Introduction to Apache Solr
Apache Solr can be defined as an open-source and fast Java search server for searching the data stored in HDFS. It is capable of improving the search features of the internet sites by allowing them to search full-text and perform indexing in real-time. It searches the data quickly regardless of its format such as tables, texts, locations, etc. This search engine is based on a Java library called Lucene.
SolrCloud is used for distributed Solr application that is used for search and query without a master node to allocate shards, replicas, and Solr nodes. The information within the ZooKeeper will be utilized for decided which servers need to handle the request. Solr Cloud also ensures automatic load balancing and fail-over for queries.
Not only the advanced search, but this technology is highly capable of giving recommendations to the users based on their previous searches. Below are the reasons which have caused its widespread adoption in the big data industry:
- Contains advanced search capabilities
- Consists of a broad administration interfaces
- Real-time indexing speeds up the search process
- Fault-tolerant and extremely scalable
Apache Solr is one of the leading technologies performing crucial operations in the big data world. Following are some of the real-world Apache Solr applications that will give you a fair idea about how it is being implemented by diverse companies to meet the ever-increasing needs of the technology landscape :
|A generic integration of Solr to the PHP projects.
|Solr is used to search large digital libraries.
|Implements auto suggestions in the search engine.
|Spatial search applications
|Finding bioinformatics, finger-print searching, facial recognition, etc.
|Duck Duck Go
|‘Zero-Click’ for showing the most relevant result.
|Clustering Support with Carrot2
|Near Real-Time Search
|The search engine takes less than 60 seconds after getting indexed
|Solandra = Solr + Cassandra
|Facilitates searching in the Facebook inbox.
|Reduces the duplicates and redundancy through filters.
Apache Solr architecture
Before knowing its merits and demerits, it is essential to learn the way Solr works. We all know that searching in a search engine is a process of fetching the documents that are required by the user. Now it is up to the user as to what he requires, the entire document or certain information from a file.
A Solr core lets you index different structures of data in the same server and offers you more control over how your data is presented to a different audience.
It performs the following operations to search a document in the database:
Indexing – The documents stored in the database can be in any format, may contain any kind of content, and may have several vocabularies. These documents need to be transformed into a machine-understandable format in order to be shown in the search results. This process is called Indexing.
Querying – The information required by the user is expressed in various terms such as keywords, images, navigation, etc. The search engine tries to understand the information required.
Matching the user requirement with the documents – The search engine tries to match the user’s query with the documents stored in the databases with the help of mapping.
Ranking the results – Since the set of results can be quite large it can be tedious for the user to parse. Therefore, the search engine sets the results in an order ranking the most relevant result as the best. This in turn appears at the top of all the outcomes and so on as per the rankings.
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Elasticsearch Vs Apache Solr
There is a broad user base for both the search engines but there are a lot of differences between the search engines. When it comes to ease of deployment, usability, and functionality there are a lot of differences between the two search engines.
When you have to search data that is growing tremendously you need to understand that there is a significant challenge to parse all that data and make sense of it all. This is where the Apache Solr search engine comes into the picture.
Comparing Solr with Elasticsearch
|Java, PHP, Perl, Python
|Depends on Elasticsearch nodes
|Depends on the Zookeeper server
|Web admin interface
|Available with Solr
|Separate app needed
About Apache Solr
Today with the ever-increasing amounts of data there is a need to have the right search engine for parsing all that data at breakneck speeds. One of the most powerful search engines that are also open source is the Apache Lucene Solr search engine.
Apache Solr is a user-friendly search engine that comes from the Lucene project. The entire Lucene framework is built on the Java programming language. Apache Lucene has been around for a long time now and it is one of the most important search engines even today.
All the Solr configuration files are contained within the Solr core which is a running instance of the Lucene index. There could be one or more Solr cores for a Solr application. The various applications like indexing and analyzing are performed using the Solr core.
Apache Solr is a user-friendly search engine that is offered by the Lucene project. Some of the features of this search engine include distributed search, load balancing, automated failover, and recovery. It has features like extreme reliability, scalability, and fault-tolerance among other strengths. Solr can index and search multiple sites at a very fast rate.
Solr is able to parse data that includes from various sources like an XML files, databases, tabular data, comma-separated values, PDF files, Microsoft Word, and others. Elasticsearch on the other hand is able to take data from various sources like DynamoDB, ActiveMQ, Git, Kafka, MongoDB, and so on.
