oxtie 0.2.1

Tools for saving NoSQL like data to various backends.

Introduction

The python oxtie package is a system for tying together data by saving and loading data to various backends (especially backends in the cloud). It fits somewhere above low-level serialization/deserialization tools like pickle, json, msgpack, etc., but below a full fledged database like postgres or MongoDB.

The goal of oxtie is to be a lightweight system for loading/saving data in a backend agnostic way.

Installation

You can install in the usual way via something like

$ pip install oxtie

Example Usage

The main question oxtie tries to answer is “which backend should I use to store my python objects in an easy and efficient way?”. The answer is “You don’t have to choose; use oxtie and you can easily change the backend when you like.”

Simplest Example

As a simple example of how you can use oxtie, consider the following example:

>>> from oxtie.backs import simple       # Import some simple backends
>>> tfbackend = simple.TempFileBackend() # Choose a temp file backend
>>> from oxtie.fronts import base        # Import front ends
>>> class MyClass(base.Frontend):        # Define your own class
...     'Simple sub-class of Frontend so it can save data'
...
>>> f = MyClass(facets={'my_name': 'test_item'}, backend=tfbackend)
>>> f.arbitrary_data = 'you can set whatever data you want'
>>> f.save()  # Backend will save

Simply by inheriting from base.Frontend and choosing a backend, you can call the save method of your object and it will save itself on the desired backend. You could have used S3 or a file system or any other supported backend simply by changing one line.

You can later load the object via something like:

>>> g = tfbackend.load({'my_name': 'test_item'}, front_cls=MyClass)
>>> f.arbitrary_data == g.arbitrary_data
True

The key which oxtie uses to save/load is the facets dictionary. By default, all keys in facets which do not start with an underscore are used but you can customize this as described later.

Another Example

You can use oxtie to handle complicated data structures. To illustrate, imagine you want to save your Pandas DataFrame somehow. First, you would do the usual thing to import pandas and create a DataFrame:

>>> import pandas  # So we can make a dataframe.
>>> data = {'estimate': [.17, None]} # Make example data
>>> frame = pandas.DataFrame(data, index=['2017-07-01', '2017-10-01'])

Next, you can create an instance of the SimpleFrame class from oxtie to store your DataFrame as a temporary file via something like:

>>> from oxtie.fronts import specials  # Illustrate special example
>>> from oxtie.backs import simple     # Import simple backends
>>> backend=simple.TempFileBackend() # Choose a temp file backend
>>> f = specials.nums.SimpleFrame({'name': 'test'},backend=backend,frame=frame)
>>> f.arbitrary = 'You can also save arbitrary data in SimpleFrame'
>>> f.save()  # Now save the frame.

You could instead have done something like from oxtie.backs import aws to get a different backend and use backend = aws.S3Backend('prefix', 'bucket') to save to Amazon S3. Or if you prefer DynamoDB, you could use backend = aws.DynamoBackend(table_name, key_fields={'primary':['name']}) to have your data saved to the table table_name with a primary key primary built from the name facet instead. The key idea is that oxtie handles dealing with the different backends so you don’t have to worry about it or change your code (apart from choosing the backend of course).

Continuing our example, once you want to load your data you can do the following (possibly in another python session):

>>> g = backend.load({'name': 'test'}, allow_load=True)
>>> g.__class__.__name__
'SimpleFrame'
>>> g.frame.to_csv() == f.frame.to_csv()  # Compare CSVs to handle Nones
True
>>> g.arbitrary == f.arbitrary  # Aribitrary properties also match
True

Note that in the above example we provide allow_load=True indicating that the backend can dynamically figure out which of your python classes to load the data into. If you don’t like dynamically loading, there are various ways to specify the class to load into.

Why Frontends?

You may wonder why we need a special front-end class like SimpleFrame. If you really wanted to, you could just use the backends by themselves as a more flexible version of pickle or a more limited version of a database object relational manager (ORM). In general, though, it is nice to have both a back-end protocol and a front-end class so that you can more intelligently do things like control serialization/deserialization, deal with headers, and so on.

As a very simple example of this, you could modify the previous example via

>>> facets = f.get_facets_dict()
>>> facets['_timezone'] = 'US/Eastern'
>>> f.save()

The dictionary returned by f.get_facets_dict() functions as a header to store simple “facets” (A “facet” is like an “attribute” but we use a different word so as not to confuse oxtie “facets” with python “attributes”). One advantage these special facets have over the usual python properties is that you can do something like.

