Streaming Analytics
===================

The toolz functions can be composed to analyze large streaming datasets.
Toolz supports common analytics patterns like the selection, grouping,
reduction, and joining of data through pure composable functions.  These
functions often have analogs to familiar operations in other data analytics
platforms like SQL or Pandas.

Throughout this document we'll use this simple dataset of accounts

.. code::

   >>> accounts = [(1, 'Alice', 100, 'F'),  # id, name, balance, gender
   ...             (2, 'Bob', 200, 'M'),
   ...             (3, 'Charlie', 150, 'M'),
   ...             (4, 'Dennis', 50, 'M'),
   ...             (5, 'Edith', 300, 'F')]

Selecting with ``map`` and ``filter``
-------------------------------------

Simple projection and linear selection from a sequence is achieved through the
standard functions ``map`` and ``filter``.

.. code::

   SELECT name, balance
   FROM accounts
   WHERE balance > 150;

These functions correspond to the SQL commands ``SELECT`` and ``WHERE``.

.. code::

   >>> from toolz.curried import pipe, map, filter, get
   >>> pipe(accounts, filter(lambda acc: acc[2] > 150),
   ...                map(get([1, 2])),
   ...                list)

*note: this uses the curried_ versions of ``map`` and ``filter``.*

Of course, these operations are also well supported with standard
list/generator comprehension syntax.  This syntax is more often used and
generally considered to be more Pythonic.

.. code::

   >>> [(name, balance) for (id, name, balance, gender) in accounts
   ...                  if balance > 150]


Split-apply-combine with ``groupby`` and ``reduceby``
-----------------------------------------------------

We separate split-apply-combine operations into the following two concepts

1.  Split the dataset into groups by some property
2.  Reduce each of the groups with some synopsis function

Toolz supports this common workflow with

1.  a simple in-memory solution
2.  a more sophisticated streaming solution.


In Memory Split-Apply-Combine
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The in-memory solution depends on the functions `groupby`_ to split, and
`valmap`_ to apply/combine.

.. code::

   SELECT gender, SUM(balance)
   FROM accounts
   GROUP BY gender;

We first show these two functions piece by piece to show the intermediate
groups.

.. code::

   >>> from toolz import groupby, valmap, compose
   >>> from toolz.curried import get, pluck

   >>> groupby(get(3), accounts)
   {'F': [(1, 'Alice', 100, 'F'), (5, 'Edith', 300, 'F')],
    'M': [(2, 'Bob', 200, 'M'), (3, 'Charlie', 150, 'M'), (4, 'Dennis', 50, 'M')]}

   >>> valmap(compose(sum, pluck(2)),
   ...        _)
   {'F': 400, 'M': 400}


Then we chain them together into a single computation

.. code::

   >>> pipe(accounts, groupby(get(3)),
   ...                valmap(compose(sum, pluck(2))))
   {'F': 400, 'M': 400}


Streaming Split-Apply-Combine
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The ``groupby`` function collects the entire dataset in memory into a
dictionary.  While convenient, the ``groupby`` operation is *not streaming* and
so this approach is limited to datasets that can fit comfortably into memory.

Toolz achieves streaming split-apply-combine with `reduceby`_, a function that
performs a simultaneous reduction on each group as the elements stream in.  To
understand this section you should first be familiar with the builtin function
``reduce``.

The ``reduceby`` operation takes a key function, like ``get(3)`` or ``lambda x:
x[3]``, and a binary operator like ``add`` or ``lesser = lambda acc, x: acc if
acc < x else x``.  It successively applies the key function to each item in
succession, accumulating running totals for each key by combining each new
value with the previous using the binary operator.  It can't accept full
reduction operations like ``sum`` or ``min`` as these require access to the
entire group at once.  Here is a simple example:

.. code::

   >>> from toolz import reduceby

   >>> def iseven(n):
   ...     return n % 2 == 0

   >>> def add(x, y):
   ...     return x + y

   >>> reduceby(iseven, add, [1, 2, 3, 4])
   {True: 6, False: 4}

The even numbers are added together ``(2 + 4 = 6)`` into group ``True``, and
the odd numbers are added together ``(1 + 3 = 4)`` into group ``False``.


Note that we have to replace the reduction ``sum`` with the binary operator
``add``.  The incremental nature of ``add`` allows us to do the summation work as
new data comes in.  The use of binary operators like ``add`` over full reductions
like ``sum`` enables computation on very large streaming datasets.

The challenge to using ``reduceby`` often lies in the construction of a
suitable binary operator. Here is the solution for our accounts example
that adds up the balances for each group:

.. code::

   >>> binop = lambda total, account: total + account[2]

   >>> reduceby(get(3), binop, accounts, 0)
   {'F': 400, 'M': 400}


This construction supports datasets that are much larger than available memory.
Only the output must be able to fit comfortably in memory and this is rarely an
issue, even for very large split-apply-combine computations.


