Highly storage efficient binary JSON format
Project description
Fractured Binary JSON
A binary JSON encoding optimized for small storage size.
For more information, see here.
Usage
import frac_json as jf
# basic usage
encoded_object = fj.encode({ key1: "value" })
decoded_object = fj.decode(encoded_object)
# with compression
large_object = {
# ...
}
encoded_object2 = encode(large_object, compression_level=3)
# with keys table
keys_table = keys_table_from_json(large_object) # one time only, save this to a file
# keys_table = keys_table_from_keys(["key", "key1", "key2", "key3"]) # or generate from keys
encoded_object3 = encode(large_object, global_keys_table_bytes=keys_table)
Functions
json_type = Union[dict, list, str, int, float, bool, None]
# Encode a JSON object (object, array, string, number, boolean, null) to bytes.
def encode(
object: json_type,
# bytes of an external keys table
# to generate a keys table from keys, use keys_table_from_keys or keys_table_from_json
global_keys_table_bytes: Optional[bytes] = None,
# compression level for zstandard. 1-22. Default is 3.
compression_level: Optional[int] = None,
# pre trained zstandard dictionary
zstd_dict: Optional[bytes] = None,
) -> bytes:
...
# Decode bytes to a JSON object (object, array, string, number, boolean, null).
def decode(
frac_json_bytes: bytes,
# bytes of an external keys table
global_keys_table_bytes: Optional[bytes] = None,
# pre trained zstandard dictionary
zstd_dict: Optional[bytes] = None,
) -> Any:
...
# Generate a keys table from a list of unique keys.
# To improve performance during encoding, keys should be sorted by frequency of occurrence.
def keys_table_from_keys(keys: List[str]) -> bytes:
...
# Generate a keys table from a JSON object.
def keys_table_from_json(
# object to recursively extract keys from
object: Any,
# maximum number of keys to extract
max_count: Optional[int] = None,
# minimum number of occurrences for a key to be included
occurrence_cutoff: Optional[int] = None,
) -> bytes:
...
Project details
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