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Our journey starts with a 30-minute discovery call to explore your project challenges, technical needs, and team diversity. Meet Carefully Matched Apache Solr Talents. Within 1-3 days, we’ll share profiles and connect you with the right talents for your project. Schedule a call to meet engineers in person. Validate Your Choice. Bring a new Apache Solr developer on board with a trial period to confirm that you’ve hired the right one. There are no termination fees or hidden costs.

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Table of Contents

    Let’s consider Difference between Junior, Middle, Senior, Expert/Team Lead developer roles.

    Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
    Junior Developer0-2 yearsAssisting in the development of software applications, troubleshooting and debugging code, implementing features under the guidance of senior developers, conducting code reviews, collaborating with other team members, and learning new technologies and frameworks.$50,000 – $75,000
    Middle Developer2-5 yearsDeveloping and maintaining software applications, designing and implementing new features, optimizing code for performance and scalability, participating in code reviews, collaborating with cross-functional teams, mentoring junior developers, and contributing to architectural decisions.$75,000 – $100,000
    Senior Developer5-8 yearsLeading the development of complex software applications, designing and implementing high-level architectural solutions, providing technical guidance and mentoring to junior and middle developers, conducting code reviews and ensuring code quality, collaborating with stakeholders to gather requirements, and driving the overall technical direction of projects.$100,000 – $130,000
    Expert/Team Lead8+ yearsLeading a team of developers, managing project timelines and deliverables, defining and enforcing coding standards and best practices, conducting performance evaluations, providing technical leadership and guidance, collaborating with stakeholders to define project scope and requirements, and staying up-to-date with industry trends and emerging technologies.$130,000 – $180,000

    TOP 10 Apache Solr Related Technologies

    • Java

      Java is the most popular language for Apache Solr software development. It provides a robust and scalable platform for building Solr applications. With its vast ecosystem and mature libraries, Java offers developers a wide range of tools and frameworks to enhance their Solr projects.

    • Spring Framework

      The Spring Framework is widely used in Apache Solr development due to its dependency injection and inversion of control features. It simplifies the integration of Solr into Java applications, providing a cohesive and modular approach to building Solr-powered solutions.

    • Python

      Python is gaining popularity among Apache Solr developers for its simplicity and ease of use. Its rich set of libraries, such as pysolr, enables developers to interact with Solr in a straightforward and efficient manner. Python’s readability and expressiveness make it a favored language for Solr development.

    • JavaScript

      JavaScript is essential for web-based Apache Solr applications. It allows developers to create interactive and dynamic user interfaces that integrate seamlessly with Solr. JavaScript frameworks like Vue.js and React.js further enhance the development experience by providing powerful tools and components.

    • Apache Lucene

      Apache Lucene is the foundational technology behind Apache Solr. It is a high-performance search library that powers Solr’s indexing and querying capabilities. Understanding Lucene is crucial for Solr developers, as it provides insights into the inner workings of Solr and enables fine-grained customization.

    • RESTful APIs

      RESTful APIs play a vital role in Apache Solr software development, allowing applications to communicate with Solr and perform various operations. They enable developers to index documents, execute queries, and manage Solr configurations programmatically, facilitating seamless integration with other systems.

    • Apache ZooKeeper

      Apache ZooKeeper is often used in Solr deployments for distributed coordination and synchronization. It helps ensure high availability and fault tolerance by managing Solr cluster configurations and maintaining consistency across nodes. Familiarity with ZooKeeper is beneficial for building scalable Solr applications.

    Cases when Apache Solr does not work

    1. Insufficient Hardware Resources: Apache Solr may not function optimally if the hardware resources allocated to it are insufficient. Inadequate memory, CPU, or disk space can hinder Solr’s performance and lead to degraded search functionality.
    2. Incorrect Configuration: Misconfigurations in the Solr setup can prevent it from working as intended. Errors in the schema definition, configuration files, or core configuration can cause indexing or searching issues.
    3. Network Connectivity Problems: Solr relies on network connectivity to communicate with clients and other components. If there are network issues, such as high latency, packet loss, or firewall restrictions, Solr may experience connectivity problems, resulting in failed requests or slow response times.
    4. Software Compatibility Issues: Compatibility issues between Solr and other software components in the system stack can cause Solr to stop functioning. Incompatible versions of Java, operating systems, or third-party libraries can disrupt Solr’s operations.
    5. Data Corruption: If the Solr index or data files become corrupted due to hardware failures, power outages, or improper shutdowns, Solr may fail to retrieve or index data correctly, leading to inconsistencies or missing search results.
    6. Heavy Query Workload: In scenarios where Solr is subjected to an extremely high query workload, it may struggle to handle the volume efficiently. This can result in increased response times or even service unavailability during peak usage periods.
    7. Security Restrictions: If Solr is operating in an environment with strict security restrictions, such as firewall rules, access control, or encryption requirements, it may not be able to function properly without the necessary configurations and permissions.
    8. Software Bugs: Like any software, Solr can have bugs or known issues that may impact its performance or functionality. It is crucial to keep Solr up to date with the latest patches and bug fixes to mitigate potential problems.

