def test_create_schema(self):
col1 = kudu.ColumnSchema.create('key', kudu.INT32)
col2 = kudu.ColumnSchema.create('int_val', kudu.INT32)
col3 = kudu.ColumnSchema.create('string_val', kudu.STRING)
cols = [col1, col2, col3]
# One key column
schema = kudu.schema_from_list(cols, 1)
self.assertEqual(len(schema), 3)
# Question whether we want to go the overloading route
self.assertTrue(schema.at(0).equals(col1))
self.assertTrue(schema.at(1).equals(col2))
self.assertTrue(schema.at(2).equals(col3))
def open_or_create_table(client, table, drop=False):
"""Based on the default dstat column names create a new table indexed by a timstamp col"""
exists = False
if client.table_exists(table):
exists = True
if drop:
client.delete_table(table)
exists = False
if not exists:
# Create the schema for the table, basically all float cols
cols = [kudu.ColumnSchema.create("ts", kudu.INT64)]
cols += [kudu.ColumnSchema.create(x, kudu.FLOAT) for x in DSTAT_COL_NAMES]
# Based on the column meta data create a new schema object, where the first column
# is the key column.
schema = kudu.schema_from_list(cols, 1)
client.create_table(table, schema)
return client.open_table(table)
def open_or_create_table(client, table, drop=False):
"""Based on the default dstat column names create a new table indexed by a timstamp col"""
exists = False
if client.table_exists(table):
exists = True
if drop:
client.delete_table(table)
exists = False
if not exists:
# Create the schema for the table, basically all float cols
cols = [kudu.ColumnSchema.create("ts", kudu.INT64)]
cols += [
kudu.ColumnSchema.create(x, kudu.FLOAT) for x in DSTAT_COL_NAMES
]
# Based on the column meta data create a new schema object, where the first column
# is the key column.
schema = kudu.schema_from_list(cols, 1)
client.create_table(table, schema)
return client.open_table(table)
def example_schema(cls):
col1 = kudu.ColumnSchema.create('key', kudu.INT32)
col2 = kudu.ColumnSchema.create('int_val', kudu.INT32)
col3 = kudu.ColumnSchema.create('string_val', kudu.STRING)
return kudu.schema_from_list([col1, col2, col3], 1)
