morsapaes commented on a change in pull request #364:
URL: https://github.com/apache/flink-web/pull/364#discussion_r464040992
##########
File path: _posts/2020-07-28-pyflink-pandas-support-flink.md
##########
@@ -0,0 +1,253 @@
+---
+layout: post
+title: "PyFlink: The integration of Pandas into PyFlink"
+date: 2020-07-28T12:00:00.000Z
+authors:
+- Jincheng:
+ name: "Jincheng Sun"
+ twitter: "sunjincheng121"
+- markos:
+ name: "Markos Sfikas"
+ twitter: "MarkSfik"
+excerpt: Flink community put some great effort in integrating Pandas into
PyFlink with the latest Flink version 1.11. Some of the added features include
support for Pandas UDF and the conversion between Pandas DataFrame and Table.
In this article, we will introduce how these functionalities work and how to
use them with a step-by-step example.
+---
+
+Python has evolved into one of the most important programming languages for
many fields of data processing. So big has been Python’s popularity, that it
has pretty much become the default data processing language for data
scientists. On top of that, there is a plethora of Python-based data
processing tools such as NumPy, Pandas, and Scikit-learn that have gained
additional popularity due to their flexibility or powerful functionalities.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="600px" alt="Python Scientific Stack"/>
Review comment:
```suggestion
<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="450px" alt="Python Scientific Stack"/>
```
##########
File path: _posts/2020-07-28-pyflink-pandas-support-flink.md
##########
@@ -0,0 +1,253 @@
+---
+layout: post
+title: "PyFlink: The integration of Pandas into PyFlink"
+date: 2020-07-28T12:00:00.000Z
+authors:
+- Jincheng:
+ name: "Jincheng Sun"
+ twitter: "sunjincheng121"
+- markos:
+ name: "Markos Sfikas"
+ twitter: "MarkSfik"
+excerpt: Flink community put some great effort in integrating Pandas into
PyFlink with the latest Flink version 1.11. Some of the added features include
support for Pandas UDF and the conversion between Pandas DataFrame and Table.
In this article, we will introduce how these functionalities work and how to
use them with a step-by-step example.
+---
+
+Python has evolved into one of the most important programming languages for
many fields of data processing. So big has been Python’s popularity, that it
has pretty much become the default data processing language for data
scientists. On top of that, there is a plethora of Python-based data
processing tools such as NumPy, Pandas, and Scikit-learn that have gained
additional popularity due to their flexibility or powerful functionalities.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="600px" alt="Python Scientific Stack"/>
+</center>
+<center>
+ <a
href="https://speakerdeck.com/jakevdp/the-unexpected-effectiveness-of-python-in-science?slide=52";>Pic
source: VanderPlas 2017, slide 52.</a>
+</center>
+<br>
+
+In an effort to meet the user needs and demands, the Flink community hopes to
leverage and make better use of these tools. Along this direction, the Flink
community put some great effort in integrating Pandas into PyFlink with the
latest Flink version 1.11. Some of the added features include support for
Pandas UDF and the conversion between Pandas DataFrame and Table. Pandas UDF
not only greatly improve the execution performance of Python UDF, but also make
it more convenient for users to leverage libraries such as Pandas and NumPy in
Python UDF. Additionally, providing support for the conversion between Pandas
DataFrame and Table enables users to switch processing engines seamlessly
without the need for an intermediate connector. In the remainder of this
article, we will introduce how these functionalities work and how to use them
with a step-by-step example.
Review comment:
```suggestion
In an effort to meet the user needs and demands, the Flink community hopes
to leverage and make better use of these tools. Along this direction, the
Flink community put some great effort in integrating Pandas into PyFlink with
the latest Flink version 1.11. Some of the added features include **support for
Pandas UDF** and the **conversion between Pandas DataFrame and Table**. Pandas
UDF not only greatly improve the execution performance of Python UDF, but also
make it more convenient for users to leverage libraries such as Pandas and
NumPy in Python UDF. Additionally, providing support for the conversion between
Pandas DataFrame and Table enables users to switch processing engines
seamlessly without the need for an intermediate connector. In the remainder of
this article, we will introduce how these functionalities work and how to use
them with a step-by-step example.
```
##########
File path: _posts/2020-07-28-pyflink-pandas-support-flink.md
##########
@@ -0,0 +1,253 @@
+---
+layout: post
+title: "PyFlink: The integration of Pandas into PyFlink"
+date: 2020-07-28T12:00:00.000Z
+authors:
+- Jincheng:
+ name: "Jincheng Sun"
+ twitter: "sunjincheng121"
+- markos:
+ name: "Markos Sfikas"
+ twitter: "MarkSfik"
+excerpt: Flink community put some great effort in integrating Pandas into
PyFlink with the latest Flink version 1.11. Some of the added features include
support for Pandas UDF and the conversion between Pandas DataFrame and Table.
In this article, we will introduce how these functionalities work and how to
use them with a step-by-step example.
