Teradata Package for R Function Reference | 17.20 - ReadNOS - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

Teradata® Package for R Function Reference

Teradata Package for R
Release Number
March 2024
English (United States)
Last Update
Product Category
Teradata Vantage



td_read_nos_sqle() function enables access to external files in JSON, CSV, or Parquet format.
User connected to Vanatge must have must have the EXECUTE FUNCTION privilege on TD_SYSFNLIB.READ_NOS.


  td_read_nos_sqle (
      data = NULL,
      location = NULL,
      authorization = NULL,
      buffer.size = 16,
      return.type = "NOSREAD_RECORD",
      sample.perc = 1.0,
      stored.as = "TEXTFILE",
      full.scan = FALSE,
      manifest = FALSE,
      row.format = NULL,
      header = TRUE,



Optional Argument.
Specifies the input tbl_teradata.
Types: tbl_teradata


Optional Argument.
Specifies the location value, which is a Uniform Resource Identifier (URI) pointing to the data in the external object storage system.
The location value includes the following components:

  • Amazon S3:

  • Azure Blob storage and Azure Data Lake Storage Gen2:

  • Google Cloud Storage:

The following fields explain each component of location value:

  • connector:
    Identifies the type of external storage system where the data is located.
    Teradata requires the storage location to start with the following for all external storage locations:

    • Amazon S3 storage location must begin with /S3 or /s3

    • Azure Blob storage location (including Azure Data Lake Storage Gen2 in Blob Interop Mode) must begin with /AZ or /az.

    • Google Cloud Storage location must begin with /GS or /gs.

  • storage-account:
    Used by Azure. The Azure storage account contains your Azure storage data objects.

  • endpoint:
    A URL that identifies the system-specific entry point for the external object storage system.

  • bucket (Amazon S3, Google Cloud Storage) or container (Azure Blob storage and Azure Data Lake Storage Gen2):
    A container that logically groups stored objects in the external storage system.

  • key_prefix:
    Identifies one or more objects in the logical organization of the bucket data. Because it is a key prefix, not an actual directory path, the key prefix may match one or more objects in the external storage. For example, the key prefix "/fabrics/cotton/colors/b/" would match objects "/fabrics/cotton/colors/blue", "/fabrics/cotton/colors/brown", and "/fabrics/cotton/colors/black". If there are organization levels below those, such as "/fabrics/cotton/colors/blue/shirts", the same key prefix would gather those objects too.

    • Vantage validates only the first file it encounters from the location key prefix.

  • For example, this location value might specify all objects on an Amazon cloud storage system for the month of December, 2001:
    location = "/S3/YOUR-BUCKET.s3.amazonaws.com/csv/US-Crimes/csv-files/2001/Dec/"

    • connector: S3

    • bucket: YOUR-BUCKET

    • endpoint: s3.amazonaws.com

    • key_prefix: csv/US-Crimes/csv-files/2001/Dec/

  • Following location could specify an individual storage object (or file), Day1.csv:
    location = "/S3/YOUR-BUCKET.s3.amazonaws.com/csv/US-Crimes/csv-files/2001/Dec/Day1.csv"

    • connector: S3

    • bucket: YOUR-BUCKET

    • endpoint: s3.amazonaws.com

    • key_prefix: csv/US-Crimes/csv-files/2001/Dec/Day1.csv

  • Following location specifies an entire container in an Azure external object store (Azure Blob storage or Azure Data Lake Storage Gen2).
    The container may contain multiple file objects:
    location = "/AZ/YOUR-STORAGE-ACCOUNT.blob.core.windows.net/nos-csv-data"

    • connector: AZ


    • endpoint: blob.core.windows.net

    • key_prefix: nos-csv-data

  • This is an example of a Google Cloud Storage location:
    location = "/gs/storage.googleapis.com/YOUR-BUCKET/CSVDATA/RIVERS/rivers.csv"

    • connector: GS

    • bucket: YOUR-BUCKET

    • endpoint: storage.googleapis.com,

    • key_prefix: CSVDATA/RIVERS/rivers.csv

Types: character


Optional Argument.
Specifies the size of the network buffer to allocate when retrieving data from the external storage repository. 16 MB is the maximum value.
Default Value: 16
Types: integer


Optional Argument.
Specifies the format in which data is returned.
Permitted Values:

  • NOSREAD_RECORD: Returns one row for each external record along with its metadata.
    Access external records by specifying one of the following:

    • Input tbl_teradata, location, and tbl_teradata on an empty table. For CSV, you can include a schema definition.

