Limit search results with RANK

Applies to: SQL Server Azure SQL Database Azure SQL Managed Instance

The CONTAINSTABLE and FREETEXTTABLE functions return a column named RANK that contains ordinal values from 0 through 1000 (rank values). These values are used to rank the rows returned according to how well they match the selection criteria. The rank values indicate only a relative order of relevance of the rows in the result set, with a lower value indicating lower relevance. The actual values are unimportant and typically differ each time the query is run.


The CONTAINS and FREETEXT predicates don't return any rank values.

The number of items matching a search condition is often large. To prevent CONTAINSTABLE or FREETEXTTABLE queries from returning too many matches, use the optional top_n_by_rank parameter, which returns only a subset of rows. top_n_by_rank is an integer value, n, which specifies that only the n highest ranked matches are to be returned, in descending order. If top_n_by_rank is combined with other parameters, the query could return fewer rows than the number of rows that actually match all the predicates.

SQL Server orders the matches by rank and returns only up to the specified number of rows. This choice can result in a dramatic increase in performance. For example, a query that would normally return 100,000 rows from a table of 1,000,000 rows are processed more quickly if only the top 100 rows are requested.

Examples of Using RANK to limit search results

Example A: Searching for only the top three matches

The following example uses CONTAINSTABLE to return only the top three matches.

USE AdventureWorks2022;

FROM Person.Address AS A
INNER JOIN CONTAINSTABLE(Person.Address, AddressLine1, 'ISABOUT ("des*",
    Rue WEIGHT(0.5),
    Bouchers WEIGHT(0.9))', 3) AS K
    ON A.AddressID = K.[KEY];

Here is the result set.

RANK        Address                          City
----------- -------------------------------- ------------------------------
172         9005, rue des Bouchers           Paris
172         5, rue des Bouchers              Orleans
172         5, rue des Bouchers              Metz

Example B: Searching for the top five matches

The following example uses CONTAINSTABLE to return the description of the top five products where the Description column contains the word "aluminum" near either the word light or the word lightweight.

USE AdventureWorks2022;

SELECT FT_TBL.ProductDescriptionID,
FROM Production.ProductDescription AS FT_TBL
INNER JOIN CONTAINSTABLE(Production.ProductDescription,
    Description, '(light NEAR aluminum) OR (lightweight NEAR aluminum)', 5) AS KEY_TBL
        ON FT_TBL.ProductDescriptionID = KEY_TBL.[KEY];

How search query results are ranked

Full-text search in SQL Server can generate an optional score (or rank value) that indicates the relevance of the data returned by a full-text query. This rank value is calculated on every row and can be used as an ordering criteria to sort the result set of a given query by relevance. The rank values indicate only a relative order of relevance of the rows in the result set. The actual values are unimportant and typically differ each time the query is run. The rank value doesn't hold any significance across queries.

Statistics for ranking

When an index is built, statistics are collected for use in ranking. The process of building a full-text catalog doesn't directly result in a single index structure. Instead, the Full-Text Engine for SQL Server creates intermediate indexes as data is indexed. The Full-Text Engine then merges these indexes into a larger index as needed. This process can be repeated many times. The Full-Text Engine then conducts a "master merge" that combines all of the intermediate indexes into one large master index.

Statistics are collected at each intermediate index level. The statistics are merged when the indexes are merged. Some statistical values can only be generated during the master merging process.

While SQL Server ranks a query result set, it uses statistics from the largest intermediate index. This depends on whether intermediate indexes are merged or not. As a result, ranking statistics can vary in accuracy if the intermediate indexes aren't merged. This explains why the same query can return different rank results over time as full-text indexed data is added, modified, and deleted, and as the smaller indexes are merged.

To minimize the size of the index and computational complexity, statistics are often rounded.

The following list includes some commonly used terms and statistical values that are important in calculating rank.

Term / value Description
Property A full-text indexed column of the row.
Document The entity that is returned in queries. In SQL Server this corresponds to a row. A document can have multiple properties, just as a row can have multiple full-text indexed columns.
Index A single inverted index of one or more documents. This might be entirely in memory or on disk. Many query statistics are relative to the individual index where the match occurred.
Full-Text Catalog A collection of intermediate indexes treated as one entity for queries. Catalogs are the unit of organization visible to the SQL Server administrator.
Word, token or item The unit of matching in the full-text engine. Streams of text from documents are tokenized into words, or tokens by language-specific word breakers.
Occurrence The word offset in a document property as determined by the word breaker. The first word is at occurrence 1, the next at 2, and so on. In order to avoid false positives in phrase and proximity queries, end-of-sentence and end-of-paragraph introduce larger occurrence gaps.
TermFrequency The number times the key value occurs in a row.
IndexedRowCount Total number of rows indexed. This is computed, based on counts maintained in the intermediate indexes. This number can vary in accuracy.
KeyRowCount Total number of rows in the full-text catalog that contain a given key.
MaxOccurrence The largest occurrence stored in a full-text catalog for a given property in a row.
MaxQueryRank The maximum rank, 1000, returned by the Full-Text Engine.

