A library provides a more easy way to describe DataFrame schema for Spark and MLSQL.

This library requires Spark 2.3+/2.4+ (tested).

You can link against this library in your program at the following coordinates:

groupId: tech.mlsql
artifactId: simple-schema_2.11
version: 0.2.0

val s = SparkSimpleSchemaParser.parse("st(field(column1,string),field(column2,string),field(column3,string))")
assert(s == StructType(Seq(StructField("column1", StringType), StructField("column2", StringType), StructField("column3", StringType))))

Spark DataFrame schema normally is represented by json, but json is not easy to write and used as plain-text in quote. Simple schema create a new format to make this easy.

st means StructType, filed means StructField,the first value in field is columnName,and the second is type. For now, simple schema supports type like following:

1. st
2. field
3. string
4. float
5. double
6. integer
7. short
8. date
9. binary
10. map
11. array
12. long
13. boolean
14. byte
15. decimal

Suppose you have a json data:

{"column1":{"key":"value"}}

you can describe it like this:

st(field(column1,map(string,string)))

st also supports nesting:

st(field(column1,map(string,array(st(field(columnx,string))))))