Skip to main content

Indexing PostgreSQL with Apache Solr

Image
By
This article explains how to offload heavy IP search and filtering workloads from PostgreSQL to Apache Solr to gain speed, scalability and richer query capabilities. PostgreSQL remains the system of record for IP ranges and geolocation data, while Solr—backed by Lucene’s inverted indexes—handles fast range lookups and flexible text queries over fields like country, state and city. The piece walks through creating a Solr core, defining an IP-focused schema, wiring PostgreSQL to Solr via the DataImportHandler, and running full imports so Solr can be queried for IP ranges and geo filters with simple syntax. The takeaway: combining Postgres for storage and Solr for search yields a robust, scalable IP management stack that outperforms pure SQL approaches for complex, high-volume search use cases.

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:

<dataConfig>
    <dataSource type="JdbcDataSource" 
                driver="org.postgresql.Driver"
                url="jdbc:postgresql://localhost/your_database"
                user="your_username" 
                password="your_password"/>
    <document>
        <entity name="ip_data" query="SELECT * FROM ip_loc">
            <field column="start_ip" name="start_ip" /> 
            </entity>
    </document>
</dataConfig>

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

http://localhost:8983/solr/ip_data/dataimport?command=full-import

Querying Solr

  • IP Range Search: start_ip:[192.168.0.1 TO 192.168.255.255]
  • Geolocation Filtering: iso2:US AND state:California
  • Combined Search: city:NewYork AND start_ip:[10.0.0.0 TO 10.255.255.255]

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.

Related guides:

If you need help with distributed systems, backend engineering, or data platforms, check my Services.

Labels:

Most read articles

Why Is Customer Obsession Disappearing?

By
Many companies trade real customer-obsession for automated, low-empathy support. Through examples from Coinbase, PayPal, GO Telecommunications and AT&T, this article shows how reliance on AI chatbots, outsourced call centers, and KPI-driven workflows erodes trust, NPS and customer retention. It argues that human-centric support—treating support as strategic investment instead of cost—is still a core growth engine in competitive markets. It's wild that even with all the cool tech we've got these days, like AI solving complex equations and doing business across time zones in a flash, so many companies are still struggling with the basics: taking care of their customers. The drama around Coinbase's customer support is a prime example of even tech giants messing up. And it's not just Coinbase — it's a big-picture issue for the whole industry. At some point, the idea of "customer obsession" got replaced with "customer automation," and no...
Read more

Building a Model-Agnostic Multi-Agent System with OpenClaw

By
Over one week we rebuilt our AI stack around OpenClaw’s multi-agent architecture to avoid provider lock-in and stop wasting premium tokens. By aligning models to tasks, diversifying fallbacks across providers, enforcing minimal tool access, and switching to memory-first workflows with ephemeral sessions, we reduced token usage per task by about 70% and cut our monthly bill by 77% while improving operational resilience. How We Achieved 77% Cost Reduction and Provider Independence Over the past week, we rebuilt our AI infrastructure around OpenClaw’s multi-agent architecture. The result was a 77% cost reduction , provider independence , and a delegation system that routes work to the most cost-effective model for each job. Below is the technical journey of optimizing a 7-agent squad with OpenClaw. The Challenge: Model Provider Lock-In We started with a simple problem: our entire squad defaulted to a single model provider. This created three issues: Cost inefficiency beca...
Read more

What are the performance implications of cross-platform execution within Wayang?

By
Apache Wayang ® enables cross-platform execution across multiple data processing platforms such as Spark, Flink, Java Streams, PostgreSQL or GraphChi. This capability fundamentally changes the performance behavior of distributed data pipelines. Wayang reduces manual data movement by selecting where each operator should run, but crossing platform boundaries still introduces serialization cost, shifts in locality, different memory strategies and new tuning constraints. Understanding these dynamics is essential before adopting Wayang for multi-platform pipelines at scale. Apache Wayang is a cross-platform data processing framework that lets developers run a single logical pipeline across engines such as Apache Spark, Apache Flink or a native Java backend. It provides an abstraction layer and a cost-based optimizer that selects the execution platform for each operator. This flexibility introduces new performance variables that do not exist in single-engine systems. Engine boundaries ...
Read more