+---
+
+Python has evolved into one of the most important programming languages for
many fields of data processing. So big has been Python’s popularity, that it
has pretty much become the default data processing language for data
scientists. On top of that, there is a plethora of Python-based data
processing tools such as NumPy, Pandas, and Scikit-learn that have gained
additional popularity due to their flexibility or powerful functionalities.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="600px" alt="Python Scientific Stack"/>
+</center>
+<center>
+ <a
href="https://speakerdeck.com/jakevdp/the-unexpected-effectiveness-of-python-in-science?slide=52";>Pic
source: VanderPlas 2017, slide 52.</a>
+</center>
+<br>
+
+In an effort to meet the user needs and demands, the Flink community hopes to
leverage and make better use of these tools. Along this direction, the Flink
community put some great effort in integrating Pandas into PyFlink with the
latest Flink version 1.11. Some of the added features include support for
Pandas UDF and the conversion between Pandas DataFrame and Table. Pandas UDF
not only greatly improve the execution performance of Python UDF, but also make
it more convenient for users to leverage libraries such as Pandas and NumPy in
Python UDF. Additionally, providing support for the conversion between Pandas
DataFrame and Table enables users to switch processing engines seamlessly
without the need for an intermediate connector. In the remainder of this
article, we will introduce how these functionalities work and how to use them
with a step-by-step example.
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Currently, only Scalar Pandas UDFs are supported in PyFlink.
+</div>
+
+<br>
+
+# Pandas UDF in Flink 1.11
+
+Using scalar Python UDF was already possible in Flink 1.10 as described in a
[previous article on the Flink
blog](https://flink.apache.org/2020/04/09/pyflink-udf-support-flink.html).
Scalar Python UDFs work based on three primary steps:
+
+ - the Java operator serializes one input row to bytes and sends them to the
Python worker;
+
+ - the Python worker deserializes the input row and evaluates the Python UDF
with it;
+
+ - the resulting row is serialized and sent back to the Java operator
+
+
+While providing support for Python UDFs in PyFlink greatly improved the user
experience, it had some drawbacks, namely resulting in:
+
+ - High serialization/deserialization overhead
+
+ - Difficulty when leveraging popular Python libraries used by data
scientists — such as Pandas or NumPy — that provide high-performance data
structure and functions.
+
+
+The introduction of Pandas UDF is used to address these drawbacks. For Pandas
UDF, a batch of rows is transferred between the JVM and PVM in a columnar
format (Arrow memory format). The batch of rows will be converted into a
collection of Pandas Series and will be transferred to the Pandas UDF to then
leverage popular Python libraries (such as Pandas, NumPy, etc.) for the Python
UDF implementation.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/vm-communication.png" width="600px"
alt="VM Communication"/>
Review comment:
```suggestion
<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/vm-communication.png" width="550px"
alt="VM Communication"/>
```
##########
File path: _posts/2020-07-28-pyflink-pandas-support-flink.md
##########
@@ -0,0 +1,253 @@
+---
+layout: post
+title: "PyFlink: The integration of Pandas into PyFlink"
+date: 2020-07-28T12:00:00.000Z
+authors:
+- Jincheng:
+ name: "Jincheng Sun"
+ twitter: "sunjincheng121"
+- markos:
+ name: "Markos Sfikas"
+ twitter: "MarkSfik"
+excerpt: Flink community put some great effort in integrating Pandas into
PyFlink with the latest Flink version 1.11. Some of the added features include
support for Pandas UDF and the conversion between Pandas DataFrame and Table.
In this article, we will introduce how these functionalities work and how to
use them with a step-by-step example.
+---
+
+Python has evolved into one of the most important programming languages for
many fields of data processing. So big has been Python’s popularity, that it
has pretty much become the default data processing language for data
scientists. On top of that, there is a plethora of Python-based data
processing tools such as NumPy, Pandas, and Scikit-learn that have gained
additional popularity due to their flexibility or powerful functionalities.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="600px" alt="Python Scientific Stack"/>
+</center>
+<center>
+ <a
href="https://speakerdeck.com/jakevdp/the-unexpected-effectiveness-of-python-in-science?slide=52";>Pic
source: VanderPlas 2017, slide 52.</a>
+</center>
+<br>
+
+In an effort to meet the user needs and demands, the Flink community hopes to
leverage and make better use of these tools. Along this direction, the Flink
community put some great effort in integrating Pandas into PyFlink with the
latest Flink version 1.11. Some of the added features include support for
Pandas UDF and the conversion between Pandas DataFrame and Table. Pandas UDF
not only greatly improve the execution performance of Python UDF, but also make
it more convenient for users to leverage libraries such as Pandas and NumPy in
Python UDF. Additionally, providing support for the conversion between Pandas
DataFrame and Table enables users to switch processing engines seamlessly
without the need for an intermediate connector. In the remainder of this
article, we will introduce how these functionalities work and how to use them
with a step-by-step example.
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Currently, only Scalar Pandas UDFs are supported in PyFlink.
+</div>
+
+<br>
+
+# Pandas UDF in Flink 1.11
+
+Using scalar Python UDF was already possible in Flink 1.10 as described in a
[previous article on the Flink
blog](https://flink.apache.org/2020/04/09/pyflink-udf-support-flink.html).