    TOP 10 Tech facts and history of creation and versions about Apache Solr Development

    • Apache Solr is an open-source search platform built on Apache Lucene, which was created in 1999 by Doug Cutting.
    • Solr was initially developed by Yonik Seeley in 2004 as an independent project, and later donated to the Apache Software Foundation in 2006.
    • The name “Solr” stands for “Searching On Lucene, Ready” and reflects its purpose of providing a ready-to-use search platform.
    • One of Solr’s groundbreaking features is its faceted search capability, which allows users to explore search results by applying filters to specific facets or categories.
    • In 2008, Solr 1.3 introduced distributed searching, enabling users to scale their search infrastructure across multiple servers for improved performance and fault tolerance.
    • Solr 3.1, released in 2011, introduced the concept of “Near Real-Time” (NRT) searching, allowing indexed documents to be available for search almost immediately after being added or updated.
    • Solr 4.0, released in 2012, introduced the new SolrCloud architecture, which provided automatic distributed indexing and querying, making it easier to build highly scalable and fault-tolerant search systems.
    • Apache Solr has gained popularity for its extensive plugin ecosystem, allowing users to easily extend its functionality with features such as spell checking, language detection, and geospatial search.
    • Solr 6.0, released in 2016, introduced the “Parallel SQL” feature, enabling users to run SQL queries against their Solr indexes, making it more accessible to developers familiar with SQL syntax.
    • The latest major release, Solr 8.0, introduced numerous improvements, including a new JSON-based request/response API, support for more efficient indexing with the “DocValues” feature, and enhanced security features.

    How and where is Apache Solr used?

    Case NameCase Description
    Case 1: E-commerce Product SearchApache Solr is widely used in e-commerce platforms to provide fast and accurate product search capabilities. With its powerful indexing and querying features, Solr can handle large catalogs of products and deliver relevant search results in real-time. It supports faceted search, spell checking, autocomplete, and advanced filtering options, allowing users to quickly find the products they are looking for.
    Case 2: Media Content DiscoveryMedia companies leverage Apache Solr to enhance content discovery on their platforms. Solr can index and search through vast amounts of multimedia content, including text, images, videos, and audio files. It enables users to perform complex searches based on metadata, tags, categories, and even content similarity. This enables personalized recommendations, content clustering, and improved search experiences for media consumers.
    Case 3: Enterprise Knowledge ManagementOrganizations utilize Apache Solr as a powerful knowledge management tool. Solr can index and search through various types of documents, such as PDFs, Word documents, spreadsheets, and presentations. It enables employees to quickly find relevant information across a wide range of enterprise data sources, including intranets, wikis, and document repositories. Solr’s advanced search capabilities, including full-text search, highlighting, and result relevancy ranking, facilitate efficient knowledge discovery within the organization.
    Case 4: News and Content AggregationNews and content aggregation platforms leverage Apache Solr to collect, index, and search through vast amounts of news articles, blogs, social media posts, and other online content sources. Solr’s distributed architecture and scalability make it an ideal choice for handling high-volume data ingestion and retrieval. It allows users to filter and search news content based on various criteria, such as source, topic, sentiment, and date, enabling timely and relevant content delivery to end-users.
    Case 5: Geographic Information Systems (GIS)Apache Solr is used in GIS applications to enable spatial search and analysis. It can index geospatial data, such as coordinates, shapes, and polygons, and provide powerful geospatial querying capabilities. Solr’s integration with popular GIS frameworks, such as OpenLayers and Leaflet, allows developers to build interactive maps with search functionalities. This enables users to perform location-based searches, find nearby points of interest, and visualize geospatial data effectively.

    Hard skills of a Apache Solr Developer

    Apache Solr is a powerful open-source search platform built on Apache Lucene. As a Apache Solr Developer, you need to possess a strong set of hard skills to effectively work with this technology.


