Search System Overview
A search system will be a system that searches a database or information source based on a given query and returns relevant results, and will be capable of targeting various types of data, such as information retrieval, image retrieval, and audio retrieval. The following describes the elements and functions of a typical search system.
- Query input: The user enters a search query into the search system. This may be in the form of text, images, audio, etc.
- Indexing: Indexes data in order to efficiently search the database or information source being searched. The index may include keywords and feature vectors for text data, feature vectors for image data, etc.
- Search processing: Retrieves indexed data based on a query. The search process involves procedures such as query interpretation and analysis, retrieval of related data, and similarity calculation.
- Ranking and filtering: Ranking and filtering of search results is performed, with the most relevant results being displayed at the top. Ranking is based on the similarity and importance of the search results.
- Result display: Display search results to the user in an appropriate format. This can be in the form of text, images, or links. It may also provide additional information and relevant content.
- Feedback and Improvement: User feedback and usage data analysis is used to improve the search system. Feedback may include modifying queries and evaluating search results.
The implementation of a search system involves elements such as database management, search algorithms, indexing, ranking models, and user interface, and a variety of technologies and algorithms are used, with the appropriate approach selected based on specific requirements and data types. The algorithms used in search systems are described below.
Algorithms used in search systems
Various algorithms are used in the search system. The main algorithms are described below.
- String Search: This is a method of finding a match by comparing strings in text data in order.
- Linear Search: This method compares strings in text data in order to find a match. This method is suitable for simple search tasks, but is less efficient for large data sets.
- Boyer-Moore method: An algorithm that efficiently searches for patterns in text, maximizing the number of comparisons that should be skipped through preprocessing.
- KMP (Knuth-Morris-Pratt) method: an algorithm that minimizes comparisons within text by preprocessing patterns.
- Index Search: Inverted index search.
- Inverted Index: Speeds up keyword-based searches by creating an index of words and their locations in text documents.
- B-tree or B+ tree: A data structure for efficiently managing large data sets, supporting queries such as keyword and range searches.
- Hash Tables: Data structures that efficiently store key/value pairs and are used to search for matching keywords.
- Similarity Search: A search for similarity between
- Cosine Similarity: A similarity calculation method often used in vector space models to evaluate vector orientation and size similarity.
- Euclidean Distance: A method to calculate the distance between vectors and evaluate their similarity, which is used to represent data such as feature vectors and distance matrices.
- Machine Learning Models
- Ranking Model: A machine learning model used to learn the ranking of search results. Typical methods include random forests, gradient boosting, and ranked SVM.
- Clustering: This is a method to group similar data and is used for clustering search results. Typical methods include k-means clustering and hierarchical clustering.
These algorithms can be combined to suit specific search tasks and requirements. In addition to the above algorithms, advanced natural language processing methods and information retrieval models (e.g., vector space models, BM25, etc.) are also used in actual retrieval systems.
The platform used to build the search system
A variety of platforms and tools are used to build search systems. Some of the major platforms are described below.
- Elasticsearch: Elasticsearch is an open source distributed search engine that is widely used to build very fast and scalable search systems. It is suitable for a variety of applications and also provides rich search capabilities, query languages, and scoring functions. For details on setting up Elasticsearch, please refer to “Search Tool Elasticsearch – Setup Instructions” etc.
- Apache Solr: Apache Solr is another open source search platform that is widely used to build fast and scalable search systems. customization.
- TensorFlow: TensorFlow is an open source machine learning framework developed by Google that is widely used in search systems to use machine learning models for image search and natural language processing tasks. construction, training, and inference.
- PyTorch: PyTorch is another open source machine learning framework used as the machine learning component of search systems; PyTorch is popular with researchers and developers for its flexible and intuitive model building and training.
- Apache Lucene: Apache Lucene is a Java-based open source information retrieval library that provides text data indexing, query processing, and search result ranking, and is used as the foundation for many search engines and information retrieval systems The following is a list of specific applications of search engines.
- FESS: An open source search engine server that will be a tool with a crawler, ES, and UI all built-in. It can be downloaded and run as a search application immediately. See “Search Tool FESS” for the specific launch of FESS.
Next, we discuss specific examples of search engine applications.
