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Original Text [setup]

The following steps install SQL Server Full-Text Search (mssql-server-fts) on Linux. Full-Text Search enables you to run full-text queries against character-based data in SQL Server tables. For known issues for this release, see the Release Notes.

Original text [setup]

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Full-Text Search uses word breakers that determine how to identify individual words based on language. You can get a list of registered word breakers by querying the sys.fulltext_languages catalog view. Word breakers for the following languages are installed with SQL Server:

Full-Text Search also works with text stored in binary files. But in this case, an installed filter is required to process the file. For more information about filters, see Configure and Manage Filters for Search.

SQL Server databases are full-text enabled by default. Before you can run full-text queries, however, you must create a full text catalog and create a full-text index on the tables or indexed views you want to search.

Each full-text index must belong to a full-text catalog. You can create a separate text catalog for each full-text index, or you can associate multiple full-text indexes with a given catalog. A full-text catalog is a virtual object and does not belong to any filegroup. The catalog is a logical concept that refers to a group of full-text indexes.

The following two-part example creates a full-text catalog named AdvWksDocFTCat on the AdventureWorks sample database and then creates a full-text index on the Document table in the sample database. This statement creates the full-text catalog in the default directory specified during SQL Server setup. The folder named AdvWksDocFTCat is in the default directory.

Before you can create a full-text index on the Document table, ensure that the table has a unique, single-column, non-nullable index. The following CREATE INDEX statement creates a unique index, ui_ukDoc, on the DocumentID column of the Document table:

The process of building a full-text index is fairly I/O intensive. In summary, it consists of reading data from SQL Server, and then propagating the filtered data to the full-text index. As a best practice, locate a full-text index in the database filegroup that is best for maximizing I/O performance or locate the full-text indexes in a different filegroup on another volume.

We recommend associating tables with the same update characteristics (such as small number of changes versus large number of changes, or tables that change frequently during a particular time of day) together under the same full-text catalog. By setting up full-text catalog population schedules, full-text indexes stay synchronous with the tables without adversely affecting the resource usage of the database server during periods of high database activity.

Consider the amount of change occurring in the tables being full-text indexed, as well as the total number of rows. If the total number of rows being changed, together with the number of rows in the table present during the last full-text population, represents millions of rows, assign the table to its own full-text catalog.

Always select the smallest unique index available for your full-text unique key. (A 4-byte, integer-based index is optimal.) This significantly reduces the resources required by Microsoft Search service in the file system. If the primary key is large (over 100 bytes), consider choosing another unique index in the table (or creating another unique index) as the full-text unique key. Otherwise, if the full-text unique key size exceeds the maximum size allowed (900 bytes), full-text population will not be able to proceed.

A stoplist is a list of stopwords, also known as noise words. A stoplist is associated with each full-text index, and the words in that stoplist are applied to full-text queries on that index. By default, the system stoplist is associated with a new full-text index. You can create and use your own stoplist too.

Like regular SQL Server indexes, full-text indexes can be automatically updated as data is modified in the associated tables. This is the default behavior. Alternatively, you can keep your full-text indexes up-to-date manually, or at specified scheduled intervals. Populating a full-text index can be time-consuming and resource-intensive. Therefore, index updating is usually performed as an asynchronous process that runs in the background and keeps the full-text index up to date after modifications in the base table.

Updating a full-text index immediately after each change in the base table is also resource-intensive. Therefore, if you have a high update/insert/delete rate, you may experience some degradation in query performance. If this occurs, consider scheduling manual change tracking updates to keep up with the numerous changes from time to time, rather than competing with queries for resources.

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Install these by adding the Python files to your /Sublime Text 3/Packages/User directory via the file menu (Sublime > Preferences > Browse Packages) and then opening the User directory. To complete the setup, bind them from the Key Bindings - User file (Sublime Text > Preferences > Package Settings > AdvancedNewFile > Key Bindings - User).

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Feature papers represent the most advanced research with significant potential for high impact in the field. A FeaturePaper should be a substantial original Article that involves several techniques or approaches, provides an outlook forfuture research directions and describes possible research applications.

Abstract:In this work, a novel and versatile experimental setup for coulometric signal transduction of ion-selective electrodes (ISEs) is introduced and studied. It is based on a constant potential coulometric measurement carried out using a one-compartment three-electrode electrochemical cell. In the setup, a potassium ion-selective electrode (K+- ISE) is connected as the reference electrode (RE). A poly(3,4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS)-based electrode with a dummy membrane (DM) and a glassy carbon (GC) rod are connected as the working electrode (WE) and counter electrode (CE), respectively. Adding a non-selective dummy membrane to the structure of the WE facilitates the regulation of the measured signal and response time. The results from electrochemical impedance spectroscopy measurements carried out on the WE showed that the time constant is profoundly influenced by the dummy membrane thickness. In addition, the redox capacitance of the PEDOT:PSS film shows a better correlation with the electrode area than the film thickness. Sequential addition/dilution experiments showed the improvement of current and cumulated charge signals in the new setup studied in this work compared to the setup used in the original coulometric signal transduction method. Both conventional ISEs and solid-contact ISEs (SCISEs) were used in this work. The results showed that the coulometric response was independent of the type of ISE used as RE, confirming the versatility of the novel set-up. Keywords: coulometric signal transduction; solid-contact ion-selective electrodes; conventional ion-selective electrodes; PEDOT:PSS; MWCNT; chronoamperometry; chronocoulometry; electrochemical impedance spectroscopy

From what I know of the ES river plugin for MongoDB, it tails the oplog for it's functionality. Since a sharded setup would have multiple oplogs and there would be no way to easily alter that code to connect via a mongos.

Mongo is not at al good for fulltext search. Obviously you need to index you fields for fast searching, and indexing fields containing BIG data (long long strings) will be failed in mongo. it has a limit of 1k for index, if you have content more thn 1k, it will be ignored by index and will not be displayed in your search results. obviously if you are trying to perform a full text search for your articles, mongo is not at al a good choice.

PostgreSQL Full Text Search refers to a technique in which you search for a single computer-stored document or a collection in your full-text database. It provides you with the capability to identify natural languages documents that satisfy a query. PostgreSQL Full Text Search allows indexing of documents which helps in the rapid search of documents. PostgreSQL Full Text Search also supports dictionaries that allow fine-grained control over token normalization. Learn more about PostgreSQL Full Text Search from here.

This function accepts a list of words that would be checked against the normalized vector created with the to_tsvector() function. You can use the following Postgres functions to convert text fields to tsquery values such as to_tsquery, plainto_tsquery, and phraseto_tsquery.

In PostgreSQL, full-text searching is done with the @@ match operator, which returns true if a tsvector (document) matches a tsquery (query). It makes no difference which type of data is written first:

A tsquery, like a tsvector, is not just raw text, as the above example shows. A tsquery is a collection of search phrases that must be already-normalized lexemes and can combine multiple terms using the AND, OR, and NOT operators. To turn user-written text into a valid tsquery, the methods to tsquery and plainto tsquery are useful, for example, by normalizing words found in the text. To parse and normalize a document string, tsvector is utilized. In practice, a text search match would seem as follows: 041b061a72

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