Scalar Python UDFs work based on three primary steps:
+
+ - the Java operator serializes one input row to bytes and sends them to the
Python worker;
+
+ - the Python worker deserializes the input row and evaluates the Python UDF
with it;
+
+ - the resulting row is serialized and sent back to the Java operator
+
+
+While providing support for Python UDFs in PyFlink greatly improved the user
experience, it had some drawbacks, namely resulting in:
+
+ - High serialization/deserialization overhead
+
+ - Difficulty when leveraging popular Python libraries used by data
scientists — such as Pandas or NumPy — that provide high-performance data
structure and functions.
+
+
+The introduction of Pandas UDF is used to address these drawbacks. For Pandas
UDF, a batch of rows is transferred between the JVM and PVM in a columnar
format (Arrow memory format). The batch of rows will be converted into a
collection of Pandas Series and will be transferred to the Pandas UDF to then
leverage popular Python libraries (such as Pandas, NumPy, etc.) for the Python
UDF implementation.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/vm-communication.png" width="600px"
alt="VM Communication"/>
+</center>
+<br>
+
+The performance of vectorized UDFs is usually much higher when compared to the
normal Python UDF, as the serialization/deserialization overhead is minimized
by falling back to [Apache Arrow](https://arrow.apache.org/), while handling
Pandas.Series as input/output allows us to take full advantage of the Pandas
and NumPy libraries, making it a popular solution to parallelize Machine
Learning and other large-scale, distributed data science workloads (e.g.
feature engineering, distributed model application).
+
+
+# Conversion between PyFlink Table and Pandas DataFrame
+
+Pandas DataFrame is the de-facto standard for working with tabular data in the
Python community while PyFlink Table is Flink’s representation of the tabular
data in Python language. Enabling the conversion between PyFlink Table and
Pandas DataFrame allows switching between PyFlink and Pandas seamlessly when
processing data in Python. Users can process data using one execution engine
and switch to a different one effortlessly. For example, in case users already
have a Pandas DataFrame at hand and want to perform some expensive
transformation, they can easily convert it to a PyFlink Table and leverage the
power of the Flink engine. On the other hand, users can also convert a PyFlink
Table to a Pandas DataFrame and perform the same transformation with the rich
functionalities provided by the Pandas ecosystem.
+
+
+## Examples
+
+Using Python in Apache Flink requires installing PyFlink. PyFlink is available
through PyPI and can be easily installed using pip:
Review comment:
```suggestion
Using Python in Apache Flink requires installing PyFlink, which is available
on [PyPI](https://pypi.org/project/apache-flink/) and can be easily installed
using `pip`. Before installing PyFlink, check the working version of Python
running in your system using:
```
##########
File path: _posts/2020-07-28-pyflink-pandas-support-flink.md
##########
@@ -0,0 +1,253 @@
+---
+layout: post
+title: "PyFlink: The integration of Pandas into PyFlink"
+date: 2020-07-28T12:00:00.000Z
+authors:
+- Jincheng:
+ name: "Jincheng Sun"
+ twitter: "sunjincheng121"
+- markos:
+ name: "Markos Sfikas"
+ twitter: "MarkSfik"
+excerpt: Flink community put some great effort in integrating Pandas into
PyFlink with the latest Flink version 1.11. Some of the added features include
support for Pandas UDF and the conversion between Pandas DataFrame and Table.
In this article, we will introduce how these functionalities work and how to
use them with a step-by-step example.
+---
+
+Python has evolved into one of the most important programming languages for
many fields of data processing. So big has been Python’s popularity, that it
has pretty much become the default data processing language for data
scientists. On top of that, there is a plethora of Python-based data
processing tools such as NumPy, Pandas, and Scikit-learn that have gained
additional popularity due to their flexibility or powerful functionalities.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="600px" alt="Python Scientific Stack"/>
+</center>
+<center>
+ <a
href="https://speakerdeck.com/jakevdp/the-unexpected-effectiveness-of-python-in-science?slide=52";>Pic
source: VanderPlas 2017, slide 52.</a>
+</center>
+<br>
+
+In an effort to meet the user needs and demands, the Flink community hopes to
leverage and make better use of these tools. Along this direction, the Flink
community put some great effort in integrating Pandas into PyFlink with the
latest Flink version 1.11. Some of the added features include support for
Pandas UDF and the conversion between Pandas DataFrame and Table. Pandas UDF
not only greatly improve the execution performance of Python UDF, but also make
it more convenient for users to leverage libraries such as Pandas and NumPy in
Python UDF. Additionally, providing support for the conversion between Pandas
DataFrame and Table enables users to switch processing engines seamlessly
without the need for an intermediate connector. In the remainder of this
article, we will introduce how these functionalities work and how to use them
with a step-by-step example.
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Currently, only Scalar Pandas UDFs are supported in PyFlink.
+</div>
+
+<br>
+
+# Pandas UDF in Flink 1.11
+
+Using scalar Python UDF was already possible in Flink 1.10 as described in a
[previous article on the Flink
blog](https://flink.apache.org/2020/04/09/pyflink-udf-support-flink.html).
