1.
Koalas tutorial
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Spark + AI Summit Europe 2019 - Tutorials
Niall Turbitt
Tim Hunter
Brooke Wenig

2.
About
Niall Turbitt, Data Scientist at Databricks
• Professional services and training
• MSc Statistics University College Dublin, B.A Mathematics & Economics
Trinity College Dublin
Tim Hunter, Software Engineer at Databricks
• Co-creator of the Koalas project
• Contributes to Apache Spark MLlib, GraphFrames, TensorFrames and
Deep Learning Pipelines libraries
• Ph.D Machine Learning from Berkeley, M.S. Electrical Engineering from
Stanford

3.
Typical journey of a data scientist
Education (MOOCs, books, universities) → pandas
Analyze small data sets → pandas
Analyze big data sets → DataFrame in Spark
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pandas
Authored by Wes McKinney in 2008
The standard tool for data manipulation and analysis in Python
Deeply integrated into Python data science ecosystem, e.g. numpy, matplotlib
Can deal with a lot of different situations, including:
- basic statistical analysis
- handling missing data
- time series, categorical variables, strings
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5.
Apache Spark
De facto unified analytics engine for large-scale data processing
(Streaming, ETL, ML)
Originally created at UC Berkeley by Databricks’ founders
PySpark API for Python; also API support for Scala, R and SQL
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Koalas
Announced April 24, 2019
Pure Python library
Aims at providing the pandas API on top of Apache Spark:
- unifies the two ecosystems with a familiar API
- seamless transition between small and large data
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Quickly gaining traction
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Bi-weekly releases!
> 500 patches merged since
announcement
> 20 significant contributors
outside of Databricks
> 8k daily downloads

8.
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pandas DataFrame Spark DataFrame
Column df['col'] df['col']
Mutability Mutable Immutable
Add a column df['c'] = df['a'] + df['b'] df.withColumn('c', df['a'] + df['b'])
Rename columns df.columns = ['a','b'] df.select(df['c1'].alias('a'),
df['c2'].alias('b'))
Value count df['col'].value_counts() df.groupBy(df['col']).count().order
By('count', ascending = False)
pandas DataFrame vs Spark DataFrame

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A short example
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import pandas as pd
df = pd.read_csv("my_data.csv")
df.columns = ['x', 'y', 'z1']
df['x2'] = df.x * df.x
df = (spark.read
.option("inferSchema", "true")
.option("comment", True)
.csv("my_data.csv"))
df = df.toDF('x', 'y', 'z1')
df = df.withColumn('x2', df.x*df.x)

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A short example
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import pandas as pd
df = pd.read_csv("my_data.csv")
df.columns = ['x', 'y', 'z1']
df['x2'] = df.x * df.x
import databricks.koalas as ks
df = ks.read_csv("my_data.csv")
df.columns = ['x', 'y', 'z1']
df['x2'] = df.x * df.x

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Current status
Bi-weekly releases, very active community with daily changes
The most common functions have been implemented:
- 60% of the DataFrame / Series API
- 60% of the DataFrameGroupBy / SeriesGroupBy API
- 15% of the Index / MultiIndex API
- to_datetime, get_dummies, …
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New features
- 80% of the plot functions (0.16.0-)
- Spark related functions (0.8.0-)
- IO: to_parquet/read_parquet, to_csv/read_csv, to_json/read_json,
to_spark_io/read_spark_io, to_delta/read_delta, ...
- SQL
- cache
- Support for multi-index columns (90%) (0.16.0-)
- Options to configure Koalas’ behavior (0.17.0-)
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Challenge: increasing scale
and complexity of data
operations
Struggling with the “Spark
switch” from pandas
More than 10X faster with
less than 1% code changes
How Virgin Hyperloop One reduced processing
time from hours to minutes with Koalas

