sbsv 0.0.1

SBSV: Square Brackets Separated Values

SBSV: square bracket separated values

Install

python3 -m pip install svsb

Use

You can read this log-like data:

[meta-data] [id 1] [format string]
[meta-data] [id 2] [format token]
[data] [string] [id 1] [actual some long string...]
[data] [token] [id 2] [actual [some] [multiple] [tokens]]
[stat] [rows 2]

import sbsv

parser = sbsv.parser()
parser.add_schema("[meta-data] [id: int] [format: str]")
parser.add_schema("[data] [string] [id: int] [actual: str]")
parser.add_schema("[data] [token] [id: int] [actual: list[str]]")
parser.add_schema("[stat] [rows: int]")
with open("testfile.svsb", "r") as f:
  result = parser.load(f)

Result would looks like:

{
  "meta-data": [{"id": 1, "format": "string"}, {"id": 2, "format": "string"}],
  "data": {
    "string": [{"id": 1, "actual": "some long string..."}],
    "token": [{"id": 2, "actual": ["some", "multiple", "tokens"]}]
  },
  "stat": [{"rows": 2}]
}

Details

Basic schema

Schema is consisted with schema name, variable name and type annotation.

[schema-name] [var-name: type]

You can use [A-Za-z0-9-_] for names.

Sub schema

[my-schema] [sub-schema] [some: int] [other: str] [data: bool]

You can add any sub schema. But if you add sub schema, you cannot add new schema with same schema name without sub schema.

[my-schema] [no: int] [sub: str] [schema: str]
# this will cause error

Ignore

[2024-03-04 13:22:56] [DEBUG] [necessary] [from] [this part]

Regular log file may contain unnecessary data. You can specify parser to ignore [2024-03-04 13:22:56] [DEBUG] part.

parser.add_schema("[$ignore] [$ignore] [necessary] [from] [this: str]")

Primitive types

Primitive types are str, int, float, bool, null.

Complex types

You can use list.