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implications on Lucene, Solr and (qualified guess) Elasticsearch search and retrieval speed. It is primarily relevant for people with We have a Solr setup for Netarchive Search at the Royal Danish Library. Below are response times grouped by the magnitude of the As can be seen, the median response time with the patch is about half that of vanilla Solr. The 95% percentile shows that the outliers has Long explanation follows as to what the patch does and why indexes with less than 100M docu*ents are not likely to see the same Lucene is a framework for building search engines. Solr is a search engine build using Lucene. Lucene, and thereby Solr, is known as an As with most things, the truth is a little more complicated. Fast searches are not enough: Quite obviously it also helps to deliver a rich docu*ent representation as part of the search. More advanced features are grouping, faceting, statistics, mas* exports etc. All of docu*ent are stored together, making is low-cost to retrieve the content for multiple fields. mechanism had a RANDOM ACCESS API, meaning that terms could be requested for docu*ents in no particular order. The implementation are needed for a docu*ent with a lower ID that previously requested, a new iterator must be created and the iteration starts from the apparent: Besides a very clean implementation with lower risk of errors, the representation is VERY COMPACT and requires VERY LITTLE HEAP to access. Indeed, the heap requirement for the search nodes in Netarchive Search at the Royal Danish Library was nearly halved when upgrading from Solr 4 to Solr 7. The compact representation is primarily the work of Adrian Grand in LUCENE-7489 When reading the wall of text below, it helps to mentally view the structures as LINKED LISTS: To get to a certain point in the list, all the entries between the current entry and the destination entry needs to be visited. determine whether a docu*ent has a value or not. If it has a value, an index into a value-structure is derived. The second step is to