Apache Lucene is a high-performance, full-text search library used for incorporating search functionality into applications.
2. Lucene creates an index of documents using which data structure?
a) B-tree
b) Hash table
c) Inverted index
d) Linked list
Answer:
c) Inverted index
Explanation:
Lucene uses an inverted index, which is a data structure used to store a mapping from content, such as words or numbers, to its locations in a document or a set of documents.
3. What is a 'Document' in the context of Lucene?
a) A file containing configuration settings
b) An entity that contains multiple fields to be indexed
c) A query submitted for search
d) A report generated after indexing
Answer:
b) An entity that contains multiple fields to be indexed
Explanation:
In Lucene, a 'Document' is an entity that consists of multiple fields. Each field has a name and textual content, and is the basic unit of indexing and search.
4. How does Lucene perform text analysis?
a) Using SQL queries
b) By utilizing machine learning algorithms
c) Through Tokenizers and Analyzers
d) Using a manual parsing method
Answer:
c) Through Tokenizers and Analyzers
Explanation:
Lucene performs text analysis using Tokenizers and Analyzers, where Tokenizers break text into tokens and Analyzers process these tokens (such as lowercasing, removing stopwords).
5. In Lucene, what is a 'Field'?
a) A type of query
b) A database table used for indexing
c) A specific part of a Document, like title or body
d) A configuration parameter
Answer:
c) A specific part of a Document, like title or body
Explanation:
In Lucene, a 'Field' represents a specific part of a Document, such as the title, body, author, etc. Each Field can be indexed and searched separately.
6. What is the role of a Lucene 'Analyzer'?
a) To analyze query performance
b) To break down text into tokens
c) To organize documents into categories
d) To optimize the index size
Answer:
b) To break down text into tokens
Explanation:
An Analyzer in Lucene is responsible for breaking down text into tokens (or terms) and applying various transformations like lowercasing, removing punctuation, etc., facilitating effective text searching.
7. What is a 'Token' in Lucene?
a) A unique identifier for each document
b) The smallest unit of search
c) An encrypted form of a document
d) A user's search query
Answer:
b) The smallest unit of search
Explanation:
In Lucene, a 'Token' is the smallest unit of text that is indexed and searched. It is typically a word, number, or other searchable text fragment.
8. What is Lucene's 'QueryParser' used for?
a) To parse configuration files
b) To optimize query execution time
c) To parse search queries into a query object
d) To parse documents into tokens
Answer:
c) To parse search queries into a query object
Explanation:
QueryParser in Lucene is used to convert a search query string into a Query object, which can then be used to perform a search against the index.
9. How does Lucene score documents in search results?
a) Based on the date of the document
b) By the number of times a document is accessed
c) Using a relevancy score calculated from the query
d) Alphabetically by document title
Answer:
c) Using a relevancy score calculated from the query
Explanation:
Lucene scores documents based on their relevancy to the search query, taking into account factors like term frequency, inverse document frequency, field norm, etc.
10. What type of search capability does Lucene provide?
a) Numeric range search
b) Full-text search
c) Geospatial search
d) All of the above
Answer:
d) All of the above
Explanation:
Lucene provides various types of search capabilities including full-text search, numeric range search, and even geospatial search, among others.
11. What is Lucene's 'IndexWriter' class used for?
a) Reading from an index
b) Writing data to an index
c) Analyzing query performance
d) Encrypting the index
Answer:
b) Writing data to an index
Explanation:
The IndexWriter class in Lucene is used for writing data to the index. It handles the creation of new documents and updating or deleting existing ones.
12. Can Lucene handle fuzzy searches?
a) No, it only supports exact matches
b) Yes, using the FuzzyQuery class
c) Fuzzy searches are handled by external plugins
d) Only in combination with other search engines
Answer:
b) Yes, using the FuzzyQuery class
Explanation:
Lucene can handle fuzzy searches, which allow for matching of terms that are similar but not identical to the search term, using the FuzzyQuery class.
13. What is a 'BooleanQuery' in Lucene?
a) A query that returns either true or false
b) A query that can combine multiple search criteria
c) A query for binary data
d) A specialized query for Boolean fields
Answer:
b) A query that can combine multiple search criteria
Explanation:
A BooleanQuery in Lucene is a composite query that combines multiple other queries using Boolean operators like AND, OR, and NOT.
14. How does Lucene support phrase searching?
a) Using the PhraseQuery class
b) By indexing each word as a separate token
c) Through regular expression matching
d) Phrase searching is not supported in Lucene
Answer:
a) Using the PhraseQuery class
Explanation:
Lucene supports phrase searching, where a sequence of words is searched exactly as it appears, using the PhraseQuery class.
