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.
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
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.
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
PandasOnDaskDataframe which inherits
generic functionality from the
PandasOnDask Dataframe implementation#
specifically for the PandasOnDask execution.
When a user calls any IO function from the
modin.pandas.io module, the API layer queries the
FactoryDispatcher which defines a factory specific for
the execution, namely, the
PandasOnDaskFactory. The factory, in turn,
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
deploy() method of
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
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.