def testSVGFormatter(self):
        t = MockTable(name='pyodps_test_svg', schema=self.schema, _client=self.odps.rest)
        expr = CollectionExpr(_source_data=t, _schema=self.schema)

        expr1 = expr.groupby('name').agg(id=expr['id'].sum())
        expr2 = expr1['name', expr1.id + 3]

        engine = MixedEngine(self.odps)
        dag = engine.compile(expr2)
        nodes = dag.nodes()
        self.assertEqual(len(nodes), 1)
        expr3 = nodes[0].expr
        self.assertIsInstance(expr3, GroupByCollectionExpr)
        dot = ExprExecutionGraphFormatter(dag)._to_dot()
        self.assertNotIn('Projection', dot)

        expr1 = expr.groupby('name').agg(id=expr['id'].sum()).cache()
        expr2 = expr1['name', expr1.id + 3]

        engine = MixedEngine(self.odps)
        dag = engine.compile(expr2)
        nodes = dag.nodes()
        self.assertEqual(len(nodes), 2)
        dot = ExprExecutionGraphFormatter(dag)._to_dot()
        self.assertIn('Projection', dot)
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', 'bigint', 'double', 'boolean', 'decimal',
                      'datetime'))
        table = MockTable(name='pyodps_test_expr_table', schema=schema)
        self.tb = DataFrame(table)

        import pandas as pd

        df = pd.DataFrame([['name1', 2, 3.14], ['name2', 100, 2.7]],
                          columns=['name', 'id', 'fid'])
        self.pd = DataFrame(df)

        self.expr = self.tb.join(self.pd, on='name')

        self.engine = MixedEngine(self.odps)

    def testMixedCompile(self):
        dag = self.engine.compile(self.expr)

        self.assertEqual(len(dag._graph), 2)

        topos = dag.topological_sort()
        root_node, expr_node = topos[0], topos[1]
        root = root_node.expr
        expr = expr_node.expr

        self.assertTrue(expr.is_ancestor(root))
        self.assertIn(id(expr_node), dag._graph[id(root_node)])
        self.assertEqual(len(available_engines(expr.data_source())), 1)

    def testCacheCompile(self):
        expr = self.tb['name', 'id'].cache()
        expr = expr.groupby('name').agg(expr.id.mean()).cache()
        expr = expr.distinct()

        dag = self.engine.compile(expr)

        self.assertEqual(len(dag._graph), 3)

        topos = dag.topological_sort()
        project_node, groupby_node, distinct_node = topos[0], topos[1], topos[
            2]
        distincted = distinct_node.expr

        self.assertIn(id(groupby_node), dag._graph[id(project_node)])
        self.assertIn(id(distinct_node), dag._graph[id(groupby_node)])
        self.assertIsInstance(distincted, DistinctCollectionExpr)

    def testDep(self):
        expr = self.tb.pivot_table(rows='id', columns='name', values='fid')

        dag = self.engine.compile(expr)

        self.assertEqual(len(dag._graph), 2)
        self.assertEqual(sum(len(v) for v in dag._graph.values()), 1)
class Test(TestBase):
    def setup(self):
        import pandas as pd

        odps_data = [
            ['name1', 1],
            ['name2', 2],
            ['name1', 3],
        ]

        pd_data = [['name1', 5], ['name2', 6]]

        names = ['name', 'id']
        types = ['string', 'bigint']

        table = tn('pyodps_df_mixed_%d' % os.getpid())
        if self.odps.exist_table(table):
            self.t = self.odps.get_table(table)
        else:
            self.t = self.odps.create_table(table,
                                            Schema.from_lists(names, types),
                                            lifecycle=1)
            with self.t.open_writer() as w:
                w.write([self.t.new_record(r) for r in odps_data])

        self.odps_df = DataFrame(self.t)
        self.pd_df = DataFrame(pd.DataFrame(pd_data, columns=names))

        self.engine = MixedEngine(self.odps)
        self.pd_engine = PandasEngine(self.odps)

    def teardown(self):
        self.engine._selecter.force_odps = False

    def testGroupReduction(self):
        expr = self.odps_df.select(self.odps_df,
                                   id2=self.odps_df.id.map(lambda x: x + 1))
        expr = expr.groupby('name').id2.sum()

        expected = [['name1', 6], ['name2', 3]]
        res = self.engine.execute(expr)
        result = self._get_result(res)
        self.assertEqual(sorted([[r[1]] for r in expected]), sorted(result))

    def assertPandasEqual(self, df1, df2):
        from odps.compat import six
        from odps import types as o_types
        from pandas.util.testing import assert_frame_equal