Semi-Streaming ``join``
-----------------------

We register multiple datasets together with `join`_.  Consider a second
dataset storing addresses by ID

.. code::

   >>> addresses = [(1, '123 Main Street'),  # id, address
   ...              (2, '5 Adams Way'),
   ...              (5, '34 Rue St Michel')]

We can join this dataset against our accounts dataset by specifying attributes
which register different elements with each other; in this case they share a
common first column, id.

.. code::

   SELECT accounts.name, addresses.address
   FROM accounts, addresses
   WHERE accounts.id = addresses.id;


.. code::

   >>> from toolz import join, first

   >>> result = join(first, accounts,
   ...               first, addresses)

   >>> for ((id, name, bal, gender), (id, address)) in result:
   ...     print((name, address))
   ('Alice', '123 Main Street')
   ('Bob', '5 Adams Way')
   ('Edith', '34 Rue St Michel')

Join takes four main arguments, a left and right key function and a left
and right sequence. It returns a sequence of pairs of matching items. In our
case the return value of ``join`` is a sequence of pairs of tuples such that the
first element of each tuple (the ID) is the same.  In the example above we
unpack this pair of tuples to get the fields that we want (``name`` and
``address``) from the result.


Join on arbitrary functions / data
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Those familiar with SQL are accustomed to this kind of join on columns.
However a functional join is more general than this; it doesn't need to operate
on tuples, and key functions do not need to get particular columns.  In the
example below we match numbers from two collections so that exactly one is even
and one is odd.

.. code::

   >>> def iseven(x):
   ...     return x % 2 == 0
   >>> def isodd(x):
   ...     return x % 2 == 1

   >>> list(join(iseven, [1, 2, 3, 4],
   ...           isodd, [7, 8, 9]))
   [(2, 7), (4, 7), (1, 8), (3, 8), (2, 9), (4, 9)]


Semi-Streaming Join
^^^^^^^^^^^^^^^^^^^

The Toolz Join operation fully evaluates the *left* sequence and streams the
*right* sequence through memory.  Thus, if streaming support is desired the
larger of the two sequences should always occupy the right side of the join.


Algorithmic Details
^^^^^^^^^^^^^^^^^^^

The semi-streaming join operation in ``toolz`` is asymptotically optimal.
Computationally it is linear in the size of the input + output.  In terms of
storage the left sequence must fit in memory but the right sequence is free to
stream.

The results are not normalized, as in SQL, in that they permit repeated values.  If
normalization is desired, consider composing with the function ``unique`` (note
that ``unique`` is not fully streaming.)


More Complex Example
^^^^^^^^^^^^^^^^^^^^

The accounts example above connects two one-to-one relationships, ``accounts``
and ``addresses``; there was exactly one name per ID and one address per ID.
This need not be the case.  The join abstraction is sufficiently flexible to
join one-to-many or even many-to-many relationships.  The following example
finds city/person pairs where that person has a friend who has a residence in
that city.  This is an example of joining two many-to-many relationships,
because a person may have many friends and because a friend may have many
residences.


.. code::

   >>> friends = [('Alice', 'Edith'),
   ...            ('Alice', 'Zhao'),
   ...            ('Edith', 'Alice'),
   ...            ('Zhao', 'Alice'),
   ...            ('Zhao', 'Edith')]

   >>> cities = [('Alice', 'NYC'),
   ...           ('Alice', 'Chicago'),
   ...           ('Dan', 'Syndey'),
   ...           ('Edith', 'Paris'),
   ...           ('Edith', 'Berlin'),
   ...           ('Zhao', 'Shanghai')]

   >>> # Vacation opportunities
   >>> # In what cities do people have friends?
   >>> result = join(second, friends,
   ...               first, cities)
   >>> for ((name, friend), (friend, city)) in sorted(unique(result)):
   ...     print((name, city))
   ('Alice', 'Berlin')
   ('Alice', 'Paris')
   ('Alice', 'Shanghai')
   ('Edith', 'Chicago')
   ('Edith', 'NYC')
   ('Zhao', 'Chicago')
   ('Zhao', 'NYC')
   ('Zhao', 'Berlin')
   ('Zhao', 'Paris')

Join is computationally powerful:

*   It is expressive enough to cover a wide set of analytics operations
*   It runs in linear time relative to the size of the input and output
*   Only the left sequence must fit in memory


Disclaimer
----------

Toolz is a general purpose functional standard library, not a library
specifically for data analytics.  While there are obvious benefits (streaming,
composition, ...) users interested in data analytics might be better served by
using projects specific to data analytics like Pandas_ or SQLAlchemy.


.. _groupby: https://toolz.readthedocs.io/en/latest/api.html#toolz.itertoolz.groupby
.. _join: https://toolz.readthedocs.io/en/latest/api.html#toolz.itertoolz.join
.. _reduceby: https://toolz.readthedocs.io/en/latest/api.html#toolz.itertoolz.reduceby
.. _valmap: https://toolz.readthedocs.io/en/latest/api.html#toolz.dicttoolz.valmap
.. _Pandas: http://pandas.pydata.org/pandas-docs/stable/groupby.html
.. _curried: https://toolz.readthedocs.io/en/latest/curry.html
