Open-source database for search applications
Search engine algorithms fetch the key elements from the web page which includes the page title, content and the keyword density. It then comes up with a ranking based on which the results are placed on the pages. Each and every search engine has its own unique algorithm. Because it’s powered by Google’s core search technology that’s constantly improving, you always get fast, relevant results. Customizable functionality You program your search engine, so you decide what content it searches and how it looks.
This project is still not complete. This is because of some unidentified problem in conversion of relative to absolute URL.
Like most search engines, this one also has a crawler whose basic aim is to retrieve the source code of a given URL and then break the content into words with which we can create an array of tag words which will represent the content of the site.
It is not a fool proof method, but can work for sites with lots of words in it, like a blog or article or a discussion forum, etc. Now these keywords are stored in a database and then used to find the relevant URL for given keywords. It seemed like an auto complete rather than a search engine, but later that night it was am, and I couldn't sleep at all because of that auto complete feature with which I was too impressed. I wanted my own After 3 sleepless nights, on the 3 rd day at a.
It took me 3 days because every day I started how to use a merkaba crystal the beginning because I was not satisfied with the performance of the crawler or there was some problem.
I have basically divided every task into small parts so that the work could become easy. So you would find lots of classes in the project. At the starting point, we have a 4 tables stored in a database named as " Crawler " by default. This table is used to provide the suggested result in the search how to add another ipad to facetime. It contains all the words which the crawler has encountered till now and no word is repeated.
For multiple keywordswe first break the keyword from " ", then find the urlhash for the word and then find what the other words are that urlhashes contain and show the words. This table contains all the words in a tag cloud of the given URL. And to identify this word belongs to which URL, we store the urlhash of that website with the word. This table is used to store all the links which it finds in the pages that it crawled till now. This table is used to store all the links of the image which are found in the webpage it crawls.
It will be used for image search, but I have not yet completed its processing and front end PHP code. So I will not discuss it right now. But I would like to tell that it will take a lot of processing power. It was very annoying to get rid of the relative URL. I tried several ways of resolving it, but every one ends up with some bug. Then I got a magic class, named as URI which solved all my problems, but while writing this article I should crawl codeproject.
It may be a problem with my code. I will try later to identify this problem and post the solution. Copy Code.
In this article. Applies to: SQL Server (all supported versions) Azure SQL Database Azure SQL Managed Instance Azure Synapse Analytics Parallel Data Warehouse Every SQL Server securable has associated permissions that can be granted to a principal. Permissions in the Database Engine are managed at the server level assigned to logins and server roles, and at the database level assigned to. Apr 17, · Engine Configuration¶. The Engine is the starting point for any SQLAlchemy application. It’s “home base” for the actual database and its DBAPI, delivered to the SQLAlchemy application through a connection pool and a Dialect, which describes how to talk to a specific kind of database/DBAPI combination.. The general structure can be illustrated as follows. The Search Engine List is the web's most comprehensive list of major and minor search engines complete with links and abstracts describing each of the search engines. You may browse them by category or find them by the alphabetical drop-down menu. You may also browse the Directory List as well. The directory list details the major web directories and is sortable by category.
The most common and effective way to describe full-text searches is "what Google, Yahoo, and Bing do with documents placed on the World Wide Web". Users input a term, or series of terms, perhaps connected by a binary operator or grouped together into a phrase, and the full-text query system finds the set of documents that best matches those terms considering the operators and groupings the user has specified. There are known issues with these older modules and their use should be avoided.
It is now developed and maintained as part of SQLite. The full-text index allows the user to efficiently query the database for all rows that contain one or more words hereafter "tokens" , even if the table contains many large documents. Then either of the two queries below may be executed to find the number of documents in the database that contain the word "linux" Of course, the two queries above are not entirely equivalent.
Both searches are case-insensitive. Using the same hardware configuration used to perform the SELECT queries above, the FTS3 table took just under 31 minutes to populate, versus 25 for the ordinary table. They share most of their code in common, and their interfaces are the same. The differences are:.
FTS4 contains query performance optimizations that may significantly improve the performance of full-text queries that contain terms that are very common present in a large percentage of table rows. FTS4 supports some additional options that may used with the matchinfo function.
Because it stores extra information on disk in two new shadow tables in order to support the performance optimizations and extra matchinfo options, FTS4 tables may consume more disk space than the equivalent table created using FTS3.
FTS4 provides hooks the compress and uncompress options allowing data to be stored in a compressed form, reducing disk usage and IO. FTS4 is sometimes significantly faster than FTS3, even orders of magnitude faster depending on the query, though in the common case the performance of the two modules is similar. The virtual table module arguments may be left empty, in which case an FTS table with a single user-defined column named "content" is created. Alternatively, the module arguments may be passed a list of comma separated column names.
The same applies to any constraints specified along with an FTS column name - they are parsed but not used or recorded by the system in any way. See below for a detailed description of using and, if necessary, implementing a tokenizer.
For example:. Attempting to insert or update a row with a docid value that already exists in the table is an error, just as it would be with an ordinary SQLite table. There is one other subtle difference between "docid" and the normal SQLite aliases for the rowid column. See below for an example. To support full-text queries, FTS maintains an inverted index that maps from each unique term or word that appears in the dataset to the locations in which it appears within the table contents.
For the curious, a complete description of the data structure used to store this index within the database file appears below. A feature of this data structure is that at any time the database may contain not one index b-tree, but several different b-trees that are incrementally merged as rows are inserted, updated and deleted. This technique improves performance when writing to an FTS table, but causes some overhead for full-text queries that use the index. This can be an expensive operation, but may speed up future queries.
