Many modern apps have the ability to search as a main feature. They must allow the end-user to quickly locate what they’re looking for while encompassing massive quantities of data. To incorporate search features, DevOps must look beyond conventional databases with complicated and non-user-friendly SQL query-based solutions. That’s where Apache Solr comes in, with features like autosuggest in search fields, range or category browsing with facets, and more to help users have a better search experience. So let’s get started and “strip” Solr down to its bare essentials. Learn about Apache Solr, including what it is, why it’s important, and how it works.
What is Apache Solr?
Apache Solr is an open-source, high-performance Java search server for searching data stored in HDFS. It has the ability to improve the search capabilities of websites by enabling full-text searches and real-time indexing. It easily scans data in any format, including tables, texts, and locations. This search engine is built using the a Java library.
Solr is therefore a much more sophisticated version of the Lucene search engine. It comes with more features and a scalability-friendly interface. REST clients, wget, curl, Chrome’s POSTMAN, native clients, and other methods are used to communicate with Solr. Both XML and JSON APIs are supported.
Why Use Apache Solr?
When we send a question to the Solr search engine, it divides it into different pieces/entities and then compares it to the document’s inverted index, which was generated earlier. Solr is a search platform that is robust, consistent, and fault-tolerant, with a rich collection of core functions that enable you to enhance both user experience and data modeling. Spell testing, geospatial search, faceting, and auto-suggest are examples of functionalities that help deliver a good user experience, while backend developers can benefit from features like joins, Clustering, the ability to import rich document formats, and several other features are available.
Powerful Full-Text Search Capabilities
Solr provides additional near-real-time scanning capabilities across a variety of data types, including fielded search, Boolean queries, word queries, fuzzy queries, spell check, wildcards, joins, grouping, auto-complete, and more.
Administration Interfaces that are Comprehensive
Solr comes with a built-in sensitive user interface that lets you manage logging, add, delete, update, and search documents.
Extensible Plugin Architecture
Solr offers extension points that make it simple to add index and query time plugins to the system.
- SSL for HTTP traffic encryption between Solr clients and Solr, as well as between nodes
- Authentication based on basic and Kerberos
- Users, roles, and permissions are defined using authorization APIs.
It’s important to understand the basics of how Solr works before diving into the following are some of the key words associated with the Solr cloud:
Node: Each Solr instance is called a node in the Solr cloud.
Cluster: When all of the nodes in the environment are combined, they form a cluster.
Collection: A cluster creates a logical index called a collection.
Shard: It’s a section of a list that has one or more index replicas.
Replica: A replica is a copy of the shard that runs in a node in the Solr core.
Leader: It’s also a copy of the shard that distributes the Solr Cloud’s queries to the remaining replicas.
Zookeeper: It’s an Apache project that Solr cloud can use to centralize configuration and coordination for cluster management and selection of a leader.
How Does Solr Work?
Solr is able provide the brief search results since it scans an index rather than searching the text directly. Since it inverts a page-centric data structure (page->words) to a keyword-centric data structure (word->pages), this type of index is known as an inverted index. Solr collects, holds, and indexes documents from a variety of sources and makes them searchable in real time. It uses a three-step method that includes indexing, querying, and rating the results — all in near real-time, despite the fact that it can handle massive amounts of data.
Indexing– The database’s documents can be in any format, contain any type of material, and have several vocabularies. In order to appear in the search results, these documents must be converted into a device format. Indexing is the term for this procedure.
Querying –The user’s knowledge needs are expressed in a variety of ways, including keywords, images, navigation, and so forth. The search engine makes an attempt to grasp the information requested.
Matching the User Requirement with the Documents –With the help of mapping, the search engine attempts to align the user’s query with the documents contained in the databases.
Solr Use Cases & Applications
Solr is a multi-purpose search engine that has proved to be crucial to business operations. But apart from its strong search capabilities, Solr is an excellent data store for analytics. Solr is thus the backbone for applications requiring sophisticated search and analytics in every area, from marketing to energy to education to HR, healthcare, retail, real estate, and many more.
Solr is used by companies such as Apple, Netflix, Instagram, NASA, Zappos, Goldman Sachs, and the White House, to name a few. It’s actually used by a lot of Fortune 500 firms. Solr is widely used for website indexing and search, as well as business search, since it can index and search documents and email attachments. It is useful not only in the IT domain, but also in scientific applications such as searching for DNA patterns or in scientific research to find specific genes or nucleotide sequences to classify an individual.
Created the list of use cases to help you better understand how you can use Solr features in the BI infrastructure:
Hiring managers are often forced to sift through hundreds of resumes in order to select a dozen or so candidates to interview. If you work in HR, you spend a lot of time looking over resumes. Solr is much better suited to review and sort resumes submitted for job openings than conventional databases, which have limited text processing capabilities.
It’s an obvious application for Solr. It can be fed native documents, such as PDF, Word, XML, or plain text, and it will index them. It can easily process unstructured text thanks to its creation as a search engine. It can extract key terms and phrases, detect languages, and deal with different word types transparently. Periodic evaluations after a hire can be combined with keywords, key phrases, and other metadata extracted from the source resume to create a predictive model that can be used in subsequent hiring processes.
As a store chain grows from a local to a regional level, new outlets improve service to existing customers while also attracting new ones. A strategic manager must make the age-old decision about the location of a company storefront. Solr has advanced features that can support here as well. Its geospatial capabilities allow the strategic planner to plot current and potential customers on a map, as well as easily factor distance into the ranking of each potential location.
Log file Analytics
Parts assembly is tracked from the time they reach inventory until they leave the line completely assembled. The assembly line’s computers all keep track of log entries. They can submit entries with a variety of structures. It’s possible the line is one of dozens or hundreds. With that much data, you’ll need ingestion and search systems that are both efficient and scalable. Solr can scale to nearly infinite volumes in SolrCloud mode. Solr can index incredibly large amounts of data when used in conjunction with an ingestion method like Apache Nifi.
Monitor Solr with Sematext
Identifying key metrics for Solr, gathering metrics and logs, and connecting it in a meaningful way are all part of comprehensive Solr monitoring. We’ve shown you how to keep track of Solr metrics and logs in one spot in this article. It provides you with insight into the health and enforcement of your application, allowing you to react quickly and accurately if a red flag appears. You can easily start tracking Solr alongside metrics, logs, and distributed request traces from all of the other technologies in your infrastructure using other open-source integrations like MySQL or Kafka. With a free Sematext beta, you can get a better understanding of Solr right now.
Apache Solr’s Ecosystem:
Apache Solr is supported by a massive open-source group of knowledgeable users. Solr accepts contributions from everyone. New Solr developers and code committers are chosen solely on the basis of their abilities. Solr has a healthy project pipeline with a number of well-known companies involved. Solr has been around for a long time and has a well-developed community with a large user base.