snowpark-extensions 0.0.22

A set of helpers to extend snowpark functionality

snowpark-extensions-py

Snowpark by itself is a powerful library, but still some utility functions can always help.

Installation

We recommended installing using PYPI

    $ pip install snowconvert-deploy-tool --upgrade

note:: If you run this command on MacOS change pip by pip3

Usage

just import it at the top of your file and it will automatically extend your snowpark package. For example:

from snowflake.snowpark import Session
import snowpark_extensions
new_session = Session.builder.from_snowsql().appName("app1").getOrCreate()

## Currently provided extensions:

## SessionBuilder extensions

| Name                          | Description |
| ----------------------------- | ----------- |
| SessionBuilder.from_snowsql   | can read the information from the snowsql config file by default at ~/snowsql/config or at a given location |
| SessionBuilder.env            | reads settings from SNOW_xxx or SNOWSQL_xxx variables |
| SessionBuilder.appName        | Sets a query tag with the given appName               |
| SessionBuilder.append_tag     | Appends a new tag to the existing query tag           | 


You can the create your session like:

``` python
from snowflake.snowpark import Session
import snowpark_extensions
new_session = Session.builder.from_snowsql().appName("app1").create()

from snowflake.snowpark import Session
import snowpark_extensions
new_session = Session.builder.env().appName("app1").create()

The appName can use to setup a query_tag like APPNAME=tag;execution_id=guid which can then be used to track job actions with a query like

You can then use a query like: To see all executions from an app or

select *
from table(information_schema.query_history())
whery query_tag like '%APPNAME=tag%'
order by start_time desc;

To see the executions for a particular execution:

select *
from table(information_schema.query_history())
whery query_tag like '%APPNAME=tag;execution_id=guid%'
order by start_time desc;

Column Extensions

Name	Description
Column.getItem	An expression that gets an item at position ordinal out of a list, or gets an item by key out of a dict.

DataFrame Extensions

Name	Description
DataFrame.dtypes	returns the list of datatypes in the DataFrame
DataFrame.map	provides an equivalent for the map function for example df.map(func,input_types=[StringType(),StringType()],output_types=[StringType(),IntegerType()],to_row=True)
DataFrame.simple_map	if a simple lambda like lambda x: x.col1 + x.col2 is used this functions can be used like df.simple_map(lambda x: x.col1 + x.col2)
DataFrame.groupby.applyInPandas	Maps each group of the current DataFrame using a pandas udf and returns the result as a DataFrame.
DataFrame.replace	extends replace to allow using a regex
DataFrame.groupBy.pivot	extends the snowpark groupby to add a pivot operator

Examples

map and simple_map

from snowflake.snowpark import Session
from snowflake.snowpark.types import *
import snowpark_extensions

session = Session.builder.from_snowsql().appName("app1").getOrCreate()
  
data = [('James','Smith','M',30),('Anna','Rose','F',41),('Robert','Williams','M',62)]
columns = ["firstname","lastname","gender","salary"]
df = session.createDataFrame(data=data, schema = columns)
df.show()


#
#--------------------------------------------------
#|"FIRSTNAME"  |"LASTNAME"  |"GENDER"  |"SALARY"  |
#--------------------------------------------------
#|James        |Smith       |M         |30        |
#|Anna         |Rose        |F         |41        |
#|Robert       |Williams    |M         |62        |
#--------------------------------------------------




# using map with a lamda, the to_row indicates that the code will pass a row as x to the lambda
# if you have a lambda like lambda x,y,z you can use to_row=False
df2=df.map(lambda x: 
        (x[0]+","+x[1],x[2],x[3]*2),
        output_types=[StringType(),StringType(),IntegerType()],to_row=True)
df2.show()

#
#-----------------------------------
#|"C_1"            |"C_2"  |"C_3"  |
#-----------------------------------
#|James,Smith      |M      |60     |
#|Anna,Rose        |F      |82     |
#|Robert,Williams  |M      |124    |
#-----------------------------------
#

# for simple lambda
# simple map will just pass the same dataframe to the function
# this approach is faster
df2 = df.simple_map(lambda x: (x[0]+","+x[1],x[2],x[3]*2))
df2.toDF(["name","gender","new_salary"]).show()

#---------------------------------------------
#|"NAME"           |"GENDER"  |"NEW_SALARY"  |
#---------------------------------------------
#|James,Smith      |M         |60            |
#|Anna,Rose        |F         |82            |
