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How to Configure Sphinx Search Plugin for WordPress

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Sphinx Search WordPress PluginNo matter how perfect your WordPress site hierarchy and navigation are, some users won’t understand it. For such users, a search box becomes essential, because it is the primary way for them to access a website. So instead of turning such users away from your site, you need to provide a search box and make their lives easy. Moreover it doesn’t hurt the usability or appeal of the site for people that don’t use it. In fact, studies suggest that, Onsite Search is the most prominent Navigation tool.

When it comes to WordPress, you don’t have to look far for a navigation tool, the open source, Sphinx Search Server integrates seamlessly with your CMS and helps your users navigate easily.

Sphinx Server Configuration

Okay let’s start from the very beginning. You first need to install the Sphinx package on the server on which you’re hosting your WordPress site. Once the installation is done the next step is configuration. You need to use the following code to configure the Sphinx Server:

[pre]<code>

#
# Sphinx configuration file sample
#
# Please refer to doc/sphinx.html for details.
#

#############################################################################
## data source definition
#############################################################################

source product_product
{
# data source type. mandatory, no default value
# known types are mysql, pgsql, mssql, xmlpipe, xmlpipe2, odbc
type = pgsql

#####################################################################
## SQL settings (for ‘mysql’ and ‘pgsql’ types)
#####################################################################

# some straightforward parameters for SQL source types
sql_host = PUT_THE_IP_ADDRESS_OR_FQDN_OF_YOUR_DATABASE_SERVER
sql_user = PUT_YOUR_DATABASE_USERNAME
sql_pass = PUT_THE_PASSWORD_FOR_THE_DATABASE_USER
sql_db = PUT_THE_DATABASE_NAME
sql_port = 5432 #default is 3306

# main document fetch query
# mandatory, integer document ID field MUST be the first selected column
sql_query = \
SELECT \
product.id AS id, \
template.name AS name, \
product.description AS description, \
FROM product_product AS product \
JOIN product_template AS template ON template.id = product.template \
WHERE \
product.id >= $start and product.id <= $end \

# range query setup, query that must return min and max ID values
# optional, default is empty
#
# sql_query will need to reference $start and $end boundaries
# if using ranged query:
#
# sql_query = \
# SELECT doc.id, doc.id AS group, doc.title, doc.data \
# FROM documents doc \
# WHERE id>=$start AND id<=$end
#
sql_query_range = SELECT MIN(id),MAX(id) FROM product_product

# range query step
# optional, default is 1024
#
sql_range_step = 1000

# combined field plus attribute declaration (from a single column)
# stores column as an attribute, but also indexes it as a full-text field
#
sql_field_string = name
sql_field_string = description

# ranged query throttling, in milliseconds
# optional, default is 0 which means no delay
# enforces given delay before each query step
sql_ranged_throttle = 0

# document info query, ONLY for CLI search (ie. testing and debugging)
# optional, default is empty
# must contain $id macro and must fetch the document by that id
sql_query_info = SELECT * FROM documents WHERE id=$id

}

# inherited source
#
# all the parameters are copied from the parent source,
# and may then be overridden in this source definition

source product_product_pt : product_product
{
sql_query = \
SELECT\
“product”.”id” AS id, get_template_translation(template.id, ‘name’, template.name, ‘pt_BR’) AS name, get_product_translation(product.id, ‘description’, product.description, ‘pt_BR’) AS description\
FROM product_product AS product\
JOIN product_template AS template ON template.id = product.template\
WHERE\
product.id >= $start and product.id <= $end\
}

#############################################################################
## index definition
#############################################################################

# local index example
#
# this is an index which is stored locally in the filesystem
#
# all indexing-time options (such as morphology and charsets)
# are configured per local index
index product_product
{
# document source(s) to index
# multi-value, mandatory
# document IDs must be globally unique across all sources
source = product_product

# index files path and file name, without extension
# mandatory, path must be writable, extensions will be auto-appended
path = /var/data/product_product

# document attribute values (docinfo) storage mode
# optional, default is ‘extern’
# known values are ‘none’, ‘extern’ and ‘inline’
docinfo = extern

# memory locking for cached data (.spa and .spi), to prevent swapping
# optional, default is 0 (do not mlock)
# requires searchd to be run from root
mlock = 0

# a list of morphology preprocessors to apply
# optional, default is empty
#
# builtin preprocessors are ‘none’, ‘stem_en’, ‘stem_ru’, ‘stem_enru’,
# ‘soundex’, and ‘metaphone’; additional preprocessors available from
# libstemmer are ‘libstemmer_XXX’, where XXX is algorithm code
# (see libstemmer_c/libstemmer/modules.txt)
#
# morphology = stem_en, stem_ru, soundex
# morphology = libstemmer_german
# morphology = libstemmer_sv
morphology = none

# minimum indexed word length
# default is 1 (index everything)
min_word_len = 1

# charset encoding type
# optional, default is ‘sbcs’
# known types are ‘sbcs’ (Single Byte CharSet) and ‘utf-8’
charset_type = utf-8

# whether to strip HTML tags from incoming documents
# known values are 0 (do not strip) and 1 (do strip)
# optional, default is 0
html_strip = 1

