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38befd2126
This patch allows the text scanner to read 'inf' or 'Infinity' from a row and correctly translate it into floating-point infinity. It also adds is_inf() and is_nan() builtins. Finally, we change the text table writer to write Infinity and NaN for compatibility with Hive. In the future, we might consider adding nan / inf literals to our grammar (postgres has this, see: http://www.postgresql.org/docs/9.3/static/datatype-numeric.html). Change-Id: I796f2852b3c6c3b72e9aae9dd5ad228d188a6ea3 Reviewed-on: http://gerrit.ent.cloudera.com:8080/2393 Reviewed-by: Henry Robinson <henry@cloudera.com> Tested-by: jenkins (cherry picked from commit 58091355142cadd2b74874d9aa7c8ab6bf3efe2f) Reviewed-on: http://gerrit.ent.cloudera.com:8080/2483
74 lines
2.8 KiB
Python
Executable File
74 lines
2.8 KiB
Python
Executable File
#!/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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