Frame of Reference and Roaring Bitmaps
Postings listsWhile it may surprise you if you are new to search engine internals, one of the most important building blocks of a search engine is the ability to efficiently compress and quickly decode sorted lists of integers. Why is this useful? As you may know, Elasticsearch shards, which are Lucene indices under the hood, split the data that they store into segments which are regularly merged
Roaring Bitmaps
A better compressed bitset Roaring bitmaps are compressed bitmaps. They can be hundreds of times faster. Grab one of our research papers Roaring Bitmaps on GitHub Widely used Roaring is found in Google Procella: YouTube’s SQL Engine, Apache Lucene and derivative systems such as Solr and Elasticsearch, Apache Druid, Apache Spark, Apache Hive, Apache Tez, Apache Zeppelin, Apache Doris, Apache Carbon
Lucene: The Good Parts | Parse.ly
Content Analytics Dashboard Build a better, smarter content strategy Content Conversion Engine Prove your content's value Content API for Developers Personalize your content experiences Data Pipeline for Analysts Stream your real-time content data Content Management System Publish better content, faster Before MongoDB, before Cassandra, before “NoSQL”, there was Lucene. Did you know that Doug Cutt
Fast Compact Sparse Bit Sets
Imagine you need a relatively compact data structure for quickly checking membership of mostly-consecutive non-negative integers. (If this sounds really specific, it is because it is precisely what I needed for a particular project.) The Ruby standard library contains a Set class which may be a good starting point. Set is actually implemented as a Hash with the Set elements as keys and true as the
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http://euphonious-intuition.com/2013/05/all-about-elasticsearch-filter-bitsets/
Working with large sets –
CONCISE: COmpressed 'N' Composable Integer SEt
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