Example Queries

import ibis
from strawberry.utils.str_converters import to_camel_case

from graphique import GraphQL


def execute(query):
    result = app.schema.execute_sync(query, root_value=app.root_value)
    for error in result.errors or []:
        raise ValueError(error)
    return result.data


table = ibis.read_parquet("../../tests/fixtures/zipcodes.parquet")
# example projection: camel-cased fields (not relevant in this dataset)
_ = table.select({to_camel_case(name): table[name] for name in table.columns})
app = GraphQL(table)

View schema.

execute("""{
  count
  schema {
    names
    types
    partitioning
  }
}""")

{'count': 41700,
 'schema': {'names': ['latitude',
   'longitude',
   'state',
   'city',
   'county',
   'zipcode'],
  'types': ['float64', 'float64', 'string', 'string', 'string', 'int32'],
  'partitioning': []}}

California counties with the most cities.

• filter state by “CA”
• group by county
  • aggregate distinct cities
• project city count
• order by city counts descending
• access columns
  • county is still known in the schema
  • cities is a new column accessed through an inline fragment

execute("""{
  filter(state: {eq: "CA"}) {
    group(by: "county", aggregate: {collect: {name: "city", distinct: true}}) {
      project(columns: {alias: "cities", array: {length: {name: "city"}}}) {
        order(by: "-cities", limit: 5) {
          columns {
            county {
              values
            }
          }
          cities: column(name: "cities") {
            ... on BigIntColumn {
              values
            }
          }
        }
      }
    }
  }
}""")

{'filter': {'group': {'project': {'order': {'columns': {'county': {'values': ['Los Angeles',
        'San Bernardino',
        'San Diego',
        'Orange',
        'Riverside']}},
     'cities': {'values': [132, 73, 52, 47, 42]}}}}}}

States with cities which match the name of their county.

• filter using where expression, because comparing two columns is not a “simple” query
• Column.distinct instead of group, because no other aggregates are needed

execute("""{
  filter(where: {eq: [{name: "county"}, {name: "city"}]}) {
    columns {
      state {
        distinct {
          values
        }
      }
    }
  }
}""")

{'filter': {'columns': {'state': {'distinct': {'values': ['VT',
      'WV',
      'NC',
      'FL',
      'MN',
      'KS',
      'AR',
      'OK',
      'TX',
      'ID',
      'AK',
      'VA',
      'OH',
      'IN',
      'WI',
      'MO',
      'NE',
      'LA',
      'NM',
      'HI',
      'OR',
      'PR',
      'MA',
      'RI',
      'NH',
      'ME',
      'CT',
      'MD',
      'KY',
      'SD',
      'CO',
      'UT',
      'AZ',
      'NV',
      'CA',
      'WA',
      'NJ',
      'NY',
      'PA',
      'DE',
      'SC',
      'AL',
      'TN',
      'MS',
      'MI',
      'IA',
      'ND',
      'MT',
      'IL',
      'WY']}}}}}

States which have no cities which match the name of their county.

The opposite of the previous example. Filtering rows would drop needed data; the “zeros” have to be counted.

• project column matching names instead of filtering
• group by state
  • aggregate whether there are any matches
• filter for no matches
• access column

execute("""{
  project(columns: {alias: "match", eq: [{name: "county"}, {name: "city"}]}) {
    group(by: "state", aggregate: {any: {name: "match"}}) {
      filter(where: {inv: {name: "match"}}) {
        columns {
          state {
            values
          }
        }
      }
    }
  }
}""")

{'project': {'group': {'filter': {'columns': {'state': {'values': ['DC',
       'GA']}}}}}}