An application’s input and output drops are accessed through its inputs and outputs members. Both of these are lists of drops, and will be sorted in the same order in which inputs and outputs were defined in the Logical Graph. Each element can also be queried for its uid.

Data can be read from input drops, and written in output drops. To read data from an input drop, one calls first the drop’s open method, which returns a descriptor to the opened drop. Using this descriptor one can perform successive calls to read, which will return the data stored in the drop. Finally, the drop’s close method should be called to ensure that all internal resources are freed.

Writing data into an output drop is similar but simpler. Application authors need only call one or more times the write method with the data that needs to be written.


Many data components are capable of storing data in multiple formats determined by the drop component. The common data io interface allows app components to be compatible with many data component types, however different app components connected to the same data component must use compatible serialization and deserialization types and utilities.

String Serialization

Raw String

The simplest deserialization format supported directly by DataDrop.write and

JSON (.json)

Portable javascript object format encoded in utf-8. JSON Schema is to be handled by the input and output apps, which may also be stored as JSON. Serialization of python dictionaries is provided by json.dump and deserialization with json.load.

INI (.ini)

Simple format for storing string key-value pairs organized by sections that is supported by the python configparser library. Due to the exclusive use of string types this format is a good for mapping directly to command line arguments.

YAML (.yaml)

Markup format with similar featureset to JSON but additionally contains features such as comments, anchors and aliases which make it more human friendly to write. Serialization of dictionaries is provided by yaml.dump and deserialization with yaml.load.

XML (.xml)

Markup format with similar features to YAML but with the addition of attributes. Serialization can be performed using dicttoxml or both serialization and deserialization using xml.etree.ElementTree.

Python Eval (.py)

Python expressions and literals are valid string serialization formats whereby the string data is iterpreted as python code. Serialization is typically performed using the __repr__ instance method and deserialization using eval or ast.eval_literal.

Binary Serialization

Data drops may also store binary formats that are typically more efficient than string formats and may utilize the python buffer protocol.

Raw Bytes

Data drops can always be read as raw bytes using droputils.allDropContents and written to using DataDROP.write. Reading as a bytes object creates a readonly in-memory data copy that may not be as performant as other drop utilities.

Pickle (.pkl)

Default serialazation format capable of serializing any python object. Use save_pickle for serialization to this format and load_pickle for deserialization.

Numpy (.npy)

Portable numpy serialization format. Use save_numpy for serialization and load_numpy for deserialization.

Numpy Zipped (.npz)

Portable zipped numpy serialization format. Consists of a .zip directory holding one or more .npy files.

Table Serialization

parquet (.parquet)

Open source column-based relational data format from Apache.

Specialized Serialization

Data drops such as RDBMSDrop drops manage their own record format and are interfaced using relational data objects such dict, pyarrow.RecordBatch or pandas.DataFrame.