#|Robert,Williams  |M         |124           |
#---------------------------------------------
#

replace with support for regex

df = session.createDataFrame([('bat',1,'abc'),('foo',2,'bar'),('bait',3,'xyz')],['A','C','B'])
# already supported replace
df.replace(to_replace=1, value=100).show()
# replace with regex
df.replace(to_replace=r'^ba.$', value='new',regex=True).show()

applyInPandas

from snowflake.snowpark import Session
import snowpark_extensions
session = Session.builder.from_snowsql().getOrCreate()
import pandas as pd  
df = session.createDataFrame(
    [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
    schema=["ID", "V"])
df1 = df.to_pandas()
def normalize(pdf):
    V = pdf.V
    return pdf.assign(V=(V - V.mean()) / V.std())
df2 = normalize(df1)
# schema can be an string or an StructType
df.group_by("ID").applyInPandas(
    normalize, schema="id long, v double").show()  

------------------------------
|"ID"  |"V"                  |
------------------------------
|2     |-0.8320502943378437  |
|2     |-0.2773500981126146  |
|2     |1.1094003924504583   |
|1     |-0.7071067811865475  |
|1     |0.7071067811865475   |
------------------------------

Functions Extensions

Name	Description
functions.array_sort	sorts the input array in ascending order or descending order. The elements of the input array must be orderable. Null elements will be placed at the end of the returned array.
functions.unix_timestamp	returns the UNIX timestamp of current time. Available in snowpark-python >= 1.1.0
functions.from_unixtimestamp	can be used to convert UNIX time to Snowflake timestamp Available in snowpark-python >= 1.1.0
functions.format_number	formats numbers using the specified number of decimal places
functions.reverse	returns a reversed string
functions.explode	returns a new row for each element in the given array
functions.explode_outer	returns a new row for each element in the given array or map. Unlike explode, if the array/map is null or empty then null is produced
functions.arrays_zip	returns a merged array of arrays
functions.array_sort	sorts the input array in ascending order. The elements of the input array must be orderable. Null elements will be placed at the end of the returned array.
functions.array_max	returns the maximon value of the array.
functions.array_min	returns the minimum value of the array.
functions.array_distinct	removes duplicate values from the array.
function.daydiff	this function returns the difference in days between two dates. This function will be direct equivalent of the spark datediff. You can simple replace spark datediff by daydiff
functions.date_add	returns the date that is n days days after
functions.date_sub	returns the date that is n days before
functions.regexp_extract	extract a specific group matched by a regex, from the specified string column.
functions.asc	returns a sort expression based on the ascending order of the given column name. Available in snowpark-python >=1.1.0
functions.desc	returns a sort expression based on the descending order of the given column name. Available in snowpark-python >=1.1.0
functions.flatten	creates a single array from an array of arrays
functions.sort_array	sorts the input array in ascending or descending order according to the natural ordering of the array elements. Null elements will be placed at the beginning of the returned array in ascending order or at the end of the returned array in descending order
functions.map_values	Returns an unordered array containing the values of the map.
functions.struct	Returns an object built with the given columns
functions.bround	This function receives a column with a number and rounds it to scale decimal places with HALF_EVEN round mode, often called as "Banker's rounding" . This means that if the number is at the same distance from an even or odd number, it will round to the even number.

Examples:

array_sort

from snowflake.snowpark import Session, DataFrame
from snowflake.snowpark.functions import col, lit
from snowflake.snowpark import functions as F
import snowpark_extensions

session = Session.builder.from_snowsql().getOrCreate()
df = session.createDataFrame([([2, 1, None, 3],),([1],),([],)], ['data'])
df.select(F.array_sort(df.data)).show()

------------
|"SORTED"  |
------------
|[         |
|  1,      |
|  2,      |
|  3,      |
|  null    |
|]         |
|[         |
|  1       |
|]         |
|[]        |
------------

explode and explode_outer

Snowflake builtin FLATTEN provide the same functionality, but the explode syntax can be somethings easier. This helper provide the same syntax.

