performs both in a functional and non-functional sense. One of the great design features of the Lucene search engine is the ability that it affords for customization of this process.

This provides tremendous flexibility that can be used to solve a wide variety of search problems. The subject of this blog is a proposed tokenization filter called automatic phrasing that can be used to deal with some of the problems associated with multi-term descriptions of singular things. Although no one tool solves all problems, the beauty of the Lucene design is that many problems can be solved by different combinations of the same basic tools.

Here is one more to consider. Language is composed of more basic elements: symbols letters, numerals and punctuation characters in Western Language, pictograms in Asian languageswords, phrases, sentences, paragraphs and so on. Each language has rules that define how these symbols and groups of symbols should be combined to form written language. These rules form the spelling and syntax or grammar of the language. Furthermore, in any communication process there is a sender and receiver s.

The sender constructs the communication as I am doing in writing this blog and the receiver parses the communication, hopefully to derive the meaning that the sender intended. The parsing process begins with lexical or phonemic analysis to extract the words followed by syntactic analysis to derive the logical structure of the sentence from its lexical units nouns, verbs, adjectives, etc.

Next is the semantic level where a great deal of other processes come into play so that the intended meaning of the communication is discerned such as detecting sentiment in the face of sarcasm, where current software does a really great job — Yeah, Right!

One of the simplest is the WhiteSpaceTokenizer which separates text streams into individual tokens separated by whitespace characters space, tab, new line, etc. Other tokenizers such as the StandardTokenizer recognize other characters as token separators punctuation characters mostly and some are wise enough to recognize certain patterns that should not be split such as internet and email addresses.

However, all of these tokenizers share the characteristic that they operate at the syntactic or pre-semantic level. Without additional processing which is provided in Lucene by the use of Token Filtersall subsequent mapping to form the inverted index is done on these individual tokens. This is not to say that all hope is lost at the semantic level because one of the things that the inverse map knows about is the relative position of words. This enables things like phrase matching and even more powerfully, fuzzy phrase matching.

This problem becomes evident when users construct queries with multiple words intending by this to improve the precision of their query. The search engine knows nothing about this because usage is a semantic not a syntactic distinction.This query may be combined with other terms or queries with a BooleanQuery. NOTE : All terms in the phrase must match, even those at the same position.

If you have terms at the same position, perhaps synonyms, you probably want MultiPhraseQuery instead which only requires one term at a position to match.

Also, Leading holes don't have any particular meaning for this query and will be ignored. For instance this query: PhraseQuery. Builder ; builder. Builder A builder for phrase queries. Term [] getTerms Returns the list of terms in this phrase. For more complicated use-cases, use PhraseQuery. The slop is an edit distance between respective positions of terms as defined in this PhraseQuery and the positions of terms in a document.

For instance, when searching for "quick fox"it is expected that the difference between the positions of fox and quick is 1. So "a quick brown fox" would be at an edit distance of 1 since the difference of the positions of fox and quick is 2.

Similarly, "the fox is quick" would be at an edit distance of 3 since the difference of the positions of fox and quick is The slop defines the maximum edit distance for a document to match. More exact matches are scored higher than sloppier matches, thus search results are sorted by exactness. Only implemented by primitive queries, which re-write to themselves. All Rights Reserved.

Skip navigation links. Object org. Query org. PhraseQuery public class PhraseQuery extends Query A Query that matches documents containing a particular sequence of terms. A PhraseQuery is built by QueryParser for input like "new york". Create a phrase query which will match documents that contain the given list of terms at consecutive positions in fieldand at a maximum edit distance of slop. Create a phrase query which will match documents that contain the given list of terms at consecutive positions in field.

Return the slop for this PhraseQuery. Expert: called to re-write queries into primitive queries. Expert: Constructs an appropriate Weight implementation for this query.

Term [].Lucene has a custom query syntax for querying its indexes. Here are some query examples demonstrating the query syntax. Search for either the phrase "foo bar" in the title field AND the phrase "quick fox" in the body field, or the word "fox" in the title field.

Note that for proximity searches, exact matches are proximity zero, and word transpositions bar foo are proximity 1. Whilst both queries are effectively equivalent with respect to the documents that are returned, the proximity query assigns a higher score to documents for which the terms foo and bar are closer together.

Range Queries allow one to match documents whose field s values are between the lower and upper bound specified by the Range Query. Range Queries can be inclusive or exclusive of the upper and lower bounds. Sorting is done lexicographically. Solr's built-in field types are very convenient for performing range queries on numbers without requiring padding. The higher the boost factor, the more relevant the term will be, and therefore the higher the corresponding document scores.

A typical boosting technique is assigning higher boosts to title matches than to body content matches:. Lucene queries can also be constructed programmatically. This can be really handy at times. Besides, there are some queries which are not possible to construct by parsing. These classes are part of the org. Lucene Query Syntax Lucene has a custom query syntax for querying its indexes. Keyword matching Search for word "foo" in the title field.