Scalar Python UDFs work based on three primary steps:
+
+ - the Java operator serializes one input row to bytes and sends them to the
Python worker;
+
+ - the Python worker deserializes the input row and evaluates the Python UDF
with it;
+
+ - the resulting row is serialized and sent back to the Java operator
+
+
+While providing support for Python UDFs in PyFlink greatly improved the user
experience, it had some drawbacks, namely resulting in:
+
+ - High serialization/deserialization overhead
+
+ - Difficulty when leveraging popular Python libraries used by data
scientists — such as Pandas or NumPy — that provide high-performance data
structure and functions.
+
+
+The introduction of Pandas UDF is used to address these drawbacks. For Pandas
UDF, a batch of rows is transferred between the JVM and PVM in a columnar
format (Arrow memory format). The batch of rows will be converted into a
collection of Pandas Series and will be transferred to the Pandas UDF to then
leverage popular Python libraries (such as Pandas, NumPy, etc.) for the Python
UDF implementation.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/vm-communication.png" width="600px"
alt="VM Communication"/>
+</center>
+<br>
+
+The performance of vectorized UDFs is usually much higher when compared to the
normal Python UDF, as the serialization/deserialization overhead is minimized
by falling back to [Apache Arrow](https://arrow.apache.org/), while handling
Pandas.Series as input/output allows us to take full advantage of the Pandas
and NumPy libraries, making it a popular solution to parallelize Machine
Learning and other large-scale, distributed data science workloads (e.g.
feature engineering, distributed model application).
+
+
+# Conversion between PyFlink Table and Pandas DataFrame
+
+Pandas DataFrame is the de-facto standard for working with tabular data in the
Python community while PyFlink Table is Flink’s representation of the tabular
data in Python language. Enabling the conversion between PyFlink Table and
Pandas DataFrame allows switching between PyFlink and Pandas seamlessly when
processing data in Python. Users can process data using one execution engine
and switch to a different one effortlessly. For example, in case users already
have a Pandas DataFrame at hand and want to perform some expensive
transformation, they can easily convert it to a PyFlink Table and leverage the
power of the Flink engine. On the other hand, users can also convert a PyFlink
Table to a Pandas DataFrame and perform the same transformation with the rich
functionalities provided by the Pandas ecosystem.
+
+
+## Examples
+
+Using Python in Apache Flink requires installing PyFlink. PyFlink is available
through PyPI and can be easily installed using pip:
+
+```bash
+$ python --version
+Python 3.7.6
+```
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Please note that Python 3.5 or higher is required to install and run PyFlink
+</div>
+
+<br>
Review comment:
```suggestion
Please note that Python 3.5 or higher is required to install and run PyFlink.
</div>
```
##########
File path: _posts/2020-07-28-pyflink-pandas-support-flink.md
##########
@@ -0,0 +1,253 @@
+---
+layout: post
+title: "PyFlink: The integration of Pandas into PyFlink"
+date: 2020-07-28T12:00:00.000Z
+authors:
+- Jincheng:
+ name: "Jincheng Sun"
+ twitter: "sunjincheng121"
+- markos:
+ name: "Markos Sfikas"
+ twitter: "MarkSfik"
+excerpt: Flink community put some great effort in integrating Pandas into
PyFlink with the latest Flink version 1.11. Some of the added features include
support for Pandas UDF and the conversion between Pandas DataFrame and Table.
In this article, we will introduce how these functionalities work and how to
use them with a step-by-step example.
+---
+
+Python has evolved into one of the most important programming languages for
many fields of data processing. So big has been Python’s popularity, that it
has pretty much become the default data processing language for data
scientists. On top of that, there is a plethora of Python-based data
processing tools such as NumPy, Pandas, and Scikit-learn that have gained
additional popularity due to their flexibility or powerful functionalities.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="600px" alt="Python Scientific Stack"/>
+</center>
+<center>
+ <a
href="https://speakerdeck.com/jakevdp/the-unexpected-effectiveness-of-python-in-science?slide=52";>Pic
source: VanderPlas 2017, slide 52.</a>
+</center>
+<br>
+
+In an effort to meet the user needs and demands, the Flink community hopes to
leverage and make better use of these tools. Along this direction, the Flink
community put some great effort in integrating Pandas into PyFlink with the
latest Flink version 1.11. Some of the added features include support for
Pandas UDF and the conversion between Pandas DataFrame and Table. Pandas UDF
not only greatly improve the execution performance of Python UDF, but also make
it more convenient for users to leverage libraries such as Pandas and NumPy in
Python UDF. Additionally, providing support for the conversion between Pandas
DataFrame and Table enables users to switch processing engines seamlessly
without the need for an intermediate connector. In the remainder of this
article, we will introduce how these functionalities work and how to use them
with a step-by-step example.
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Currently, only Scalar Pandas UDFs are supported in PyFlink.
+</div>
+
+<br>
+
+# Pandas UDF in Flink 1.11
+
+Using scalar Python UDF was already possible in Flink 1.10 as described in a
[previous article on the Flink
blog](https://flink.apache.org/2020/04/09/pyflink-udf-support-flink.html).
