Example data set

Let’s consider a table transactions that contains records about purchases in some stores. The purchase can be made by cash, with a credit or a debit card, which adds an extra dimension to the data. Here is the code for creating and populating the table.

create table transactions (
    transaction_id int,
    user_id int,
    transaction_date date,
    store_id int,
    payment_method varchar(10),
    amount float
)
;

insert into transactions
(transaction_id, user_id, transaction_date, store_id, payment_method, amount)
values
    (1, 1234, '2019-03-02', 1, 'cash', 5.25),
    (1, 1234, '2019-03-01', 1, 'credit', 10.75),
    (1, 1234, '2019-03-02', 2, 'cash', 25.50),
    (1, 1234, '2019-03-03', 2, 'credit', 17.00),
    (1, 4321, '2019-03-01', 2, 'cash', 20.00),
    (1, 4321, '2019-03-02', 2, 'debit', 30.00),
    (1, 4321, '2019-03-03', 1, 'cash', 3.00)
;

Metrics to compute

When exploring a data set, it is common to look at the main performance metrics across all dimensions. In this example, we want to compute the following metrics:

• number of transactions
• average transaction amount
• the total amount of transactions

We want these metrics for each user, store, and payment method. We also want to look at these metrics by store and payment method together.

Template for a single dimension

The query to obtain the metrics for each store is:

select
    store_id
    , count(*) as num_transactions
    , sum(amount) as total_amount
    , avg(amount) as avg_amount
from
    transactions
group by
    store_id
order by total_amount desc

To get the same metrics for other dimensions, we only need to change the store_id into user_id or payment_method in both the select and group by clauses. So the JinjaSql template may look like

_BASIC_STATS_TEMPLATE = '''
select
    {{ dim | sqlsafe }}
    , count(*) as num_transactions
    , sum(amount) as total_amount
    , avg(amount) as avg_amount
from
    transactions
group by
    {{ dim | sqlsafe }}
order by total_amount desc
'''

with parameters, for example, as

params = {
    'dim': 'payment_method'
}
sql = apply_sql_template(_BASIC_STATS_TEMPLATE, params)

The above template works for a single dimension, but what if we have more than one? To generate a generic query that works with any number of dimensions, let’s create a skeleton of a function that takes a list of dimension column names as an argument and returns the SQL.

def get_basic_stats_sql(dimensions):
  '''
  Returns the sql computing the number of transactions,
  as well as the total and the average transaction amounts
  for the provided list of column names as dimensions.
  '''
  # TODO: construct params
  return apply_sql_template(_BASIC_STATS_TEMPLATE, params)

Essentially, we want to transform a list of column names, such as ['payment_method'] or ['store_id', 'payment_method'] into a single string containing the column names as a comma-separated list. Here we have some options, as it can be done either in Python or in the template.

Passing a string generated outside the template

The first option is to generate the comma-separated string before passing it to the template. We can do it simply by joining the members of the list together:

def get_basic_stats_sql(dimensions):
    '''
    Returns the sql computing the number of transactions,
    as well as the total and the average transaction amounts
    for the provided list of column names as dimensions.
    '''
    params = {
      'dim': '\n    , '.join(dimensions)
    }
    return apply_sql_template(_BASIC_STATS_TEMPLATE, params)

It so happens that the template parameter dim is in the right place, so the resulting query is

>>> print(get_basic_stats_sql(['store_id', 'payment_method']))
select
    store_id
    , payment_method
    , count(*) as num_transactions
    , sum(amount) as total_amount
    , avg(amount) as avg_amount
from
    transactions
group by
    store_id
    , payment_method
order by total_amount desc

Now we can quickly generate SQL queries for all desired dimensions using

dimension_lists = [
    ['user_id'],
    ['store_id'],
    ['payment_method'],
    ['store_id', 'payment_method'],
]

dimension_queries = [get_basic_stats_sql(dims) for dims in dimension_lists]

Preset variables inside the template

An alternative to passing a pre-built string as a template parameter is to move the column list SQL generation into the template itself by setting a new variable at the top:

_PRESET_VAR_STATS_TEMPLATE = '''
{% set dims = '\n    , '.join(dimensions) %}
select
    {{ dims | sqlsafe }}
    , count(*) as num_transactions
    , sum(amount) as total_amount
    , avg(amount) as avg_amount
from
    transactions
group by
    {{ dims | sqlsafe }}
order by total_amount desc
'''

This template is more readable than the previous version since all transformations happen in one place in the template, and at the same time, there’s no clutter. The function should change to

def get_stats_sql(dimensions):
    '''
    Returns the sql computing the number of transactions,
    as well as the total and the average transaction amounts
    for the provided list of column names as dimensions.
    '''
    params = {
      'dimensions': dimensions
    }
    return apply_sql_template(_PRESET_VAR_STATS_TEMPLATE, params)

Loops inside the template

We can also use loops inside the template to generate the columns.

_LOOPS_STATS_TEMPLATE = '''
select
    {{ dimensions[0] | sqlsafe }}\
    {% for dim in dimensions[1:] %}
    , {{ dim | sqlsafe }}{% endfor %}
    , count(*) as num_transactions
    , sum(amount) as total_amount
    , avg(amount) as avg_amount
from
    transactions
group by
    {{ dimensions[0] | sqlsafe }}\
    {% for dim in dimensions[1:] %}
    , {{ dim | sqlsafe }}{% endfor %}
order by total_amount desc
'''

This example may not be the best use of the loops because a preset variable does the job just fine without the extra complexity. However, loops are a powerful construct, especially when there is additional logic inside the loop, such as conditions ({% if ... %} - {% endif %}) or nested loops.