Proximity matching Lucene supports finding words are a within a specific distance away. Search for "foo bar" within 4 words from each other. The trade-off, is that the proximity query is slower to perform and requires more CPU. Solr DisMax and eDisMax query parsers can add phrase proximity matches to a user query. Range searches Range Queries allow one to match documents whose field s values are between the lower and upper bound specified by the Range Query.

Parsing Queries Queries can be parsed by constructing a QueryParser object and invoking the parse method.

lucene phrase query

Available query objects as of 3. MUST ; bq. About Me Siblings SolrTutorial.Although Lucene provides the ability to create your own queries through its API, it also provides a rich query language through the Query Parser, a lexer which interprets a string into a Lucene Query using JavaCC.

Generally, the query parser syntax may change from release to release. This page describes the syntax as of the current release. Before choosing to use the provided Query Parser, please consider the following: If you are programmatically generating a query string and then parsing it with the query parser then you should seriously consider building your queries directly with the query API.

In other words, the query parser is designed for human-entered text, not for program-generated text. Untokenized fields are best added directly to queries, and not through the query parser. If a field's values are generated programmatically by the application, then so should query clauses for this field.

An analyzer, which the query parser uses, is designed to convert human-entered text to terms. Program-generated values, like dates, keywords, etc. In a query form, fields which are general text should use the query parser. All others, such as date ranges, keywords, etc.

A field with a limit set of values, that can be specified with a pull-down menu should not be added to a query string which is subsequently parsed, but rather added as a TermQuery clause. A query is broken up into terms and operators. There are two types of terms: Single Terms and Phrases. Multiple terms can be combined together with Boolean operators to form a more complex query see below.

Note: The analyzer used to create the index will be used on the terms and phrases in the query string. So it is important to choose an analyzer that will not interfere with the terms used in the query string. Lucene supports fielded data. When performing a search you can either specify a field, or use the default field. The field names and default field is implementation specific.

You can search any field by typing the field name followed by a colon ":" and then the term you are looking for. As an example, let's assume a Lucene index contains two fields, title and text and text is the default field.

If you want to find the document entitled "The Right Way" which contains the text "don't go this way", you can enter:.

Will only find "Do" in the title field. It will find "it" and "right" in the default field in this case the text field. Lucene supports single and multiple character wildcard searches within single terms not within phrase queries. The single character wildcard search looks for terms that match that with the single character replaced.

For example, to search for "text" or "test" you can use the search:. Multiple character wildcard searches looks for 0 or more characters. For example, to search for test, tests or tester, you can use the search:. Lucene supports fuzzy searches based on the Levenshtein Distance, or Edit Distance algorithm.

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For example to search for a term similar in spelling to "roam" use the fuzzy search:. Starting with Lucene 1. The value is between 0 and 1, with a value closer to 1 only terms with a higher similarity will be matched.Create a project with a name LuceneFirstApplication under a package com.

You can also use the project created in Lucene - First Application chapter as such for this chapter to understand the searching process. Create LuceneConstants. Keep the rest of the files unchanged. Clean and Build the application to make sure the business logic is working as per the requirements.

This class is used to read the indexes made on raw data and searches data using the Lucene library. We have used 10 text files from record1. Test Data. After running the indexing program during chapter Lucene - Indexing Processyou can see the list of index files created in that folder.

Once you are done with the creation of the source, the raw data, the data directory, the index directory and the indexes, you can proceed by compiling and running your program.

To do this, keep the LuceneTester. Lucene - PhraseQuery Advertisements. Previous Page. Next Page. Previous Page Print Page. Dashboard Logout. Weight createWeight Searcher searcher Expert: Constructs an appropriate Weight implementation for this query. Query rewrite IndexReader reader Expert: Called to re-write queries into primitive queries.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I come up with solution to programmaticlly create query to search for phrase with wildcards using this code:. Which works great, and fast enough for most cases. For instance, if I create such query and search with it, It will output desired results, for example:. I have mentioned that query work fast enough in most cases. Currently I have an index with size of aprox. Depending on many factors like: cache, size of subsets of documents matching single word in phrase since lucene will perform set intersections between founded terms.

This query is very slow that I haven't waited long enough to get results over 1h and sometimes causes GC overhead limit exceeded exception.

I am aware of that I could query "an? This is where I am confused. And it is still unacceptable slow I killed process before it returned anythong. Yes, wildcards can be performance hogs, especially if they match a lot of terms, but what you describe does seem surprisingly so. Hard to say for sure why that is occuring, but for an attempt. On it's own, is performing very badly, as described. Since the wildcards you are looking for are both prefix style queries, it's a better idea to use a PrefixQuery instead.

Though I don't think that will make much of a difference if any at all.

lucene phrase query

What might just make a different is changing you rewrite method. You could try limiting the number of Terms the query is rewritten into:. Learn more. Lucene phrase query with wildcards Ask Question. Asked 6 years ago. Active 6 years ago.

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