        # compare column types
        def get_odps_type(p_type):
            for data_type, builtin_type in six.iteritems(
                    o_types._odps_primitive_to_builtin_types):
                if issubclass(p_type.type, builtin_type):
                    return data_type

        types1 = [get_odps_type(dt) for dt in df1.dtypes]
        types2 = [get_odps_type(dt) for dt in df2.dtypes]
        self.assertSequenceEqual(types1, types2)
        assert_frame_equal(df1, df2, check_dtype=False)

    def testJoin(self):
        expr = self.odps_df.join(self.pd_df, 'name').sort('id_x')
        result = self.engine.execute(expr).values

        df = DataFrame(self.odps_df.to_pandas())
        expected = self.pd_engine.execute(
            df.join(self.pd_df, 'name').sort('id_x')).values
        self.assertTrue(result.equals(expected))

    def testUnion(self):
        expr = self.odps_df.union(self.pd_df).sort(['id', 'name'])
        result = self.engine.execute(expr).values

        df = DataFrame(self.odps_df.to_pandas())
        expected = self.pd_engine.execute(
            df.union(self.pd_df).sort(['id', 'name'])).values
        self.assertTrue(result.equals(expected))

        schema = Schema.from_lists(
            [c.name for c in self.t.schema.columns if c.name != 'name'],
            [c.type for c in self.t.schema.columns if c.name != 'name'],
            ['name'], ['string'])
        t = self.odps.create_table(
            'tmp_pyodps_%s' % str(uuid.uuid4()).replace('-', '_'), schema)
        try:
            expr = self.odps_df.union(self.pd_df)
            expr.persist(t.name, create_table=False, partitions=['name'])

            self.assertEqual(self.engine.execute(DataFrame(t).count()), 5)

            self.engine._selecter.force_odps = False
            df = DataFrame(t)
            self.assertGreaterEqual(
                len(
                    self.engine.execute(df.filter(df.name > 'a',
                                                  df.name < 'b'))), 0)
        finally:
            t.drop()

    def testIsIn(self):
        expr = self.odps_df['name'].isin(self.pd_df['name']).rename('isin')
        result = self.engine.execute(expr).values

        df = DataFrame(self.odps_df.to_pandas())
        expected = self.pd_engine.execute(df['name'].isin(
            self.pd_df['name']).rename('isin')).values
        self.assertTrue(result.equals(expected))

        expr = (self.odps_df.id + 2).isin(self.pd_df['id']).rename('isin')
        res = self.engine.execute(expr)
        result = self._get_result(res)

        expected = [[False], [False], [True]]
        self.assertEqual(result, expected)

    def testMixed(self):
        expr = self.odps_df.union(
            self.odps_df.join(self.pd_df,
                              'name')[lambda x: x.name,
                                      lambda x: x.id_x.rename('id')]).sort(
                                          ['name', 'id'])
        expr = expr[expr['name'].isin(self.pd_df['name'])]
        expr = expr[expr, func.rand(rtype='float').rename('rand')]
        result = self.engine.execute(expr).values[['name', 'id']]

        df = DataFrame(self.odps_df.to_pandas())
        test_expr = df.union(
            df.join(self.pd_df, 'name')[lambda x: x.name,
                                        lambda x: x.id_x.rename('id')]).sort(
                                            ['name', 'id'])
        test_expr = test_expr[test_expr['name'].isin(self.pd_df['name'])]
        expected = self.pd_engine.execute(test_expr).values

        self.assertTrue(result.equals(expected))

    def testPandasPersist(self):
        import pandas as pd, numpy as np

        tmp_table_name = tn('pyodps_test_mixed_persist')
        self.odps.delete_table(tmp_table_name, if_exists=True)
        t = self.odps.create_table(
            tmp_table_name, ('a bigint, b bigint, c bigint', 'ds string'))
        t.create_partition('ds=today')
        try:
            pd_df = pd.DataFrame(np.arange(9).reshape(3, 3),
                                 columns=list('abc'))
            df = DataFrame(pd_df).persist(tmp_table_name,
                                          partition='ds=today',
                                          odps=self.odps)

