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.

Parameters

  • 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.