In 2015, they evaluated Apache Solr and Elasticsearch and decided to build their own (Firefly). They said, other solutions did not scale. So, instead of contributing to scaling (like Apple and Bloomberg and Cloudera did), they went the other way. Now, they seem to be doing it again (at least they are using Tika).

In a meanwhile, Solr implemented most of the features they are describing in their architecture document.

Specifically:

1)General scaling: https://lucene.apache.org/solr/guide/7_5/introduction-to-sca... (using ZooKeeper and SolrCloud)

2) Search Ranking and click-data training: https://lucene.apache.org/solr/guide/7_5/learning-to-rank.ht... (Contributed by Bloomberg)

3) Offline builds with substitution into production: https://lucene.apache.org/solr/guide/7_5/collections-api.htm...

4) Near-Real-Time: https://lucene.apache.org/solr/guide/7_5/near-real-time-sear...

5) Sharding specifically: https://lucene.apache.org/solr/guide/7_5/shards-and-indexing...

6) Extraction pipeline, they are doing all together. We have:

a) pre-Solr extraction (usually done in a stand-alone client, though we do include Tika and DataImportHandler for quick start),

b) in-Solr pre-schema processing with Update Request Processors https://lucene.apache.org/solr/guide/7_5/update-request-proc...

c) Actual per-field text processing pipelines, separate both for index and query (they call query part later "query understanding": https://lucene.apache.org/solr/guide/7_5/understanding-analy... Also, my own: http://www.solr-start.com/info/analyzers/

7) Pluggable internal index formats? Here is the latest (FST50): https://lucene.apache.org/solr/guide/7_5/the-tagger-handler....

8) Update system configuration live, over API? https://lucene.apache.org/solr/guide/7_5/configuration-apis.... https://lucene.apache.org/solr/guide/7_5/schema-api.html

9) Tolerate small failures, but abort if something is definitely not right: http://www.solr-start.com/javadoc/solr-lucene/org/apache/sol...

10) Retrieval root: https://lucene.apache.org/solr/guide/7_5/solrcloud-query-rou...

11) Retrieval leaf: That's Solr's basic shard/core

12) The inverted and forward indexes look like standard Lucene index and maybe docValues: https://lucene.apache.org/solr/guide/7_5/docvalues.html

13) Search orchestrator seems to be a couple of features on top of Solr's existing routing linked earlier. There were individual approaches/3rd-party modules doing some of these (shadow, federation, ACL). Some of this is definitely unique to Dropbox though.

14) Precision vs Recall vs Ranking is too many links, but there is a whole book on this: https://www.manning.com/books/relevant-search (mostly about Elasticsearch, but Solr has added some new features recently to make it even better)

15) BM25, we had it back in 2015: https://opensourceconnections.com/blog/2015/10/16/bm25-the-n...

16) (Future for Nautilus): Distance Based embeddings, such as Word2Vec. Commercial offering on top of Solr has it: https://lucidworks.com/2016/11/16/word2vec-fusion-nlp-search... but I remember discussion for Solr as well

17) (Future for Nautilus): Searching images/videos/etc: https://lucidworks.com/2015/08/28/shutterstock-searches-35-m...

And a lot more (Solr Reference manual is more than 1300 pages....).

Obviously, this is a bit of a dig at a Dropbox reinventing the wheel again (or perhaps this time actually using Lucene, but forgetting to attribute it so far).

But more importantly, it is a message to others that got excited by their architecture post. You can have a similar battle-tested system for yourself, for free. And if something is not perfect, you can fix it and help the rest of the world too. We are always happy to see new contributors.

Finally, if you know Apache Solr well, it is not just Dropbox you can work for, but also Lucidworks, Bloomberg, Cloudera, Alfresco, Shutterstock, Dice, CareerBuilder, and many others.