Scalar Python UDFs work based on three primary steps:
+
+ - the Java operator serializes one input row to bytes and sends them to the
Python worker;
+
+ - the Python worker deserializes the input row and evaluates the Python UDF
with it;
+
+ - the resulting row is serialized and sent back to the Java operator
+
+
+While providing support for Python UDFs in PyFlink greatly improved the user
experience, it had some drawbacks, namely resulting in:
+
+ - High serialization/deserialization overhead
+
+ - Difficulty when leveraging popular Python libraries used by data
scientists — such as Pandas or NumPy — that provide high-performance data
structure and functions.
+
+
+The introduction of Pandas UDF is used to address these drawbacks. For Pandas
UDF, a batch of rows is transferred between the JVM and PVM in a columnar
format (Arrow memory format). The batch of rows will be converted into a
collection of Pandas Series and will be transferred to the Pandas UDF to then
leverage popular Python libraries (such as Pandas, NumPy, etc.) for the Python
UDF implementation.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/vm-communication.png" width="600px"
alt="VM Communication"/>
+</center>
+<br>
+
+The performance of vectorized UDFs is usually much higher when compared to the
normal Python UDF, as the serialization/deserialization overhead is minimized
by falling back to [Apache Arrow](https://arrow.apache.org/), while handling
Pandas.Series as input/output allows us to take full advantage of the Pandas
and NumPy libraries, making it a popular solution to parallelize Machine
Learning and other large-scale, distributed data science workloads (e.g.
feature engineering, distributed model application).
+
+
+# Conversion between PyFlink Table and Pandas DataFrame
+
+Pandas DataFrame is the de-facto standard for working with tabular data in the
Python community while PyFlink Table is Flink’s representation of the tabular
data in Python language. Enabling the conversion between PyFlink Table and
Pandas DataFrame allows switching between PyFlink and Pandas seamlessly when
processing data in Python. Users can process data using one execution engine
and switch to a different one effortlessly. For example, in case users already
have a Pandas DataFrame at hand and want to perform some expensive
transformation, they can easily convert it to a PyFlink Table and leverage the
power of the Flink engine. On the other hand, users can also convert a PyFlink
Table to a Pandas DataFrame and perform the same transformation with the rich
functionalities provided by the Pandas ecosystem.
+
+
+## Examples
Review comment:
```suggestion
# Examples
### Installation
```
##########
File path: _posts/2020-07-28-pyflink-pandas-support-flink.md
##########
@@ -0,0 +1,253 @@
+---
+layout: post
+title: "PyFlink: The integration of Pandas into PyFlink"
+date: 2020-07-28T12:00:00.000Z
+authors:
+- Jincheng:
+ name: "Jincheng Sun"
+ twitter: "sunjincheng121"
+- markos:
+ name: "Markos Sfikas"
+ twitter: "MarkSfik"
+excerpt: Flink community put some great effort in integrating Pandas into
PyFlink with the latest Flink version 1.11. Some of the added features include
support for Pandas UDF and the conversion between Pandas DataFrame and Table.
In this article, we will introduce how these functionalities work and how to
use them with a step-by-step example.
+---
+
+Python has evolved into one of the most important programming languages for
many fields of data processing. So big has been Python’s popularity, that it
has pretty much become the default data processing language for data
scientists. On top of that, there is a plethora of Python-based data
processing tools such as NumPy, Pandas, and Scikit-learn that have gained
additional popularity due to their flexibility or powerful functionalities.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="600px" alt="Python Scientific Stack"/>
+</center>
+<center>
+ <a
href="https://speakerdeck.com/jakevdp/the-unexpected-effectiveness-of-python-in-science?slide=52";>Pic
source: VanderPlas 2017, slide 52.</a>
+</center>
+<br>
+
+In an effort to meet the user needs and demands, the Flink community hopes to
leverage and make better use of these tools. Along this direction, the Flink
community put some great effort in integrating Pandas into PyFlink with the
latest Flink version 1.11. Some of the added features include support for
Pandas UDF and the conversion between Pandas DataFrame and Table. Pandas UDF
not only greatly improve the execution performance of Python UDF, but also make
it more convenient for users to leverage libraries such as Pandas and NumPy in
Python UDF. Additionally, providing support for the conversion between Pandas
DataFrame and Table enables users to switch processing engines seamlessly
without the need for an intermediate connector. In the remainder of this
article, we will introduce how these functionalities work and how to use them
with a step-by-step example.
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Currently, only Scalar Pandas UDFs are supported in PyFlink.
+</div>
+
+<br>
+
+# Pandas UDF in Flink 1.11
+
+Using scalar Python UDF was already possible in Flink 1.10 as described in a
[previous article on the Flink
blog](https://flink.apache.org/2020/04/09/pyflink-udf-support-flink.html).
Scalar Python UDFs work based on three primary steps:
+
+ - the Java operator serializes one input row to bytes and sends them to the
Python worker;
+
+ - the Python worker deserializes the input row and evaluates the Python UDF
with it;
+
+ - the resulting row is serialized and sent back to the Java operator
+
+
+While providing support for Python UDFs in PyFlink greatly improved the user
experience, it had some drawbacks, namely resulting in:
+
+ - High serialization/deserialization overhead
+
+ - Difficulty when leveraging popular Python libraries used by data
scientists — such as Pandas or NumPy — that provide high-performance data
structure and functions.