So what is happening in the template above? The first element of the list dimensions[0] stands alone because it doesn’t need a comma in front of the column name. We wouldn’t need that if there were a defined first column in the query, and the for-loop would look simply as

    {% for dim in dimensions %}
    , {{ dim | sqlsafe }}
    {% endfor %}

Then, the for-loop construct goes over the remaining elements dimensions[1:]. The same code appears in the group by clause, which is also not ideal and only serves the purpose of showing the loop functionality.

One may wonder why the formatting of the loop is so strange. The reason is that the flow elements of the SQL template, such as {% endfor %}, generate a blank line if they appear on a separate line. To avoid that, in the template above, both {% for ... %} and {% endfor %} are technically on the same line as the previous code (hence the backslash \ after the first column name). SQL, of course, doesn’t care about whitespace, but humans who read SQL may (and should) care. An alternative to fighting with formatting within the template that would make it more readable is to strip the blank lines from the generated query before printing or logging it. A useful function for that purpose is

import os
def strip_blank_lines(text):
    '''
    Removes blank lines from the text, including those containing only spaces.
    https://stackoverflow.com/questions/1140958/whats-a-quick-one-liner-to-remove-empty-lines-from-a-python-string
    '''
    return os.linesep.join([s for s in text.splitlines() if s.strip()])

A better-formatted template then becomes

_LOOPS_STATS_TEMPLATE = '''
select
    {{ dimensions[0] | sqlsafe }}
    {% for dim in dimensions[1:] %}
    , {{ dim | sqlsafe }}
    {% endfor %}
    , count(*) as num_transactions
    , sum(amount) as total_amount
    , avg(amount) as avg_amount
from
    transactions
group by
    {{ dimensions[0] | sqlsafe }}
    {% for dim in dimensions[1:] %}
    , {{ dim | sqlsafe }}
    {% endfor %}
order by total_amount desc
'''

And the call to print the query is

print(strip_blank_lines(get_loops_stats_sql(['store_id', 'payment_method'])))

All the SQL templates so far used a list of dimensions to produce precisely the same query.

Custom dimensions with looping over a dictionary

In the loop example above, we see how to iterate over a list. It is also possible to iterate over a dictionary. This comes in handy, for example, when we want to alias or transform some or all of the columns that form dimensions. Suppose we wanted to combine the debit and credit cards as a single value and compare it to cash transactions. We can accomplish that by first creating a dictionary defining a transformation for the payment_method and keeping the store_id unchanged.

custom_dimensions = {
    'store_id': 'store_id',
    'card_or_cash': "case when payment_method = 'cash' then 'cash' else 'card' end",
}

Here, both credit and debit values are replaced with card. Then, the template may look like the following:

_CUSTOM_STATS_TEMPLATE = '''
{% set dims = '\n    , '.join(dimensions.keys()) %}
select
    sum(amount) as total_amount
    {% for dim, def in dimensions.items() %}
    , {{ def | sqlsafe }} as {{ dim | sqlsafe }}
    {% endfor %}
    , count(*) as num_transactions
    , avg(amount) as avg_amount
from
    transactions
group by
    {{ dims | sqlsafe }}
order by total_amount desc
'''

Note that I moved the total_amount as the first column just to simplify this example and avoid having to deal with the first element in the loop separately. Also, note that the group by clause is using a preset variable and is different from the code in the select query because it only lists the names of the generated columns. The resulting SQL query is

>>> print(strip_blank_lines(
...     apply_sql_template(template=_CUSTOM_STATS_TEMPLATE,
...                        parameters={'dimensions': custom_dimensions})))
select
    sum(amount) as total_amount
    , store_id as store_id
    , case when payment_method = 'cash' then 'cash' else 'card' end as card_or_cash
    , count(*) as num_transactions
    , avg(amount) as avg_amount
from
    transactions
group by
    store_id
    , card_or_cash
order by total_amount desc

Calling custom Python functions from within JinjaSql templates

What if we want to use a Python function to generate a portion of the code? Jinja2 allows one to register custom functions and functions from other packages for use within the SQL templates. Let’s start with defining a function that generates the string that we insert into the SQL for transforming custom dimensions.

def transform_dimensions(dimensions: dict) -> str:
    '''
    Generate SQL for aliasing or transforming the dimension columns.
    '''
    return '\n    , '.join([
        '{val} as {key}'.format(val=val, key=key)
        for key, val in dimensions.items()
    ])

The output of this function is what we expect to appear in the select clause:

>>> print(transform_dimensions(custom_dimensions))
store_id as store_id
    , case when payment_method = 'cash' then 'cash' else 'card' end as card_or_cash

Now we need to register this function with Jinja2. To do that, we’ll modify the apply_sql_template function from the previous blog as follows.

from jinjasql import JinjaSql

def apply_sql_template(template, parameters, func_list=None):
    '''
    Apply a JinjaSql template (string) substituting parameters (dict) and return
    the final SQL. Use the func_list to pass any functions called from the template.
    '''
    j = JinjaSql(param_style='pyformat')
    if func_list:
        for func in func_list:
            j.env.globals[func.__name__] = func
    query, bind_params = j.prepare_query(template, parameters)
    return get_sql_from_template(query, bind_params)

This version has an additional optional argument func_list that needs to be a list of functions.