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Key Differences
Spark is more lazy by nature:
- most operations only happen when displaying or writing a
DataFrame
Spark does not maintain row order
Performance when working at scale
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InternalFrame
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Koalas
DataFrame
InternalFrame
- column_index
- index_map
Spark
DataFrame

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InternalFrame
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Koalas
DataFrame
InternalFrame
- column_index
- index_map
Spark
DataFrame
InternalFrame
- column_index
- index_map
Spark
DataFrame
Koalas
DataFrame
API call copy with new state

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Notebooks
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bit.ly/koalas_1_sseu
bit.ly/koalas_2_sseu

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Thank you!
github.com/databricks/koalas
Get Started at databricks.com/try
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Internal Frame
Internal immutable metadata.
- holds the current Spark DataFrame
- manages mapping from Koalas column names to Spark
column names
- manages mapping from Koalas index names to Spark column
names
- converts between Spark DataFrame and pandas DataFrame
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InternalFrame
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Koalas
DataFrame
InternalFrame
- column_index
- index_map
Spark
DataFrame
InternalFrame
- column_index
- index_map
Koalas
DataFrame
API call copy with new state
Only updates metadata
kdf.set_index(...)

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InternalFrame
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Koalas
DataFrame
InternalFrame
- column_index
- index_map
Spark
DataFrame
InternalFrame
- column_index
- index_map
Spark
DataFrame
API call copy with new state
e.g., inplace=True
kdf.dropna(...,
inplace=True)

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Operations on different DataFrames
We only allow Series derived from the same DataFrame by default.
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OK
- df.a + df.b
- df['c'] = df.a * df.b
Not OK
- df1.a + df2.b
- df1['c'] = df2.a * df2.b

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Operations on different DataFrames
We only allow Series derived from the same DataFrame by default.
Equivalent Spark code?
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OK
- df.a + df.b
- df['c'] = df.a * df.b
sdf.select(
sdf['a'] + sdf['b'])
Not OK
- df1.a + df2.b
- df1['c'] = df2.a * df2.b
???
sdf1.select(
sdf1['a'] + sdf2['b'])

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Operations on different DataFrames
We only allow Series derived from the same DataFrame by default.
Equivalent Spark code?
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OK
- df.a + df.b
- df['c'] = df.a * df.b
sdf.select(
sdf['a'] + sdf['b'])
Not OK
- df1.a + df2.b
- df1['c'] = df2.a * df2.b
???
sdf1.select(
sdf1['a'] + sdf2['b'])AnalysisException!!

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Operations on different DataFrames
ks.set_option('compute.ops_on_diff_frames', True)
Equivalent Spark code?
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OK
- df.a + df.b
- df['c'] = df.a * df.b
sdf.select(
sdf['a'] + sdf['b'])
OK
- df1.a + df2.b
- df1['c'] = df2.a * df2.b
sdf1.join(sdf2,
on="_index_")
.select('a * b')

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Default Index
Koalas manages a group of columns as index.
The index behaves the same as pandas’.
If no index is specified when creating a Koalas DataFrame:
it attaches a “default index” automatically.
Each “default index” has Pros and Cons.
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Default Indexes
Configurable by the option “compute.default_index_type”
See also: https://koalas.readthedocs.io/en/latest/user_guide/options.html#default-index-type
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requires to collect data
into single node
requires shuffle continuous increments
sequence YES YES YES
distributed-sequence NO YES / NO YES / NO
distributed NO NO NO

