from graphql.utils.schema_printer import print_schema
from graphql_compiler import (
    get_graphql_schema_from_orientdb_schema_data, graphql_to_match
)
from graphql_compiler.schema.schema_info import CommonSchemaInfo
from graphql_compiler.schema_generation.orientdb.utils import ORIENTDB_SCHEMA_RECORDS_QUERY

# Step 1: Get schema metadata from hypothetical Animals database.
client = your_function_that_returns_an_orientdb_client()
schema_records = client.command(ORIENTDB_SCHEMA_RECORDS_QUERY)
schema_data = [record.oRecordData for record in schema_records]

# Step 2: Generate GraphQL schema from metadata.
schema, type_equivalence_hints = get_graphql_schema_from_orientdb_schema_data(schema_data)

print(print_schema(schema))
# schema {
#    query: RootSchemaQuery
# }
#
# directive @filter(op_name: String!, value: [String!]!) on FIELD | INLINE_FRAGMENT
#
# directive @tag(tag_name: String!) on FIELD
#
# directive @output(out_name: String!) on FIELD
#
# directive @output_source on FIELD
#
# directive @optional on FIELD
#
# directive @recurse(depth: Int!) on FIELD
#
# directive @fold on FIELD
#
# type Animal {
#     name: String
#     net_worth: Int
#     limbs: Int
# }
#
# type RootSchemaQuery{
#     Animal: [Animal]
# }

# Step 3: Write GraphQL query that returns the names of all animals with a certain net worth.
# Note that we prefix net_worth with '$' and surround it with quotes to indicate it's a parameter.
graphql_query = '''
{
    Animal {
        name @output(out_name: "animal_name")
        net_worth @filter(op_name: "=", value: ["$net_worth"])
    }
}
'''
parameters = {
    'net_worth': '100',
}

# Step 4: Use autogenerated GraphQL schema to compile query into the target database language.
common_schema_info = CommonSchemaInfo(schema, type_equivalence_hints)
compilation_result = graphql_to_match(common_schema_info, graphql_query, parameters)
print(compilation_result.query)
# SELECT Animal___1.name AS `animal_name`
# FROM  ( MATCH  { class: Animal, where: ((net_worth = decimal("100"))), as: Animal___1 }
# RETURN $matches)

from graphql_compiler import get_sqlalchemy_schema_info, graphql_to_sql
from sqlalchemy import MetaData, create_engine

engine = create_engine('<connection string>')

# Reflect the default database schema. Each table must have a primary key. Otherwise see:
# https://graphql-compiler.readthedocs.io/en/latest/supported_databases/sql.html#including-tables-without-explicitly-enforced-primary-keys
metadata = MetaData(bind=engine)
metadata.reflect()

# Wrap the schema information into a SQLAlchemySchemaInfo object.
sql_schema_info = get_sqlalchemy_schema_info(metadata.tables, {}, engine.dialect)

# Write GraphQL query.
graphql_query = '''
{
    Animal {
        name @output(out_name: "animal_name")
    }
}
'''
parameters = {}

# Compile and execute query.
compilation_result = graphql_to_sql(sql_schema_info, graphql_query, parameters)
query_results = [dict(row) for row in engine.execute(compilation_result.query)]