Let’s change the template to take advantage of the transform_dimensions function.

_FUNCTION_STATS_TEMPLATE = '''
{% set dims = '\n    , '.join(dimensions.keys()) %}
select
    {{ transform_dimensions(dimensions) | sqlsafe }}
    , sum(amount) as total_amount
    , count(*) as num_transactions
    , avg(amount) as avg_amount
from
    transactions
group by
    {{ dims | sqlsafe }}
order by total_amount desc
'''

Now we also don’t need to worry about the first column not having a comma. The following call produces a SQL query similar to that in the previous section.

>>> print(strip_blank_lines(
...     apply_sql_template(template=_FUNCTION_STATS_TEMPLATE,
...                        parameters={'dimensions': custom_dimensions},
...                        func_list=[transform_dimensions])))

select
    store_id as store_id
    , case when payment_method = 'cash' then 'cash' else 'card' end as card_or_cash
    , sum(amount) as total_amount
    , count(*) as num_transactions
    , avg(amount) as avg_amount
from
    transactions
group by
    store_id
    , card_or_cash
order by total_amount desc

Note how we pass transform_dimensions to apply_sql_template as a list [transform_dimensions]. Multiple functions can be passed into SQL templates as a list of functions, for example, [func1, func2].

Conclusion

This tutorial is a follow up to my first post on the basic use of JinjaSql. It demonstrates the use of preset variables, loops over lists and dictionaries, and custom Python functions within JinjaSql templates for advanced SQL code generation. In particular, the addition of custom functions registration to the apply_sql_template function makes templating much more powerful and versatile. Parameterized SQL queries continue to be indispensable for automated report generation and for reducing the amount of SQL code that needs to be maintained. An added benefit is that with reusable SQL code snippets, it becomes easier to use standard Python unit testing techniques to verify that the generated SQL is correct.

The code in this post is licensed under the MIT License.

Photo by Sergei Izrailev

The post Advanced SQL Templates In Python with JinjaSql by Sergei Izrailev appeared first on Life Around Data.

Merging Pandas data frames is covered extensively in a StackOverflow article Pandas Merging 101. However, my experience of grading data science take-home tests leads me to believe that left joins remain to be a challenge for many people. In this post, I show how to properly handle cases when the right table (data frame) in a Pandas left join contains nulls.

Let’s consider a scenario where we have a table transactions containing transactions performed by some users and a table users containing some user properties, for example, their favorite color. We want to annotate the transactions with the users’ properties. Here are the data frames:

import numpy as np
import pandas as pd

np.random.seed(0)
# transactions
left = pd.DataFrame({'transaction_id': ['A', 'B', 'C', 'D'],
                     'user_id': ['Peter', 'John', 'John', 'Anna'],
                     'value': np.random.randn(4),
                    })

# users
right = pd.DataFrame({'user_id': ['Paul', 'Mary', 'John', 'Anna'],
                      'favorite_color': ['blue', 'blue', 'red', np.NaN],
                     })

Note that Peter is not in the users table and Anna doesn’t have a favorite color.

>>> left
  transaction_id user_id     value
0              A   Peter  1.867558
1              B    John -0.977278
2              C    John  0.950088
3              D    Anna -0.151357

>>> right
  user_id favorite_color
0    Paul           blue
1    Mary           blue
2    John            red
3    Anna            NaN

Adding the user’s favorite color to the transaction table seems straightforward using a left join on the user id:

>>> left.merge(right, on='user_id', how='left')
  transaction_id user_id     value favorite_color
0              A   Peter  1.867558            NaN
1              B    John -0.977278            red
2              C    John  0.950088            red
3              D    Anna -0.151357            NaN

We see that Peter and Anna have NaNs in the favorite_color column. However, the missing values are there for two different reasons: Peter’s record didn’t have a match in the users table, while Anna didn’t have a value for the favorite color. In some cases, this subtle difference is important. For example, it can be critical to understanding the data during initial exploration and to improving data quality.

Here are two simple methods to track the differences in why a value is missing in the result of a left join. The first is provided directly by the merge function through the indicator parameter. When set to True, the resulting data frame has an additional column _merge:

>>> left.merge(right, on='user_id', how='left', indicator=True)
  transaction_id user_id     value favorite_color     _merge
0              A   Peter  1.867558            NaN  left_only
1              B    John -0.977278            red       both
2              C    John  0.950088            red       both
3              D    Anna -0.151357            NaN       both

The second method is related to how it would be done in the SQL world and explicitly adds a column representing the user_id in the right table. We note that if the join columns in the two tables have different names, both columns appear in the resulting data frame, so we rename the user_id column in the users table before merging.

>>> left.merge(right.rename({'user_id': 'user_id_r'}, axis=1),
               left_on='user_id', right_on='user_id_r', how='left')
  transaction_id user_id     value user_id_r favorite_color
0              A   Peter  1.867558       NaN            NaN
1              B    John -0.977278      John            red
2              C    John  0.950088      John            red
3              D    Anna -0.151357      Anna            NaN

An equivalent SQL query is

select
    t.transaction_id
    , t.user_id
    , t.value
    , u.user_id as user_id_r
    , u.favorite_color
from
    transactions t
    left join
    users u
    on t.user_id = u.user_id
;

In conclusion, adding an extra column that indicates whether there was a match in the Pandas left join allows us to subsequently treat the missing values for the favorite color differently depending on whether the user was known but didn’t have a favorite color or the user was missing from the users table.