    • Input tbl_teradata with a row for each external file. For CSV, this method does not support a schema definition.

    For an empty single-column input table, do the following:

    • Define an input tbl_teradata with a single column, Payload, with the appropriate data type:
      JSON and CSV

    • This column determines the output Payload column return type.
      Specify the filepath in the "location" argument.

    For a multiple-column input table, define a tbl_teradata with the following columns:

    Column Name Data Types
    OffsetIntoObject BIGINT
    ObjectLength BIGINT
    Payload JSON

    This tbl_teradata can be populated using the output of the 'NOSREAD_KEYS' return type.

  • NOSREAD_KEYS: Retrieve the list of files from the path specified in the "location" argument.
    A schema definition is not necessary.
    'NOSREAD_KEYS' returns Location, ObjectVersionID, ObjectTimeStamp, and ObjectLength (size of external file).

  • NOSREAD_PARQUET_SCHEMA: Returns information about the Parquet data schema. If you are using the "full.scan" option, use 'NOSREAD_PARQUET_SCHEMA'; otherwise you can use 'NOSREAD_SCHEMA' to get information about the Parquet schema. For information about the mapping between Parquet data types and Teradata data types, see Parquet External Files in Teradata Vantage - SQL Data Definition Language Syntax and Examples.

  • NOSREAD_SCHEMA: Returns the name and data type of each column of the file specified in the "location" argument. Schema format can be JSON, CSV, or Parquet.

Default Value: "NOSREAD_RECORD"
Types: character


Optional Argument.
Specifies the percentage of rows to retrieve from the external storage repository when "return.type" is 'NOSREAD_RECORD'. The valid range of values is from 0.0 to 1.0, where 1.0 represents 100% of the rows.
Default Value: 1.0
Types: float


Optional Argument.
Specifies the formatting style of the external data.
Permitted Values:

  • PARQUET- The external data is formatted as Parquet. This is a required parameter for Parquet data.

  • TEXTFILE- The external data uses a text-based format, such as CSV or JSON.

Default Value: "TEXTFILE"
Types: character


Optional Argument.
Specifies whether td_read_nos_sqle() function scans columns of variable length types (CHAR, VARCHAR, BYTE, VARBYTE, JSON, and BSON) to discover the maximum length.
When set to TRUE, the size of variable length data is determined from the Parquet data.

  • Choosing this value can impact performance because all variable length data type columns in each Parquet file at the location must be scanned to assess the value having the greatest length.

  • When set to FALSE, variable length field sizes are assigned the Vantage maximum value for the particular data type.


  • "full.scan" is only used with a "return.type" of 'NOSREAD_PARQUET_SCHEMA'.

Default Value: FALSE
Types: logical


Optional Argument.
Specifies whether the location value points to a manifest file (a file containing a list of files to read) or object name. The object name can include the full path or a partial path. It must identify a single file containing the manifest.

  • Individual entries within the manifest file must show complete paths.

Below is an example of a manifest file that contains a list of locations in JSON format.
{ "entries": [ {"url":"s3://nos-core-us-east-1/UNICODE/JSON/mln-key/data-10/data-8_9_02-10.json"}, {"url":"s3://nos-core-us-east-1/UNICODE/JSON/mln-key/data-10/data-8_9_02-101.json"}, {"url":"s3://nos-core-us-east-1/UNICODE/JSON/mln-key/data-10/data-10-01/data-8_9_02-102.json"}, {"url":"s3://nos-core-us-east-1/UNICODE/JSON/mln-key/data-10/data-10-01/data-8_9_02-103.json"} ] }
Default Value: FALSE
Types: logical


Optional Argument.
Specifies the encoding format of the external row.

  • For example:
    row.format = list('field_delimiter'= ',', 'record_delimiter'= '\n', 'character_set'= 'LATIN')

  • If string value is used, JSON format must be used to specify the row format. For example:
    row.format = '{"field_delimiter": ",", "record_delimiter": "\n", "character_set": "LATIN"}'

Format can include only the three keys shown above. Key names and values are case-specific, except for the value for "character_set", which can use any combination of letter cases.
The character set specified in "row.format" must be compatible with the character set of the Payload column.
Do not specify "row.format" for Parquet format data.
For a JSON column, these are the default values:

  • UNICODE: row.format = {"record_delimiter":"\n", "character_set":"UTF8"}

  • LATIN: row.format = {"record_delimiter":"\n", "character_set":"LATIN"}

For a CSV column, these are the default values:

  • UNICODE: row.format = '{"character_set":"UTF8"}'

  • LATIN: row.format = '{"character_set":"LATIN"}'

User can specify the following options:

  • field_delimiter: The default is "," (comma). User can also specify a custom field delimiter, such as tab "\t".

