/// <summary> Create weight in multiple index scenario.
///
/// Distributed query processing is done in the following steps:
/// 1. rewrite query
/// 2. extract necessary terms
/// 3. collect dfs for these terms from the Searchables
/// 4. create query weight using aggregate dfs.
/// 5. distribute that weight to Searchables
/// 6. merge results
///
/// Steps 1-4 are done here, 5+6 in the search() methods
///
/// </summary>
/// <returns> rewritten queries
/// </returns>
public /*protected internal*/ override Weight CreateWeight(Query original)
{
// step 1
Query rewrittenQuery = Rewrite(original);
// step 2
ISet <Term> terms = Lucene.Net.Support.Compatibility.SetFactory.CreateHashSet <Term>();
rewrittenQuery.ExtractTerms(terms);
// step3
Term[] allTermsArray = terms.ToArray();
int[] aggregatedDfs = new int[terms.Count];
for (int i = 0; i < searchables.Length; i++)
{
int[] dfs = searchables[i].DocFreqs(allTermsArray);
for (int j = 0; j < aggregatedDfs.Length; j++)
{
aggregatedDfs[j] += dfs[j];
}
}
var dfMap = new Dictionary <Term, int>();
for (int i = 0; i < allTermsArray.Length; i++)
{
dfMap[allTermsArray[i]] = aggregatedDfs[i];
}
// step4
int numDocs = MaxDoc;
CachedDfSource cacheSim = new CachedDfSource(dfMap, numDocs, Similarity);
return(rewrittenQuery.Weight(cacheSim));
}
// inherit javadoc
public override void ExtractTerms(System.Collections.Generic.ISet <Term> terms)
{
Query.ExtractTerms(terms);
}