}

# inherited index
#
# all the parameters are copied from the parent index,
# and may then be overridden in this index definition
index product_product_en : product_product
{
path = /var/data/product_product_en
morphology = stem_en
}

index product_product_pt : product_product
{
source = product_product_pt
path = /var/data/product_product_pt
morphology = libstemmer_pt
}

#############################################################################
## indexer settings
#############################################################################

indexer
{
# memory limit, in bytes, kiloytes (16384K) or megabytes (256M)
# optional, default is 32M, max is 2047M, recommended is 256M to 1024M
mem_limit = 2047M

}

#############################################################################
## searchd settings
#############################################################################

searchd
{
# [hostname:]port[:protocol], or /unix/socket/path to listen on
# known protocols are ‘sphinx’ (SphinxAPI) and ‘mysql41’ (SphinxQL)
#
# multi-value, multiple listen points are allowed
# optional, defaults are 9312:sphinx and 9306:mysql41, as below
#
# listen = 127.0.0.1
# listen = 192.168.0.1:9312
# listen = 9312
# listen = /var/run/searchd.sock
listen = PUT_PUBLIC_OR_PRIVATE_IP_ADDRESS_OF_THE_SEARCHD_SERVER:9312
listen = PUT_PUBLIC_OR_PRIVATE_IP_ADDRESS_OF_THE_SEARCHD_SERVER:9306:mysql41

# log file, searchd run info is logged here
# optional, default is ‘searchd.log’
log = /var/log/sphinx/searchd.log

# query log file, all search queries are logged here
# optional, default is empty (do not log queries)
query_log = /var/log/sphinx/query.log

# client read timeout, seconds
# optional, default is 5
read_timeout = 5

# request timeout, seconds
# optional, default is 5 minutes
client_timeout = 300

# maximum amount of children to fork (concurrent searches to run)
# optional, default is 0 (unlimited)
max_children = 30

# PID file, searchd process ID file name
# mandatory
pid_file = /var/run/searchd.pid

# max amount of matches the daemon ever keeps in RAM, per-index
# WARNING, THERE’S ALSO PER-QUERY LIMIT, SEE SetLimits() API CALL
# default is 1000 (just like Google)
max_matches = 1000

# seamless rotate, prevents rotate stalls if precaching huge datasets
# optional, default is 1
seamless_rotate = 1

# whether to forcibly preopen all indexes on startup
# optional, default is 1 (preopen everything)
preopen_indexes = 1

# whether to unlink .old index copies on succesful rotation.
# optional, default is 1 (do unlink)
unlink_old = 1

# MVA updates pool size
# shared between all instances of searchd, disables attr flushes!
# optional, default size is 1M
mva_updates_pool = 1M

# max allowed network packet size
# limits both query packets from clients, and responses from agents
# optional, default size is 8M
max_packet_size = 8M

# max allowed per-query filter count
# optional, default is 256
max_filters = 256

# max allowed per-filter values count
# optional, default is 4096
max_filter_values = 4096

# max allowed per-batch query count (aka multi-query count)
# optional, default is 32
max_batch_queries = 32

# multi-processing mode (MPM)
# known values are none, fork, prefork, and threads
# optional, default is fork
#
workers = threads # for RT to work
}
# –eof–

</code>
[/pre]
This code can be customized according to your website’s database. Once you install your WordPress website, create a file named ‘sphinx.conf’ and paste the code there. You have to put this code in the following path:
Open your server, goto public_html -> wp-content -> (any file)

Please Note:
This file can be uploaded in any file in wp-content. However sometimes the ‘sphinx.conf’ file is not readable to all servers. For the sphinx code to be readable to all the servers, upload this file in the uploads directory of the wp-content. You need to give this path: public_html -> wp-content -> uploads while starting the sphinx server. You will also need this path while indexing. We just created a sphinx directory on the server, which is needed to store all the sphinx packages.

[space]

Auto-Complete Functionality for Sphinx Server

Autocomplete is not a built-in functionality in the Sphinx Server. We can however, implement this functionality using a source. The source can be a word dictionary or can be built from indexed data.

To create a table from indexed data, we need to do the following:

  1. Run the following command in the terminal to create a text file (word_freq.txt): $ indexer myindex –buildstops word_freq.txt 1000

  1. Upload the word_freq.txt file data in mysql table using PHP

We now have a source. Thus using jQuery and PHP we can implement the autocomplete functionality.

We used Buildstops here; –buildstops <outputfile.text> <N> . Buildstops reviews the index source, as if it were indexing the data, and produces a list of the terms that are being indexed. Basically, it produces a list of all the searchable terms that become part of the index. The output file (outputfile.text or word_freq.txt in our case) will contain the list of words, one word per line (sorted by frequency) and N (1000) specifies the maximum number of words that will be listed.
Using Buildstops is the simplest way to implement the autocomplete feature. In case you wanted to explore other ways to implement autocomplete, you could refer this link.

[space]

The Sphinx Search Server Plugin in WordPress is used to search content within your website but it has certain limitations. The plugin does not immediately recognize content added in real-time to your website in the search query. Stay tuned to read more about how to overcome this limitation in our upcoming posts.

Ankita Kawde

Ankita Kawde

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