+
+
+The introduction of Pandas UDF is used to address these drawbacks. For Pandas
UDF, a batch of rows is transferred between the JVM and PVM in a columnar
format (Arrow memory format). The batch of rows will be converted into a
collection of Pandas Series and will be transferred to the Pandas UDF to then
leverage popular Python libraries (such as Pandas, NumPy, etc.) for the Python
UDF implementation.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/vm-communication.png" width="600px"
alt="VM Communication"/>
+</center>
+<br>
+
+The performance of vectorized UDFs is usually much higher when compared to the
normal Python UDF, as the serialization/deserialization overhead is minimized
by falling back to [Apache Arrow](https://arrow.apache.org/), while handling
Pandas.Series as input/output allows us to take full advantage of the Pandas
and NumPy libraries, making it a popular solution to parallelize Machine
Learning and other large-scale, distributed data science workloads (e.g.
feature engineering, distributed model application).
+
+
+# Conversion between PyFlink Table and Pandas DataFrame
+
+Pandas DataFrame is the de-facto standard for working with tabular data in the
Python community while PyFlink Table is Flink’s representation of the tabular
data in Python language. Enabling the conversion between PyFlink Table and
Pandas DataFrame allows switching between PyFlink and Pandas seamlessly when
processing data in Python. Users can process data using one execution engine
and switch to a different one effortlessly. For example, in case users already
have a Pandas DataFrame at hand and want to perform some expensive
transformation, they can easily convert it to a PyFlink Table and leverage the
power of the Flink engine. On the other hand, users can also convert a PyFlink
Table to a Pandas DataFrame and perform the same transformation with the rich
functionalities provided by the Pandas ecosystem.
+
+
+## Examples
+
+Using Python in Apache Flink requires installing PyFlink. PyFlink is available
through PyPI and can be easily installed using pip:
+
+```bash
+$ python --version
+Python 3.7.6
+```
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Please note that Python 3.5 or higher is required to install and run PyFlink
+</div>
+
+<br>
+
+If you don't have a version above 3.5, you can use virtualenv with follows
commands:
+
+```bash
+$ pip install virtualenv
+$ virtualenv --python /usr/local/bin/python3 py37
+$ source py37/bin/activate
+
+```
+<br>
Review comment:
```suggestion
And then install PyFlink:
```
##########
File path: _posts/2020-07-28-pyflink-pandas-support-flink.md
##########
@@ -0,0 +1,253 @@
+---
+layout: post
+title: "PyFlink: The integration of Pandas into PyFlink"
+date: 2020-07-28T12:00:00.000Z
+authors:
+- Jincheng:
+ name: "Jincheng Sun"
+ twitter: "sunjincheng121"
+- markos:
+ name: "Markos Sfikas"
+ twitter: "MarkSfik"
+excerpt: Flink community put some great effort in integrating Pandas into
PyFlink with the latest Flink version 1.11. Some of the added features include
support for Pandas UDF and the conversion between Pandas DataFrame and Table.
In this article, we will introduce how these functionalities work and how to
use them with a step-by-step example.
+---
+
+Python has evolved into one of the most important programming languages for
many fields of data processing. So big has been Python’s popularity, that it
has pretty much become the default data processing language for data
scientists. On top of that, there is a plethora of Python-based data
processing tools such as NumPy, Pandas, and Scikit-learn that have gained
additional popularity due to their flexibility or powerful functionalities.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="600px" alt="Python Scientific Stack"/>
+</center>
+<center>
+ <a
href="https://speakerdeck.com/jakevdp/the-unexpected-effectiveness-of-python-in-science?slide=52";>Pic
source: VanderPlas 2017, slide 52.</a>
+</center>
+<br>
+
+In an effort to meet the user needs and demands, the Flink community hopes to
leverage and make better use of these tools. Along this direction, the Flink
community put some great effort in integrating Pandas into PyFlink with the
latest Flink version 1.11. Some of the added features include support for
Pandas UDF and the conversion between Pandas DataFrame and Table. Pandas UDF
not only greatly improve the execution performance of Python UDF, but also make
it more convenient for users to leverage libraries such as Pandas and NumPy in
Python UDF. Additionally, providing support for the conversion between Pandas
DataFrame and Table enables users to switch processing engines seamlessly
without the need for an intermediate connector. In the remainder of this
article, we will introduce how these functionalities work and how to use them
with a step-by-step example.
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Currently, only Scalar Pandas UDFs are supported in PyFlink.
+</div>
+
+<br>
+
Review comment:
```suggestion
```
##########
File path: _posts/2020-07-28-pyflink-pandas-support-flink.md
##########
@@ -0,0 +1,253 @@
+---
+layout: post
+title: "PyFlink: The integration of Pandas into PyFlink"
+date: 2020-07-28T12:00:00.000Z
+authors:
+- Jincheng:
+ name: "Jincheng Sun"
+ twitter: "sunjincheng121"
+- markos:
+ name: "Markos Sfikas"
+ twitter: "MarkSfik"
+excerpt: Flink community put some great effort in integrating Pandas into
PyFlink with the latest Flink version 1.11. Some of the added features include
support for Pandas UDF and the conversion between Pandas DataFrame and Table.