About the application of the search system
Search systems are widely applied in a variety of areas, some of which are discussed below.
- Web search engines: Search engines index information on the Web and serve as a retrieval system for users to search for keywords and retrieve relevant pages. Typical web search engines include Google and Bing.
- Electronic Document Management: Search systems are used to manage large sets of documents. This allows companies, libraries, and other organizations to index documents, reports, manuals, and other data so that users can search for the information they need.
- Product Search: Search systems are used by online shopping sites to allow users to search for products and obtain detailed information and similar products. This contributes to the user experience by allowing customers to quickly find products that match their requirements.
- Image search: Image search systems are also used to allow users to enter images and retrieve related images. Here, searches for similar images and searches based on image content are performed, allowing users to find images with specific objects or characteristics in the image database.
- News Search: Search systems are used to retrieve articles related to specific keywords or topics from sources such as news articles and blogs. Users can search and view the latest news.
- Legal Information Search: Law firms and law-related organizations index legal documents, case law, and other information so that lawyers and researchers can find the information they need.
- Medical Data Search: Search systems are used by medical professionals to retrieve information related to specific symptoms or diseases from medical-related databases and research literature.
Next, we describe the implementation procedure for Elasticsearch, a typical search engine PF.
Implementation Procedure for Elasticsearch
Elasticsearch is a distributed search engine and a tool for efficiently performing tasks such as indexing, searching, analyzing, and visualizing data (for details, please refer to “About Elastic Search: Overview“). The following is a detailed description of the steps involved in implementing Elasticsearch.
- Java Installation: Elasticsearch runs on Java, so Java must first be installed. This can be done by downloading the official Java Development Kit (JDK).
- Download Elasticsearch: Download the latest release version from the official Elasticsearch website. Select the appropriate version and obtain the download package for your OS.
- Start Elasticsearch: Extract the downloaded package and navigate to the extracted directory in a terminal or command prompt. Next, execute the following commands to start Elasticsearch.
> bin/elasticsearch
By default, Elasticsearch accepts HTTP requests on port 9200.
- Cluster configuration: Elasticsearch can usually be configured in clusters of multiple nodes. Therefore, it is recommended to configure cluster settings even when running in a local environment on a single node (it is possible to run without any configuration). Edit the default configuration file, elasticsearch.yml, to properly configure settings such as cluster name, node name, and network bind address.
- Submitting data to Elasticsearch: To submit data to Elasticsearch, prepare the data in JSON format and send it to Elasticsearch using HTTP requests. The following is an example of submitting data using the Curl command.
curl -XPOST "localhost:9200/{index_name}/{document_type}/{document_id}" -d '{ "field1": "value1", "field2": "value2" }'
index_name is the index name of the data, document_type is the document type (optional in Elasticsearch 7.x and later), and document_id is the unique identifier of the document.
- Data retrieval: Elasticsearch allows you to retrieve data using a variety of queries. The following is an example of data retrieval using the Curl command.
curl -XGET "localhost:9200/{index_name}/{document_type}/_search?q={field}:{query}"
field is the name of the field to be searched and query is the search query.
For detailed instructions on how to set up the above, please refer to “Search Tool Elastic Search – Starting ElasticSearch” and so on.
Next, we will discuss a more concrete example of implementing a search system.
Example implementation of a search system using python
Describes how to implement a search system using Python.
- Example implementation of a text search system:.
import re
# text data list
documents = [
"This is the first document.",
"This document is the second document.",
"And this is the third one.",
"Is this the first document?"
