One of the many great things about Solr is that it allows you to add dynamic fields – you can define a certain pattern that a field will have to follow, but it can then use any field name that matches the pattern.
We’ve been using one such dynamic field to add a sort field for our documents:
This would allow us to sort by this field to get the priority of our documents in this particular category and subcategory. A document would contain somewhere between 1 and 15 such fields. The total selection of unique field names is somewhere around 1200 across all documents.
Be small, be happy
As long as our collection were quite small (<10k documents) this scheme worked great. When our collection grew to around 500k documents, we started seeing out of memory errors quite often. At the worst rate we got an out of memory exception every 30 minutes, and had to restart the Solr server. Performance didn't suffer, but obviously we couldn't continue restarting servers until we got bored. After removing a few other possible issues (such as our stable random sort) I were rather stumped that things didn't improve. The total amount of data in our dynamic fields were rather low, somewhere around 2.5 - 3.5m integers, or possibly somewhere around 50-70MB in total. The JVM should be able to fit everything about these fields in memory and query them for the fields we're trying to find, but a heap dump of the jvm just before it hit the out of memory exception revealed that we were getting quite a few GBs of Lucene's FieldCache objects. These objects cache the value of a field for the total set of documents available in the index, and you're sadly not able to tune this cache through the Solr configuration (at least not for 1.4 as far as I could find).
Less Dynamic Fields, More Manual Labor
After pondering this issue a bit I came to the conclusion that our problem was related to the dynamic fields we had, and the fact that we used them for sorting. Lucene / Solr keeps one set of field caches for each field when it’s used for sorting, to avoid having to do duplicate work later. For us, this meant that each time we sorted a new field, an array had to be created with the size of the total document set. As long as we just had 10k documents, these arrays were small enough that we had enough memory available – when the document set grew to almost 500k documents, not so much.
This means that the total memory required for field caches will be limited by DocumentsInIndex * FieldsSortedBy. As long as our DocumentsInIndex were just 10k, the available memory to the jvm was enough to keep sorting by the number of fields we did. When the number of documents grew, the memory usage grew by the same factor and we got our OutOfMemoryException.
Our solution could probably be more elegant, but currently we’ve moved the sorting to our application layer instead of the data provider layer. We’re requesting the complete set of hits from the Solr-server in the category anyway, so we’re able to sort it in the application – and by using a response format other than XML we’re also doing it rather quickly. This means that we’re not using sorting at all, and are only querying against one multivalued field to see if the category key is present there at all.
Note: Other solutions we considered were to divide our index into several Solr cores. This would allow us to keep the number of documents in each core low, and therefor also keep the fieldcache size in check. We know that each category could very well live on just on core as we won’t be mixing it with data from the other cores (and for that we could keep a separate core with all the documents, just not use it for searching across dynamic fields). We dropped this plan because of the rather worrying increase in complexity in our Solr installation. This could however help in your own case. :-)