Turn complex GraphQL queries into optimized database queries.
pip install graphql-compiler
GraphQL compiler is a library that simplifies data querying and exploration by exposing one simple query language written using GraphQL syntax to target multiple database backends. It currently supports OrientDB. and multiple SQL database management systems, such as PostgreSQL, MSSQL and MySQL.
For a detailed overview, see our blog post. To get started, see our Read the Docs documentation. To contribute, please see our contributing guide.
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)]Licensed under the Apache 2.0 License. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Copyright 2017-present Kensho Technologies, LLC. The present date is determined by the timestamp of the most recent commit in the repository.