15. What is the purpose of Lucene's 'TermVector'?
a) To vectorize documents for machine learning
b) To store additional information about how terms occur in documents
c) To create a vector image of the index
d) To encrypt term information in the index
Answer:
b) To store additional information about how terms occur in documents
Explanation:
TermVectors in Lucene are used to store additional information about how terms occur in documents, such as their frequency, positions, offsets, etc., which can be useful for certain types of analysis.
16. What is the advantage of using a 'MultiFieldQueryParser' in Lucene?
a) It reduces the size of the index
b) It allows searching across multiple fields with a single query
c) It enhances the security of the index
d) It speeds up the indexing process
Answer:
b) It allows searching across multiple fields with a single query
Explanation:
The MultiFieldQueryParser class in Lucene allows a single query to be executed across multiple fields, making it possible to search for a term in more than one field at the same time.
17. How are updates handled in a Lucene index?
a) By directly modifying the existing document
b) Updates are not supported in Lucene
c) By deleting the old document and adding a new one
d) Through real-time streaming updates
Answer:
c) By deleting the old document and adding a new one
Explanation:
In Lucene, updates to an indexed document are handled by first deleting the old document and then adding the new version of the document.
18. Can Lucene index binary data?
a) Yes, directly without any processing
b) No, Lucene can only index text data
c) Yes, but the binary data needs to be encoded as text
d) Only if the binary data is in a specific format
Answer:
c) Yes, but the binary data needs to be encoded as text
Explanation:
Lucene can index binary data, but it needs to be converted or encoded into a text representation, as Lucene is fundamentally a text search engine.
19. What is the role of Lucene's 'Similarity' class?
a) To check the similarity between two indexes
b) To determine how closely a document matches a query
c) To find similar terms in the index
d) To compare the performance of different queries
Answer:
b) To determine how closely a document matches a query
Explanation:
The Similarity class in Lucene is used to score documents based on how closely they match a given query, influencing the search results ranking.
20. In Lucene, what is 'IndexSearcher' used for?
a) To create and maintain the index
b) To search for documents in the index
c) To optimize the index for faster search
d) To monitor the health of the index
Answer:
b) To search for documents in the index
Explanation:
The IndexSearcher class in Lucene is used to search for documents in an index. It executes queries against the index and retrieves matching documents.
21. What is a 'WildcardQuery' in Lucene?
a) A query that searches for documents with missing fields
b) A type of fuzzy query
c) A query that uses wildcards like '*' or '?'
d) A query that returns random documents
Answer:
c) A query that uses wildcards like '*' or '?'
Explanation:
A WildcardQuery in Lucene allows for the use of wildcard characters such as '*' (any character sequence) and '?' (any single character) in search terms.
22. How does Lucene handle text stemming?
a) Through its built-in dictionary
b) Using external libraries like Snowball
c) Lucene does not support stemming
d) By default in all Analyzers
Answer:
b) Using external libraries like Snowball
Explanation:
Lucene handles text stemming, which is reducing words to their root form, by using external libraries like Snowball. Stemming is not built-in in Lucene but can be implemented through custom Analyzers using these libraries.
23. What is 'Near Real-Time Search' in Lucene?
a) Searching in an index that is updated in real-time
b) A search feature for time-sensitive data
c) Searching with almost no latency
d) A specialized search for near-duplicate documents
Answer:
a) Searching in an index that is updated in real-time
Explanation:
Near Real-Time Search in Lucene refers to the capability of searching in an index that is updated in near real-time, allowing users to search for content that has been recently indexed with minimal delay.
24. In Lucene, what does the 'Commit' operation do?
a) It saves the current search state
b) It writes changes to the index from memory to disk
c) It finalizes a query
d) It locks the index for exclusive access
Answer:
b) It writes changes to the index from memory to disk
Explanation:
The 'Commit' operation in Lucene writes any changes made to the index (such as adding, updating, or deleting documents) from memory to disk, making them permanent and searchable.
25. Can Lucene be integrated with big data technologies like Hadoop?
a) No, Lucene operates independently of big data technologies
b) Yes, but only for data extraction purposes
c) Yes, Lucene can be used alongside Hadoop for indexing and searching large datasets
d) Integration is possible but not recommended due to performance issues
Answer:
c) Yes, Lucene can be used alongside Hadoop for indexing and searching large datasets
Explanation:
Lucene can be integrated with big data technologies like Hadoop to handle the indexing and searching of large datasets stored in Hadoop's file system, combining Lucene's search capabilities with Hadoop's scalability.