]
# Function to search for keywords
def search(keyword, documents):
results = []
for doc in documents:
if re.search(r'b' + re.escape(keyword) + r'b', doc, re.IGNORECASE):
results.append(doc)
return results
# search execution
keyword = "document"
search_results = search(keyword, documents)
# Search result display
print(f"Search results for '{keyword}':")
for result in search_results:
print(result)- Example implementation of an index search system (using Elasticsearch):
from elasticsearch import Elasticsearch
# Creating an Elasticsearch Client
es = Elasticsearch()
# Indexing of documents
def create_index(index_name):
es.indices.create(index=index_name, ignore=400)
# Adding Documents
def add_document(index_name, doc_id, document):
es.index(index=index_name, id=doc_id, body=document)
# Search for keywords
def search(index_name, keyword):
res = es.search(index=index_name, body={"query": {"match": {"content": keyword}}})
hits = res["hits"]["hits"]
results = [hit["_source"]["content"] for hit in hits]
return results
# Indexing and Adding Documents
index_name = "my_index"
create_index(index_name)
add_document(index_name, 1, {"content": "This is the first document."})
add_document(index_name, 2, {"content": "This document is the second document."})
add_document(index_name, 3, {"content": "And this is the third one."})
add_document(index_name, 4, {"content": "Is this the first document?"})
# search execution
keyword = "document"
search_results = search(index_name, keyword)
# Search result display
print(f"Search results for '{keyword}':")
for result in search_results:
print(result)Search systems often perform automatic acquisition (crawling) of target data. The following describes an implementation for a combination of crawling and searching.
Implement crawling and searching using Elasticsearch
Below is an example implementation of crawling and searching using Elasticsearch. This example uses the Python elasticsearch and requests modules.
from elasticsearch import Elasticsearch
import requests
from bs4 import BeautifulSoup
# Creating an Elasticsearch Client
es = Elasticsearch()
# Crawling of web pages and indexing of documents
def crawl_and_index(url, index_name):
# Retrieving Web Pages
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
# Text extraction in web pages
text = soup.get_text().strip()
# Indexing of documents
es.index(index=index_name, body={"content": text})
# Search for keywords
def search(index_name, keyword):
res = es.search(index=index_name, body={"query": {"match": {"content": keyword}}})
hits = res["hits"]["hits"]
results = [hit["_source"]["content"] for hit in hits]
return results
# Crawling of web pages and indexing of documents
url = "https://example.com"
index_name = "my_index"
crawl_and_index(url, index_name)
# search execution
keyword = "example"
search_results = search(index_name, keyword)
# Search result display
print(f"Search results for '{keyword}':")
for result in search_results:
print(result)In the above example, the web page with the specified URL is crawled, the text is extracted, and registered as an index in Elasticsearch. It then performs a search using the specified keywords and displays the text of the relevant documents.
This example is simplified and should be customized to meet actual crawling and data processing needs. Additional appropriate filtering, data preprocessing, and error handling should also be considered. The actual crawling also requires compliance with robot countermeasures and website terms of use.
For details on crawling technology, see “Overview of web crawling technology and its implementation using Python/Clojure” etc.
Elasticsearch is often used as a tool for log data acquisition and analysis. Examples of their implementations are shown below.
Implementation of log data collection and analysis using Elasticsearch
An example implementation using Elasticsearch to collect and analyze log data is shown below. This example uses the Python elasticsearch module.
from elasticsearch import Elasticsearch
# Creating an Elasticsearch Client
es = Elasticsearch()
# Log data collection and indexing
def collect_and_index_log(log_data, index_name):
# Indexing of log data
es.index(index=index_name, body=log_data)
# Log data retrieval
def search_logs(index_name, query):
res = es.search(index=index_name, body={"query": {"match": query}})
hits = res["hits"]["hits"]
results = [hit["_source"] for hit in hits]
return results
# Log data collection and indexing
log_data = {
"timestamp": "2023-05-29T12:00:00",
"message": "Example log message",
"severity": "INFO",
"source": "example.py"
}
index_name = "my_logs"
collect_and_index_log(log_data, index_name)
# Log data retrieval
query = {"message": "Example"}
search_results = search_logs(index_name, query)
# Search result display
print("Search results:")
for result in search_results:
print(result)In the above example, log data is collected and registered as an index in Elasticsearch. Then, log data matching the specified query is retrieved and displayed.
This example is simplified and should be customized according to the actual log data format and collection method. Additional appropriate filtering, data preprocessing, and error handling should also be considered. Log data collection may require log collection agents and log format settings for integration into the actual application or system, and it is also common to combine tools such as Kibana for log data analysis and visualization.
For more information on log data acquisition and analysis using Elasticsearch, please refer to “Using ElasticStash to monitor system operations including microservices“.
Reference Information and Books
The details of the search system are described in detail in “About Search Technology“. Please refer to it as well.
For reference books, please refer to “Search Technology” “User Interfaces for Information Retrieval” and “Search tool Elastic Search -reference books“.
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