Spark can be used to improve the performance of importing and searching large datasets in Solr. Data can be imported from HDFS files into Solr in parallel using Spark, speeding up the import process. Spark can also be used to stream data from Solr into RDDs for further processing, such as aggregation, filtering, and joining with other data. Techniques like column-based denormalization and compressed storage of event data in Solr documents can reduce data volume and improve import and query speeds by orders of magnitude.