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What to expect?
• Improve pandas API coverage
- rolling/expanding
• Support categorical data types
• More time-series related functions
• Improve performance
- Minimize the overhead at Koalas layer
- Optimal implementation of APIs
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Getting started
pip install koalas
conda install koalas
Look for docs on https://koalas.readthedocs.io/en/latest/
and updates on github.com/databricks/koalas
10 min tutorial in a Live Jupyter notebook is available from the docs.
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Do you have suggestions or requests?
Submit requests to github.com/databricks/koalas/issues
Very easy to contribute
koalas.readthedocs.io/en/latest/development/contributing.html
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Koalas Sessions
Koalas: Pandas on Apache Spark (Tutorial)
- 14:30 - @ROOM: G104
AMA: Koalas
- 16:00 - @EXPO HALL
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Apache Spark: I unify data engineering and data science!
Data Scientist: I like pandas!
Apache Spark: pandas does not scale!
Data Scientist: I still like pandas!
Apache Spark: pandas lacks parallelism!
Data Scientist: I still like pandas!
Apache Spark: OK… How about Koalas?
Data Scientist: I like Koalas! Awesome!

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Why do we need Koalas?
1. Data scientists write a bunch of models and codes in pandas
2. Data scientists are told to make it distributed at some point because
the business got bigger.
3. Struggling and frustrated at all other popular distributed systems.
4. Ask some help to some engineers
5. Engineers have no idea what models the data scientists wrote in pandas are.
Data scientists have no idea why the engineers can’t make it working.
6. They fight with each other and get fed up with that.
7. End up with something lousy and just-work-for-now.
8. Thousands of bugs are found. Both engineers and data scientists don’t know
what’s going on and keep tossing those bugs to each other.
9. Go to 10. or they quit their job

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import pandas as pd
df = pd.read_csv('my_data.csv')
df.columns = ['x', 'y', 'z1']
df['x2'] = df.x * df.x
import databricks.koalas as ks
df = ks.read_csv('my_data.csv')
df.columns = ['x', 'y', 'z1']
df['x2'] = df.x * df.x
Instead of pandas … … simply say Koalas

35.
10,000+
Downloads per day
204,452
Downloads this Sept
~100%
Month-over-month
download growth
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Bi-weekly releases
- pip install koalas
- conda install koalas
- Docs and updates on github.com/databricks/koalas
- Project docs are published on koalas.readthedocs.io

36.
New Features
Upcoming Features
Multiindex column support
DataFrame.expanding()
DataFrame.groupby.expanding()
Series.groupby.expanding()
...
pandas API coverage
DataFrame.rolling()
DataFrame.groupby.rolling()
Series.groupby.rolling()
...
Performance improvement
Minimized the overhead at Koalas layer
Optimal implementation of APIs
...
DataFrame.plot and Series.plot (≈80 %)
Multiindex column support (≈80 %)
DataFrame.applymap() koalas.concat()
DataFrame.shift() koalas.get_dummies()
DataFrame.diff() DataFrame.pivot_table()
… …
pandas API coverage (≈50 %)
DataFrame.transform() Series.apply()
DataFrame.groupby.apply() Series.groupby.apply()
DataFrame.T Series.aggregate()
… …
Spark APIs
read_delta() DataFrame.to_delta()
read_spark_io() DataFrame.to_spark_io()
read_table() DataFrame.to_table()
read_sql() cache()
… …

37.
Conversions
.to_pandas() : koalas → pandas
.toDF(): koalas → Spark
ks.DataFrame(...): pandas, Spark → Koalas
all the other to_XXX (excel, html, hdf5, ...) from pandas available
too
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Feature engineering at scale
get_dummies and categorical variables
timestamp manipulations
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The current ecosystem
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pandas Spark
Machine Learning
Graph processing
Time series
Plotting

40.
Differences
Spark needs a bit more information about types. This is only
required when using .apply or functions.
Example:
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df = ... # Contains information about employees
def change_title(old_title):
if old_title.beginswith("Senior"):
return "Hello"
return "Hi"
def make_greetings(title_col):
return title_col.apply(change_title)
kdf = ... # Contains information about employees
from databricks.koalas import Col, pandas_wraps
kdf = ... # Contains information about employees
@pandas_wraps
def make_greetings(title_col) -> Col[str]:
return title_col.applykdf = ... # Contains information about em
kdf['greeting'] = make_greetings(kdf['title'])