    • Apache Solr installation and configuration: Proficiency in setting up and configuring Apache Solr instances.
    • Schema design and management: Ability to design and manage schemas for indexing and searching data using Apache Solr.
    • Indexing data: Knowledge of indexing various types of data such as structured, unstructured, and semi-structured data into Apache Solr.
    • Querying and filtering: Understanding of querying and filtering techniques in Apache Solr to retrieve relevant search results.
    • Basic troubleshooting: Familiarity with basic troubleshooting techniques to identify and resolve common issues in Apache Solr.


    • Performance optimization: Proficiency in optimizing the performance of Apache Solr by tuning query parameters, caching strategies, and shard configuration.
    • Advanced schema design: Ability to design complex schemas with features like multi-valued fields, dynamic fields, and field types.
    • Query analysis and relevance tuning: Expertise in analyzing search queries and fine-tuning relevance parameters to improve search results.
    • Security implementation: Knowledge of implementing security measures such as authentication, authorization, and SSL/TLS encryption in Apache Solr.
    • Cluster management: Understanding of managing Apache Solr clusters, including replication, sharding, and load balancing.
    • Monitoring and logging: Familiarity with monitoring tools and techniques to monitor the performance and health of Apache Solr instances.
    • Data import and integration: Ability to import and integrate data from various sources such as databases, APIs, and file systems into Apache Solr.


    • Distributed search: Expertise in setting up and managing distributed search across multiple Apache Solr nodes for high availability and scalability.
    • Custom plugin development: Proficiency in developing custom plugins and extensions for Apache Solr to add custom functionality.
    • Advanced query and filter parsing: In-depth understanding of query and filter parsing techniques, including query syntax, boosting, and faceted search.
    • Advanced troubleshooting: Ability to diagnose and resolve complex issues in Apache Solr, including performance bottlenecks and data inconsistencies.
    • Advanced security implementation: Knowledge of implementing advanced security measures such as role-based access control and encryption at rest.
    • Integration with other systems: Experience in integrating Apache Solr with other systems such as content management systems, e-commerce platforms, and data analytics tools.
    • Capacity planning and scalability: Ability to perform capacity planning and scale Apache Solr infrastructure to handle increasing data and query loads.

    Expert/Team Lead

    • Architecture design: Expertise in designing scalable and fault-tolerant architectures for Apache Solr deployments, including multi-region setups.
    • Performance tuning: Mastery in fine-tuning Apache Solr performance by optimizing JVM settings, query caching, and query rewriting.
    • Advanced cluster management: Ability to manage and optimize large-scale Apache Solr clusters with features like distributed indexing, replication, and failover.
    • Continuous integration and deployment: Knowledge of setting up automated CI/CD pipelines for Apache Solr deployments, including testing and version control integration.
    • Capacity planning and resource allocation: Proficiency in analyzing resource requirements, estimating capacity needs, and allocating resources for Apache Solr clusters.
    • Team leadership and mentoring: Experience in leading and mentoring a team of Apache Solr Developers, providing guidance and support for project delivery.
    • Vendor management: Ability to evaluate and manage relationships with Apache Solr-related vendors, including support contracts and third-party integrations.
    • Community involvement: Active participation in the Apache Solr community, contributing to open-source projects, and sharing knowledge with the community.
    • Industry trends and best practices: Continual learning and staying updated with the latest industry trends and best practices in Apache Solr development and search technologies.
    • Business acumen: Understanding of business requirements and ability to align Apache Solr solutions with organizational goals and objectives.
    • Project management: Proficiency in project management methodologies and tools to effectively plan, execute, and deliver Apache Solr projects.

    TOP 10 Facts about Apache Solr

    • Apache Solr is an open-source search platform built on Apache Lucene, which is a high-performance, full-featured text search engine library.
    • It is widely used by organizations to implement fast and scalable search functionality in their applications.
    • Apache Solr provides advanced features like faceted search, spell checking, geospatial search, and hit highlighting to enhance the search experience.
    • With its powerful indexing and querying capabilities, Solr can handle large volumes of data efficiently, making it suitable for use cases with big data.
    • Solr supports distributed searching and indexing, allowing it to handle high traffic and provide fault tolerance.
    • It offers multiple integration options, including RESTful APIs, XML, JSON, and Java, making it easy to integrate Solr with different programming languages and frameworks.
    • Solr’s extensible plugin architecture allows developers to customize and extend its functionality to meet specific requirements.
    • Apache Solr has a vibrant and active community of developers who contribute to its ongoing development and provide support through forums, mailing lists, and online resources.
    • It is used by major companies and organizations, such as Netflix, Apple, Instagram, and NASA, to power their search functionality.
    • Apache Solr is continuously updated and improved, with regular releases that introduce new features, enhancements, and bug fixes.