NOTE: explode can be used with arrays and maps/structs. In this helper at least for now you need to specify if you want to process this as array or map. We provide explode and explode outer our you can just use explode with the outer=True flag.

from snowflake.snowpark import Session
import snowpark_extensions
from snowflake.snowpark.functions import explode
session = Session.builder.appName('snowpark_extensions_unittest').from_snowsql().getOrCreate()
schema = StructType([StructField("id", IntegerType()), StructField("an_array", ArrayType()), StructField("a_map", MapType()) ])
sf_df = session.createDataFrame([(1, ["foo", "bar"], {"x": 1.0}), (2, [], {}), (3, None, None)],schema)

#  +---+----------+----------+                                                     
# | id|  an_array|     a_map|
# +---+----------+----------+
# |  1|[foo, bar]|{x -> 1.0}|
# |  2|        []|        {}|
# |  3|      null|      null|
# +---+----------+----------+

sf_df.select("id", "an_array", explode("an_array")).show()

# +---+----------+---+
# | id|  an_array|col|
# +---+----------+---+
# |  1|[foo, bar]|foo|
# |  1|[foo, bar]|bar|
# +---+----------+---+

sf_df.select("id", "an_array", explode_outer("an_array")).show()

# +---+----------+----+
# | id|  an_array| COL|
# +---+----------+----+
# |  1|[foo, bar]| foo|
# |  1|[foo, bar]| bar|
# |  2|        []|    |
# |  3|          |    |
# +---+----------+----+

For a map use

results = sf_df.select("id", "an_array", explode_outer("an_array",map=True))

# +---+----------+----+-----+
# | id|  an_array| KEY| VALUE|
# +---+----------+----+-----+
# |  1|[foo, bar]|   x|   1 |
# |  2|        []|    |     |
# |  3|          |    |     |
# +---+----------+----+-----+

regexp_extract

session = Session.builder.from_snowsql().create()


df = session.createDataFrame([('100-200',)], ['str'])
res = df.select(F.regexp_extract('str',r'(\d+)-(\d+)',1).alias('d')).collect()
print(str(res))
# [Row(D='1')]

df = session.createDataFrame([['id_20_30', 10], ['id_40_50', 30]], ['id', 'age'])
df.show()
# --------------------
# |"ID"      |"AGE"  |
# --------------------
# |id_20_30  |10     |
# |id_40_50  |30     |
# --------------------


df.select(F.regexp_extract('id', r'(\d+)', 1)).show()
# ------------------------------------------------------
# |"COALESCE(REGEXP_SUBSTR(""ID"", '(\\D+)', 1, 1,...  |
# ------------------------------------------------------
# |20                                                  |
# |40                                                  |
# ------------------------------------------------------


df.select(F.regexp_extract('id', r'(\d+)_(\d+)', 2)).show()
# ------------------------------------------------------
# |"COALESCE(REGEXP_SUBSTR(""ID"", '(\\D+)_(\\D+)'...  |
# ------------------------------------------------------
# |30                                                  |
# |50                                                  |
# ------------------------------------------------------

utilities

Name	Description
utils.map_to_python_type	maps from DataType to python type
utils.map_string_type_to_datatype	maps a type by name to a snowpark DataType
utils.schema_str_to_schema	maps an schema specified as an string to a StructType()

Experimental

Jupyter Notebook support

%load_ext snowpark_extensions

This extension provides simple integration with Jupyter notebooks It will preload the snowpark libraries and adds a simple magic.

A %%sql magic can be used to run queries. Queries can use Jinja2 syntax. For example:

If a previous cell you had something like:

COL1=1

Then on following cells you can do:

%%sql
select * from tables where col={{COL1}}

You can use give a name that you can use later for example:

%%sql tables
select * from information_schema.tables limit 5

and then use that as a normal dataframe:

if tables.count() > 5:
    print("There are more that 5 tables")

If you dont specify a name you can still access the last result using __df.

NOTE: By default only 50 rows are displays. You can customize this limit for example to 100 rows with:

import snowpark_extensions
snowpark_extensions.rows_limit = 100

You can configure Jupyter to run some imports and initialization code at the start of a notebook by creating a file called startup.ipy in the ~/.ipython/profile_default/startup directory.

Any code written in this file will be executed when you start a new Jupyter notebook.

An example startup.ipy is provided

Running notebooks in Snowpark

There is an small script that uploads your notebook to snowflake into an stage and executes it. It will record an html with the results of the notebook execution.

To use it:

• install snowpark_extensions
• runner --notebook notebook1.ipynb --stage my_stage --package ""