$ cnpm install reactabular-search
Reactabular comes with search helpers. It consists of search algorithms that can be applied to the rows. Just like with sorting, you have to apply it to the rows just before rendering. A column is considered searchable in case it has a unique property
defined.
The general workflow goes as follows:
Search
control that outputs a query in {<column>: <query>}
format. If <column>
is all
, then the search will work against all columns. Otherwise it will respect the exact columns set. You'll most likely want to use either reactabular-search-field
or reactabular-search-columns
(or both) for this purpose or provide an implementation of your own.search.multipleColumns({ columns, query })(rows)
. This will filter the rows based on the passed rows
, columns
definition, and query
. A lazy way to do this is to filter at render()
although you can do it elsewhere too to optimize rendering.Table
.The Search API consists of three parts. Out of these search.multipleColumns
and search.matches
are the most useful ones for normal usage. If the default search strategies aren't enough, it's possible to implement more as long as you follow the same interface.
search.multipleColumns({ castingStrategy: <castingStrategy>, columns: [<object>], query: {<column>: <query>}, strategy: <strategy>, transform: <transform> })([<rows to query>]) => [<filtered rows>]
This is the highest level search function available. It expects rows
and columns
in the same format the Table
uses. query
object describes column specific search queries.
It uses infix
strategy underneath although it is possible to change it. By default it matches in a case insensitive manner. If you want case sensitive behavior, pass a => a
(identity function) as transform
.
It will cast everything but arrays to a string by default. If you want a custom casting behavior, pass a custom function to castingStrategy
.
search.singleColumn({ castingStrategy: <castingStrategy>, columns: [<object>], searchColumn: <string>, query: <string>, strategy: <strategy>, transform: <transform> })([<rows to query>]) => [<filtered rows>]
This is a more specialized version of search.multipleColumns
. You can use it to search a specific column through searchColumn
and query
.
search._columnMatches({ query: <string>, castingStrategy: <castingStrategy>, column: <object>, row: <object>, strategy: <strategy>, transform: <transform> }) => <boolean>
This is a function that can be used to figure out all column specific matches. It is meant only for internal usage of the library.
When dealing with strings:
search.matches({ value: <string>, query: <string>, strategy: <strategy>, transform: <transform> }) => [{ startIndex: <number>, length: <number> }]
Returns an array with the matches.
When dealing with arrays:
search.matches({ value: <string>, query: [<string|[...]>], strategy: <strategy>, transform: <transform> }) => [[{ startIndex: <number>, length: <number> }], ...]
Returns a sparse array with the same shape as the original query. If there was a match for an item, it will have the same shape as the string version above, otherwise the array will have a hole in that location.
This function returns matches against the given value and query. This is particularly useful with highlighting.
search.strategies.infix(queryTerm: <string>) => { evaluate(searchText: <string>) => <string>, matches(searchText) => [{ startIndex: <number>, length: <number> }]
Search uses infix
strategy by default. This means it will match even if the result is in the middle of a searchText
.
The strategies operate in two passes - evaluation and matching. The evaluation pass allows us to implement perform fast boolean check on whether or not a search will match. Matching gives exact results.
search.strategies.prefix(queryTerm: <string>) => { evaluate(searchText: <string>) => <string>, matches(searchText) => [{ startIndex: <number>, length: <number> }]
prefix
strategy matches from the start.
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