The statement above may appear syntactically incorrect to some. Refer to the section describing the simple fts queries for an explanation. Query by rowid. Full-text query. If neither of these two query strategies can be used, all queries on FTS tables are implemented using a linear scan of the entire table. If the table contains large amounts of data, this may be an impractical approach the first example on this page shows that a linear scan of 1.
In all of the full-text queries above, the right-hand operand of the MATCH operator is a string consisting of a single term. In this case, the MATCH expression evaluates to true for all documents that contain one or more instances of the specified word "sqlite", "search" or "database", depending on which example you look at.
Specifying a single term as the right-hand operand of the MATCH operator results in the simplest and most common type of full-text query possible. However more complicated queries are possible, including phrase searches, term-prefix searches and searches for documents containing combinations of terms occurring within a defined proximity of each other. The various ways in which the full-text index may be queried are described below.
Normally, full-text queries are case-insensitive. However, this is dependent on the specific tokenizer used by the FTS table being queried. Refer to the section on tokenizers for details. The paragraph above notes that a MATCH operator with a simple term as the right-hand operand evaluates to true for all documents that contain the specified term.
In this context, the "document" may refer to either the data stored in a single column of a row of an FTS table, or to the contents of all columns in a single row, depending on the identifier used as the left-hand operand to the MATCH operator.
If the identifier specified as the left-hand operand of the MATCH operator is an FTS table column name, then the document that the search term must be contained in is the value stored in the specified column. The following example demonstrates this:. At first glance, the final two full-text queries in the example above seem to be syntactically incorrect, as there is a table name "mail" used as an SQL expression.
The value stored in this column is not meaningful to the application, but can be used as the left-hand operand to a MATCH operator. This special column may also be passed as an argument to the FTS auxiliary functions. The following example illustrates the above. The expressions "docs", "docs. However, the expression "main. It could be used to refer to a table, but a table name is not allowed in the context in which it is used below.
The following list summarizes the differences between FTS and ordinary tables:. As with all virtual table types, it is not possible to create indices or triggers attached to FTS tables. Instead of the normal rules for applying type affinity to inserted values, all values inserted into FTS table columns except the special rowid column are converted to type TEXT before being stored. FTS tables permit the special alias "docid" to be used to refer to the rowid column supported by all virtual tables.
The FTS auxiliary functions , snippet , offsets , and matchinfo are available to support full-text queries. Every FTS table has a hidden column with the same name as the table itself. The value contained in each row for the hidden column is a blob that is only useful as the left operand of a MATCH operator, or as the left-most argument to one of the FTS auxiliary functions.
Usually, this is done by adding the following two switches to the compiler command line:. If using the amalgamation autoconf based build system, setting the CPPFLAGS environment variable while running the 'configure' script is an easy way to set these macros. For example, the following command:. The error message returned will be similar to "no such module: ftsN" where N is either 3 or 4.
Compiling with this macro enables an FTS tokenizer that uses the ICU library to split a document into terms words using the conventions for a specified language and locale.
The most useful thing about FTS tables is the queries that may be performed using the built-in full-text index. Simple FTS queries that return all documents that contain a given term are described above.
In that discussion the right-hand operand of the MATCH operator was assumed to be a string consisting of a single term. This section describes the more complex query types supported by FTS tables, and how they may be utilized by specifying a more complex query expression as the right-hand operand of a MATCH operator.
Token or token prefix queries. An FTS table may be queried for all documents that contain a specified term the simple case described above , or for all documents that contain a term with a specified prefix. As we have seen, the query expression for a specific term is simply the term itself. Or, if the special column with the same name as the FTS table itself is specified, against all columns. This may be overridden by specifying a column-name followed by a ":" character before a basic term query.
There may be space between the ":" and the term to query for, but not between the column-name and the ":" character. In this case, in order to match the token must appear as the very first token in any column of the matching row. Phrase queries. A phrase query is a query that retrieves all documents that contain a nominated set of terms or term prefixes in a specified order with no intervening tokens.
Phrase queries are specified by enclosing a space separated sequence of terms or term prefixes in double quotes ". NEAR queries. A NEAR query is a query that returns documents that contain a two or more nominated terms or phrases within a specified proximity of each other by default with 10 or less intervening terms. More than one NEAR operator may appear in a single query. In this case each pair of terms or phrases separated by a NEAR operator must appear within the specified proximity of each other in the document.
Using the same table and data as in the block of examples above:. The three basic query types described above may be used to query the full-text index for the set of documents that match the specified criteria. Using the FTS query expression language it is possible to perform various set operations on the results of basic queries. There are currently three supported operations:. The FTS modules may be compiled to use one of two slightly different versions of the full-text query syntax, the "standard" query syntax and the "enhanced" query syntax.
The basic term, term-prefix, phrase and NEAR queries described above are the same in both versions of the syntax. The way in which set operations are specified is slightly different.
The following two sub-sections describe the part of the two query syntaxes that pertains to set operations. Refer to the description of how to compile fts for compilation notes. Operators must be entered using capital letters. Otherwise, they are interpreted as basic term queries instead of set operators. The AND operator may be implicitly specified. If two basic queries appear with no operator separating them in an FTS query string, the results are the same as if the two basic queries were separated by an AND operator.
For example, the query expression "implicit operator" is a more succinct version of "implicit AND operator". The examples above all use basic full-text term queries as both operands of the set operations demonstrated.