This topic provides an overview of Greenplum Database full text search, basic text search expressions, configuring, and customizing text search. Greenplum Database full text search is compared with Tanzu Greenplum Text.
This section contains the following subtopics:
Full Text Searching (or just "text search") provides the capability to identify natural-language documents that satisfy a query, and optionally to rank them by relevance to the query. The most common type of search is to find all documents containing given query terms and return them in order of their similarity to the query.
Greenplum Database provides a data type
tsvector to store preprocessed documents, and a data type
tsquery to store processed queries (Text Search Data Types). There are many functions and operators available for these data types (Text Search Functions and Operators), the most important of which is the match operator
@@, which we introduce in Basic Text Matching. Full text searches can be accelerated using indexes (GiST and GIN Indexes for Text Search).
Notions of query and similarity are very flexible and depend on the specific application. The simplest search considers query as a set of words and similarity as the frequency of query words in the document.
Greenplum Database supports the standard text matching operators
ILIKE for textual data types, but these operators lack many essential properties required for searching documents:
There is no linguistic support, even for English. Regular expressions are not sufficient because they cannot easily handle derived words, e.g.,
satisfy. You might miss documents that contain
satisfies, although you probably would like to find them when searching for
satisfy. It is possible to use OR to search for multiple derived forms, but this is tedious and error-prone (some words can have several thousand derivatives).
They provide no ordering (ranking) of search results, which makes them ineffective when thousands of matching documents are found.
They tend to be slow because there is no index support, so they must process all documents for every search.
Full text indexing allows documents to be preprocessed and an index saved for later rapid searching. Preprocessing includes:
Dictionaries allow fine-grained control over how tokens are normalized. With appropriate dictionaries, you can:
A document is the unit of searching in a full text search system; for example, a magazine article or email message. The text search engine must be able to parse documents and store associations of lexemes (key words) with their parent document. Later, these associations are used to search for documents that contain query words.
For searches within Greenplum Database, a document is normally a textual field within a row of a database table, or possibly a combination (concatenation) of such fields, perhaps stored in several tables or obtained dynamically. In other words, a document can be constructed from different parts for indexing and it might not be stored anywhere as a whole. For example:
SELECT title || ' ' || author || ' ' || abstract || ' ' || body AS document FROM messages WHERE mid = 12; SELECT m.title || ' ' || m.author || ' ' || m.abstract || ' ' || d.body AS document FROM messages m, docs d WHERE mid = did AND mid = 12;
In these example queries,
coalesce should be used to prevent a single
NULL attribute from causing a
NULL result for the whole document.
Another possibility is to store the documents as simple text files in the file system. In this case, the database can be used to store the full text index and to run searches, and some unique identifier can be used to retrieve the document from the file system. However, retrieving files from outside the database requires superuser permissions or special function support, so this is usually less convenient than keeping all the data inside Greenplum Database. Also, keeping everything inside the database allows easy access to document metadata to assist in indexing and display.
For text search purposes, each document must be reduced to the preprocessed
tsvector format. Searching and ranking are performed entirely on the tsvector representation of a document — the original text need only be retrieved when the document has been selected for display to a user. We therefore often speak of the
tsvector as being the document, but of course it is only a compact representation of the full document.
Full text searching in Greenplum Database is based on the match operator
@@, which returns
true if a
tsvector (document) matches a
tsquery (query). It does not matter which data type is written first:
SELECT 'a fat cat sat on a mat and ate a fat rat'::tsvector @@ 'cat & rat'::tsquery; ?column? ---------- t SELECT 'fat & cow'::tsquery @@ 'a fat cat sat on a mat and ate a fat rat'::tsvector; ?column? ---------- f
As the above example suggests, a
tsquery is not just raw text, any more than a
tsvector is. A
tsquery contains search terms, which must be already-normalized lexemes, and may combine multiple terms using AND, OR, and NOT operators. (For details see.) There are functions
plainto_tsquery that are helpful in converting user-written text into a proper
tsquery, for example by normalizing words appearing in the text. Similarly,
to_tsvector is used to parse and normalize a document string. So in practice a text search match would look more like this:
SELECT to_tsvector('fat cats ate fat rats') @@ to_tsquery('fat & rat'); ?column? ---------- t
Observe that this match would not succeed if written as
SELECT 'fat cats ate fat rats'::tsvector @@ to_tsquery('fat & rat'); ?column? ---------- f
since here no normalization of the word
rats will occur. The elements of a
tsvector are lexemes, which are assumed already normalized, so
rats does not match
@@ operator also supports
text input, allowing explicit conversion of a text string to
tsquery to be skipped in simple cases. The variants available are:
tsvector @@ tsquery tsquery @@ tsvector text @@ tsquery text @@ text
The first two of these we saw already. The form
text @@ tsquery is equivalent to
to_tsvector(x) @@ y. The form
text @@ text is equivalent to
to_tsvector(x) @@ plainto_tsquery(y).
The above are all simple text search examples. As mentioned before, full text search functionality includes the ability to do many more things: skip indexing certain words (stop words), process synonyms, and use sophisticated parsing, e.g., parse based on more than just white space. This functionality is controlled by text search configurations. Greenplum Database comes with predefined configurations for many languages, and you can easily create your own configurations. (psql's
\dF command shows all available configurations.)
During installation an appropriate configuration is selected and default_text_search_config is set accordingly in
postgresql.conf. If you are using the same text search configuration for the entire cluster you can use the value in
postgresql.conf. To use different configurations throughout the cluster but the same configuration within any one database, use
ALTER DATABASE ... SET. Otherwise, you can set
default_text_search_config in each session.
Each text search function that depends on a configuration has an optional
regconfig argument, so that the configuration to use can be specified explicitly.
default_text_search_config is used only when this argument is omitted.
To make it easier to build custom text search configurations, a configuration is built up from simpler database objects. Greenplum Database's text search facility provides four types of configuration-related database objects:
Text search parsers and templates are built from low-level C functions; therefore it requires C programming ability to develop new ones, and superuser privileges to install one into a database. (There are examples of add-on parsers and templates in the
contrib/ area of the Greenplum Database distribution.) Since dictionaries and configurations just parameterize and connect together some underlying parsers and templates, no special privilege is needed to create a new dictionary or configuration. Examples of creating custom dictionaries and configurations appear later in this chapter.
Greenplum Database text search is PostgreSQL text search ported to the Greenplum Database MPP platform. VMware also offers Tanzu Greenplum Text, which integrates Greenplum Database with the Apache Solr text search platform. Tanzu Greenplum Text installs an Apache Solr cluster alongside your Greenplum Database cluster and provides Greenplum Database functions you can use to create Solr indexes, query them, and receive results in the database session.
Both of these systems provide powerful, enterprise-quality document indexing and searching services. Greenplum Database text search is immediately available to you, with no need to install and maintain additional software. If it meets your applications' requirements, you should use it.
Tanzu Greenplum Text, with Solr, has many capabilities that are not available with Greenplum Database text search. In particular, it is better for advanced text analysis applications. Following are some of the advantages and capabilities available to you when you use Tanzu Greenplum Text for text search applications.
See the Tanzu Greenplum Text Documentation web site for more information.
Parent topic: Using Full Text Search