We offer installation services for Solr and its required applications and tools across both high and low environments. High environment installations involve clustering, which necessitates specific platform and environment preparations. Lower environment setups may include clustering, single-node configurations, or containerized solutions (e.g., Docker). For high environments, Solr installations can be configured as either a Master-Slave (Replication) setup or using Solr Shards, ensuring failover, fault tolerance, and high availability.

Additionally, we manage the entire lifecycle of the installation process. The complexity and intensity of these installations can vary significantly based on business requirements. While some clients may have this process well-defined and planned, others may benefit greatly from our expertise in overseeing all aspects of the installation.

We provide services for upgrading to the latest version of Solr (6.x, 7.x, 8.x, 9.x) along with any necessary upgrades to related applications and tools.

The upgrade process requires thorough analysis and careful planning, ensuring a deep understanding of existing customizations, application modifications, and platform changes. It’s also essential to assess the impact of the upgrade on various business units. While some clients may have these considerations addressed, others may find our expertise invaluable for ensuring a successful upgrade process.

• Establishing and maintaining monitoring tools.
• Troubleshooting errors and exceptions within Solr.
• Executing upgrades, patches, and customization deployments, including any scheduled activities that necessitate shutdowns and restarts.
• Producing regular usage and statistics reports.

• Development of automated Solr Health Checks.
• Schema Configuration, query reviews and optimization.
• Performance tuning to enhance query response and indexing times.
• Creation of ingestion engines or tools for data intake from various sources.
• Enhancement of keyword relevancy.
• Implementation of features such as typeahead, spellchecker, faceting, pivot facets, statistics, and boosting.
• Creation of filter queries, sorting, multilingual search, cognitive search, and natural language processing.
• Construction of an indexing pipeline for bulk and real-time indexing of large-scale datasets.