When it comes to searching, the Apache Solr is more adept at searching text files while Elasticsearch is more useful in deploying analytical querying, filtering, and grouping. The elasticsearch can be made more efficient through the method of decreasing memory footprint, CPU usage, and so on. Both the Apache Solr and the Elasticsearch are using various analyzers and tokenizers that can break the text into tokens and texts that can be later indexed. When using the analyzer with the elasticsearch the output of one analyzer becomes the input for the next analyzer. Solr does not deploy such a feature.
Solr is much more oriented toward text search while Elasticsearch is often used for analytical querying, filtering, and grouping. The team behind Elasticsearch is always trying to make these queries more efficient (through methods including the lowering of memory footprint and CPU usage) and improve performance at both the Lucene and Elasticsearch levels. When comparing both, it’s clear that Elasticsearch is a better choice for applications that require not only text search but also complex time series search and aggregations.
The major features of the Apache Solr search engine include :
- You can switch between schema and schemaless mode with ease
- You can even index rich content through the use of powerful extensions
- Slice and dice your data using powerful algorithms
- Location-based search is simple and effective
- Text search can be done in an advanced and configurable manner
- You can improve performance using built-in caching
- Optimizing the performance for parsing any kind of data.
Both search engines use various analyzers and tokenizers that break up text into terms or tokens that are later indexed. Elasticsearch allows you to specify the query analyzer chain, which is comprised of a sequence of analyzers or tokenizers on a per-document or per-query basis. This helps when you have multiple analyzers attached so that the output of one analyzer becomes the input of a second analyzer. In contrast, Solr does not support this feature.
Both the Apache Solr and the Elasticsearch are using the stopwords and synonyms that are matching the document. The join index in a Solr has a single shard and this is replicated across the nodes for searching the inter-document relationship.
The elasticsearch is an open-source search engine that is having full-text capabilities, which is fully distributed and is multitenant as well. It is based on the RESTful API and is extensively used for rich text data. The way in which the elasticsearch works is through shards that have many replicas. Within the elasticsearch node, there are one or more shards. the task of the elasticsearch engine is to be a coordinator for delegating the operations.
Some of the main features of Elasticsearch include :
- It has an analytical search feature
- It can be used for grouping and aggregation
- It is a multi-tenant engine
- It is distributed engine.
Future prospect of Apache Solr
Having familiarity with the process and features of Apache Solr, one would definitely like to know the future prospect of Apache Solr before learning it. The following discussion will help you make your decision:
Solr is not only helpful in the domain of software technology but has a huge scope in scientific applications, such as searching for a DNA pattern. Similarly, it can also be used in scientific research, where an organism could be found by searching for certain genes or nucleotide sequences.
Solr can also refine its searches by allowing the users to drag and drop the websites and query about them. Also, Solr can be built into the desktop search to parse and filter the files more quickly.
While these are some ideas for Solr to come up with in the future, there is a broad spectrum of opportunities available for it to modify and improve its existing features.
Who is the right audience to learn Apache Solr?
The candidates aspiring to become Solr Developers, Project Managers, Mainframe Professionals, System Administrators, Search Analysts, etc., learning Apache Solr will help you launch your career in this domain in an efficient manner.
Especially the candidates who have thorough knowledge about Hadoop and HBase have a great opportunity waiting for you.
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How Apache Solr will help you in your career?
According to Dice, in the year 2013, there were about 318 Solr jobs available. Many of these job listings had a phrase like, “Solr experience is a must.”
As of 19th November 2016, the trend showing the average salary of Solr professionals can be estimated from the following graph:
This clearly depicts that the technology domain is craving big data professionals with hands-on experience in this technology. Therefore if you want to make your career in the big data world, learn this technology and take a big step towards your success.
With the massive amounts of data-generating each second, the requirement for big data professionals has also increased making it a dynamic field. Numerous technologies are competing with each other offering diverse facilities, from which Apache Solr is a trending one. Its ability to improve and speed up the search engine has made it the choice of top companies.
Not only the big ones, but the medium and small-sized companies are improving their operations by implementing Solr in their architectures. Therefore the job opportunities are not limited to the top brands, but many other firms are offering attractive packages to professionals having a good grasp of this technology. Learning this technology will give you a definitive advantage in your career.
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