In this article, we will introduce how these functionalities work and how to
use them with a step-by-step example.
+---
+
+Python has evolved into one of the most important programming languages for
many fields of data processing. So big has been Python’s popularity, that it
has pretty much become the default data processing language for data
scientists. On top of that, there is a plethora of Python-based data
processing tools such as NumPy, Pandas, and Scikit-learn that have gained
additional popularity due to their flexibility or powerful functionalities.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="600px" alt="Python Scientific Stack"/>
+</center>
+<center>
+ <a
href="https://speakerdeck.com/jakevdp/the-unexpected-effectiveness-of-python-in-science?slide=52";>Pic
source: VanderPlas 2017, slide 52.</a>
+</center>
+<br>
+
+In an effort to meet the user needs and demands, the Flink community hopes to
leverage and make better use of these tools. Along this direction, the Flink
community put some great effort in integrating Pandas into PyFlink with the
latest Flink version 1.11. Some of the added features include support for
Pandas UDF and the conversion between Pandas DataFrame and Table. Pandas UDF
not only greatly improve the execution performance of Python UDF, but also make
it more convenient for users to leverage libraries such as Pandas and NumPy in
Python UDF. Additionally, providing support for the conversion between Pandas
DataFrame and Table enables users to switch processing engines seamlessly
without the need for an intermediate connector. In the remainder of this
article, we will introduce how these functionalities work and how to use them
with a step-by-step example.
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Currently, only Scalar Pandas UDFs are supported in PyFlink.
+</div>
+
+<br>
+
+# Pandas UDF in Flink 1.11
+
+Using scalar Python UDF was already possible in Flink 1.10 as described in a
[previous article on the Flink
blog](https://flink.apache.org/2020/04/09/pyflink-udf-support-flink.html).
Scalar Python UDFs work based on three primary steps:
+
+ - the Java operator serializes one input row to bytes and sends them to the
Python worker;
+
+ - the Python worker deserializes the input row and evaluates the Python UDF
with it;
+
+ - the resulting row is serialized and sent back to the Java operator
+
+
+While providing support for Python UDFs in PyFlink greatly improved the user
experience, it had some drawbacks, namely resulting in:
+
+ - High serialization/deserialization overhead
+
+ - Difficulty when leveraging popular Python libraries used by data
scientists — such as Pandas or NumPy — that provide high-performance data
structure and functions.
+
+
+The introduction of Pandas UDF is used to address these drawbacks. For Pandas
UDF, a batch of rows is transferred between the JVM and PVM in a columnar
format (Arrow memory format). The batch of rows will be converted into a
collection of Pandas Series and will be transferred to the Pandas UDF to then
leverage popular Python libraries (such as Pandas, NumPy, etc.) for the Python
UDF implementation.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/vm-communication.png" width="600px"
alt="VM Communication"/>
+</center>
+<br>
+
+The performance of vectorized UDFs is usually much higher when compared to the
normal Python UDF, as the serialization/deserialization overhead is minimized
by falling back to [Apache Arrow](https://arrow.apache.org/), while handling
Pandas.Series as input/output allows us to take full advantage of the Pandas
and NumPy libraries, making it a popular solution to parallelize Machine
Learning and other large-scale, distributed data science workloads (e.g.
feature engineering, distributed model application).
+
+
+# Conversion between PyFlink Table and Pandas DataFrame
+
+Pandas DataFrame is the de-facto standard for working with tabular data in the
Python community while PyFlink Table is Flink’s representation of the tabular
data in Python language. Enabling the conversion between PyFlink Table and
Pandas DataFrame allows switching between PyFlink and Pandas seamlessly when
processing data in Python. Users can process data using one execution engine
and switch to a different one effortlessly. For example, in case users already
have a Pandas DataFrame at hand and want to perform some expensive
transformation, they can easily convert it to a PyFlink Table and leverage the
power of the Flink engine. On the other hand, users can also convert a PyFlink
Table to a Pandas DataFrame and perform the same transformation with the rich
functionalities provided by the Pandas ecosystem.
+
+
+## Examples
+
+Using Python in Apache Flink requires installing PyFlink. PyFlink is available
through PyPI and can be easily installed using pip:
+
+```bash
+$ python --version
+Python 3.7.6
+```
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Please note that Python 3.5 or higher is required to install and run PyFlink
+</div>
+
+<br>
+
+If you don't have a version above 3.5, you can use virtualenv with follows
commands:
Review comment:
```suggestion
If you don't have a version above 3.5, you can create a virtual environment
(i.e. [virtualenv](https://docs.python-guide.org/dev/virtualenvs/)) with the
following commands:
```
##########
File path: _posts/2020-07-28-pyflink-pandas-support-flink.md
##########
@@ -0,0 +1,253 @@
+---
+layout: post
+title: "PyFlink: The integration of Pandas into PyFlink"
+date: 2020-07-28T12:00:00.000Z
+authors:
+- Jincheng:
+ name: "Jincheng Sun"
+ twitter: "sunjincheng121"
+- markos:
+ name: "Markos Sfikas"
+ twitter: "MarkSfik"
+excerpt: Flink community put some great effort in integrating Pandas into
PyFlink with the latest Flink version 1.11. Some of the added features include
support for Pandas UDF and the conversion between Pandas DataFrame and Table.
