Python implementation of the Initial State events api to easily send data via a super simple python api
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Latest commit d6213e5 Jan 22, 2017 @jrbail01 jrbail01 committed on GitHub Update example_read_csv.py

README.md

Python Data Streamer

Join the chat at https://gitter.im/InitialState/python_appender

Build Status

This is a Python module currently built for python >= 2.7

Installation

Using the automated script

On a Unix based system: (including Raspberry Pi, Mac OS X, Ubuntu)

\curl -sSL https://get.initialstate.com/python -o - | sudo bash

If you don't have curl you should get it

sudo apt-get install curl
sudo yum install curl

Using Package Management

The package is hosted in PyPI under the package name ISStreamer.

If you don't have pip:

  1. (optional) Check if you have python setup tools installed:

    $ easy_install --version
    

    if you don't see a version number come back, you should install setuptools:

    $ sudo apt-get install python-setuptools
    
  2. (optional) Check if you have pip installed:

    $ pip --version
    

    if you don't see a version number after running the above command, install pip using easy_install:

    $ sudo easy_install pip
    

I've got pip what next?:

$ sudo pip install ISStreamer

This command installs the ISStreamer module

Basic Usage

After getting the ISStreamer module, usage is really simple. With the following example, you can do most of what you need, and you don't need to read further! However

from ISStreamer.Streamer import Streamer

# create a Streamer instance
streamer = Streamer(bucket_name="Some Bucket Name", bucket_key="bucket_key", access_key="YourAccessKey")

# send some data
streamer.log("test", "hi")
streamer.log("temperature", 32)

# flush and close the stream
streamer.close()

Advanced Usage, Troubleshooting, and Concepts

Concepts

  • Buckets

    In order to keep your event streams and visualizations contextually appropriate, we have implemented a concept called buckets. A new bucket is automatically created when the Streamer is constructed, however, if you want to append to an existing bucket, or use a key that is more memorable than the uuid that will otherwise be used, you can use the optional bucket_key constructor parameter. If a Streamer is constructed with a bucket_key that already exists, then any data sent in that Stream will append to the existing bucket.

    NOTE: bucket_key's uniqueness is scoped to a specific access_key.

  • Event Stream

    An event stream is a key with an associated set of values with timestamps. These individual events are created every time the Strmeaer.log method is called. If an event_key is the same for different pieces of data, those pieces of data are represented together in an event stream. This is more of an Initial State concept than a Python Streamer specific concept.

    Legacy Note: Event Streams use to be called Signals.

Most Used Methods

  • Streamer.log(key, value[, epoch])

    This is the core method and api for the event streamer. This is an asyncronous method that pushes your data to a queue that handles sending it off to Initial State's servers. You don't have to worry about anything but calling the method where you want! For the sake of clarity (for those new to python or programming) the Streamer would be replaced with the variable reference to a Streamer instance.

    The log method expects two parameters, key and value:

    • key is a string that represents the source of the value and is limited to 250 characters
    • value is either a string, boolean, or number and it represents a value at the time of the method call.
    • epoch is an optional parameter to override the epoch timestamp, recommended for advanced uses.
  • Streamer.log_object(obj[, key_prefix[, epoch]])

    This is an enhanced method to abstract having to write a bunch of log statements to stream all of the values of an object with multiple data points at a specific time.

    The log_object method expects one parameter, obj:

    • obj is either a list, dict, or simple object with attributes.
      • If obj is a list, it will use the key name list_n unless the optional key_prefix is supplied, then it will use key_prefix_n where n - in both cases - is an index.
      • If obj is a dict, it will use the key name dict_key where unless the optional key_prefix is supplied, then it will use key_prefix_key where key - in both cases - is the key of the dictionary value.
      • If obj is a simple object, it will iterate over the objects attributes and produce values for keys with the name of the key as obj_attr unless the key_prefix is supplied, then it will use key_prefix_attr. In all cases, attr is the attribute name.
    • key_prefix is an optional string that, when supplied, overrrides the default key prefixes.
    • epoch is an optional number that represents the current time in epoch format.

    Here is a working example.

