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The Apache Solr Certification course also provides essential knowledge about the design, use cases, installation, and application of Apache Solr for enhanced scalability and fault tolerance in the search engine. The program includes sections covering the components, analysers, indexers, and searchers of Lucene Library. You will also become familiar with Apache Lucene Library and its significant components. The Apache Solr Certification Training syllabus focuses primarily on the features of Apache Solr.
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You will learn how to enhance the search and navigation features of a search engine create fast and highly scalable search platforms using Apache Solr. Ingersoll said that, by creating Mahout, he hoped to "further unlock the mysteries of Google and companies like it to provide these capabilities to the masses and spur on new innovation in the space" - for those with an interest in this new project, there are both a project plan and an incubator proposal available.The Apache Solr Certification Training Course by Edureka will help you become a certified Apache Solr developer or Apache Solr engineer. I have seen a fair amount of interest already, and hope to have this project underway in the coming month. The goal of this project is to provide commercial quality, large scale machine learning (ML) algorithms built on Hadoop under an Apache license. There are currently some patches in JIRA for Lucene that implement ML algorithms.
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Ingersoll also described a new project named Mahout which he is in the process of launching: That will be a separate project, but may be beneficial to Lucene users. The Lucene community as a whole was also discussed, with Ingersoll indicating that Lucene and Solr have a strong integration, and that Nutch, Tika and Hadoop also enjoyed a fair amount of intercommunication. The 3.0 version will be a major release which will involve moving the codebase to JDK 5 as the minimum supported codebase - the other major features of 3.0 are yet to be determined. The 2.9 release will be a relatively minor, with items being marked as deprecated and other clean-up being performed in preparation for Lucene 3.0. Ingersoll also discussed the future plans for Lucene, saying that the next release would be 2.9. A comprehensive changelog is also available. In addition, 2.3 is intended to be a drop-in replacement for 2.2, with no recompilation required. Easier IndexWriter tuning - The setMaxBufferedDocs method has been supplanted by the more intuitive setRAMBufferSizeMB method.IndexReader reopening - Reopening an IndexReader to capture the latest changes in an index is now much faster with the new reopen() method, which loads in only those index segments which have changed rather than reloading the entire index.Object pooling - Document, Field and Token instances can now be reused during indexing analysis, which both speeds up analysis and reduces the number of allocations during indexing.Improved index management - long pauses which were occasionally seen during indexing due to merging of internal index files have been eliminated, and other approaches to managing the indexing process are now easy to implement.According to Ingersoll, simply switching the existing Lucene 2.2 JAR for a Lucene 2.3 JAR resulted in speed-ups of 500% in indexing performance in several tests which were performed. Ingersoll indicated that the largest change in this release is a new indexing algorithm, which uses new in-memory models to achieve large speed improvements. InfoQ spoke with committer and Project Management Committee (PMC) member Grant Ingersoll to learn more about this release and the future plans for Lucene. The Apache Lucene project, a high-performance full-featured text search engine library written entirely in Java, released version 2.3 today.