Photo by Ilona Froehlich on Unsplash.

The post Left Join with Pandas Data Frames in Python by Sergei Izrailev appeared first on Life Around Data.

Business leaders often ask how to accelerate data science projects. It is well established that data scientists spend as much as 80% of their time on data wrangling. Reducing this time leads to faster data science project turnaround and allows data scientists to spend a larger fraction of their time on high-value activities that cannot be performed by others. Economic and productivity benefits grow quickly with the size of the data science team. This post makes a case for a moderate investment in data engineering that drastically reduces the time spent on interactive data exploration by satisfying the need for smaller representative data sets.

Reduce the turnaround time of analytics queries

The key to reducing the time spent in data exploration and code development is minimizing the time it takes to get an answer to basic questions about the data. The dynamics of the development process changes drastically when the typical run time of an analytics query goes from several hours to an hour to 10 minutes to under a minute to under 10 seconds. Once the query response time gets into the range of a few seconds, one can and does ask many questions that test different hypotheses and ultimately arrives at a better result in a significantly shorter time.

Big Data is too big

During research projects as well as the development of data pipelines, data scientists necessarily run their queries multiple times, refining and improving them to get the data they need in the format they want. However, working with Big Data is time-consuming because processing a large data set can take a long time whether you are using Hadoop or a modern distributed data warehouse such as Snowflake, Redshift, or Vertica. This problem is often exacerbated by a pattern of using shared computational resources where interactive queries compete with larger batch processes.

Data scientists, frustrated by how long it takes to move their research forward, invent shortcuts that avoid long wait times and often sacrifice the accuracy or applicability of the results of their analyses. For example, working with a single hour of log data makes the query time bearable so that one can make progress. However, the results cannot be used to draw conclusions about the whole data set.

Data engineers can alleviate the pain of data wrangling by employing several simple approaches that reduce the query time with only a moderate amount of work and little extra cost. These approaches, reviewed in detail below, aim at reducing the size and the scope of the data to a subset that is relevant to the questions asked.

Use columnar data stores

The way the data is stored makes a massive difference to how easily it is accessible for analysis. Columnar data formats, such as Parquet, are widely used and provide many benefits. In addition to limiting the data scans to just the columns that are present in the query and allowing better compression, these formats store the data in a binary format rather than text. Even in a highly efficient CSV reader, parsing the text into binary data types can consume about 80% of the time needed to load the data into computer memory. In addition, parsing text fields correctly can itself be a challenge. Pre-processing once and storing the data in a binary format avoids the additional computation every time the data is accessed. Data warehouses typically provide binary columnar data storage out-of-the-box.

Create always-on sampled data sets

Many data questions can be answered by using a representative sample of the data. Sampled data sets are much better than a small time slice of data because they are representative of the whole data set and are sufficient for answering a variety of questions. Even though extracting and updating a sample is tedious, every data scientist sooner or later is enticed to do it merely to shorten the turnaround time of their queries. A much more efficient solution that doesn’t cost much is providing an automatically generated sample of the data at regular intervals. Generation of sampled data should ideally be tied to the pipeline that produces the full data set.

In a typical situation when the data represents some events (e.g., ad impressions, purchase transactions, site visits, etc.) related to some entities (e.g., online users, companies, etc.), it is beneficial to create data sets with different types of sampling. An obvious one is sampling the events, where a given percentage of events is randomly chosen to be included in the data set. Another sampling strategy, potentially more valuable and harder for a data scientist or analyst to implement, is covering all records related to a sample of entities. For example, we may want to include all purchases for 5% of our site users. Such a sample allows one to perform a user-based analysis efficiently. Sampling strategies, including adaptive sampling, is a topic for another post.

Extract smaller sets of relevant data

When events of interest are rare, sampling may not be an option. For example, in the digital advertising setting, one may be interested in extracting all available data for a specific advertising campaign within a limited time frame. Such a data set, while complete with every necessary field, is a small subset of all of the data. Analysts and data scientists interacting with this data are likely to issue hundreds of queries while they work on a project. The process of extracting such a data set and keeping it up-to-date can be automated if the data engineering team builds tools that allow data scientists to create a query for the initial extract, with another query responsible for periodic updates. After the project is complete, the data is no longer needed and can be archived or deleted.

Put smaller data sets in efficient SQL stores

SQL is undoubtedly the most common language of data analytics. Thus, making data sets available for querying using SQL is expanding the number of people that can interact with the data. Democratization of the data aside, making smaller data sets available in efficient analytics SQL query engines further reduces the query time and, therefore, the wasted time for data scientists and analysts. Such query engines range, depending on the size of the data and the requirements to infrastructure, from MySQL and PostgreSQL to Snowflake and Redshift to fully managed services such as Amazon’s Athena and Google’s BigQuery.

Provide dedicated computational resources

One of the biggest frustrations in data exploration is having to wait in a shared queue for results of a query that eventually runs in under a minute. Separate high-priority resource pools for interactive queries on a shared cluster, possibly limited to business hours, go a long way in improving the overall query turnaround time. In addition, providing data science and analytics teams with dedicated computational resources that have sufficient CPU and memory capacity allows loading of the smaller data sets into a tool of their choice to perform development and in-depth analyses.

The investment is worth it!

Human time is an order of magnitude more expensive than computer time and data storage costs. One can observe this easily by comparing an effective hourly rate of a data scientist to the hourly cost of a powerful computer. Also, human time creates net new value for the company. Thus, reducing the typical analytics query completion time to under a minute is likely the most impactful investment in technology available to accelerate data science and analytics projects.

