class Test(TestBase): def setup(self): datatypes = lambda *types: [validate_data_type(t) for t in types] schema = Schema.from_lists( ['name', 'id', 'fid', 'isMale', 'scale', 'birth'], datatypes('string', 'int64', 'float64', 'boolean', 'decimal', 'datetime')) self.schema = df_schema_to_odps_schema(schema) table_name = 'pyodps_test_engine_table' self.odps.delete_table(table_name, if_exists=True) self.table = self.odps.create_table(name='pyodps_test_engine_table', schema=self.schema) self.expr = CollectionExpr(_source_data=self.table, _schema=schema) self.engine = ODPSEngine(self.odps) class FakeBar(object): def update(self, *args, **kwargs): pass self.faked_bar = FakeBar() def _gen_data(self, rows=None, data=None, nullable_field=None, value_range=None): if data is None: data = [] for _ in range(rows): record = [] for t in self.schema.types: method = getattr(self, '_gen_random_%s' % t.name) if t.name == 'bigint': record.append(method(value_range=value_range)) else: record.append(method()) data.append(record) if nullable_field is not None: j = self.schema._name_indexes[nullable_field] for i, l in enumerate(data): if i % 2 == 0: data[i][j] = None self.odps.write_table(self.table, 0, [self.table.new_record(values=d) for d in data]) return data def testTunnelCases(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) expr = self.expr.name.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) res = self.engine._handle_cases(self.expr, self.faked_bar) result = self._get_result(res) self.assertEqual(data, result) expr = self.expr['name', self.expr.id.rename('new_id')] res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual([it[:2] for it in data], result) table_name = 'pyodps_test_engine_partitioned' self.odps.delete_table(table_name, if_exists=True) df = self.engine.persist(self.expr, table_name, partitions=['name']) try: expr = df.count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertIsNone(res) expr = df[df.name == data[0][0]]['fid', 'id'].count() expr = self.engine._pre_process(expr) res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(res, 0) expr = df[df.name == data[0][0]]['fid', 'id'] res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(len(res), 0) finally: self.odps.delete_table(table_name, if_exists=True) df = self.engine.persist(self.expr, table_name, partitions=['name', 'id']) try: expr = df.count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertIsNone(res) expr = df[(df.name == data[0][0]) & (df.id == data[0][1])]['fid', 'ismale'].count() expr = self.engine._pre_process(expr) res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(res, 0) expr = df[(df.name == data[0][0]) & (df.id == data[0][1])]['fid', 'ismale'] res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(len(res), 0) finally: self.odps.delete_table(table_name, if_exists=True) def testAsync(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.id.sum() res = self.engine.execute(expr, async=True) self.assertNotEqual(res.instance.status, Instance.Status.TERMINATED) res.wait() self.assertEqual(sum(it[1] for it in data), res.fetch()) def testBase(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr[self.expr.id < 10]['name', lambda x: x.id] result = self._get_result(self.engine.execute(expr).values) self.assertEqual(len([it for it in data if it[1] < 10]), len(result)) if len(result) > 0: self.assertEqual(2, len(result[0])) expr = self.expr[Scalar(3).rename('const'), self.expr.id, (self.expr.id + 1).rename('id2')] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual([c.name for c in res.columns], ['const', 'id', 'id2']) self.assertTrue(all(it[0] == 3 for it in result)) self.assertEqual(len(data), len(result)) self.assertEqual([it[1] + 1 for it in data], [it[2] for it in result]) expr = self.expr.sort('id')[:5] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual(sorted(data, key=lambda it: it[1])[:5], result) expr = self.expr.sort('id')[:5] # test do not use tunnel res = self.engine.execute(expr, use_tunnel=False) result = self._get_result(res.values) self.assertEqual(sorted(data, key=lambda it: it[1])[:5], result) def testElement(self): data = self._gen_data(5, nullable_field='name') fields = [ self.expr.name.isnull().rename('name1'), self.expr.name.notnull().rename('name2'), self.expr.name.fillna('test').rename('name3'), self.expr.id.isin([1, 2, 3]).rename('id1'), self.expr.id.isin(self.expr.fid.astype('int')).rename('id2'), self.expr.id.notin([1, 2, 3]).rename('id3'), self.expr.id.notin(self.expr.fid.astype('int')).rename('id4'), self.expr.id.between(self.expr.fid, 3).rename('id5'), self.expr.name.fillna('test').switch( 'test', 'test' + self.expr.name.fillna('test'), 'test2', 'test2' + self.expr.name.fillna('test'), default=self.expr.name).rename('name4'), self.expr.id.cut([100, 200, 300], labels=['xsmall', 'small', 'large', 'xlarge'], include_under=True, include_over=True).rename('id6') ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual(len([it for it in data if it[0] is None]), len([it[0] for it in result if it[0]])) self.assertEqual(len([it[0] for it in data if it[0] is not None]), len([it[1] for it in result if it[1]])) self.assertEqual([(it[0] if it[0] is not None else 'test') for it in data], [it[2] for it in result]) self.assertEqual([(it[1] in (1, 2, 3)) for it in data], [it[3] for it in result]) fids = [int(it[2]) for it in data] self.assertEqual([(it[1] in fids) for it in data], [it[4] for it in result]) self.assertEqual([(it[1] not in (1, 2, 3)) for it in data], [it[5] for it in result]) self.assertEqual([(it[1] not in fids) for it in data], [it[6] for it in result]) self.assertEqual([(it[2] <= it[1] <= 3) for it in data], [it[7] for it in result]) self.assertEqual( [to_str('testtest' if it[0] is None else it[0]) for it in data], [to_str(it[8]) for it in result]) def get_val(val): if val <= 100: return 'xsmall' elif 100 < val <= 200: return 'small' elif 200 < val <= 300: return 'large' else: return 'xlarge' self.assertEqual([to_str(get_val(it[1])) for it in data], [to_str(it[9]) for it in result]) def testArithmetic(self): data = self._gen_data(5, value_range=(-1000, 1000)) fields = [ (self.expr.id + 1).rename('id1'), (self.expr.fid - 1).rename('fid1'), (self.expr.scale * 2).rename('scale1'), (self.expr.scale + self.expr.id).rename('scale2'), (self.expr.id / 2).rename('id2'), (self.expr.id**-2).rename('id3'), abs(self.expr.id).rename('id4'), (~self.expr.id).rename('id5'), (-self.expr.fid).rename('fid2'), (~self.expr.isMale).rename('isMale1'), (-self.expr.isMale).rename('isMale2'), (self.expr.id // 2).rename('id6'), (self.expr.birth + day(1).rename('birth1')), (self.expr.birth - (self.expr.birth - millisecond(10))).rename('birth2'), ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual([it[1] + 1 for it in data], [it[0] for it in result]) self.assertAlmostEqual([it[2] - 1 for it in data], [it[1] for it in result]) self.assertEqual([it[4] * 2 for it in data], [it[2] for it in result]) self.assertEqual([it[4] + it[1] for it in data], [it[3] for it in result]) self.assertAlmostEqual([float(it[1]) / 2 for it in data], [it[4] for it in result]) self.assertEqual([int(it[1]**-2) for it in data], [it[5] for it in result]) self.assertEqual([abs(it[1]) for it in data], [it[6] for it in result]) self.assertEqual([~it[1] for it in data], [it[7] for it in result]) self.assertAlmostEqual([-it[2] for