    What are top Apache Solr instruments and tools?

    • Apache Lucene: Apache Lucene is a high-performance, full-featured text search engine library written entirely in Java. It was first released in 1999 and has since become the de facto standard for text retrieval in many applications, including Apache Solr.
    • Apache Tika: Apache Tika is a content analysis toolkit that provides a way to extract metadata and text from various file formats such as Word documents, PDFs, and HTML. It can be seamlessly integrated with Apache Solr to enhance the indexing and searching capabilities.
    • SolrCloud: SolrCloud is a distributed cluster management system for Apache Solr. It allows for automatic sharding and replication of indexes across multiple nodes, providing high availability and fault tolerance. SolrCloud simplifies the process of scaling Solr to handle large volumes of data.
    • SolrJ: SolrJ is the official Java client library for Apache Solr. It provides a convenient and easy-to-use interface for interacting with Solr programmatically. SolrJ allows developers to perform indexing, querying, and administration tasks seamlessly from their Java applications.
    • Apache Solr Admin UI: The Apache Solr Admin UI is a web-based user interface that provides a graphical interface for managing and monitoring Apache Solr instances. It offers a rich set of features, including schema management, query analysis, and real-time monitoring of indexing and search performance.
    • Solr Data Import Handler (DIH): The Solr Data Import Handler is a powerful tool for importing data from various external sources into Apache Solr. It supports importing data from databases, XML files, CSV files, and more. The DIH simplifies the process of ingesting large volumes of data into Solr.
    • Solr Query Parser: The Solr Query Parser is a component of Apache Solr that parses user queries and translates them into internal Solr queries. It supports a wide range of query syntax and provides advanced features such as faceting, highlighting, and spatial search. The query parser is highly customizable and allows for fine-grained control over the search behavior.
    • Solr Collections API: The Solr Collections API is a RESTful API that allows for the management of collections in a SolrCloud cluster. It provides endpoints for creating, deleting, and updating collections, as well as querying the status and configuration of existing collections. The Collections API makes it easy to automate administrative tasks and integrate Solr with other systems.
    • Solr Spatial Search: Solr Spatial Search is a feature of Apache Solr that enables searching and filtering of geospatial data. It supports various spatial operations such as bounding box queries, distance calculations, and spatial filtering. Solr’s spatial search capabilities make it an excellent choice for applications that require location-based search functionality.
    • Apache Solr Plugins: Apache Solr offers a wide range of plugins that extend its functionality and provide additional features. These plugins include query parsers, analyzers, tokenizers, and more. Developers can leverage the plugin architecture to customize and enhance the behavior of Solr to suit their specific requirements.

    Soft skills of a Apache Solr Developer

    Soft skills are essential for Apache Solr Developers as they collaborate with teams, communicate with stakeholders, and solve complex problems. Here are the soft skills required at different levels of experience:


    • Strong problem-solving skills: Ability to analyze and troubleshoot issues related to Apache Solr implementation.
    • Communication skills: Effective verbal and written communication to collaborate with team members and report project progress.
    • Attention to detail: Being meticulous in indexing, configuring, and optimizing Solr documents to ensure accuracy.
    • Teamwork: Ability to work well in a team, contribute ideas, and collaborate on projects.
    • Adaptability: Willingness to learn new technologies and adapt to changing project requirements.


    • Leadership skills: Ability to guide and mentor junior developers, provide technical guidance, and delegate tasks.
    • Project management: Experience in managing Solr projects, setting timelines, and coordinating with stakeholders.
    • Analytical thinking: Strong analytical skills to identify performance bottlenecks and optimize Solr configurations.
    • Problem-solving: Proficiency in troubleshooting complex Solr issues and proposing effective solutions.
    • Collaboration: Ability to work closely with cross-functional teams, including developers, testers, and business analysts.
    • Client management: Experience in understanding client requirements and delivering solutions that meet their needs.
    • Continuous learning: Staying updated with the latest trends and advancements in Solr technology.