    NOTE: log_object will log multiple keys and values, but will override the epoch timestamp of each value so that there is no cpu or iteration skew in the timestamp reported for when those values were logged and streamed.

  • Streamer.close()

    This method ensures that the log buffer is flushed and should be called when a program is exiting. It is also called during the __del__ magic method of the Streamer by python, but it is a best practice to explicitly call it at the end of a program to ensure it is executed.

Advanced Use

  • Manual Streamer.flush()

    You can manually flush on your own accord by calling Streamer.flush(). This will ensure that anything that has been queued or buffered locally will get sent to Initial State's servers asap.

  • Changing buffer size

    You can override the default event buffer size (the count of events) by passing the optional buffer_size parameter into the Streamer constructor. Here is an example:

    streamer = Streamer(bucket_name="Hi!", access_key="YourAccessKey", buffer_size=200)

    In this example, the buffer_size is being increased to 200 from the default, 10. The decision to override this value should be based on how many log statements you make in a loop before sleeping. You can typically play around with this number to help tune the performance of the Streame. Additionally, a higher buffer_size may help if getting throttled by the api.

    Another great example of overriding the buffer_size would be to increase to a large value and use the Streamer.flush() method at the end of each iteration to effectively have a dynamic buffer size. Here is an example:

    ...
    
    streamer = Streamer(bucket_name="Dynamic Buffer", access_key="YourAccessKey", buffer_size=200)
    
    counter = 0
    while 1:
        streamer.log("loop_counter", counter)
    
        some_dict = {
            "a": 1,
            "b": 2,
            "c": 3
        }
        streamer.log_object(some_dict, key_prefix="some_dict")
    
        dynamic_list = SomeOtherModule.PracticalClass.get_stuff()
    
        # We don't know how many items will be in dynamic_list, so we just
        # log the list to get them all. However, since we don't know how many
        # there will be, we don't know the optimal buffer size, so we set the
        # buffer to a high value and flush at the end of the iteration
        streamer.log_object(dynamic_list)
    
        # Here we will flush the log buffer to insure that there isn't a delay
        # in sending the logs that we've created previous to this point while
        # waiting for the sleep to finish
        streamer.flush()
    
        # increment counter
        counter += 1
    
        # sleep for 10 seconds
        time.sleep(10)
    
    ...
  • Overriding the timestamp

    Some have asked for the ability to override the timestamp. Currently, the timestamp is automatically associated with data by retrieving the most accurate timestamp possible from the device as soon as a log or log_object method is called. However, you can override this by doing the following:

    time = time.time()
    
    streamer.log("siganl", 5, epoch=time)
    

    For a full example checkout this

  • Creating a new bucket

    When you construct a Streamer the constructor expects a name or a key that it will use to ensure there is a bucket that it will use as the context for Streamer.log(key, value). Buckets are either created or consumed based on the unique combination of a access_key and a bucket_key. If you want to switch which to a new bucket, because say you've started a new session or run, simply call Streamer.set_bucket(bucket_name='some_bucket_name'[, bucket_key='some_bucket_key']). Note that bucket_key is optional, if not provided the module will create a uuid4 as the bucket_key. Here is an example:

    streamer = Streamer(bucket_name="Starting Bucket", access_key="YourAccessKey")
    
    streamer.log("key1", "starting")
    streamer.set_bucket(bucket_name="New Bucket")
    streamer.log("key1", "starting")

    In this example, you will get a key1=starting in two different buckets: "Starting Bucket" and "New Bucket".

Troubleshooting

Missing Events

If the Streamer cannot ship a set of events during a flush, it will retry a few times before deeming it a failure. If it does fail, it will attempt to save it's payload to a local file. This payload will be in a json format inside a json array. Each array can be individually submitted to Initial State's events api to fill in any missed events.

Setting debug_level

If you're having issues with your data you might want to try running ISStreamer at a higher debug level:

logger = Streamer(bucket_name="SomeBucketName", access_key="YourAccessKey", debug_level=2)

With a debug_level at or greater than 2 the streamer will throw exceptions on logging errors. Otherwise, it will assume logging errors are not fundamentally exceptional. It will also display more verbouse logging information.

If you want to simply see what's being logged in the console, construct the Streamer object with the debug_level equal to 1.