it in data], [it[8] for it in result]) self.assertEqual([not it[3] for it in data], [it[9] for it in result]) self.assertEqual([it[1] // 2 for it in data], [it[11] for it in result]) self.assertEqual([it[5] + timedelta(days=1) for it in data], [it[12] for it in result]) self.assertEqual([10] * len(data), [it[13] for it in result]) def testMath(self): data = self._gen_data(5, value_range=(1, 90)) import numpy as np methods_to_fields = [ (np.sin, self.expr.id.sin()), (np.cos, self.expr.id.cos()), (np.tan, self.expr.id.tan()), (np.sinh, self.expr.id.sinh()), (np.cosh, self.expr.id.cosh()), (np.tanh, self.expr.id.tanh()), (np.log, self.expr.id.log()), (np.log2, self.expr.id.log2()), (np.log10, self.expr.id.log10()), (np.log1p, self.expr.id.log1p()), (np.exp, self.expr.id.exp()), (np.expm1, self.expr.id.expm1()), (np.arccosh, self.expr.id.arccosh()), (np.arcsinh, self.expr.id.arcsinh()), (np.arctanh, self.expr.id.arctanh()), (np.arctan, self.expr.id.arctan()), (np.sqrt, self.expr.id.sqrt()), (np.abs, self.expr.id.abs()), (np.ceil, self.expr.id.ceil()), (np.floor, self.expr.id.floor()), (np.trunc, self.expr.id.trunc()), ] fields = [ it[1].rename('id' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[1]) for it in data] second = [it[i] for it in result] self.assertEqual(len(first), len(second)) for it1, it2 in zip(first, second): if np.isnan(it1) and np.isnan(it2): continue self.assertAlmostEqual(it1, it2) def testString(self): data = self._gen_data(5) methods_to_fields = [ (lambda s: s.capitalize(), self.expr.name.capitalize()), (lambda s: data[0][0] in s, self.expr.name.contains(data[0][0], regex=False)), (lambda s: s.count(data[0][0]), self.expr.name.count(data[0][0])), (lambda s: s.endswith(data[0][0]), self.expr.name.endswith(data[0][0])), (lambda s: s.startswith(data[0][0]), self.expr.name.startswith(data[0][0])), (lambda s: s.find(data[0][0]), self.expr.name.find(data[0][0])), (lambda s: s.rfind(data[0][0]), self.expr.name.rfind(data[0][0])), (lambda s: s.replace(data[0][0], 'test'), self.expr.name.replace(data[0][0], 'test')), (lambda s: s[0], self.expr.name.get(0)), (lambda s: len(s), self.expr.name.len()), (lambda s: s.ljust(10), self.expr.name.ljust(10)), (lambda s: s.ljust(20, '*'), self.expr.name.ljust(20, fillchar='*')), (lambda s: s.rjust(10), self.expr.name.rjust(10)), (lambda s: s.rjust(20, '*'), self.expr.name.rjust(20, fillchar='*')), (lambda s: s * 4, self.expr.name.repeat(4)), (lambda s: s[2:10:2], self.expr.name.slice(2, 10, 2)), (lambda s: s[-5:-1], self.expr.name.slice(-5, -1)), (lambda s: s.title(), self.expr.name.title()), (lambda s: s.rjust(20, '0'), self.expr.name.zfill(20)), (lambda s: s.isalnum(), self.expr.name.isalnum()), (lambda s: s.isalpha(), self.expr.name.isalpha()), (lambda s: s.isdigit(), self.expr.name.isdigit()), (lambda s: s.isspace(), self.expr.name.isspace()), (lambda s: s.isupper(), self.expr.name.isupper()), (lambda s: s.istitle(), self.expr.name.istitle()), (lambda s: to_str(s).isnumeric(), self.expr.name.isnumeric()), (lambda s: to_str(s).isdecimal(), self.expr.name.isdecimal()), ] fields = [ it[1].rename('id' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[0]) for it in data] second = [it[i] for it in result] self.assertEqual(first, second) def testApply(self): data = self._gen_data(5) def my_func(row): return row.name, expr = self.expr['name', 'id'].apply(my_func, axis=1, names='name') res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([r[0] for r in result], [r[0] for r in data]) def my_func2(row): yield len(row.name) yield row.id expr = self.expr['name', 'id'].apply(my_func2, axis=1, names='cnt', types='int') res = self.engine.execute(expr) result = self._get_result(res) def gen_expected(data): for r in data: yield len(r[0]) yield r[1] self.assertEqual([r[0] for r in result], [r for r in gen_expected(data)]) def testDatetime(self): data = self._gen_data(5) import pandas as pd methods_to_fields = [ (lambda s: list(s.birth.dt.year.values), self.expr.birth.year), (lambda s: list(s.birth.dt.month.values), self.expr.birth.month), (lambda s: list(s.birth.dt.day.values), self.expr.birth.day), (lambda s: list(s.birth.dt.hour.values), self.expr.birth.hour), (lambda s: list(s.birth.dt.minute.values), self.expr.birth.minute), (lambda s: list(s.birth.dt.second.values), self.expr.birth.second), (lambda s: list(s.birth.dt.weekofyear.values), self.expr.birth.weekofyear), (lambda s: list(s.birth.dt.dayofweek.values), self.expr.birth.dayofweek), (lambda s: list(s.birth.dt.weekday.values), self.expr.birth.weekday), (lambda s: list(s.birth.dt.date.values), self.expr.birth.date), (lambda s: list(s.birth.dt.strftime('%Y%d')), self.expr.birth.strftime('%Y%d')) ] fields = [ it[1].rename('birth' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) def conv(v): if isinstance(v, pd.Timestamp): return v.to_datetime().date() else: return v second = [conv(it[i]) for it in result] self.assertEqual(first, second) def testSortDistinct(self): data = [ ['name1', 4, None, None, None, None], ['name2', 2, None, None, None, None], ['name1', 4, None, None, None, None], ['name1', 3, None, None, None, None], ] self._gen_data(data=data) expr = self.expr.sort(['name', -self.expr.id ]).distinct(['name', lambda x: x.id + 1])[:50] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 3) expected = [['name1', 5], ['name1', 4], ['name2', 3]] self.assertEqual(expected, result) def testGroupbyAggregation(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby(['name', 'id'])[lambda x: x.fid.min() * 2 < 8] \ .agg(self.expr.fid.max() + 1, new_id=self.expr.id.sum()) res = self.engine.execute(expr) result = self._get_result(res) expected = [['name1', 3, 5.1, 6], ['name2', 2, 4.5, 2]] result = sorted(result, key=lambda k: k[0]) self.assertEqual(expected, result) field = self.expr.groupby('name').sort(['id', -self.expr.fid]).row_number() expr = self.expr['name', 'id', 'fid', field] res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 4.1, 1], ['name1', 3, 2.2, 2], ['name1', 4, 5.3, 3], ['name1', 4, 4.2, 4], ['name2', 2, 3.5, 1], ] result = sorted(result, key=lambda k: (k[0], k[1], -k[2])) self.assertEqual(expected, result) expr = self.expr.name.value_counts()[:25] expected = [['name1', 4], ['name2', 1]] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.name.topk(25) res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.groupby('name').count() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([it[1:] for it in expected], result) expected = [['name1', 2], ['name2', 1]] expr = self.expr.groupby('name').id.nunique() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([it[1:] for it in expected], result) expr = self.expr[self.expr['id'] > 2].name.value_counts()[:25] expected = [['name1', 4]] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) def testJoinGroupby(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [['name1', 4, -1], ['name2', 1, -2]] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) expr = self.expr.join(expr2, on='name')[self.expr] expr = expr.groupby('id').agg(expr.fid.sum()) res = self.engine.execute(expr) result = self._get_result(res) import pandas as pd expected = pd.DataFrame(data, columns=self.expr.schema.names).groupby('id').agg({'fid': 'sum'})\ .reset_index().values.tolist() for it in zip(sorted(expected, key=lambda it: it[0]), sorted(result, key=lambda