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Indexing PostgreSQL with Apache Solr


Searching and filtering large IP address datasets within PostgreSQL can be challenging. Why? Databases excel at data storage and structured queries, but often struggle with full-text search and complex analysis. Apache Solr, a high-performance search engine built on top of Lucene, is designed to handle these tasks with remarkable speed and flexibility.

What do we need?

  • A running PostgreSQL database with a table containing IP address information (named "ip_loc" in our example).
  • A basic installation of Apache Solr.

Setting up Apache Solr

1. Create a Solr Core:

solr create -c ip_data -d /path/to/solr/configsets/

2. Define the Schema (schema.xml)

<field name="start_ip" type="ip" indexed="true" stored="true"/>
<field name="end_ip" type="ip" indexed="true" stored="true"/>
<field name="iso2" type="string" indexed="true" stored="true"/>
<field name="state" type="text_general" indexed="true" stored="true"/>
<field name="city" type="text_general" indexed="true" stored="true"/>

Integrating PostgreSQL and Solr

Solr's DataImportHandler (DIH): Add the following DIH configuration to your solrconfig.xml:

    <dataSource type="JdbcDataSource" 
        <entity name="ip_data" query="SELECT * FROM ip_loc">
            <field column="start_ip" name="start_ip" /> 

Import Data: Initiate the data import using the Solr admin interface or the command line:


Querying Solr

  • IP Range Search: start_ip:[ TO]
  • Geolocation Filtering: iso2:US AND state:California
  • Combined Search: city:NewYork AND start_ip:[ TO]

Benefits vs. Pure PostgreSQL

  1. Performance: Solr's inverted indexes provide superior search speed.
  2. Scalability: Solr easily distributes across multiple machines.
  3. Flexibility: Solr's query syntax offers rich search capabilities.

My take

By combining PostgreSQL and Apache Solr, you create a robust IP address management system that scales efficiently while providing lightning-fast search functionality.


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