Slings from a data source to a data target.

Installation

pip install sling

Then you should be able to run sling --help from command line.

Running a Extract-Load Task

CLI

sling run --src-conn MY_PG --src-stream myschema.mytable \
  --tgt-conn YOUR_SNOWFLAKE --tgt-object yourschema.yourtable \
  --mode full-refresh

Or passing a yaml/json string or file

cat '
source: MY_POSTGRES
target: MY_SNOWFLAKE

# default config options which apply to all streams
defaults:
  mode: full-refresh
  object: new_schema.{stream_schema}_{stream_table}

streams:
  my_schema.*:
' > /path/to/replication.yaml

sling run -r /path/to/replication.yaml

Using the Replication class

Run a replication from file:

import yaml
from sling import Replication

# From a YAML file
replication = Replication(file_path="path/to/replication.yaml")
replication.run()

# Or load into object
with open('path/to/replication.yaml') as file:
  config = yaml.load(file, Loader=yaml.FullLoader)

replication = Replication(**config)

replication.run()

Build a replication dynamically:

from sling import Replication, ReplicationStream, Mode

# build sling replication
streams = {}
for (folder, table_name) in list(folders):
  streams[folder] = ReplicationStream(
    mode=Mode.FULL_REFRESH, object=table_name, primary_key='_hash_id')

replication = Replication(
  source='aws_s3',
  target='snowflake',
  streams=streams,
  env=dict(SLING_STREAM_URL_COLUMN='true', SLING_LOADED_AT_COLUMN='true'),
  debug=True,
)

replication.run()

Using the Sling Class

For more direct control and streaming capabilities, you can use the Sling class, which mirrors the CLI interface.

Basic Usage with run() method

import os
from sling import Sling, Mode

# Set postgres & snowflake connection
# see https://docs.slingdata.io/connections/database-connections
os.environ["POSTGRES"] = 'postgres://...'
os.environ["SNOWFLAKE"] = 'snowflake://...'

# Database to database transfer
Sling(
    src_conn="postgres",
    src_stream="public.users",
    tgt_conn="snowflake",
    tgt_object="public.users_copy",
    mode=Mode.FULL_REFRESH
).run()

# Database to file
Sling(
    src_conn="postgres", 
    src_stream="select * from users where active = true",
    tgt_object="file:///tmp/active_users.csv"
).run()

# File to database
Sling(
    src_stream="file:///path/to/data.csv",
    tgt_conn="snowflake",
    tgt_object="public.imported_data"
).run()

Input Streaming - Python Data to Target

💡 Tip: Install pip install sling[arrow] for better streaming performance and improved data type handling.

import os
from sling import Sling

# Set postgres connection
# see https://docs.slingdata.io/connections/database-connections
os.environ["POSTGRES"] = 'postgres://...'

# Stream Python data to CSV file
data = [
    {"id": 1, "name": "John", "age": 30},
    {"id": 2, "name": "Jane", "age": 25},
    {"id": 3, "name": "Bob", "age": 35}
]

Sling(
    input=data,
    tgt_object="file:///tmp/output.csv"
).run()

# Stream Python data to database
Sling(
    input=data,
    tgt_conn="postgres",
    tgt_object="public.users"
).run()

# Stream Python data to JSON Lines file
Sling(
    input=data,
    tgt_object="file:///tmp/output.jsonl",
    tgt_options={"format": Format.JSONLINES}
).run()

# Stream from generator (memory efficient for large datasets)
def data_generator():
    for i in range(10000):
        yield {"id": i, "value": f"item_{i}", "timestamp": "2023-01-01"}

Sling(input=data_generator(), tgt_object="file:///tmp/large_dataset.csv").run()

Output Streaming with stream()

import os
from sling import Sling

# Set postgres connection
# see https://docs.slingdata.io/connections/database-connections
os.environ["POSTGRES"] = 'postgres://...'

# Stream data from database
sling = Sling(
    src_conn="postgres",
    src_stream="public.users",
    limit=1000
)

for record in sling.stream():
    print(f"User: {record['name']}, Age: {record['age']}")

# Stream data from file
sling = Sling(
    src_stream="file:///path/to/data.csv"
)

# Process records one by one (memory efficient)
for record in sling.stream():
    # Process each record
    processed_data = transform_record(record)
    # Could save to another system, send to API, etc.

# Stream with parameters
sling = Sling(
    src_conn="postgres",
    src_stream="public.orders",
    select=["order_id", "customer_name", "total"],
    where="total > 100",
    limit=500
)

records = list(sling.stream())
print(f"Found {len(records)} high-value orders")

Round-trip Examples

import os
from sling import Sling

# Set postgres connection
# see https://docs.slingdata.io/connections/database-connections
os.environ["POSTGRES"] = 'postgres://...'

# Python → File → Python
original_data = [
    {"id": 1, "name": "Alice", "score": 95.5},
    {"id": 2, "name": "Bob", "score": 87.2}
]

# Step 1: Python data to file
sling_write = Sling(
    input=original_data,
    tgt_object="file:///tmp/scores.csv"
)
sling_write.run()

# Step 2: File back to Python
sling_read = Sling(
    src_stream="file:///tmp/scores.csv"
)
loaded_data = list(sling_read.stream())

# Python → Database → Python (with transformations)
sling_to_db = Sling(
    input=original_data,
    tgt_conn="postgres",
    tgt_object="public.temp_scores"
)
sling_to_db.run()

sling_from_db = Sling(
    src_conn="postgres", 
    src_stream="select *, score * 1.1 as boosted_score from public.temp_scores",
)
transformed_data = list(sling_from_db.stream())

Using the Pipeline class

Run a Pipeline:

from sling import Pipeline
from sling.hooks import StepLog, StepCopy, StepReplication, StepHTTP, StepCommand

# From a YAML file
pipeline = Pipeline(file_path="path/to/pipeline.yaml")
pipeline.run()

# Or using Hook objects for type safety
pipeline = Pipeline(
    steps=[
        StepLog(message="Hello world"),
        StepCopy(from_="sftp//path/to/file", to="aws_s3/path/to/file"),
        StepReplication(path="path/to/replication.yaml"),
        StepHTTP(url="https://trigger.webhook.com"),
        StepCommand(command=["ls", "-l"], print_output=True)
    ],
    env={"MY_VAR": "value"}
)
pipeline.run()

# Or programmatically using dictionaries
pipeline = Pipeline(
    steps=[
        {"type": "log", "message": "Hello world"},
        {"type": "copy", "from": "sftp//path/to/file", "to": "aws_s3/path/to/file"},
        {"type": "replication", "path": "path/to/replication.yaml"},
        {"type": "http", "url": "https://trigger.webhook.com"},
        {"type": "command", "command": ["ls", "-l"], "print": True}
    ],
    env={"MY_VAR": "value"}
)
pipeline.run()

Testing

pytest sling/tests/tests.py -v
pytest sling/tests/test_sling_class.py -v