structy | irresponsibly dumb and simple struct serialization
kandi X-RAY | structy Summary
kandi X-RAY | structy Summary
Structy is an irresponsibly dumb and simple serialization/deserialization library for C, Python, and vanilla JavaScript. You can think of it like protobuf, thrift, flatbuffers, etc. but imagine that instead of a team of engineers maintaining it, it's instead written by a single moron. Structy was created to exchange data between C-based firmware on embedded devices and Python- and JavaScript-based programming, test, and calibration scripts running on a big-girl computer. As such, its C implementation is designed specifically for embedded devices: it doesn't do any dynamic allocation and it doesn't have any fancy code for optimizations. Be small, be simple, be useful. Be fast, be clever. If you want something far more thought out and comprehensive I'd suggest checking out things like Kaitai Struct.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of structy
structy Key Features
structy Examples and Code Snippets
class UserSettings:
brightness : uint8 = 127
dark_mode: bool = False
user_id : uint32
$ python3 -m pip install structy
$ structy user_settings.schema --c generated
#import "generated/user_settings.h"
struct UserSettings settings = {
void StructName_print(const struct StructName* inst);
struct UserSettings @ 0x0B00B135
- brightness: 127
- dark_mode: 1
- user_id: 6
#include "super_cool_printf.h"
#define STRUCTY_PRINTF(...) super_cool_printf(__VA_ARGS__)
# Create an instance with default values for all fields.
inst = StructName()
# Or, override fields with a custom value while creating an instance.
inst = StructName(field_name=something)
Community Discussions
Trending Discussions on structy
QUESTION
I have a DataFrame which contains multiple nested columns. The schema is not static and could change upstream of my Spark application. Schema evolution is guaranteed to always be backward compatible. An anonymized, shortened version of the schema is pasted below
...ANSWER
Answered 2018-May-19 at 06:12You could try using the DataFrame.withColumn()
. It allows you to reference nested fields. You could add a new map
column and drop the flat one. This question shows how to handle structs with withColumn
.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install structy
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page