Python SDK for V3IO

Python (2.7 and 3.5+) client for the Iguazio Data Science Platform (the "platform"). Designed to allow fast access to the data layer and basic access to the control layer. This library can be used in Nuclio functions, Jupyter notebooks, local Python IDEs; anywhere with a Python interpreter and access to a platform.

Installing

Simply get with pip (locking to a specific version is recommended, as always):

pip install v3io

Dataplane client

With the dataplane client you can manipulate data in the platform's multi-model data layer, including:

Objects
Key-values (NoSQL)
Streams
Containers

Under the hood, the client connects through the platform's web API (https://www.iguazio.com/docs/reference/latest-release/api-reference/web-apis/) and wraps each low level API with an interface. Calls are blocking, but you can use the batching interface to send multiple requests in parallel for greater performance.

Creating a client

Create a dataplane client, passing in the web API endpoint and your access key:

import v3io.dataplane


v3io_client = v3io.dataplane.Client(endpoint='https://v3io-webapi:8081', access_key='some_access_key')

Note: In some environments (like Jupyter notebooks) you do not need to pass the endpoint and access key, as they will automatically be inferred from the environment

Making requests

The client supports a handful of low level APIs, each receiving different arguments but all returning a Response object. A Response object holds 4 fields:

status_code: The HTTP status code returned
output: An object containing the parsed response (each API returns a different object)
headers and body: The raw headers and body of the response

You would normally only access the output field unless an API was called that returns raw data like get_object (in which case body holds the response). Consult the reference for each API call to see how to handle its Response object. In the example below, we perform a simple request to get the containers available in our tenant, print the returned status code and containers:

# list the containers we can access
response = v3io_client.get_containers()

# print the status code. outputs:
# 
#  Status code: 200
#
print(f'Status code: {response.status_code}')

# iterate over the containers and print them
# 
#   #0: bigdata
#   #1: users
#
for container_idx, container in enumerate(response.output.containers):
    print(f'#{container_idx}: {container.name}')

We can also get help information about the parameters this API call receives:

help(v3io_client.get_containers)

Handling errors

By default, making a request will raise an exception if any non-200 status code is returned. We can override this default behavior in two ways.

The first is to simply never raise an exception and handle the status manually:

# list the containers we can access and never raise an exception
response = v3io_client.get_containers(raise_for_status=v3io.dataplane.RaiseForStatus.never)

# do anything we want with the status code
# some_logic(response.status_code)

The second is to indicate which status codes are acceptable:

# list the containers and raise if the status code is not 200 or 204
response = v3io_client.get_containers(raise_for_status=[200, 204])

Creating batches

To get the highest possible throughput, we can send many requests towards the data layer and wait for all the responses to arrive (rather than send each request and wait for the response). The SDK supports this through batching. Any API call can be made through the client's built in batch object. The API call receives the exact same arguments it would normally receive (except for raise_for_status), and does not block until the response arrives. To wait for all pending responses, call wait() on the batch object:

# do 16 writes in parallel
for idx in range(16):

    # returns immediately
    v3io_client.batch.put_object(container='bigdata',
                                 path=f'/object{idx}',
                                 body=f'object-{idx}')

# wait for all writes to complete
v3io_client.batch.wait()

The looped put_object interface above will send 16 put object requests to the data layer in parallel. When wait is called, it will block until either all responses arrive (in which case it will return a Responses object, containing the responses of each call) or an error occurs - in which case an exception is thrown. You can pass raise_for_status to wait, and it behaves as explained above.

Note: The batch object is stateful, so you can only create one batch at a time. However, you can create multiple parallel batches yourself through the client's create_batch() interface

Examples

Accessing objects

Put data in an object, get it back and then delete the object:

# put contents to some object
v3io_client.put_object(container='users',
                       path='/my-object',
                       body='hello, there')

# get the object
response = v3io_client.get_object(container='users', path='/my-object')

# print the contents. outputs:
#
#   hello, there
#
print(response.body.decode('utf-8'))

# delete the object
v3io_client.delete_object(container='users', path='/my-object')

Accessing key-values (NoSQL)

Create a table, update a record and run a query.

items = {
    'bob': {'age': 42, 'feature': 'mustache'},
    'linda': {'age': 41, 'feature': 'singing'},
    'louise': {'age': 9, 'feature': 'bunny ears'},
}

# add the records to the table
for item_key, item_attributes in items.items():
    v3io_client.put_item(container='users', path='/bobs-burgers/' + item_key, attributes=item_attributes)

# adds two fields (height, quip) to the louise record
v3io_client.update_item(container='users',
                        path='/bobs-burgers/louise',
                        attributes={
                            'height': 130,
                            'quip': 'i can smell fear on you'
                        })

# get a record by key, specifying specific arguments
response = v3io_client.get_item(container='users', 
                                path='/bobs-burgers/louise', 
                                attribute_names=['__size', 'age', 'quip', 'height'])


# print the item from the response. outputs:
#
#   {'__size': 0.0, 'quip': 'i can smell fear on you', 'height': 130.0}
#
print(response.output.item)

# create a query, and use an items cursor to iterate the results
items_cursor = v3io_client.new_items_cursor(container='users',
                                            path='/bobs-burgers/',
                                            attribute_names=['age', 'feature'],
                                            filter_expression='age > 15')

# print the output
for item in items_cursor.all():
    print(item)

Accessing streams

Creates a stream with several partitions, writes records to it, reads the records and deletes the stream:

# create a stream w/8 shards
v3io_client.create_stream(container='users',
                           path='/my-test-stream',
                           shard_count=8)

# write 4 records - 3 with explicitly specifying the shard and 1 using hashing
records = [
    {'shard_id': 1, 'data': 'first shard record #1'},
    {'shard_id': 1, 'data': 'first shard record #2'},
    {'shard_id': 2, 'data': 'second shard record #1'},
    {'data': 'some shard record #1'}
]

v3io_client.put_records(container='users', path='/my-test-stream', records=records)

# seek to the beginning of the shard of #1 so we know where to read from 
response = v3io_client.seek_shard(container='users', path='/my-test-stream/1', seek_type='EARLIEST')

# get records from the shard (should receive 2)
response = v3io_client.get_records(container='users', path='/my-test-stream/1', location=response.output.location)

# print the records. outputs:
#
#   first shard record #1
#   first shard record #2
#
for record in response.output.records:
    print(record.data.decode('utf-8'))

# delete the stream
v3io_client.delete_stream(container='users', path='/my-test-stream')

Controlplane client

Coming soon.

Hedingber/v3io-py

README