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This is the first set of changes required to start getting our functional test infrastructure moved from JUnit to Python. After investigating a number of option, I decided to go with a python test executor named py.test (http://pytest.org/). It is very flexible, open source (MIT licensed), and will enable us to do some cool things like parallel test execution. As part of this change, we now use our "test vectors" for query test execution. This will be very nice because it means if load the "core" dataset you know you will be able to run the "core" query tests (specified by --exploration_strategy when running the tests). You will see that now each combination of table format + query exec options is treated like an individual test case. this will make it much easier to debug exactly where something failed. These new tests can be run using the script at tests/run-tests.sh
60 lines
2.3 KiB
Python
60 lines
2.3 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 *
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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_dataset(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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# Only execute against text format, restrict bool types to min/max
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cls.TestMatrix.add_constraint(\
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lambda v: v.get_value('table_format').file_format == 'text')
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cls.TestMatrix.add_constraint(lambda v: v.get_value('exec_option')['batch_size'] == 0)
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cls.TestMatrix.add_constraint(lambda v: v.get_value('agg_func') in ['min', 'max'] if\
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v.get_value('data_type') == 'bool' else 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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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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