Photos by Kelly Sikkema and Glen Noble on Unsplash combined by Sergei Izrailev

The post Faster Data Science: From Big To Small Data by Sergei Izrailev appeared first on Life Around Data.

Basic parameter substitution

Let’s assume we have a table transactions holding records about financial transactions. The columns in this table could be transaction_id, user_id, transaction_date, and amount. To compute the number of transactions and the total amount for a given user on a given day, a query directly to the database may look something like

select
    user_id
    , count(*) as num_transactions
    , sum(amount) as total_amount
from
    transactions
where
    user_id = 1234
    and transaction_date = '2019-03-02'
group by
    user_id

Here, we assume that the database will automatically convert the YYYY-MM-DD format of the string representation of the date into a proper date type.

If we want to run the query above for an arbitrary user and date, we need to parameterize the user_id and the transaction_date values. In JinjaSql, the corresponding template would simply become

select
    user_id
    , count(*) as num_transactions
    , sum(amount) as total_amount
from
    transactions
where
    user_id = {{ uid }}
    and transaction_date = {{ tdate }}
group by
    user_id

Here, the values were replaced by placeholders with python variable names enclosed in double curly braces {{ }}. Note that the variable names uid and tdate were picked only to demonstrate that they are variable names and don’t have anything to do with the column names themselves. A more readable version of the same template stored in a python variable is

user_transaction_template = '''
select
    user_id
    , count(*) as num_transactions
    , sum(amount) as total_amount
from
    transactions
where
    user_id = {{ user_id }}
    and transaction_date = {{ transaction_date }}
group by
    user_id
'''

Next, we need to set the parameters for the query.

params = {
    'user_id': 1234,
    'transaction_date': '2019-03-02',
}

Now, generating a SQL query from this template is straightforward.

from jinjasql import JinjaSql
j = JinjaSql(param_style='pyformat')
query, bind_params = j.prepare_query(user_transaction_template, params)

If we print query and bind_params, we find that the former is a parameterized string, and the latter is an OrderedDict of parameters:

>>> print(query)
select
    user_id
    , count(*) as num_transactions
    , sum(amount) as total_amount
from
    transactions
where
    user_id = %(user_id)s
    and transaction_date = %(transaction_date)s
group by
    user_id
>>> print(bind_params)
OrderedDict([('user_id', 1234), ('transaction_date', '2018-03-01')])

Running parameterized queries

Many database connections have an option to pass bind_params as an argument to the method executing the SQL query on a connection. For a data scientist, it may be natural to get results of the query in a Pandas data frame. Once we have a connection conn, it is as easy as running read_sql:

import pandas as pd
frm = pd.read_sql(query, conn, params=bind_params)

See the JinjaSql docs for other examples.

From a template to the final SQL query

It is often desired to fully expand the query with all parameters before running it. For example, logging the full query is invaluable for debugging batch processes because one can copy-paste the query from the logs directly into an interactive SQL interface. It is tempting to substitute the bind_params into the query using python built-in string substitution. However, we quickly find that string parameters need to be quoted to result in proper SQL. For example, in the template above, the date value must be enclosed in single quotes.

>>> print(query % bind_params)

select
    user_id
    , count(*) as num_transactions
    , sum(amount) as total_amount
from
    transactions
where
    user_id = 1234
    and transaction_date = 2018-03-01
group by
    user_id

To deal with this, we need a helper function to correctly quote parameters that are strings. We detect whether a parameter is a string, by calling

from six import string_types
isinstance(value, string_types)

This works for both python 3 and 2.7. The string parameters are converted to the str type, single quotes in the names are escaped by another single quote, and finally, the whole value is enclosed in single quotes.

from six import string_types

def quote_sql_string(value):
    '''
    If `value` is a string type, escapes single quotes in the string
    and returns the string enclosed in single quotes.
    '''
    if isinstance(value, string_types):
        new_value = str(value)
        new_value = new_value.replace("'", "''")
        return "'{}'".format(new_value)
    return value

Finally, to convert the template to proper SQL, we loop over bind_params, quote the strings, and then perform string substitution.

from copy import deepcopy

def get_sql_from_template(query, bind_params):
    if not bind_params:
        return query
    params = deepcopy(bind_params)
    for key, val in params.items():
        params[key] = quote_sql_string(val)
    return query % params

Now we can easily get the final query that we can log or run interactively:

>>> print(get_sql_from_template(query, bind_params))

select
    user_id
    , count(*) as num_transactions
    , sum(amount) as total_amount
from
    transactions
where
    user_id = 1234
    and transaction_date = '2018-03-01'
group by
    user_id

Putting it all together, another helper function wraps the JinjaSql calls and simply takes the template and a dict of parameters, and returns the full SQL.

from jinjasql import JinjaSql

def apply_sql_template(template, parameters):
    '''
    Apply a JinjaSql template (string) substituting parameters (dict) and return
    the final SQL.
    '''
    j = JinjaSql(param_style='pyformat')
    query, bind_params = j.prepare_query(template, parameters)
    return get_sql_from_template(query, bind_params)

Compute statistics on a column

Computing statistics on the values stored in a particular database column is handy both when first exploring the data and for data validation in production. Since we only want to demonstrate some features of the templates, for simplicity, let’s just work with integer columns, such as the column user_id in the table transactions above. For integer columns, we are interested in the number of unique values, min and max values, and the number of nulls. Some columns may have a default of say, -1, the drawbacks of which are beyond the scope of this post, however, we do want to capture that by reporting the number of default values.

