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@@ -60,7 +60,7 @@ index and re-runs the original query criteria through Lucene's query execution
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planner to get access to low-level match information for the current document.
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This is repeated for every field and every document that needs to be highlighted.
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If you want to highlight a lot of fields in a lot of documents with complex
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-queries, we recommend using one of the other highlighters.
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+queries, we recommend using the `unified` highlighter on `postings` or `term_vector` fields.
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[[fast-vector-highlighter]]
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==== Fast vector highlighter
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@@ -68,8 +68,7 @@ The `fvh` highlighter uses the Lucene Fast Vector highlighter.
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This highlighter can be used on fields with `term_vector` set to
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`with_positions_offsets` in the mapping. The fast vector highlighter:
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-* Is faster especially for large fields (> `1MB`)
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-* Can be customized with a <<boundary-scanners,`boundary_scanner`>>.
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+* Can be customized with a <<boundary-scanners,`boundary_scanner`>>.
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* Requires setting `term_vector` to `with_positions_offsets` which
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increases the size of the index
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* Can combine matches from multiple fields into one result. See
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@@ -95,12 +94,11 @@ disk space than using `term_vectors`.
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* Term vectors. If `term_vector` information is provided by setting
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`term_vector` to `with_positions_offsets` in the mapping, the `unified`
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highlighter automatically uses the `term_vector` to highlight the field.
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-Term vector highlighting is faster for highlighting multi-term queries like
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-`prefix` or `wildcard` because it can access the dictionary of terms for
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-each document, but it can be slower than using the postings list. The `fvh`
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-highlighter always uses term vectors.
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+It's fast especially for large fields (> `1MB`) and for highlighting multi-term queries like
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+`prefix` or `wildcard` because it can access the dictionary of terms for each document.
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+The `fvh` highlighter always uses term vectors.
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-* Plain highlighting. This mode is used when there is no other alternative.
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+* Plain highlighting. This mode is used by the `unified` when there is no other alternative.
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It creates a tiny in-memory index and re-runs the original query criteria through
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Lucene's query execution planner to get access to low-level match information on
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the current document. This is repeated for every field and every document that
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Jim Ferenczi