Lucene indexing strategy for documents that change often

I'm integrating search functionality into a desktop application and I'm using vanilla Lucene to do so. The application handles (potentially thousands) of POJOs each with its own set of key/value(s) properties. When mapping models between my application and Lucene I originally thought of assigning each POJO a Document and add the properties as Fields. This approach works great as far as indexing and searching goes but the main downside is that whenever a POJO changes its properties I have to reindex ALL the properties again, even the ones that didn't change, in order to update the index. I have been thinking of changing my approach and instead create a Document per property and assign the same id to all the Documents from the same POJO. This way when a POJO property changes I only update its corresponding Document without reindexing all the other unchanged properties. I think that the graph db Neo4J follows a similar approach when comes to indexing, but I'm not completely su开发者_开发问答re. Could anyone comment on possible impact on performance, querying, etc?

It depends fundamentally on what you want to return as a Document in a search result.

But indexing is pretty cheap. Does a changed POJO really have so many properties that reindexing them all is a major problem?

If you only search one field in every search request, splitting one POJO to several documents will speed up reindexing. But it will cause another problem if search one multiple fields, a POJO may appear many times. Actually, I agree with EJP, building index is very fast in small dataset.