Consider the following template and function. The function takes the table name, the column name and the default value as arguments, and returns the SQL for computing the statistics.

COLUMN_STATS_TEMPLATE = '''
select
    {{ column_name | sqlsafe }} as column_name
    , count(*) as num_rows
    , count(distinct {{ column_name | sqlsafe }}) as num_unique
    , sum(case when {{ column_name | sqlsafe }} is null then 1 else 0 end) as num_nulls
    {% if default_value %}
    , sum(case when {{ column_name | sqlsafe }} = {{ default_value }} then 1 else 0 end) as num_default
    {% else %}
    , 0 as num_default
    {% endif %}
    , min({{ column_name | sqlsafe }}) as min_value
    , max({{ column_name | sqlsafe }}) as max_value
from
    {{ table_name | sqlsafe }}
'''


def get_column_stats_sql(table_name, column_name, default_value):
    '''
    Returns the SQL for computing column statistics.
    Passing None for the default_value results in zero output for the number
    of default values.
    '''
    # Note that a string default needs to be quoted first.
    params = {
        'table_name': table_name,
        'column_name': column_name,
        'default_value': quote_sql_string(default_value),
    }
    return apply_sql_template(COLUMN_STATS_TEMPLATE, params)

This function is straightforward and very powerful because it applies to any column in any table. Note the {% if default_value %} syntax in the template. If the default value that is passed to the function is None, the SQL returns zero in the num_default field.

The function and template above will also work with strings, dates, and other data types if the default_value is set to None. However, to handle different data types more intelligently, it is necessary to extend the function to also take the data type as an argument and build the logic specific to different data types. For example, one might want to know the min and max of the string length instead of the min and max of the value itself.

Let’s look at the output for the transactions.user_id column.

>>> print(get_column_stats_sql('transactions', 'user_id', None))

select
    user_id as column_name
    , count(*) as num_rows
    , count(distinct user_id) as num_unique
    , sum(case when user_id is null then 1 else 0 end) as num_nulls

    , 0 as num_default

    , min(user_id) as min_value
    , max(user_id) as max_value
from
    transactions

Note that blank lines appear in place of the {% %} clauses and could be removed.

Summary

With the helper functions above, creating and running templated SQL queries in python is very easy. Because the details of parameter substitution are hidden, one can focus on building the template and the set of parameters, and then call a single function to get the final SQL.

One important caveat is the risk of code injection. For batch processes, it should not be an issue, but using the sqlsafe construct in web applications could be dangerous. The sqlsafe keyword indicates that the user (you) is confident that no code injection is possible and takes responsibility for simply putting whatever string is passed in the parameters directly into the query.

On the other hand, the ability to put an arbitrary string in the query allows one to pass whole code blocks into a template. For example, instead of passing table_name='transactions' above, one could pass '(select * from transactions where transaction_date = 2018-03-01) t', and the query would still work.

The code in this post is licensed under the MIT License.

Photo and image by Sergei Izrailev

The post A Simple Approach To Templated SQL Queries In Python by Sergei Izrailev appeared first on Life Around Data.

Such a pattern of development attempts to solve for the following:

• Quality: We want production code to be of high quality and maintained by engineering teams. Since most data scientists are not great software engineers, they are not expected to write end-to-end production-quality code.

• Resource Allocation: Building and maintaining production systems requires special expertise, and data scientists can contribute more value solving problems for which they were trained rather than spend the time acquiring such expertise.

• Skills: The programming language used in production may be different from what the data scientist is normally using.

However, there are numerous pitfalls in the over-the-wall development pattern that can be avoided with proper planning and resourcing.

What is over-the-wall data science?

A data scientist writes some code and spends a lot of time to get it to behave correctly. For example, the code may assemble data in a certain way and build a machine learning model that performs well on test data. Getting to this point is where data scientists spend most of their time iterating over the code and the data. The work product could be a set of scripts, or a Jupyter or RStudio notebook containing code snippets, documentation, and reproducible test results. In the extreme, the data scientist produces a document detailing the algorithm, using mathematical formulas and references to library calls, and doesn’t even give any code to the engineering team.

At this point, the code is thrown over the wall to Engineering.

An engineer is then tasked with productionizing the data scientist’s code. If the data scientist used R, and the production applications use Java, that could be a real challenge that in the worst case leads to rewriting everything in a different language. Even in a common and much simpler case of Python on both sides, the engineer may want to rewrite the code to satisfy coding standards, add tests, optimize it for performance, etc. As a result, the ownership of the production code lies with the engineer, and the data scientist can’t modify it.

This is, of course, an oversimplification, and there are many variations of such a process.

What is wrong with having a wall?

Let’s assume that the engineer successfully built the new code, the data scientist compared its results to the results of their own code, and the new code is released to production. Time goes by, and the data scientist needs to change something in the algorithm. The data engineer in the meantime moved on to other projects. Changing the algorithm in production becomes a lengthy process, involving waiting for an engineer (hopefully the same one) to become available. In many cases, after going through the process a couple of times, the data scientist simply gives up, and only critical updates are ever released.

Such interaction between data science and engineering frustrates data scientists because it makes it hard to make changes and strips them of ownership of the final code. It also makes it very difficult to troubleshoot production issues. It is also frustrating for engineers because they feel that they are excluded from the original design, don’t participate in the most interesting part of the project, and have to fix someone else’s code. The frustration on both sides makes the whole process even more difficult.

