ReadNOS
Description
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.
Usage
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,
...
)
Arguments
data |
Optional Argument.
Specifies the input tbl_teradata.
Types: tbl_teradata
|
location |
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:
"/connector/bucket.endpoint/[key_prefix]".
Azure Blob storage and Azure Data Lake Storage Gen2:
"/connector/container.endpoint/[key_prefix]".
Google Cloud Storage:
"/connector/endpoint/bucket/[key_prefix]".
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.
Note:
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/"
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"
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"
This is an example of a Google Cloud Storage location:
location = "/gs/storage.googleapis.com/YOUR-BUCKET/CSVDATA/RIVERS/rivers.csv"
Types: character
|
buffer.size |
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
|
return.type |
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 |
|
------------------ | ------------------------------------- |
|
Location | VARCHAR(2048) CHARACTER SET UNICODE |
|
ObjectVersionID | VARCHAR(1024) CHARACTER SET UNICODE |
|
OffsetIntoObject | BIGINT |
|
ObjectLength | BIGINT |
|
Payload | JSON |
|
CSV | VARCHAR |
|
------------------ | ------------------------------------- |
|
|
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.
Default Value: "NOSREAD_RECORD"
Types: character
|
sample.perc |
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
|
stored.as |
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
|
full.scan |
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.
Note:
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.
Note:
Default Value: FALSE
Types: logical
|
manifest |
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.
Note:
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
|
row.format |
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:
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:
persist:
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
volatile:
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
Note:
|
Value
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 "$" operator
using the name(s):result
Examples
# Get the current context/connection.
con <- td_get_context()$connection
# Check the list of available analytic functions.
display_analytic_functions()
# Example 1: Reading PARQUET file from AWS S3 location.
obj <- td_read_nos_sqle(
location="/S3/s3.amazonaws.com/Your-Bucket/location/",
authorization=list("Access_ID"= "YOUR-ID",
"Access_Key"= "YOUR-KEY"),
stored.as="PARQUET")
# print the result.
print(obj$result)
# Example 2: Read PARQUET file in external storage with one row for each
# external record along with its metadata.
obj <- td_read_nos_sqle(
location="/S3/s3.amazonaws.com/Your-Bucket/location/",
authorization=list("Access_ID"= "YOUR-ID",
"Access_Key"= "YOUR-KEY"),
return.type="NOSREAD_KEYS")
# print the result.
print(obj$result)
# Example 3: Read CSV file from external storage.
obj <- td_read_nos_sqle(
location="/S3/s3.amazonaws.com/Your-Bucket/csv-location/",
authorization=list("Access_ID"= "YOUR-ID",
"Access_Key"= "YOUR-KEY"),
stored.as="TEXTFILE")
# print the result.
print(obj$result)
# Example 4: Read CSV file in external storage using "data" argument.
# Create a table to store the data.
query <- "CREATE TABLE read_nos_support_tab
(payload dataset storage format csv) NO PRIMARY INDEX;"
dbExecute(con, query)
read_nos_support_tab = tbl(con, "read_nos_support_tab")
# Read the CSV data using "data" argument.
obj <- td_read_nos_sqle(data=read_nos_support_tab,
location="/S3/s3.amazonaws.com/Your-Bucket/csv-location/",
authorization=list("Access_ID"= "YOUR-ID",
"Access_Key"= "YOUR-KEY"),
row.format=list("field_delimiter"= ",",
"record_delimiter"= "\n",
"character_set"= "LATIN"),
stored.as="TEXTFILE")
# print the result.
print(obj$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(
data=read_nos_support_tab,
row.format=list("field_delimiter"= ",",
"record_delimiter"= "\n",
"character_set"= "LATIN"),
stored.as="TEXTFILE")
# print the result.
print(obj$result)