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bb09b5270f
Some tests have constraints that were there only to help reduce runtime which reduces coverage when running in exhaustive mode. The majority of the constraints are because it adds no value to run the test across additional dimensions (or it is invalid to run with those dimensions). Updates the tests that have legitimate constraints to use two new helper methods for constraining the table format dimension: create_uncompressed_text_dimension() create_parquet_dimension() These will create a dimension that will produce a single test vector, either uncompressed text or parquet respectively. Change-Id: Id85387c1efd5d192f8059ef89934933389bfe247 Reviewed-on: http://gerrit.ent.cloudera.com:8080/2149 Reviewed-by: Lenni Kuff <lskuff@cloudera.com> Tested-by: jenkins (cherry picked from commit e02acbd469bc48c684b2089405b4a20552802481) Reviewed-on: http://gerrit.ent.cloudera.com:8080/2290
74 lines
2.8 KiB
Python
74 lines
2.8 KiB
Python
#!/usr/bin/env python
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# Copyright (c) 2012 Cloudera, Inc. All rights reserved.
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# Validates all aggregate functions across all datatypes
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#
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import logging
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import pytest
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from tests.common.test_vector import *
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from tests.common.impala_test_suite import ImpalaTestSuite
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from tests.util.test_file_parser import QueryTestSectionReader
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agg_functions = ['sum', 'count', 'min', 'max', 'avg']
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data_types = ['int', 'bool', 'double', 'bigint', 'tinyint',
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'smallint', 'float', 'timestamp']
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result_lut = {
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# TODO: Add verification for other types
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'sum-tinyint': 45000, 'avg-tinyint': 5, 'count-tinyint': 9000,
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'min-tinyint': 1, 'max-tinyint': 9,
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'sum-smallint': 495000, 'avg-smallint': 50, 'count-smallint': 9900,
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'min-smallint': 1, 'max-smallint': 99,
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'sum-int': 4995000, 'avg-int': 500, 'count-int': 9990,
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'min-int': 1, 'max-int': 999,
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'sum-bigint': 49950000, 'avg-bigint': 5000, 'count-bigint': 9990,
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'min-bigint': 10, 'max-bigint': 9990,
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}
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class TestAggregation(ImpalaTestSuite):
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@classmethod
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def get_workload(self):
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return 'functional-query'
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@classmethod
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def add_test_dimensions(cls):
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super(TestAggregation, cls).add_test_dimensions()
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# Add two more dimensions
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cls.TestMatrix.add_dimension(TestDimension('agg_func', *agg_functions))
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cls.TestMatrix.add_dimension(TestDimension('data_type', *data_types))
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cls.TestMatrix.add_constraint(lambda v: cls.is_valid_vector(v))
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@classmethod
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def is_valid_vector(cls, vector):
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# Reduce execution time when exploration strategy is 'core'
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if cls.exploration_strategy() == 'core':
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if vector.get_value('exec_option')['batch_size'] != 0: return False
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data_type, agg_func = vector.get_value('data_type'), vector.get_value('agg_func')
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file_format = vector.get_value('table_format').file_format
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# Avro doesn't have timestamp type
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if file_format == 'avro' and data_type == 'timestamp':
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return False
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elif agg_func not in ['min', 'max', 'count'] and data_type == 'bool':
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return False
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elif agg_func == 'sum' and data_type == 'timestamp':
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return False
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return True
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def test_aggregation(self, vector):
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data_type, agg_func = (vector.get_value('data_type'), vector.get_value('agg_func'))
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query = 'select %s(%s_col) from alltypesagg' % (agg_func, data_type)
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result = self.execute_scalar(query, vector.get_value('exec_option'),
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table_format=vector.get_value('table_format'))
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if 'int' in data_type:
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assert result_lut['%s-%s' % (agg_func, data_type)] == int(result)
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# AVG
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if vector.get_value('data_type') == 'timestamp' and\
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vector.get_value('agg_func') == 'avg':
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return
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query = 'select %s(DISTINCT(%s_col)) from alltypesagg' % (agg_func, data_type)
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result = self.execute_scalar(query, vector.get_value('exec_option'))
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