Breaking down the wall between data science and engineering

The need for over-the-wall data science can be eliminated entirely if data scientists are self-sufficient and can safely deploy their own code to production. This can be achieved by minimizing the footprint of data scientist’s code on production systems and by making engineers part of the AI system design and development process upfront. AI system development is a team sport, and both engineers and data scientists are required for success. Hiring and resource allocation must take that into account.

Make the teams cross-functional

Involving engineering early in the data science projects avoids the “us” and “them” mentality, makes the product much better, and encourages knowledge sharing. Even when a full cross-functional team of engineers and data scientists is not practical, forming a project team working together towards a common goal solves most of the problems of over-the-wall data science.

Expect data scientists to become better engineers

In the end, data scientists should own the logic of the AI code in the production application, and that logic needs to be isolated in the application so that data scientists could modify it themselves. In order to do so, data scientists must follow the same best practices as engineers. For example, writing unit and integration tests may feel like a lot of overhead for data scientists at first, however, the value of knowing that your code still works after you’ve made a change soon overcomes that feeling. Also, engineers must be part of the data scientists’ code review process to make sure the code is of production quality and there are no scalability or other issues.

Provide production tooling for data scientists

Engineers should build production-ready reusable components and wrappers, testing, deployment, and monitoring tools, as well as infrastructure and automation for data science related code. Data scientists can then focus on a much smaller portion of the code containing the main logic of the AI application. When the tooling is not in place, data scientists tend to spend much of their time on building the tools themselves.

Avoid rewriting the code in another language

The production environment is one of the constraints on the types of acceptable machine learning packages, algorithms, and languages. This constraint has to be enforced at the beginning of the project to avoid rewrites. A number of companies are offering production-oriented data science platforms and AI model deployment strategies both in open source and commercial products. These products, such as TensorFlow and H2O.ai, help solve the problem of a production environment being very different from that normally used by data scientists.

Images by MabelAmber and Wokadandapix on Pixabay

The post Over-the-Wall Data Science and How to Avoid Its Pitfalls by Sergei Izrailev appeared first on Life Around Data.

Let’s look at each of the four stages of AI systems development cycle in more detail.

Discovery

The discovery phase is responsible for defining the project: what are its goals, what is the business problem it is solving, why solving it is important, what is the value of solving it, what are the constraints, and how will we know that we’ve succeeded. Frequently, such information is captured in a Product Requirements Document (PRD) or a similar document, defining the “what” of the project. Some aspects of discovery are described in another article on reducing the risk of machine learning projects.

For AI systems, feasibility and quality of a solution to the problem at hand are usually not obvious from the start. Carefully defining the constraints can dramatically narrow down the choice of technology and algorithms. However, creating new machine learning models still largely remains to be a task for an expert. As a result, a research stage is needed in order to find whether or not an AI solution is possible, as well as to estimate its value and cost.

Research

The research phase answers in detail how we are going to solve the business problem. Relevant documentation of a typical software project may include a system design, various options considered during design and their trade-offs, specifications, etc., with enough information for an engineering team to build the software.

The research phase of AI systems development cycle is highly iterative, often manual, and heavy on visualizations and analytics. First, we need to check whether we can solve the problem with machine learning given the available data and constraints established in the discovery phase. We collect the data, extract it and transform it into inputs to a machine learning algorithm. We usually build many variants of a model, experiment with input data and algorithms, test and evaluate the models. Then we frequently go back to collecting and transforming the data. This cycle stops when, after a few (and sometimes many) iterations at every step, there’s a model that makes predictions with an acceptable accuracy. Information gathered during this process is passed back into the discovery phase.

Prototype

A prototype for an AI system is proof that a system reflecting the production design, without all the bells and whistles, can run end-to-end as code and produce predictions within the predefined constraints. Sometimes, the output of the research phase is close to a prototype, after a little clean-up and converting some manual steps into scripts. As we are getting closer to production, it is better to keep the prototype code at production quality and involve engineers who will be working on the production AI system.

Note that the goal of the prototype stage is not for a data scientist to create something that will then be rewritten by an engineer in a different language. Often referred to as “over the wall” development, such a pattern is extremely inefficient and should be avoided.

Production

The production stage of the AI systems development cycle is responsible for the final system that is able to reliably build, deploy and operate machine learning models. The reliability requirements lead to a plethora of components that can easily take much of the time and effort of the whole project. Such components include testing, validation, model tracking and versioning, deployment, automation, logging, monitoring, alerting, and error handling, to name a few.

Summary

The AI systems development cycle has the same stages as most other software. It is different in the much higher proportion of the effort allocated to the research and prototype stages. The operational components of AI systems at the production stage may also require much effort, especially in the first iteration of the whole cycle. Once the first AI system is in production, the frameworks used for operationalizing machine learning can be reused and improved on in the subsequent cycles.

The post AI Systems Development Cycle And How It’s Different From Other Software by Sergei Izrailev appeared first on Life Around Data.

Is this the right problem to solve – right now?

This is by far the most important question, and it lies squarely in the product management and business area. To answer it effectively, strong communication must be established between the data science team and the product and business teams. On one hand, it is important to have a process which enables ideas generated in the engineering world to be validated quickly with customers. This avoids situations when a product feature is developed on the premise of “Wouldn’t it be cool if…” and there’s no demand for the feature. On the other hand, there needs to be an efficient way to prototype and validate solutions for inbound ideas and requests from customers.