    • Strategic thinking: Ability to align Solr implementation with business goals and provide strategic recommendations.
    • Technical expertise: In-depth knowledge of Solr internals, query optimization, and performance tuning.
    • Mentorship: Mentoring junior and middle-level developers, sharing best practices, and guiding their professional growth.
    • Critical thinking: Ability to analyze complex problems, identify root causes, and propose innovative solutions.
    • Communication: Excellent communication skills to effectively convey technical concepts to non-technical stakeholders.
    • Conflict resolution: Ability to handle conflicts within the team and find mutually beneficial solutions.
    • Decision-making: Strong decision-making skills in choosing the right Solr features and techniques for specific use cases.
    • Quality focus: Ensuring high-quality code, documentation, and adherence to coding standards.

    Expert/Team Lead

    • Strategic planning: Ability to devise long-term Solr implementation plans and drive their execution.
    • Team leadership: Experience in leading Solr development teams, setting goals, and managing team dynamics.
    • Innovation: Driving innovation by exploring new Solr features, techniques, and integrations.
    • Collaboration with stakeholders: Building strong relationships with business stakeholders and understanding their needs.
    • Project management: Overseeing multiple Solr projects, ensuring timely delivery, and managing resources effectively.
    • Technical authority: Being the go-to person for Solr-related technical queries and providing expert guidance.
    • Continuous improvement: Advocating for process improvements, automation, and adopting industry best practices.
    • Business acumen: Understanding the business context and aligning Solr solutions to drive business outcomes.
    • Strategic partnerships: Building partnerships with external vendors, Solr community, and industry experts.
    • Risk management: Identifying and mitigating risks associated with Solr implementation and data security.
    • Thought leadership: Contributing to the Solr community through conference presentations, blogs, and knowledge sharing.

    Pros & cons of Apache Solr

    7 Pros of Apache Solr

    • 1. High Performance: Apache Solr is known for its fast and efficient search capabilities. It is built on the Apache Lucene library, which provides powerful indexing and retrieval features.
    • 2. Scalability: Solr is designed to handle large volumes of data and can easily scale horizontally by adding more servers to the cluster. It supports distributed searching and indexing, making it suitable for high traffic websites and big data applications.
    • 3. Full-Text Search: Solr excels at full-text search, allowing users to perform complex searches on large text fields. It supports features like faceted search, highlighting, and spell checking, enhancing the search experience for users.
    • 4. Rich Query Support: Solr offers a wide range of query options, including Boolean operators, wildcard searches, fuzzy searches, and more. It supports advanced filtering, sorting, and grouping of search results, providing flexibility to developers and users.
    • 5. Extensibility: Solr provides a highly extensible architecture, allowing developers to customize and extend its functionality. It supports various plug-ins and integrations with other technologies, such as Apache Kafka, Spark, and Hadoop.
    • 6. Document-oriented: Solr treats data as documents, making it suitable for handling structured and unstructured data alike. It supports various document formats like XML, JSON, and CSV, facilitating easy data ingestion and retrieval.
    • 7. Active Community: Solr has a vibrant and active community of developers and users who contribute to its development and provide support. This ensures regular updates, bug fixes, and a wealth of resources for troubleshooting and learning.

    7 Cons of Apache Solr

    • 1. Complexity: Setting up and configuring Solr can be complex, especially for users with limited experience in search technologies. It requires understanding of various concepts like schema design, query optimization, and distributed architecture.
    • 2. Resource Intensive: Solr can consume significant system resources, especially when dealing with large datasets or handling high query loads. Adequate hardware and infrastructure planning are necessary to ensure optimal performance.
    • 3. Learning Curve: Solr has a steep learning curve for developers who are new to search technologies. Understanding its query syntax, configuration options, and advanced features may require time and effort.
    • 4. Lack of Real-Time Updates: Solr is primarily designed for batch indexing and searching, which may not be suitable for applications requiring real-time updates. Although near-real-time updates are possible, they involve additional configuration and considerations.
    • 5. Limited Machine Learning Capabilities: While Solr provides powerful search capabilities, it lacks built-in machine learning functionality. To incorporate machine learning models, additional integrations or custom implementations may be required.
    • 6. Documentation Gaps: The official Solr documentation, while comprehensive, may have occasional gaps or outdated information. Relying on community forums or external resources may be necessary for addressing specific issues or advanced use cases.
    • 7. Maintenance and Support: As with any software, maintaining and supporting Solr requires dedicated resources. Organizations need to allocate time and expertise for ongoing maintenance, upgrades, and troubleshooting.

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