  • record_delimiter: New line feed character: "\n". A line feed is the only acceptable record delimiter.

  • character_set: "UTF8" or "LATIN". If you do not specify a "row.format" or payload column, Vantage assumes UTF8 Unicode.

Types: character or list


Optional Argument.
Specifies whether the first row of data in an input CSV file is interpreted as column headings for the subsequent rows of data. Use this parameter only when a CSV input file is not associated with a separate schema object that defines columns for the CSV data.
Default Value: TRUE
Types: logical


Specifies the generic keyword arguments SQLE functions accept.
Below are the generic keyword arguments:

Optional Argument.
Specifies whether to persist the results of the function in table or not.
When set to TRUE, results are persisted in table; otherwise, results are garbage collected at the end of the session.
Default Value: FALSE
Types: logical

Optional Argument.
Specifies whether to put the results of the function in volatile table or not.
When set to TRUE, results are stored in volatile table, otherwise not.
Default Value: FALSE
Types: logical

Function allows the user to partition, hash, order or local order the input data. These generic arguments are available for each argument that accepts tbl_teradata as input and can be accessed as:

  • "<input.data.arg.name>.partition.column" accepts character or vector of character (Strings)

  • "<input.data.arg.name>.hash.column" accepts character or vector of character (Strings)

  • "<input.data.arg.name>.order.column" accepts character or vector of character (Strings)

  • "local.order.<input.data.arg.name>" accepts logical


  • These generic arguments are supported by tdplyr if the underlying Analytic Database function supports, else an exception is raised.


Function returns an object of class "td_read_nos_sqle" which is a named list containing object of class "tbl_teradata".
Named list member(s) can be referenced directly with the "$" operator using the name(s):result


    # Get the current context/connection.
    con <- td_get_context()$connection
    # Check the list of available analytic functions.
    # Example 1: Reading PARQUET file from AWS S3 location.
    obj <-  td_read_nos_sqle(
              authorization=list("Access_ID"= "YOUR-ID",
                                 "Access_Key"= "YOUR-KEY"),
    # print the result.
    # Example 2: Read PARQUET file in external storage with one row for each  
    #            external record along with its metadata.
    obj <- td_read_nos_sqle(
            authorization=list("Access_ID"= "YOUR-ID",
                               "Access_Key"= "YOUR-KEY"),
    # print the result.
    # Example 3: Read CSV file from external storage.
    obj <- td_read_nos_sqle(
            authorization=list("Access_ID"= "YOUR-ID",
                               "Access_Key"= "YOUR-KEY"),
    # print the result.
    # Example 4: Read CSV file in external storage using "data" argument.
    # Create a table to store the data.
    query <- "CREATE TABLE read_nos_support_tab 
              (payload dataset storage format csv) NO PRIMARY INDEX;"
    dbExecute(con, query)
    read_nos_support_tab = tbl(con, "read_nos_support_tab")
    # Read the CSV data using "data" argument.
    obj <- td_read_nos_sqle(data=read_nos_support_tab,
            authorization=list("Access_ID"= "YOUR-ID",
                               "Access_Key"= "YOUR-KEY"),
            row.format=list("field_delimiter"= ",",
                            "record_delimiter"= "\n",
                            "character_set"= "LATIN"),
    # print the result.
    # Note: 
    #   Before proceeding, verify with your database administrator that  
    #   you have the correct privileges, an authorization object, 
    #   and a function mapping (for READ_NOS function).
    # If function mapping for READ_NOS Analytic database function is created 
    # as 'READ_NOS_FM' and location and authorization object are mapped,
    # then set function mapping with tdplyr options as below.
    # Example 5: Setting function mapping using options.
    options(read.nos.function.mapping = "READ_NOS_FM")
    obj <-  td_read_nos_sqle(
              row.format=list("field_delimiter"= ",",
                              "record_delimiter"= "\n",
                              "character_set"= "LATIN"),
    # print the result.