This section outlines design principles guiding the Koalas project.
Koalas targets Python data scientists. We want to stick to the convention that users are already familiar with as much as possible. Here are some examples:
Function names and parameters use snake_case, rather than CamelCase. This is different from PySpark’s design. For example, Koalas has to_pandas(), whereas PySpark has toPandas() for converting a DataFrame into a pandas DataFrame. In limited cases, to maintain compatibility with Spark, we also provide Spark’s variant as an alias.
Koalas respects to the largest extent the conventions of the Python numerical ecosystem, and allows the use of NumPy types, etc. that can be supported by Spark.
Koalas docs’ style and infrastructure simply follow rest of the PyData projects’.
The Koalas DataFrame is meant to provide the best of pandas and Spark under a single API, with easy and clear conversions between each API when necessary. When Spark and pandas have similar APIs with subtle differences, the principle is to honor the contract of the pandas API first.
There are different classes of functions:
Functions that are found in both Spark and pandas under the same name (count, dtypes, head). The return value is the same as the return type in pandas (and not Spark’s). Functions that are found in Spark but that have a clear equivalent in pandas, e.g. alias and rename. These functions will be implemented as the alias of the pandas function, but should be marked that they are aliases of the same functions. They are provided so that existing users of PySpark can get the benefits of Koalas without having to adapt their code. Functions that are only found in pandas. When these functions are appropriate for distributed datasets, they should become available in Koalas. Functions that are only found in Spark that are essential to controlling the distributed nature of the computations, e.g. cache. These functions should be available in Koalas.
Functions that are found in both Spark and pandas under the same name (count, dtypes, head). The return value is the same as the return type in pandas (and not Spark’s).
Functions that are found in Spark but that have a clear equivalent in pandas, e.g. alias and rename. These functions will be implemented as the alias of the pandas function, but should be marked that they are aliases of the same functions. They are provided so that existing users of PySpark can get the benefits of Koalas without having to adapt their code.
Functions that are only found in pandas. When these functions are appropriate for distributed datasets, they should become available in Koalas.
Functions that are only found in Spark that are essential to controlling the distributed nature of the computations, e.g. cache. These functions should be available in Koalas.
We are still debating whether data transformation functions only available in Spark should be added to Koalas, e.g. select. We would love to hear your feedback on that.
Often developers face the question whether a particular function should return a Koalas DataFrame/Series, or a pandas DataFrame/Series. The principle is: if the returned object can be large, use a Koalas DataFrame/Series. If the data is bound to be small, use a pandas DataFrame/Series. For example, DataFrame.dtypes return a pandas Series, because the number of columns in a DataFrame is bounded and small, whereas DataFrame.head() or Series.unique() returns a Koalas DataFrame/Series, because the resulting object can be large.
At the risk of overgeneralization, there are two API design approaches: the first focuses on providing APIs for common tasks; the second starts with abstractions, and enable users to accomplish their tasks by composing primitives. While the world is not black and white, pandas takes more of the former approach, while Spark has taken more of the later.
One example is value count (count by some key column), one of the most common operations in data science. pandas DataFrame.value_count returns the result in sorted order, which in 90% of the cases is what users prefer when exploring data, whereas Spark’s does not sort, which is more desirable when building data pipelines, as users can accomplish the pandas behavior by adding an explicit orderBy.
Similar to pandas, Koalas should also lean more towards the former, providing discoverable APIs for common data science tasks. In most cases, this principle is well taken care of by simply implementing pandas’ APIs. However, there will be circumstances in which pandas’ APIs don’t address a specific need, e.g. plotting for big data.
All functions and parameters should be documented. Most functions should be documented with examples, because those are the easiest to understand than a blob of text explaining what the function does.
A recommended way to add documentation is to start with the docstring of the corresponding function in PySpark or pandas, and adapt it for Koalas. If you are adding a new function, also add it to the API reference doc index page in docs/source/reference directory. The examples in docstring also improve our test coverage.
Certain operations in pandas are prohibitively expensive as data scales, and we don’t want to give users the illusion that they can rely on such operations in Koalas. That is to say, methods implemented in Koalas should be safe to perform by default on large datasets. As a result, the following capabilities are not implemented in Koalas:
Capabilities that are fundamentally not parallelizable: e.g. imperatively looping over each element
Capabilities that require materializing the entire working set in a single node’s memory. This is why we do not implement pandas.DataFrame.to_xarray. Another example is the _repr_html_ call caps the total number of records shown to a maximum of 1000, to prevent users from blowing up their driver node simply by typing the name of the DataFrame in a notebook.
A few exceptions, however, exist. One common pattern with “big data science” is that while the initial dataset is large, the working set becomes smaller as the analysis goes deeper. For example, data scientists often perform aggregation on datasets and want to then convert the aggregated dataset to some local data structure. To help data scientists, we offer the following:
DataFrame.to_pandas(): returns a pandas DataFrame, koalas only
DataFrame.to_pandas()
DataFrame.to_numpy(): returns a numpy array, works with both pandas and Koalas
DataFrame.to_numpy()
Note that it is clear from the names that these functions return some local data structure that would require materializing data in a single node’s memory. For these functions, we also explicitly document them with a warning note that the resulting data structure must be small.
Koalas is designed as an API overlay layer on top of Spark. The project should be lightweight, and most functions should be implemented as wrappers around Spark or pandas. Koalas does not accept heavyweight implementations, e.g. execution engine changes.
This approach enables us to move fast. For the considerable future, we aim to be making bi-weekly releases. If we find a critical bug, we will be making a new release as soon as the bug fix is available.
Koalas should be well tested. The project tracks its test coverage with over 90% across the entire codebase, and close to 100% for critical parts. Pull requests will not be accepted unless they have close to 100% statement coverage from the codecov report.