PandasOnDask Execution#

Queries that perform data transformation, data ingress or data egress using the pandas on Dask execution pass through the Modin components detailed below.

To enable pandas on Dask execution, please refer to the usage section in pandas on Dask.

Data Transformation#


When a user calls any DataFrame API, a query starts forming at the API layer to be executed at the Execution layer. The API layer is responsible for processing the query appropriately, for example, determining whether the final result should be a DataFrame or Series object. This layer is also responsible for sanitizing the input to the PandasQueryCompiler, e.g. validating a parameter from the query and defining specific intermediate values to provide more context to the query compiler. The PandasQueryCompiler is responsible for processing the query, received from the DataFrame API layer, to determine how to apply it to a subset of the data - either cell-wise or along an axis-wise partition backed by the pandas storage format. The PandasQueryCompiler maps the query to one of the Core Algebra Operators of the PandasOnDaskDataframe which inherits generic functionality from the PandasDataframe.

PandasOnDask Dataframe implementation#

Modin implements Dataframe, PartitionManager, AxisPartition and Partition classes specifically for the PandasOnDask execution.

Data Ingress#


Data Egress#


When a user calls any IO function from the module, the API layer queries the FactoryDispatcher which defines a factory specific for the execution, namely, the PandasOnDaskFactory. The factory, in turn, exposes the PandasOnDaskIO class whose responsibility is to perform a parallel read/write from/to a file.

When reading data from a CSV file, for example, the PandasOnDaskIO class forwards the user query to the _read() method of CSVDispatcher, where the query’s parameters are preprocessed to check if they are supported by the execution (defaulting to pandas if they are not) and computes some metadata common for all partitions to be read. Then, the file is split into row chunks, and this data is used to launch remote tasks on the Dask workers via the deploy() method of DaskWrapper. On each Dask worker, the PandasCSVParser parses data. After the remote tasks are finished, additional result postprocessing is performed, and a new query compiler with the data read is returned.

When writing data to a CSV file, for example, the PandasOnDaskIO processes the user query to execute it on Dask workers. Then, the PandasOnDaskIO asks the PandasOnDaskDataframe to decompose the data into row-wise partitions that will be written into the file in parallel in Dask workers.


Currently, data egress uses default pandas implementation for pandas on Dask execution.