如何直接从SAS服务器或SFTP服务器读取SAS数据集？大文件场景实现方案咨询

1. Directly Reading SAS Data from a SAS Server #

Absolutely! There are several reliable ways to pull SAS data directly from a SAS server without downloading files first:

• SASPy (Official SAS-Python Integration)
  This is the most straightforward method if you have access to the SAS server. SASPy lets you establish a connection to a SAS session (local or remote), run SAS code, and transfer datasets between SAS and Python seamlessly.
  Example workflow:

  import saspy
  # Configure connection to remote SAS server (adjust parameters based on your setup)
  sas = saspy.SASsession(cfgname='remote')
  # Read a SAS dataset directly into a pandas DataFrame
  df = sas.sasdata2dataframe(table='statfile', libref='sasdatasets')
  

  Note: You'll need to set up the SAS configuration file (sascfg_personal.py) with your server's connection details (like host, port, authentication method) first.

• SAS ODBC Driver
  If your SAS server is configured to support ODBC connections, you can use the SAS ODBC driver with pandas to query and load data:

  import pandas as pd
  import pyodbc
  # Establish ODBC connection
  conn = pyodbc.connect('DRIVER=SAS ODBC Driver;SERVER=<SAS_SERVER_HOST>;PORT=<PORT>;UID=<USER>;PWD=<PASSWORD>;')
  # Read data via SQL query
  df = pd.read_sql('SELECT * FROM sasdatasets.statfile', conn)
  conn.close()
  

• SAS Viya with SWAT Library
  If you're working with SAS Viya, the SWAT (SAS Scripting Wrapper for Analytics Transfer) library lets you interact directly with the CAS (Cloud Analytic Services) server, handling large datasets efficiently in-memory or distributed:

  import swat
  # Connect to CAS server
  cas = swat.CAS('<CAS_SERVER_HOST>', <PORT>, '<USER>', '<PASSWORD>')
  # Load a SAS dataset from the server
  tbl = cas.load_table('sasdatasets.statfile')
  # Convert to pandas DataFrame (or work directly with CAS table)
  df = tbl.to_frame()
  

2. Optimizing Large SAS File Reads from SFTP #

First, let's fix a critical issue in your current code: you're using open("/sas/...") which tries to read a local file, not the one on the SFTP server. Instead, you need to use pysftp's built-in open() method to get a file-like object pointing to the remote file.

For large files, you'll also want to avoid loading the entire dataset into memory at once. Pyreadstat supports chunked reading with the chunksize parameter, which is perfect for this scenario.

Here's the revised, optimized code:

import pysftp
import pyreadstat

class My_Connection(pysftp.Connection):
    def __init__(self, *args, **kwargs):
        try:
            if kwargs.get('cnopts') is not None:
                return
            kwargs['cnopts'] = pysftp.CnOpts()
            kwargs['cnopts'].hostkeys = None
        except pysftp.HostKeysException as e:
            self._init_error = True
            print('Warning: Failed to load Host-keys')
        else:
            self._init_error = False
            self._sftp_live = False
            self._transport = None
        super().__init__(*args, **kwargs)
    
    def __del__(self):
        if not self._init_error:
            self.close()

# Connect to SFTP and read the large file in chunks
with My_Connection(SFTP_HOST, username=SFTP_USER, password=SFTP_PASSWORD) as conn:
    conn.cwd('/sas/sasdata/sasdev/sasdatasets')
    # Use pysftp's open() to get remote file object (binary mode is required for sas7bdat)
    with conn.open('statfile.sas7bdat', 'rb') as remote_fp:
        # Read in chunks (adjust chunksize based on your memory capacity)
        for df_chunk, meta in pyreadstat.read_sas7bdat(remote_fp, chunksize=10000):
            # Process each chunk here (e.g., analyze, save to database, etc.)
            print(f"Processed chunk with {len(df_chunk)} rows")

Key improvements here:

• Uses conn.open() instead of local open() to access the remote SFTP file
• Opens the file in binary mode (rb) (required for reading binary SAS dataset files)
• Enables chunked reading with chunksize to handle large files without overwhelming memory
• Processes each chunk incrementally, which is ideal for very large datasets that don't fit in RAM

内容的提问来源于stack exchange，提问作者Rakesh Dash