In this article, we will introduce how these functionalities work and how to
use them with a step-by-step example.
+---
+
+Python has evolved into one of the most important programming languages for
many fields of data processing. So big has been Python’s popularity, that it
has pretty much become the default data processing language for data
scientists. On top of that, there is a plethora of Python-based data
processing tools such as NumPy, Pandas, and Scikit-learn that have gained
additional popularity due to their flexibility or powerful functionalities.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/python-scientific-stack.png"
width="600px" alt="Python Scientific Stack"/>
+</center>
+<center>
+ <a
href="https://speakerdeck.com/jakevdp/the-unexpected-effectiveness-of-python-in-science?slide=52";>Pic
source: VanderPlas 2017, slide 52.</a>
+</center>
+<br>
+
+In an effort to meet the user needs and demands, the Flink community hopes to
leverage and make better use of these tools. Along this direction, the Flink
community put some great effort in integrating Pandas into PyFlink with the
latest Flink version 1.11. Some of the added features include support for
Pandas UDF and the conversion between Pandas DataFrame and Table. Pandas UDF
not only greatly improve the execution performance of Python UDF, but also make
it more convenient for users to leverage libraries such as Pandas and NumPy in
Python UDF. Additionally, providing support for the conversion between Pandas
DataFrame and Table enables users to switch processing engines seamlessly
without the need for an intermediate connector. In the remainder of this
article, we will introduce how these functionalities work and how to use them
with a step-by-step example.
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Currently, only Scalar Pandas UDFs are supported in PyFlink.
+</div>
+
+<br>
+
+# Pandas UDF in Flink 1.11
+
+Using scalar Python UDF was already possible in Flink 1.10 as described in a
[previous article on the Flink
blog](https://flink.apache.org/2020/04/09/pyflink-udf-support-flink.html).
Scalar Python UDFs work based on three primary steps:
+
+ - the Java operator serializes one input row to bytes and sends them to the
Python worker;
+
+ - the Python worker deserializes the input row and evaluates the Python UDF
with it;
+
+ - the resulting row is serialized and sent back to the Java operator
+
+
+While providing support for Python UDFs in PyFlink greatly improved the user
experience, it had some drawbacks, namely resulting in:
+
+ - High serialization/deserialization overhead
+
+ - Difficulty when leveraging popular Python libraries used by data
scientists — such as Pandas or NumPy — that provide high-performance data
structure and functions.
+
+
+The introduction of Pandas UDF is used to address these drawbacks. For Pandas
UDF, a batch of rows is transferred between the JVM and PVM in a columnar
format (Arrow memory format). The batch of rows will be converted into a
collection of Pandas Series and will be transferred to the Pandas UDF to then
leverage popular Python libraries (such as Pandas, NumPy, etc.) for the Python
UDF implementation.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-07-28-pyflink-pandas/vm-communication.png" width="600px"
alt="VM Communication"/>
+</center>
+<br>
+
+The performance of vectorized UDFs is usually much higher when compared to the
normal Python UDF, as the serialization/deserialization overhead is minimized
by falling back to [Apache Arrow](https://arrow.apache.org/), while handling
Pandas.Series as input/output allows us to take full advantage of the Pandas
and NumPy libraries, making it a popular solution to parallelize Machine
Learning and other large-scale, distributed data science workloads (e.g.
feature engineering, distributed model application).
+
+
+# Conversion between PyFlink Table and Pandas DataFrame
+
+Pandas DataFrame is the de-facto standard for working with tabular data in the
Python community while PyFlink Table is Flink’s representation of the tabular
data in Python language. Enabling the conversion between PyFlink Table and
Pandas DataFrame allows switching between PyFlink and Pandas seamlessly when
processing data in Python. Users can process data using one execution engine
and switch to a different one effortlessly. For example, in case users already
have a Pandas DataFrame at hand and want to perform some expensive
transformation, they can easily convert it to a PyFlink Table and leverage the
power of the Flink engine. On the other hand, users can also convert a PyFlink
Table to a Pandas DataFrame and perform the same transformation with the rich
functionalities provided by the Pandas ecosystem.
+
+
+## Examples
+
+Using Python in Apache Flink requires installing PyFlink. PyFlink is available
through PyPI and can be easily installed using pip:
+
+```bash
+$ python --version
+Python 3.7.6
+```
+
+<div class="alert alert-info" markdown="1">
+<span class="label label-info" style="display: inline-block"><span
class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span>
+Please note that Python 3.5 or higher is required to install and run PyFlink
+</div>
+
+<br>
+
+If you don't have a version above 3.5, you can use virtualenv with follows
commands:
+
+```bash
+$ pip install virtualenv
+$ virtualenv --python /usr/local/bin/python3 py37
+$ source py37/bin/activate
+
+```
+<br>
+
+```bash
+
+$ python -m pip install apache-flink
+
+```
+<br>
Review comment:
```suggestion
```
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