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?

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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?

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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.