it: it[0])): self.assertAlmostEqual(it[0][0], it[1][0]) self.assertAlmostEqual(it[0][1], it[1][1]) def testFilterGroupby(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby( ['name']).agg(id=self.expr.id.max())[lambda x: x.id > 3] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 1) expected = [['name1', 4]] self.assertEqual(expected, result) def testWindowRewrite(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr[self.expr.id - self.expr.id.mean() < 10][[ lambda x: x.id - x.id.max() ]][[lambda x: x.id - x.id.min()]][lambda x: x.id - x.id.std() > 0] res = self.engine.execute(expr) result = self._get_result(res) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) expected = df.id - df.id.max() expected = expected - expected.min() expected = list(expected[expected - expected.std() > 0]) self.assertEqual(expected, [it[0] for it in result]) def testReduction(self): data = self._gen_data(rows=5, value_range=(-100, 100)) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) methods_to_fields = [ (lambda s: df.id.mean(), self.expr.id.mean()), (lambda s: len(df), self.expr.count()), (lambda s: df.id.var(ddof=0), self.expr.id.var(ddof=0)), (lambda s: df.id.std(ddof=0), self.expr.id.std(ddof=0)), (lambda s: df.id.median(), self.expr.id.median()), (lambda s: df.id.sum(), self.expr.id.sum()), (lambda s: df.id.min(), self.expr.id.min()), (lambda s: df.id.max(), self.expr.id.max()), (lambda s: df.isMale.min(), self.expr.isMale.min()), (lambda s: df.name.max(), self.expr.name.max()), (lambda s: df.birth.max(), self.expr.birth.max()), (lambda s: df.isMale.sum(), self.expr.isMale.sum()), (lambda s: df.isMale.any(), self.expr.isMale.any()), (lambda s: df.isMale.all(), self.expr.isMale.all()), (lambda s: df.name.nunique(), self.expr.name.nunique()), ] fields = [ it[1].rename('f' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) second = [it[i] for it in result][0] if isinstance(first, float): self.assertAlmostEqual(first, second) else: self.assertEqual(first, second) def testMapReduceByApplyDistributeSort(self): data = [ ['name key', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key name', 3, 4.1, None, None, None], ] self._gen_data(data=data) def mapper(row): for word in row[0].split(): yield word, 1 class reducer(object): def __init__(self): self._curr = None self._cnt = 0 def __call__(self, row): if self._curr is None: self._curr = row.word elif self._curr != row.word: yield (self._curr, self._cnt) self._curr = row.word self._cnt = 0 self._cnt += row.count def close(self): if self._curr is not None: yield (self._curr, self._cnt) expr = self.expr['name', ].apply(mapper, axis=1, names=['word', 'count'], types=['string', 'int']) expr = expr.groupby('word').sort('word').apply(reducer, names=['word', 'count'], types=['string', 'int']) res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) def testMapReduce(self): data = [ ['name key', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key name', 3, 4.1, None, None, None], ] self._gen_data(data=data) @output(['word', 'cnt'], ['string', 'int']) def mapper(row): for word in row[0].split(): yield word, 1 @output(['word', 'cnt'], ['string', 'int']) def reducer(keys): cnt = [ 0, ] def h(row, done): cnt[0] += row[1] if done: yield keys[0], cnt[0] return h expr = self.expr['name', ].map_reduce(mapper, reducer, group='word') res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) @output(['word', 'cnt'], ['string', 'int']) class reducer2(object): def __init__(self, keys): self.cnt = 0 def __call__(self, row, done): self.cnt += row.cnt if done: yield row.word, self.cnt expr = self.expr['name', ].map_reduce(mapper, reducer2, group='word') res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) def testDistributeSort(self): data = [ ['name', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key', 3, 4.1, None, None, None], ] self._gen_data(data=data) @output_names('name', 'id') @output_types('string', 'int') class reducer(object): def __init__(self): self._curr = None self._cnt = 0 def __call__(self, row): if self._curr is None: self._curr = row.name elif self._curr != row.name: yield (self._curr, self._cnt) self._curr = row.name self._cnt = 0 self._cnt += 1 def close(self): if self._curr is not None: yield (self._curr, self._cnt) expr = self.expr['name', ].groupby('name').sort('name').apply(reducer) res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 2], ['name', 3]] self.assertEqual(sorted(expected), sorted(result)) def testJoin(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [['name1', 4, -1], ['name2', 1, -2]] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr.join(expr2)['name', 'id2'] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 5) expected = [[to_str('name1'), 4], [to_str('name2'), 1]] self.assertTrue(all(it in expected for it in result)) expr = self.expr.join(expr2, on=['name', ('id', 'id2')])[self.expr.name, expr2.id2] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 2) expected = [to_str('name1'), 4] self.assertTrue(all(it == expected for it in result)) finally: table2.drop() def testUnion(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [['name3', 5, -1], ['name4', 6, -2]] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr['name', 'id'].distinct().union( expr2[expr2.id2.rename('id'), 'name']) res = self.engine.execute(expr) result = self._get_result(res) expected = [['name1', 4], ['name1', 3], ['name2', 2], ['name3', 5], ['name4', 6]] result = sorted(result) expected = sorted(expected) self.assertEqual(len(result), len(expected)) for e, r in zip(result, expected): self.assertEqual([to_str(t) for t in e], [to_str(t) for t in r]) finally: table2.drop() def testPersist(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) table_name = 'pyodps_test_engine_persist_table' try: df = self.engine.persist(self.expr, table_name) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, result) finally: self.odps.delete_table(table_name, if_exists=True) try: schema = Schema.from_lists(self.schema.names, self.schema.types, ['ds'], ['string']) self.odps.create_table(table_name, schema) df = self.engine.persist(self.expr, table_name, partition='ds=today', create_partition=True) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, [d[:-1] for d in result]) finally: self.odps.delete_table(table_name, if_exists=True) try: self.engine.persist(self.expr, table_name, partitions=['name']) t = self.odps.get_table(table_name) self.assertEqual(2, len(list(t.partitions))) with t.open_reader(partition='name=name1', reopen=True) as r: self.assertEqual(4, r.count) with t.open_reader(partition='name=name2', reopen=True) as r: self.assertEqual(1, r.count) finally: self.odps.delete_table(table_name, if_exists=True) def teardown(self): self.table.drop()