Rank computation issues

The process of computing rank, depends on many factors. Different language word breakers tokenize text differently. For example, the string "dog-house" could be broken into "dog" "house" by one word breaker and into "dog-house" by another. This means that matching and ranking varies based on the language specified, because not only are the words different, but so is the document length. The document length difference can affect ranking for all queries.

Statistics such as IndexRowCount can vary widely. For example, if a catalog has 2 billion rows in the master index, then one new document is indexed into an in-memory intermediate index, and ranks for that document based on the number of documents in the in-memory index could be skewed compared with ranks for documents from the master index. For this reason, we recommended that after any population that results in large number of rows being indexed or reindexed, you merge the indexes into a master index using the ALTER FULLTEXT CATALOG ... REORGANIZE Transact-SQL statement. The Full-Text Engine also automatically merges the indexes based on parameters such as the number and size of intermediate indexes.

MaxOccurrence values are normalized into 1 of 32 ranges. This means, for example, that a document 50 words long, is treated the same as a document 100 words long. Following is the table used for normalization. Because the document lengths are in the range between adjacent table values 32 and 128, they're effectively treated as having the same length, 128 (32 < docLength <= 128).

{ 16, 32, 128, 256, 512, 725, 1024, 1450, 2048, 2896, 4096, 5792, 8192, 11585,
16384, 23170, 28000, 32768, 39554, 46340, 55938, 65536, 92681, 131072, 185363,
262144, 370727, 524288, 741455, 1048576, 2097152, 4194304 };


CONTAINSTABLE ranking uses the following algorithm:

StatisticalWeight = Log2( ( 2 + IndexedRowCount ) / KeyRowCount )
Rank = min( MaxQueryRank, HitCount * 16 * StatisticalWeight / MaxOccurrence )

Phrase matches are ranked just like individual keys except that KeyRowCount (the number of rows containing the phrase) is estimated and can be inaccurate and higher than the actual number.

Rank of NEAR

CONTAINSTABLE supports querying for two or more search terms in proximity to each other by using the NEAR option. The rank value of each returned row is based on several parameters. One major ranking factor is the total number of matches (or hits) relative to the length of the document. Thus, for example, if a 100-word document and a 900-word document contain identical matches, the 100-word document is ranked higher.

The total length of each hit in a row also contributes to the ranking of that row, based on the distance between the first and last search terms of that hit. The smaller the distance, the more the hit contributes to the rank value of the row. If a full-text query doesn't specify an integer as the maximum distance, a document that contains only hits whose distances are greater than 100 logical terms apart, has a ranking of 0.


CONTAINSTABLE supports querying for weighted terms by using the ISABOUT option. ISABOUT is a vector-space query in traditional information retrieval terminology. The default ranking algorithm used is Jaccard, a widely known formula. The ranking is computed for each term in the query and then combined, as described in the following algorithm.

ContainsRank = same formula used for CONTAINSTABLE ranking of a single term (above).
Weight = the weight specified in the query for each term. Default weight is 1.
WeightedSum = Σ[key=1 to n] ContainsRankKey * WeightKey
Rank =  ( MaxQueryRank * WeightedSum ) / ( ( Σ[key=1 to n] ContainsRankKey^2 )
      + ( Σ[key=1 to n] WeightKey^2 ) - ( WeightedSum ) )


FREETEXTTABLE ranking is based on the OKAPI BM25 ranking formula. FREETEXTTABLE queries add words to the query via inflectional generation (inflected forms of the original query words); these words are treated as separate words, with no special relationship to the words from which they were generated. Synonyms generated from the Thesaurus feature are treated as separate, equally weighted terms. Each word in the query contributes to the rank.

Rank = Σ[Terms in Query] w ( ( ( k1 + 1 ) tf ) / ( K + tf ) ) * ( ( k3 + 1 ) qtf / ( k3 + qtf ) ) )
w is the Robertson-Sparck Jones weight.
In simplified form, w is defined as:
w = log10 ( ( ( r + 0.5 ) * ( N - R + r + 0.5 ) ) / ( ( R - r + 0.5 ) * ( n - r + 0.5 ) )
N is the number of indexed rows for the property being queried.
n is the number of rows containing the word.
K is ( k1 * ( ( 1 - b ) + ( b * dl / avdl ) ) ).
dl is the property length, in word occurrences.
avdl is the average length of the property being queried, in word occurrences.
k1, b, and k3 are the constants 1.2, 0.75, and 8.0, respectively.
tf is the frequency of the word in the queried property in a specific row.
qtf is the frequency of the term in the query.