read_s3

getml.database.read_s3(name, bucket, keys, region, sep=',', num_lines_read=0, skip=0, colnames=None, conn=None)

Reads a list of CSV files located in an S3 bucket.

Args:

name (str): Name of the table in which the data is to be inserted.

bucket (str):

The bucket from which to read the files.

keys (List[str]): The list of keys (files in the bucket) to be read.

region (str):

The region in which the bucket is located.

sep (str, optional): The separator used for separating fields. Default:,

num_lines_read (int, optional): Number of lines read from each file.

Set to 0 to read in the entire file.

skip (int, optional): Number of lines to skip at the beginning of each

file (Default: 0).

colnames(List[str] or None, optional): The first line of a CSV file

usually contains the column names. When this is not the case, you need to explicitly pass them.

conn (Connection, optional): The database connection to be used.

If you don’t explicitly pass a connection, the engine will use the default connection.

Example:

Let’s assume you have two CSV files - file1.csv and file2.csv - in the bucket. You can import their data into the getML engine using the following commands:

>>> getml.engine.set_s3_access_key_id("YOUR-ACCESS-KEY-ID")
>>>
>>> getml.engine.set_s3_secret_access_key("YOUR-SECRET-ACCESS-KEY")
>>>
>>> stmt = data.database.sniff_s3(
...         bucket="your-bucket-name",
...         keys=["file1.csv", "file2.csv"],
...         region="us-east-2",
...         name="MY_TABLE",
...         sep=';'
... )
>>>
>>> getml.database.execute(stmt)
>>>
>>> stmt = data.database.read_s3(
...         bucket="your-bucket-name",
...         keys=["file1.csv", "file2.csv"],
...         region="us-east-2",
...         name="MY_TABLE",
...         sep=';'
... )

You can also set the access credential as environment variables before you launch the getML engine.