class Test(TestBase): def setup(self): datatypes = lambda *types: [validate_data_type(t) for t in types] schema = Schema.from_lists(['name', 'id', 'fid', 'isMale', 'scale', 'birth'], datatypes('string', 'int64', 'float64', 'boolean', 'decimal', 'datetime')) self.schema = df_schema_to_odps_schema(schema) table_name = 'pyodps_test_engine_table' self.odps.delete_table(table_name, if_exists=True) self.table = self.odps.create_table( name='pyodps_test_engine_table', schema=self.schema) self.expr = CollectionExpr(_source_data=self.table, _schema=schema) self.engine = ODPSEngine(self.odps) class FakeBar(object): def update(self, *args, **kwargs): pass self.faked_bar = FakeBar() def _gen_data(self, rows=None, data=None, nullable_field=None, value_range=None): if data is None: data = [] for _ in range(rows): record = [] for t in self.schema.types: method = getattr(self, '_gen_random_%s' % t.name) if t.name == 'bigint': record.append(method(value_range=value_range)) else: record.append(method()) data.append(record) if nullable_field is not None: j = self.schema._name_indexes[nullable_field] for i, l in enumerate(data): if i % 2 == 0: data[i][j] = None self.odps.write_table(self.table, 0, [self.table.new_record(values=d) for d in data]) return data def testTunnelCases(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) expr = self.expr.name.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) res = self.engine._handle_cases(self.expr, self.faked_bar) result = self._get_result(res) self.assertEqual(data, result) expr = self.expr['name', self.expr.id.rename('new_id')] res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual([it[:2] for it in data], result) table_name = 'pyodps_test_engine_partitioned' self.odps.delete_table(table_name, if_exists=True) df = self.engine.persist(self.expr, table_name, partitions=['name']) try: expr = df.count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertIsNone(res) expr = df[df.name == data[0][0]]['fid', 'id'].count() expr = self.engine._pre_process(expr) res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(res, 0) expr = df[df.name == data[0][0]]['fid', 'id'] res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(len(res), 0) finally: self.odps.delete_table(table_name, if_exists=True) df = self.engine.persist(self.expr, table_name, partitions=['name', 'id']) try: expr = df.count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertIsNone(res) expr = df[(df.name == data[0][0]) & (df.id == data[0][1])]['fid', 'ismale'].count() expr = self.engine._pre_process(expr) res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(res, 0) expr = df[(df.name == data[0][0]) & (df.id == data[0][1])]['fid', 'ismale'] res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(len(res), 0) finally: self.odps.delete_table(table_name, if_exists=True) def testAsync(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.id.sum() res = self.engine.execute(expr, async=True) self.assertNotEqual(res.instance.status, Instance.Status.TERMINATED) res.wait() self.assertEqual(sum(it[1] for it in data), res.fetch()) def testBase(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr[self.expr.id < 10]['name', lambda x: x.id] result = self._get_result(self.engine.execute(expr).values) self.assertEqual(len([it for it in data if it[1] < 10]), len(result)) if len(result) > 0: self.assertEqual(2, len(result[0])) expr = self.expr[Scalar(3).rename('const'), self.expr.id, (self.expr.id + 1).rename('id2')] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual([c.name for c in res.columns], ['const', 'id', 'id2']) self.assertTrue(all(it[0] == 3 for it in result)) self.assertEqual(len(data), len(result)) self.assertEqual([it[1]+1 for it in data], [it[2] for it in result]) expr = self.expr.sort('id')[:5] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual(sorted(data, key=lambda it: it[1])[:5], result) expr = self.expr.sort('id')[:5] # test do not use tunnel res = self.engine.execute(expr, use_tunnel=False) result = self._get_result(res.values) self.assertEqual(sorted(data, key=lambda it: it[1])[:5], result) def testElement(self): data = self._gen_data(5, nullable_field='name') fields = [ self.expr.name.isnull().rename('name1'), self.expr.name.notnull().rename('name2'), self.expr.name.fillna('test').rename('name3'), self.expr.id.isin([1, 2, 3]).rename('id1'), self.expr.id.isin(self.expr.fid.astype('int')).rename('id2'), self.expr.id.notin([1, 2, 3]).rename('id3'), self.expr.id.notin(self.expr.fid.astype('int')).rename('id4'), self.expr.id.between(self.expr.fid, 3).rename('id5'), self.expr.name.fillna('test').switch('test', 'test' + self.expr.name.fillna('test'), 'test2', 'test2' + self.expr.name.fillna('test'), default=self.expr.name).rename('name4'), self.expr.id.cut([100, 200, 300], labels=['xsmall', 'small', 'large', 'xlarge'], include_under=True, include_over=True).rename('id6') ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual(len([it for it in data if it[0] is None]), len([it[0] for it in result if it[0]])) self.assertEqual(len([it[0] for it in data if it[0] is not None]), len([it[1] for it in result if it[1]])) self.assertEqual([(it[0] if it[0] is not None else 'test') for it in data], [it[2] for it in result]) self.assertEqual([(it[1] in (1, 2, 3)) for it in data], [it[3] for it in result]) fids = [int(it[2]) for it in data] self.assertEqual([(it[1] in fids) for it in data], [it[4] for it in result]) self.assertEqual([(it[1] not in (1, 2, 3)) for it in data], [it[5] for it in result]) self.assertEqual([(it[1] not in fids) for it in data], [it[6] for it in result]) self.assertEqual([(it[2] <= it[1] <= 3) for it in data], [it[7] for it in result]) self.assertEqual([to_str('testtest' if it[0] is None else it[0]) for it in data], [to_str(it[8]) for it in result]) def get_val(val): if val <= 100: return 'xsmall' elif 100 < val <= 200: return 'small' elif 200 < val <= 300: return 'large' else: return 'xlarge' self.assertEqual([to_str(get_val(it[1])) for it in data], [to_str(it[9]) for it in result]) def testArithmetic(self): data = self._gen_data(5, value_range=(-1000, 1000)) fields = [ (self.expr.id + 1).rename('id1'), (self.expr.fid - 1).rename('fid1'), (self.expr.scale * 2).rename('scale1'), (self.expr.scale + self.expr.id).rename('scale2'), (self.expr.id / 2).rename('id2'), (self.expr.id ** -2).rename('id3'), abs(self.expr.id).rename('id4'), (~self.expr.id).rename('id5'), (-self.expr.fid).rename('fid2'), (~self.expr.isMale).rename('isMale1'), (-self.expr.isMale).rename('isMale2'), (self.expr.id // 2).rename('id6'), (self.expr.birth + day(1).rename('birth1')), (self.expr.birth - (self.expr.birth - millisecond(10))).rename('birth2'), ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual([it[1] + 1 for it in data], [it[0] for it in result]) self.assertAlmostEqual([it[2] - 1 for it in data], [it[1] for it in result]) self.assertEqual([it[4] * 2 for it in data], [it[2] for it in result]) self.assertEqual([it[4] + it[1] for it in data], [it[3] for it in result]) self.assertAlmostEqual([float(it[1]) / 2 for it in data], [it[4] for it in result]) self.assertEqual([int(it[1] ** -2) for it in data], [it[5] for it in result]) self.assertEqual([abs(it[1]) for it in data], [it[6] for it in result]) self.assertEqual([~it[1] for it in data], [it[7] for it in result]) self.assertAlmostEqual([-it[2] for it in data], [it[8] for it in result]) self.assertEqual([not it[3] for it in data], [it[9] for it in result]) self.assertEqual([it[1] // 2 for it in data], [it[11] for it in result]) self.assertEqual([it[5] + timedelta(days=1) for it in data], [it[12] for it in result]) self.assertEqual([10] * len(data), [it[13] for it in result]) def testMath(self): data = self._gen_data(5, value_range=(1, 90)) import numpy as np methods_to_fields = [ (np.sin, self.expr.id.sin()), (np.cos, self.expr.id.cos()), (np.tan, self.expr.id.tan()), (np.sinh, self.expr.id.sinh()), (np.cosh, self.expr.id.cosh()), (np.tanh, self.expr.id.tanh()), (np.log, self.expr.id.log()), (np.log2, self.expr.id.log2()), (np.log10, self.expr.id.log10()), (np.log1p, self.expr.id.log1p()), (np.exp, self.expr.id.exp()), (np.expm1, self.expr.id.expm1()), (np.arccosh, self.expr.id.arccosh()), (np.arcsinh, self.expr.id.arcsinh()), (np.arctanh, self.expr.id.arctanh()), (np.arctan, self.expr.id.arctan()), (np.sqrt, self.expr.id.sqrt()), (np.abs, self.expr.id.abs()), (np.ceil, self.expr.id.ceil()), (np.floor, self.expr.id.floor()), (np.trunc, self.expr.id.trunc()), ] fields = [it[1].rename('id'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[1]) for it in data] second = [it[i] for it in result] self.assertEqual(len(first), len(second)) for it1, it2 in zip(first, second): if np.isnan(it1) and np.isnan(it2): continue self.assertAlmostEqual(it1, it2) def testString(self): data = self._gen_data(5) methods_to_fields = [ (lambda s: s.capitalize(), self.expr.name.capitalize()), (lambda s: data[0][0] in s, self.expr.name.contains(data[0][0], regex=False)), (lambda s: s.count(data[0][0]), self.expr.name.count(data[0][0])), (lambda s: s.endswith(data[0][0]), self.expr.name.endswith(data[0][0])), (lambda s: s.startswith(data[0][0]), self.expr.name.startswith(data[0][0])), (lambda s: s.find(data[0][0]), self.expr.name.find(data[0][0])), (lambda s: s.rfind(data[0][0]), self.expr.name.rfind(data[0][0])), (lambda s: s.replace(data[0][0], 'test'), self.expr.name.replace(data[0][0], 'test')), (lambda s: s[0], self.expr.name.get(0)), (lambda s: len(s), self.expr.name.len()), (lambda s: s.ljust(10), self.expr.name.ljust(10)), (lambda s: s.ljust(20, '*'), self.expr.name.ljust(20, fillchar='*')), (lambda s: s.rjust(10), self.expr.name.rjust(10)), (lambda s: s.rjust(20, '*'), self.expr.name.rjust(20, fillchar='*')), (lambda s: s * 4, self.expr.name.repeat(4)), (lambda s: s[2: 10: 2], self.expr.name.slice(2, 10, 2)), (lambda s: s[-5: -1], self.expr.name.slice(-5, -1)), (lambda s: s.title(), self.expr.name.title()), (lambda s: s.rjust(20, '0'), self.expr.name.zfill(20)), (lambda s: s.isalnum(), self.expr.name.isalnum()), (lambda s: s.isalpha(), self.expr.name.isalpha()), (lambda s: s.isdigit(), self.expr.name.isdigit()), (lambda s: s.isspace(), self.expr.name.isspace()), (lambda s: s.isupper(), self.expr.name.isupper()), (lambda s: s.istitle(), self.expr.name.istitle()), (lambda s: to_str(s).isnumeric(), self.expr.name.isnumeric()), (lambda s: to_str(s).isdecimal(), self.expr.name.isdecimal()), ] fields = [it[1].rename('id'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[0]) for it in data] second = [it[i] for it in result] self.assertEqual(first, second) def testApply(self): data = self._gen_data(5) def my_func(row): return row.name, expr = self.expr['name', 'id'].apply(my_func, axis=1, names='name') res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([r[0] for r in result], [r[0] for r in data]) def my_func2(row): yield len(row.name) yield row.id expr = self.expr['name', 'id'].apply(my_func2, axis=1, names='cnt', types='int') res = self.engine.execute(expr) result = self._get_result(res) def gen_expected(data): for r in data: yield len(r[0]) yield r[1] self.assertEqual([r[0] for r in result], [r for r in gen_expected(data)]) def testDatetime(self): data = self._gen_data(5) import pandas as pd methods_to_fields = [ (lambda s: list(s.birth.dt.year.values), self.expr.birth.year), (lambda s: list(s.birth.dt.month.values), self.expr.birth.month), (lambda s: list(s.birth.dt.day.values), self.expr.birth.day), (lambda s: list(s.birth.dt.hour.values), self.expr.birth.hour), (lambda s: list(s.birth.dt.minute.values), self.expr.birth.minute), (lambda s: list(s.birth.dt.second.values), self.expr.birth.second), (lambda s: list(s.birth.dt.weekofyear.values), self.expr.birth.weekofyear), (lambda s: list(s.birth.dt.dayofweek.values), self.expr.birth.dayofweek), (lambda s: list(s.birth.dt.weekday.values), self.expr.birth.weekday), (lambda s: list(s.birth.dt.date.values), self.expr.birth.date), (lambda s: list(s.birth.dt.strftime('%Y%d')), self.expr.birth.strftime('%Y%d')) ] fields = [it[1].rename('birth'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) def conv(v): if isinstance(v, pd.Timestamp): return v.to_datetime().date() else: return v second = [conv(it[i]) for it in result] self.assertEqual(first, second) def testSortDistinct(self): data = [ ['name1', 4, None, None, None, None], ['name2', 2, None, None, None, None], ['name1', 4, None, None, None, None], ['name1', 3, None, None, None, None], ] self._gen_data(data=data) expr = self.expr.sort(['name', -self.expr.id]).distinct(['name', lambda x: x.id + 1])[:50] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 3) expected = [ ['name1', 5], ['name1', 4], ['name2', 3] ] self.assertEqual(expected, result) def testGroupbyAggregation(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby(['name', 'id'])[lambda x: x.fid.min() * 2 < 8] \ .agg(self.expr.fid.max() + 1, new_id=self.expr.id.sum()) res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 5.1, 6], ['name2', 2, 4.5, 2] ] result = sorted(result, key=lambda k: k[0]) self.assertEqual(expected, result) field = self.expr.groupby('name').sort(['id', -self.expr.fid]).row_number() expr = self.expr['name', 'id', 'fid', field] res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 4.1, 1], ['name1', 3, 2.2, 2], ['name1', 4, 5.3, 3], ['name1', 4, 4.2, 4], ['name2', 2, 3.5, 1], ] result = sorted(result, key=lambda k: (k[0], k[1], -k[2])) self.assertEqual(expected, result) expr = self.expr.name.value_counts()[:25] expected = [ ['name1', 4], ['name2', 1] ] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.name.topk(25) res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.groupby('name').count() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([it[1:] for it in expected], result) expected = [ ['name1', 2], ['name2', 1] ] expr = self.expr.groupby('name').id.nunique() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([it[1:] for it in expected], result) expr = self.expr[self.expr['id'] > 2].name.value_counts()[:25] expected = [ ['name1', 4] ] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) def testJoinGroupby(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [ ['name1', 4, -1], ['name2', 1, -2] ] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) expr = self.expr.join(expr2, on='name')[self.expr] expr = expr.groupby('id').agg(expr.fid.sum()) res = self.engine.execute(expr) result = self._get_result(res) import pandas as pd expected = pd.DataFrame(data, columns=self.expr.schema.names).groupby('id').agg({'fid': 'sum'})\ .reset_index().values.tolist() for it in zip(sorted(expected, key=lambda it: it[0]), sorted(result, key=lambda it: it[0])): self.assertAlmostEqual(it[0][0], it[1][0]) self.assertAlmostEqual(it[0][1], it[1][1]) def testFilterGroupby(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby(['name']).agg(id=self.expr.id.max())[lambda x: x.id > 3] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 1) expected = [ ['name1', 4] ] self.assertEqual(expected, result) def testWindowRewrite(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr[self.expr.id - self.expr.id.mean() < 10][ [lambda x: x.id - x.id.max()]][[lambda x: x.id - x.id.min()]][lambda x: x.id - x.id.std() > 0] res = self.engine.execute(expr) result = self._get_result(res) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) expected = df.id - df.id.max() expected = expected - expected.min() expected = list(expected[expected - expected.std() > 0]) self.assertEqual(expected, [it[0] for it in result]) def testReduction(self): data = self._gen_data(rows=5, value_range=(-100, 100)) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) methods_to_fields = [ (lambda s: df.id.mean(), self.expr.id.mean()), (lambda s: len(df), self.expr.count()), (lambda s: df.id.var(ddof=0), self.expr.id.var(ddof=0)), (lambda s: df.id.std(ddof=0), self.expr.id.std(ddof=0)), (lambda s: df.id.median(), self.expr.id.median()), (lambda s: df.id.sum(), self.expr.id.sum()), (lambda s: df.id.min(), self.expr.id.min()), (lambda s: df.id.max(), self.expr.id.max()), (lambda s: df.isMale.min(), self.expr.isMale.min()), (lambda s: df.name.max(), self.expr.name.max()), (lambda s: df.birth.max(), self.expr.birth.max()), (lambda s: df.isMale.sum(), self.expr.isMale.sum()), (lambda s: df.isMale.any(), self.expr.isMale.any()), (lambda s: df.isMale.all(), self.expr.isMale.all()), (lambda s: df.name.nunique(), self.expr.name.nunique()), ] fields = [it[1].rename('f'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) second = [it[i] for it in result][0] if isinstance(first, float): self.assertAlmostEqual(first, second) else: self.assertEqual(first, second) def testMapReduceByApplyDistributeSort(self): data = [ ['name key', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key name', 3, 4.1, None, None, None], ] self._gen_data(data=data) def mapper(row): for word in row[0].split(): yield word, 1 class reducer(object): def __init__(self): self._curr = None self._cnt = 0 def __call__(self, row): if self._curr is None: self._curr = row.word elif self._curr != row.word: yield (self._curr, self._cnt) self._curr = row.word self._cnt = 0 self._cnt += row.count def close(self): if self._curr is not None: yield (self._curr, self._cnt) expr = self.expr['name', ].apply( mapper, axis=1, names=['word', 'count'], types=['string', 'int']) expr = expr.groupby('word').sort('word').apply( reducer, names=['word', 'count'], types=['string', 'int']) res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) def testMapReduce(self): data = [ ['name key', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key name', 3, 4.1, None, None, None], ] self._gen_data(data=data) @output(['word', 'cnt'], ['string', 'int']) def mapper(row): for word in row[0].split(): yield word, 1 @output(['word', 'cnt'], ['string', 'int']) def reducer(keys): cnt = [0, ] def h(row, done): cnt[0] += row[1] if done: yield keys[0], cnt[0] return h expr = self.expr['name', ].map_reduce(mapper, reducer, group='word') res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) @output(['word', 'cnt'], ['string', 'int']) class reducer2(object): def __init__(self, keys): self.cnt = 0 def __call__(self, row, done): self.cnt += row.cnt if done: yield row.word, self.cnt expr = self.expr['name', ].map_reduce(mapper, reducer2, group='word') res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) def testDistributeSort(self): data = [ ['name', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key', 3, 4.1, None, None, None], ] self._gen_data(data=data) @output_names('name', 'id') @output_types('string', 'int') class reducer(object): def __init__(self): self._curr = None self._cnt = 0 def __call__(self, row): if self._curr is None: self._curr = row.name elif self._curr != row.name: yield (self._curr, self._cnt) self._curr = row.name self._cnt = 0 self._cnt += 1 def close(self): if self._curr is not None: yield (self._curr, self._cnt) expr = self.expr['name', ].groupby('name').sort('name').apply(reducer) res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 2], ['name', 3]] self.assertEqual(sorted(expected), sorted(result)) def testJoin(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [ ['name1', 4, -1], ['name2', 1, -2] ] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr.join(expr2)['name', 'id2'] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 5) expected = [ [to_str('name1'), 4], [to_str('name2'), 1] ] self.assertTrue(all(it in expected for it in result)) expr = self.expr.join(expr2, on=['name', ('id', 'id2')])[self.expr.name, expr2.id2] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 2) expected = [to_str('name1'), 4] self.assertTrue(all(it == expected for it in result)) finally: table2.drop() def testUnion(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [ ['name3', 5, -1], ['name4', 6, -2] ] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr['name', 'id'].distinct().union(expr2[expr2.id2.rename('id'), 'name']) res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 4], ['name1', 3], ['name2', 2], ['name3', 5], ['name4', 6] ] result = sorted(result) expected = sorted(expected) self.assertEqual(len(result), len(expected)) for e, r in zip(result, expected): self.assertEqual([to_str(t) for t in e], [to_str(t) for t in r]) finally: table2.drop() def testPersist(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) table_name = 'pyodps_test_engine_persist_table' try: df = self.engine.persist(self.expr, table_name) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, result) finally: self.odps.delete_table(table_name, if_exists=True) try: schema = Schema.from_lists(self.schema.names, self.schema.types, ['ds'], ['string']) self.odps.create_table(table_name, schema) df = self.engine.persist(self.expr, table_name, partition='ds=today', create_partition=True) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, [d[:-1] for d in result]) finally: self.odps.delete_table(table_name, if_exists=True) try: self.engine.persist(self.expr, table_name, partitions=['name']) t = self.odps.get_table(table_name) self.assertEqual(2, len(list(t.partitions))) with t.open_reader(partition='name=name1', reopen=True) as r: self.assertEqual(4, r.count) with t.open_reader(partition='name=name2', reopen=True) as r: self.assertEqual(1, r.count) finally: self.odps.delete_table(table_name, if_exists=True) def teardown(self): self.table.drop()
class Test(TestBase): def setup(self): datatypes = lambda *types: [validate_data_type(t) for t in types] schema = Schema.from_lists(['name', 'id', 'fid', 'isMale', 'scale', 'birth'], datatypes('string', 'int64', 'float64', 'boolean', 'decimal', 'datetime')) self.schema = df_schema_to_odps_schema(schema) table_name = 'pyodps_test_engine_table' self.odps.delete_table(table_name, if_exists=True) self.table = self.odps.create_table( name='pyodps_test_engine_table', schema=self.schema) self.expr = CollectionExpr(_source_data=self.table, _schema=schema) self.engine = ODPSEngine(self.odps) class FakeBar(object): def update(self, *args, **kwargs): pass self.faked_bar = FakeBar() def _gen_random_bigint(self, value_range=None): return random.randint(*(value_range or types.bigint._bounds)) def _gen_random_string(self, max_length=15): gen_letter = lambda: letters[random.randint(0, 51)] return to_str(''.join([gen_letter() for _ in range(random.randint(1, 15))])) def _gen_random_double(self): return random.uniform(-2**32, 2**32) def _gen_random_datetime(self): return datetime.fromtimestamp(random.randint(0, int(time.time()))) def _gen_random_boolean(self): return random.uniform(-1, 1) > 0 def _gen_random_decimal(self): return Decimal(str(self._gen_random_double())) def _gen_data(self, rows=None, data=None, nullable_field=None, value_range=None): if data is None: data = [] for _ in range(rows): record = [] for t in self.schema.types: method = getattr(self, '_gen_random_%s' % t.name) if t.name == 'bigint': record.append(method(value_range=value_range)) else: record.append(method()) data.append(record) if nullable_field is not None: j = self.schema._name_indexes[nullable_field] for i, l in enumerate(data): if i % 2 == 0: data[i][j] = None self.odps.write_table(self.table, 0, [self.table.new_record(values=d) for d in data]) return data def _get_result(self, res): if isinstance(res, ResultFrame): res = res.values try: import pandas if isinstance(res, pandas.DataFrame): return [list(it) for it in res.values] else: return res except ImportError: return res def testTunnelCases(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) expr = self.expr.name.