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    Platform For AI:String similarity - Top N

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    Platform For AI:String similarity - Top N

    Last Updated:Mar 11, 2026

    Calculates string similarity between input strings and mapping table entries, then returns the top N matches for each input string.

    Configuration

    Configure the component on the Designer workflow page or using PAI commands.

    Configure via GUI

    Configure parameters on the Designer workflow page.

    Tab

    Parameter

    Description

    Field settings

    Columns to append from input table

    Columns from the input table to include in the output.

    Columns to append from mapping table

    Columns from the mapping table to include in the output.

    Left table column for similarity calculation

    Column from the left table to use for similarity calculation.

    Mapping table column for similarity calculation

    Column from the mapping table to use for similarity calculation. The component calculates similarity between each row in the left table and all strings in the mapping table, then returns the top N results.

    Similarity column name in output table

    Name for the similarity column in the output table. Must contain only letters (a-z, A-Z), digits, and underscores (_), start with a letter, and have a maximum length of 128 bytes. Default: output.

    Parameter settings

    Number of top similarity values

    Number of top similarity values to return for each input string. Must be a positive integer. Default: 10.

    Similarity calculation method

    Similarity calculation method. Valid values:

    • levenshtein_sim (default)

    • lcs_sim

    • ssk

    • cosine

    • simhash_hamming_sim

    Substring length

    Required only when Similarity calculation method is set to ssk, cosine, or simhash_hamming_sim. Value range: (0, 100). Default: 2.

    Matching string weight

    Required only when Similarity calculation method is set to ssk or simhash_hamming_sim. Value range: (0, 1). Default: 0.5.

    Execution tuning

    Number of cores

    Allocated by default.

    Memory per core (MB)

    Automatically allocated by default.

    Configure via PAI command

    Configure parameters using PAI commands. Use the SQL Script component to run PAI commands. For more information, see SQL Script.

    PAI -name string_similarity_topn
    -project algo_public
    -DinputTableName="pai_test_string_similarity_topn"
    -DoutputTableName="pai_test_string_similarity_topn_output"
    -DmapTableName="pai_test_string_similarity_map_topn"
    -DinputSelectedColName="col0"
    -DmapSelectedColName="col1"

    Parameter name

    Required

    Description

    Default value

    inputTableName

    Yes

    Name of the input table.

    None

    mapTableName

    Yes

    Name of the mapping table.

    None

    outputTableName

    Yes

    Name of the output table.

    None

    inputSelectedColName1

    No

    Name of the column from the left table to use for similarity calculation.

    First STRING column in the table

    inputSelectedColName2

    No

    Name of the column from the mapping table to use for similarity calculation.

    First STRING column in the table

    inputAppendColNames

    No

    Names of columns from the input table to include in the output table.

    None

    inputAppendRenameColNames

    No

    Aliases for columns from the input table to include in the output table.

    None

    mapSelectedColName

    Yes

    Name of the column from the mapping table to use for similarity calculation.

    None

    mapAppendColNames

    No

    Names of columns from the mapping table to include in the output table.

    None

    mapAppendRenameColNames

    No

    Aliases for columns from the mapping table to include in the output table.

    None

    inputTablePartitions

    No

    Names of partitions in the input table.

    All partitions

    mapTablePartitions

    No

    Names of partitions in the mapping table.

    All partitions

    outputColName

    No

    Name of the similarity column in the output table. Must contain only letters (a-z, A-Z), digits, or underscores (_), start with a letter, and be no more than 128 bytes long.

    output

    method

    No

    Similarity calculation method. Valid values:

    • levenshtein_sim

    • lcs_sim

    • ssk

    • cosine

    • simhash_hamming_sim

    levenshtein_sim

    lambda

    No

    Required only when Similarity calculation method is set to ssk or simhash_hamming_sim. Value range: (0, 1).

    0.5

    k

    No

    Required only when Similarity calculation method is set to ssk, cosine, or simhash_hamming_sim. Value range: (0, 100).

    2

    lifecycle

    No

    Number of days to retain the output table. Must be a positive integer.

    None

    coreNum

    No

    Number of CPU cores to allocate for calculation.

    System-assigned

    memSizePerCore

    No

    Amount of memory to allocate per CPU core.

    Automatically assigned

    Resource usage

    This component uses M × N computational complexity. To find the closest strings for N records within a set of M records, the algorithm calculates the distance between each pair of samples, resulting in M × N calculations. Resources required are directly proportional to M × N.

    To find the nearest records for N records within a set of M records, the required worker count is (M × N) / (1024 × 1024 × 32), up to a maximum of 1,000. Memory per worker is N/8 MB, ranging from 4 GB to 64 GB. According to the billing model, one computing unit (CU) provides 4 GB of memory. The maximum CU request for this algorithm is 1,000 × 64 / 4 = 16,000 CUs.

    Reference