>>> h = backend.load({'name': 'test'}, only_hdr=True)
>>> print('name:tz = %s:%s' % (
...     h['_facets']['name'], h['_facets']['_timezone']))
name:tz = test:US/Eastern

In the above, we use the only_hdr=True option to backend.load to first load only the header. This is generally a much cheaper and faster operation than de-serializing and loading the full object. Among other things, this header dictionary contains a '_facets key containing the dictionary provided by get_facets_dict(). As a result, we can look at the header to see the timezone and do things like:

1. Skip the full load for objects with the wrong timezone.

2. Deserialize differently depending on things in the facets such as the timezone.

Indeed, the SimpleFrame class does just that. If you load the full object and print the frame, you will see that although we saved a pandas DataFrame with no timezone information, the SimpleFrame class looks for the '_timezone' in the facets and localize appropriately on loading:

>>> print(backend.load({'name': 'test'},allow_load=True).frame.index[0])
2017-07-01 00:00:00-04:00

Key Features

The oxtie package is designed to simplify loading and saving data into different backends with the following key features:

1. Built-in oxtie backends can easily store data out to local databases, local files, Amazon S3, Amazon DynamoDB, and other cloud providers.

2. Support for serializing and storing Pandas DataFrame objects.

3. Ability to easily implement your own backend storage to save/load to/from.

4. Ability to separate backend from serialization.

   • You may want to serialize in a format like JSON, BSON, msgpack, CSV, pickle, etc., but store on backends like local files, S3, and so on. With oxtie you can mix and match these as you like.

5. Ability to quickly load/scan header information before loading the full object so you can cheaply scan through stored data deciding what/how to load.

6. Ability to build intelligent front ends to do validation or other actions on loading or saving.

These features may seem somewhat standard and generic so it may be worth emphasizing the one which most influenced creation of oxtie: quickly load/scan header information. In the model of a modern major data system used by many organizations you generally want to do 3 things well:

1. Save data.

2. Get data for which you know the key.

3. Look for a key based on some simple search criteria.

In the early days, SQL databases implemented #3 very well. You can could write arbitrarily complicated queries which let you use all kinds of conditions to find which data to load. Later, people realized that you often have binary large objects (BLOBs) which you don’t need to search plus header information which you do want to search. This led to NoSQL databases which greatly improved performance at the cost of more limited search criteria. Since NoSQL was successful, cloud storage moved things even further providing even more limited search capability (e.g., Amazon S3 or Amazon DynamoDB).

In many cloud storage systems, you effectively divide your data into some simple header information or facets such as a name, or last update time, or region along with your non-searchable data. Your storage and search operations essentially only involve the header so that everything can be efficient.

Assuming you subscribe to this small header + big body paradigm, you may start to realize that the simple save/load/scan methodology works on lots of different systems so why should you hard-code your application to depend on a particular backend? The answer is you shouldn’t! Use oxtie!

Ideally, one could even go a step further and define a simple language independent protocol for search-load-scan (SLS) operations similar to the SQL standard which could be implemented in various languages. With an SLS standard, you could flexibly save something like a Pandas DataFrame from python to S3 (or some other backend) and then load into into the appropriate structure in some other language. The oxtie package is the first step on that path.

Frequently Asked Questions (FAQ)

Do We Need Yet Another Serialization System?

Unfortunately, yes. If you try to naively store things like pandas DataFrames its easy to run into multiple issues:

1. JSON doesn’t support certain types (even simple things like datetime let alone DataFrames).

2. JSON is not so efficient in terms of storage.

3. You could use BSON but that has similar issues in type support.

4. You could use msgpack but you need a little help to support things like pandas DataFrames. Even with msgpack, though, you probably may want additional features as described below.

5. Most existing serialization formats don’t provide support for automatic validation on deserialization.

6. Often you want to serialize a time series or data table (e.g., a pandas DataFrame) but also include additional facets such as when the data was collected, the source of the data, and so on). This generally means you want a class that holds your time series along with some other facets.

7. Sometimes you want to mix and match your serialization formats. For example, you may want to serialize an object in both msgpack and CSV formats or you may want the option to sometimes serializes/deserialize to/from one or the other.

For these reasons and more, it seems useful to have a lightweight “cloud serialization” system like oxtie to manage persisting data in a flexible way.

What determines an objects key for saving/loading?

By default, the facets dictionary provided to the Frontend class on __init__ is used to determine the object’s key. By default, we use all the keys in the facets dict that do NOT start with an underscore. This lets you easily set facets to contain both key information (i.e., those not starting with underscore) as well as other meta-data which you do not want to be part of the key (i.e., by starting the key with an underscore such as _timezone).

If desired, you can override what the key is by inheriting from the Frontend class in the oxtie.fronts.base module and overriding either get_key or preferably facets2key.

How do you deal with special key requirements?

Some back-ends like Amazon’s DynamoDB have specific requirements for the key. For example, DynamoDB requires a single primary key and an optional secondary key. If you have a front-end with a facets dictionary with three keys like {'k1': 'a', 'k2': 'b', 'k3': 'c'} you can define things on the backend to translate the frontend key into the form the backend can work with.

In the case of DynamoDB, we might have a frontend key like {'k1': 'a', 'k2': 'b', 'k3': 'c'}. The backend could be told that k1 and k2 are part of the primary key called pkey and k3 is part of the secondary key called skey. The backend would then produce a backend key of the form {'pkey': 'a#b', 'skey': 'c'} by joining together the fields for pkey. See the documentation for the DynamoBackend class in the oxtie.backs.aws module for more details.

Basically, the frontend key is always a simple dict with string keys and the backend can then transform this further as required.