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) res = self.engine._handle_cases(self.expr, self.faked_bar) result = self._get_result(res) self.assertEqual(data, result) expr = self.expr['name', self.expr.id.rename('new_id')] res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual([it[:2] for it in data], result) table_name = 'pyodps_test_engine_partitioned' self.odps.delete_table(table_name, if_exists=True) df = self.expr.persist(table_name, partitions=['name']) try: expr = df.count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertIsNone(res) expr = df[df.name == data[0][0]]['fid', 'id'].count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(res, 0) expr = df[df.name == data[0][0]]['fid', 'id'] res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(len(res), 0) finally: self.odps.delete_table(table_name, if_exists=True) def testBase(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr[self.expr.id < 10]['name', lambda x: x.id] result = self._get_result(self.engine.execute(expr).values) self.assertEqual(len([it for it in data if it[1] < 10]), len(result)) if len(result) > 0: self.assertEqual(2, len(result[0])) expr = self.expr[Scalar(3).rename('const'), self.expr.id, (self.expr.id + 1).rename('id2')] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual([c.name for c in res.columns], ['const', 'id', 'id2']) self.assertTrue(all(it[0] == 3 for it in result)) self.assertEqual(len(data), len(result)) self.assertEqual([it[1]+1 for it in data], [it[2] for it in result]) expr = self.expr.sort('id')[:5] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual(sorted(data, key=lambda it: it[1])[:5], result) def testElement(self): data = self._gen_data(5, nullable_field='name') fields = [ self.expr.name.isnull().rename('name1'), self.expr.name.notnull().rename('name2'), self.expr.name.fillna('test').rename('name3'), self.expr.id.isin([1, 2, 3]).rename('id1'), self.expr.id.isin(self.expr.fid.astype('int')).rename('id2'), self.expr.id.notin([1, 2, 3]).rename('id3'), self.expr.id.notin(self.expr.fid.astype('int')).rename('id4'), self.expr.id.between(self.expr.fid, 3).rename('id5'), self.expr.name.fillna('test').switch('test', 'test' + self.expr.name.fillna('test'), 'test2', 'test2' + self.expr.name.fillna('test'), default=self.expr.name).rename('name4'), self.expr.id.cut([100, 200, 300], labels=['xsmall', 'small', 'large', 'xlarge'], include_under=True, include_over=True).rename('id6') ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual(len([it for it in data if it[0] is None]), len([it[0] for it in result if it[0]])) self.assertEqual(len([it[0] for it in data if it[0] is not None]), len([it[1] for it in result if it[1]])) self.assertEqual([(it[0] if it[0] is not None else 'test') for it in data], [it[2] for it in result]) self.assertEqual([(it[1] in (1, 2, 3)) for it in data], [it[3] for it in result]) fids = [int(it[2]) for it in data] self.assertEqual([(it[1] in fids) for it in data], [it[4] for it in result]) self.assertEqual([(it[1] not in (1, 2, 3)) for it in data], [it[5] for it in result]) self.assertEqual([(it[1] not in fids) for it in data], [it[6] for it in result]) self.assertEqual([(it[2] <= it[1] <= 3) for it in data], [it[7] for it in result]) self.assertEqual([to_str('testtest' if it[0] is None else it[0]) for it in data], [to_str(it[8]) for it in result]) def get_val(val): if val <= 100: return 'xsmall' elif 100 < val <= 200: return 'small' elif 200 < val <= 300: return 'large' else: return 'xlarge' self.assertEqual([to_str(get_val(it[1])) for it in data], [to_str(it[9]) for it in result]) def testArithmetic(self): data = self._gen_data(5, value_range=(-1000, 1000)) fields = [ (self.expr.id + 1).rename('id1'), (self.expr.fid - 1).rename('fid1'), (self.expr.scale * 2).rename('scale1'), (self.expr.scale + self.expr.id).rename('scale2'), (self.expr.id / 2).rename('id2'), (self.expr.id ** -2).rename('id3'), abs(self.expr.id).rename('id4'), (~self.expr.id).rename('id5'), (-self.expr.fid).rename('fid2'), (~self.expr.isMale).rename('isMale1'), (-self.expr.isMale).rename('isMale2'), ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual([it[1] + 1 for it in data], [it[0] for it in result]) self.assertAlmostEqual([it[2] - 1 for it in data], [it[1] for it in result]) self.assertEqual([it[4] * 2 for it in data], [it[2] for it in result]) self.assertEqual([it[4] + it[1] for it in data], [it[3] for it in result]) self.assertAlmostEqual([float(it[1]) / 2 for it in data], [it[4] for it in result]) self.assertEqual([int(it[1] ** -2) for it in data], [it[5] for it in result]) self.assertEqual([abs(it[1]) for it in data], [it[6] for it in result]) self.assertEqual([~it[1] for it in data], [it[7] for it in result]) self.assertAlmostEqual([-it[2] for it in data], [it[8] for it in result]) self.assertEqual([not it[3] for it in data], [it[9] for it in result]) # TODO: test the datetime add and substract def testMath(self): data = self._gen_data(5, value_range=(1, 90)) import numpy as np methods_to_fields = [ (np.sin, self.expr.id.sin()), (np.cos, self.expr.id.cos()), (np.tan, self.expr.id.tan()), (np.sinh, self.expr.id.sinh()), (np.cosh, self.expr.id.cosh()), (np.tanh, self.expr.id.tanh()), (np.log, self.expr.id.log()), (np.log2, self.expr.id.log2()), (np.log10, self.expr.id.log10()), (np.log1p, self.expr.id.log1p()), (np.exp, self.expr.id.exp()), (np.expm1, self.expr.id.expm1()), (np.arccosh, self.expr.id.arccosh()), (np.arcsinh, self.expr.id.arcsinh()), (np.arctanh, self.expr.id.arctanh()), (np.arctan, self.expr.id.arctan()), (np.sqrt, self.expr.id.sqrt()), (np.abs, self.expr.id.abs()), (np.ceil, self.expr.id.ceil()), (np.floor, self.expr.id.floor()), (np.trunc, self.expr.id.trunc()), ] fields = [it[1].rename('id'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[1]) for it in data] second = [it[i] for it in result] self.assertEqual(len(first), len(second)) for it1, it2 in zip(first, second): if np.isnan(it1) and np.isnan(it2): continue self.assertAlmostEqual(it1, it2) def testString(self): data = self._gen_data(5) methods_to_fields = [ (lambda s: s.capitalize(), self.expr.name.capitalize()), (lambda s: data[0][0] in s, self.expr.name.contains(data[0][0], regex=False)), (lambda s: s.count(data[0][0]), self.expr.name.count(data[0][0])), (lambda s: s.endswith(data[0][0]), self.expr.name.endswith(data[0][0])), (lambda s: s.startswith(data[0][0]), self.expr.name.startswith(data[0][0])), (lambda