If we solved the problem, what would be the value?

The value is the net difference between the benefit of having the feature and the cost of building it. While it may be hard to estimate either of these quantities accurately, some idea of the financial or other impact should provide guidance on whether the benefit is worth the risk of investing in the project. The company is exposed to a potentially large opportunity and financial risk, for example, when a product feature is built without an estimate of either what it would cost to build and maintain, or what revenue impact it is expected to have.

Is machine learning the right tool to solve the problem?

Building a production machine learning system is hard and can be expensive. In many cases, an outcome that is good enough to solve the business problem can be achieved with simpler methods that are much easier to implement. During a panel discussion at H2O World 2015, Monica Rogati made this point beautifully: “My favorite data science algorithm is division because you can actually get very far with just division…” Understanding that using machine learning is not the goal and is not necessarily an appropriate tool can sometimes be disappointing to data scientists. However, the satisfaction of solving a real problem and having a business impact easily outweighs this disappointment.

What are the constraints?

Any project has its constraints, and machine learning systems are not an exception. The starting point is the available people and their skills. If the existing skills do not match the task at hand, the project will depend heavily on the ability to train, hire or outsource in order to fill the gaps. More on this in my post Four machine learning skills of a successful AI team.

Further, if machine learning models have to run in a production environment, the data scientists who build the models need to understand upfront the existing production environment architecture, the technology stack, and the requirements for scale, which typically limit the choice of programming language and algorithms that are acceptable in production. Scalability of machine learning systems is a large topic in itself, and I leave it for another blog post.

Summary

By solving the right problem, understanding the value of the solution, being confident that machine learning is the right tool to solve the problem, and defining the constraints upfront we drastically reduce the overall risk of machine learning projects. Answering the questions above helps avoid wasted funds, time, and effort, as well as frustration across the organization. Not all questions can be readily answered, and some discovery with customers and proof-of-concept projects may be needed. While it may appear as unnecessary extra work, the resulting clarity about the project is so powerful that it is worth the investment.

Photo by rawpixel on Unsplash

The post How Can We Reduce the Risk of Machine Learning Projects? by Sergei Izrailev appeared first on Life Around Data.

Kitchen builders, chefs, cooks, and microwave builders

In her blog Why businesses fail at machine learning, Cassie Kozyrkov, Chief Decision Scientist at Google, points out two main types of machine learning: research and applied, and draws a compelling analogy with cooking. In this analogy, machine learning researchers who develop new algorithms are likened to engineers who build microwaves and other appliances. The applied machine learning specialists, on the other hand, are cooks who use appliances to produce tasty dishes.

Extending this line of thought, just like individual dishes don’t necessarily make a meal, predictions coming from machine learning systems are usually not the end goal. Someone needs to define how to use them in a product that solves a specific customer or business problem. For example, if the AI system predicts a user’s music preferences, there’s still work on how this information is optimally used in a music streaming app, and how to measure its impact on key performance indicators. Such a person is similar to a chef in a restaurant, who is capable of selecting and combining dishes together to serve a full dinner.

Another important aspect of AI systems is their day-to-day operation, and so people who develop and integrate machine learning platforms are those who build automated and scalable kitchens. Kitchen builders are making the cooks efficient so that the chefs are able to deliver more meals.

To summarize, there are four broad AI skillsets:

• Development of new and improvement of existing algorithms (microwave and other appliance builders).
• Application of existing algorithms to build machine learning models and produce predictions that are useful in solving a business problem (cooks, who prepare individual dishes).
• Defining how predictions are used to solve specific business problems (chefs who create a full meal and dining experience).
• Building platforms that facilitate and automate machine learning (kitchen builders).

It is important to keep these skillsets in mind when building a team that is tasked with developing AI-driven products. So which of them are necessary, which are optional, and which can be outsourced?

How to assemble a successful AI team

Considering a large number of readily available machine learning algorithms, one would normally not start with developing a new algorithm or modifying an existing one. Therefore, we usually would not need microwave builders to start (unless it’s a microwave design business). Unfortunately, these are the most common skills taught in machine learning and data science courses.

Cooking skills are definitely required, and sometimes cooks specializing in certain dishes (pastries, sauces) may be needed. For example, if solving the business problem involves text analysis, knowledge of NLP could come in handy.

Having a chef, whose skills cross into the product management area, is absolutely critical to making AI valuable for the business. It doesn’t have to be a separate role, and it can be synthesized from more than one person.

Finally, if there is a lot of cooking to be done, kitchen builders are also required in order for the whole process to scale. While one can certainly rent a ready-to-go kitchen, some of the kitchen builder skills are usually needed in-house to make it operational. The main reason is that while platforms make certain aspects of machine learning easier, integration with existing production systems and processes remains a challenge.

To assemble a successful AI team, one first needs to evaluate what business problems need solving, which skill sets are needed, and which of them are required in-house. In some cases, it is sufficient to rent a kitchen, hire a chef who can also cook various dishes, and provide adequate engineering support to make the chef effective. In others, the competitive advantage comes from the ability to cook simple meals at scale and serve many customers. Then kitchen builders become a key to success. In yet other cases, none of the existing algorithms can solve the business problem well, and one has to develop better microwaves and hire microwave builders.

Image by olafBroeker on Pixabay

The post Four Machine Learning Skills of a Successful AI Team by Sergei Izrailev appeared first on Life Around Data.