s: s.find(data[0][0]), self.expr.name.find(data[0][0])), (lambda s: s.rfind(data[0][0]), self.expr.name.rfind(data[0][0])), (lambda s: s.replace(data[0][0], 'test'), self.expr.name.replace(data[0][0], 'test')), (lambda s: s[0], self.expr.name.get(0)), (lambda s: len(s), self.expr.name.len()), (lambda s: s.ljust(10), self.expr.name.ljust(10)), (lambda s: s.ljust(20, '*'), self.expr.name.ljust(20, fillchar='*')), (lambda s: s.rjust(10), self.expr.name.rjust(10)), (lambda s: s.rjust(20, '*'), self.expr.name.rjust(20, fillchar='*')), (lambda s: s * 4, self.expr.name.repeat(4)), (lambda s: s[2: 10: 2], self.expr.name.slice(2, 10, 2)), (lambda s: s[-5: -1], self.expr.name.slice(-5, -1)), (lambda s: s.title(), self.expr.name.title()), (lambda s: s.rjust(20, '0'), self.expr.name.zfill(20)), (lambda s: s.isalnum(), self.expr.name.isalnum()), (lambda s: s.isalpha(), self.expr.name.isalpha()), (lambda s: s.isdigit(), self.expr.name.isdigit()), (lambda s: s.isspace(), self.expr.name.isspace()), (lambda s: s.isupper(), self.expr.name.isupper()), (lambda s: s.istitle(), self.expr.name.istitle()), (lambda s: to_str(s).isnumeric(), self.expr.name.isnumeric()), (lambda s: to_str(s).isdecimal(), self.expr.name.isdecimal()), ] fields = [it[1].rename('id'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[0]) for it in data] second = [it[i] for it in result] self.assertEqual(first, second) def testDatetime(self): data = self._gen_data(5) import pandas as pd methods_to_fields = [ (lambda s: list(s.birth.dt.year.values), self.expr.birth.year), (lambda s: list(s.birth.dt.month.values), self.expr.birth.month), (lambda s: list(s.birth.dt.day.values), self.expr.birth.day), (lambda s: list(s.birth.dt.hour.values), self.expr.birth.hour), (lambda s: list(s.birth.dt.minute.values), self.expr.birth.minute), (lambda s: list(s.birth.dt.second.values), self.expr.birth.second), (lambda s: list(s.birth.dt.weekofyear.values), self.expr.birth.weekofyear), (lambda s: list(s.birth.dt.dayofweek.values), self.expr.birth.dayofweek), (lambda s: list(s.birth.dt.weekday.values), self.expr.birth.weekday), ] fields = [it[1].rename('birth'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) second = [it[i] for it in result] self.assertEqual(first, second) def testSortDistinct(self): data = [ ['name1', 4, None, None, None, None], ['name2', 2, None, None, None, None], ['name1', 4, None, None, None, None], ['name1', 3, None, None, None, None], ] self._gen_data(data=data) expr = self.expr.sort(['name', -self.expr.id]).distinct(['name', lambda x: x.id + 1])[:50] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 3) expected = [ ['name1', 5], ['name1', 4], ['name2', 3] ] self.assertEqual(expected, result) def testGroupbyAggregation(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby(['name', 'id'])[lambda x: x.fid.min() * 2 < 8] \ .agg(self.expr.fid.max() + 1, new_id=self.expr.id.sum()) res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 5.1, 6], ['name2', 2, 4.5, 2] ] result = sorted(result, key=lambda k: k[0]) self.assertEqual(expected, result) field = self.expr.groupby('name').sort(['id', -self.expr.fid]).row_number() expr = self.expr['name', 'id', 'fid', field] res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 4.1, 1], ['name1', 3, 2.2, 2], ['name1', 4, 5.3, 3], ['name1', 4, 4.2, 4], ['name2', 2, 3.5, 1], ] result = sorted(result, key=lambda k: (k[0], k[1], -k[2])) self.assertEqual(expected, result) expr = self.expr.name.value_counts()[:25] expected = [ ['name1', 4], ['name2', 1] ] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.name.topk(25) res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.groupby('name').count() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) def testFilterGroupby(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby(['name']).agg(id=self.expr.id.max())[lambda x: x.id > 3] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 1) expected = [ ['name1', 4] ] self.assertEqual(expected, result) def testWindowRewrite(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr[self.expr.id - self.expr.id.mean() < 10][ [lambda x: x.id - x.id.max()]][[lambda x: x.id - x.id.min()]][lambda x: x.id - x.id.std() > 0] # FIXME compiling too slow res = self.engine.execute(expr) result = self._get_result(res) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) expected = df.id - df.id.max() expected = expected - expected.min() expected = list(expected[expected - expected.std() > 0]) self.assertEqual(expected, [it[0] for it in result]) def testReduction(self): data = self._gen_data(rows=5, value_range=(-100, 100)) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) methods_to_fields = [ (lambda s: df.id.mean(), self.expr.id.mean()), (lambda s: len(df), self.expr.count()), (lambda s: df.id.var(ddof=0), self.expr.id.var(ddof=0)), (lambda s: df.id.std(ddof=0), self.expr.id.std(ddof=0)), (lambda s: df.id.median(), self.expr.id.median()), (lambda s: df.id.sum(), self.expr.id.sum()), (lambda s: df.id.min(), self.expr.id.min()), (lambda s: df.id.max(), self.expr.id.max()), (lambda s: df.isMale.min(), self.expr.isMale.min()), (lambda s: df.name.max(), self.expr.name.max()), (lambda s: df.birth.max(), self.expr.birth.max()), (lambda s: df.name.sum(), self.expr.name.sum()), (lambda s: df.isMale.sum(), self.expr.isMale.sum()), ] fields = [it[1].rename('f'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) second = [it[i] for it in result][0] self.assertAlmostEqual(first, second) def testJoin(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [ ['name1', 4, -1], ['name2', 1, -2] ] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr.join(expr2)['name', 'id2'] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 5) expected = [ [to_str('name1'), 4], [to_str('name2'), 1] ] self.assertTrue(all(it in expected for it in result)) expr = self.expr.join(expr2, on=['name', ('id', 'id2')])[self.expr.name, expr2.id2] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 2) expected = [to_str('name1'), 4] self.assertTrue(all(it == expected for it in result)) finally: table2.drop() def testUnion(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [ ['name3', 5, -1], ['name4', 6, -2] ] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr['name', 'id'].distinct().union(expr2[expr2.id2.rename('id'), 'name']) res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 4], ['name1', 3], ['name2', 2], ['name3', 5], ['name4', 6] ] result = sorted(result) expected = sorted(expected) self.assertEqual(len(result), len(expected)) for e, r in zip(result, expected): self.assertEqual([to_str(t) for t in e], [to_str(t) for t in r]) finally: table2.drop() def testPersist(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) table_name = 'pyodps_test_engine_persist_table' try: df = self.expr.persist(table_name) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, result) finally: self.odps.delete_table(table_name, if_exists=True) try: self.expr.persist(table_name, partitions=['name']) t = self.odps.get_table(table_name) self.assertEqual(2, len(list(t.partitions))) with t.open_reader(partition='name=name1', reopen=True) as r: self.assertEqual(4, r.count) with t.open_reader(partition='name=name2', reopen=True) as r: self.assertEqual(1, r.count) finally: self.odps.delete_table(table_name, if_exists=True) def teardown(self): self.table.drop()