            self.assertPandasEqual(df[list('abc')].to_pandas(), pd_df)
        finally:
            self.odps.delete_table(tmp_table_name)

        self.odps.to_global()

        tmp_table_name = tn('pyodps_test_mixed_persist2')
        self.odps.delete_table(tmp_table_name, if_exists=True)

        try:
            pd_df = pd.DataFrame(np.arange(9).reshape(3, 3),
                                 columns=list('abc'))
            df = DataFrame(pd_df).persist(tmp_table_name)

            self.assertPandasEqual(df.to_pandas(), pd_df)
        finally:
            self.odps.delete_table(tmp_table_name)

    def testExecuteCacheTable(self):
        df = self.odps_df[self.odps_df.name == 'name1']
        result = df.execute().values
        self.assertEqual(len(result), 2)
        self.assertTrue(context.is_cached(df))

        dag = self.engine.compile(df)
        calls = dag.topological_sort()
        self.assertEqual(len(calls), 1)
        self.assertTrue(is_source_collection(calls[0].expr))

        df2 = df[:5]
        result = df2.execute()
        self.assertEqual(len(result), 2)

    def testHandleCache(self):
        df = self.pd_df['name', self.pd_df.id + 1]
        df.execute()
        self.assertTrue(context.is_cached(df))

        df2 = df[df.id < 10]
        dag = self.engine.compile(df2)
        self.assertEqual(len(dag.nodes()), 1)
        self.assertTrue(is_source_collection(dag.nodes()[0].expr.input))

        df3 = self.pd_df[self.pd_df.id < 10].count()
        i = df3.execute()
        self.assertTrue(context.is_cached(df3))

        df4 = df3 + 1
        dag = self.engine.compile(df4)
        self.assertEqual(len(dag.nodes()), 1)
        self.assertIsNotNone(dag.nodes()[0].expr._fields[0].lhs.value)
        self.assertEqual(df4.execute(), i + 1)

    def testCacheTable(self):
        self.engine._selecter.force_odps = True

        df = self.odps_df.join(self.pd_df, 'name').cache()
        df2 = df.sort('id_x')

        dag = self.engine.compile(df2)
        self.assertEqual(len(dag.nodes()), 3)

        result = self.engine.execute(df2).values

        df3 = DataFrame(self.odps_df.to_pandas())
        expected = self.pd_engine.execute(
            df3.join(self.pd_df, 'name').sort('id_x')).values
        self.assertTrue(result.equals(expected))

        self.assertEqual(len(self.engine._generated_table_names), 2)

        table = context.get_cached(df)
        self.assertEqual(len(self.engine.execute(df)), len(expected))

        self.assertIs(context.get_cached(df), table)
        if not isinstance(table, SeahawksTable):
            self.assertEqual(context.get_cached(df).lifecycle, 1)

        df4 = df[df.id_x < 3].count()
        result = self.engine.execute(df4)
        self.assertEqual(result, 2)

        self.assertEqual(context.get_cached(df4), 2)

    def testUseCache(self):
        self.engine._selecter.force_odps = True

        df_cache = self.odps_df[self.odps_df['name'] == 'name1'].cache()
        df = df_cache[df_cache.id * 2, df_cache.exclude('id')]
        self.assertEqual(len(self.engine.execute(df, head=10)), 2)

        context.get_cached(df_cache).drop()

        self.assertEqual(
            len(self.engine.execute(df_cache['name', df_cache.id * 2],
                                    head=10)), 2)
        self.assertTrue(context.is_cached(df_cache))
        self.assertTrue(
            self.odps.exist_table(context.get_cached(df_cache).name))

    def testHeadAndTail(self):
        res = self.odps_df.head(2)
        self.assertEqual(len(res), 2)

        df = self.odps_df[self.odps_df['name'] == 'name1']
        res = df.head(1)
        self.assertEqual(len(res), 1)
        self.assertTrue(context.is_cached(df))

        res = self.odps_df.tail(2)
        self.assertEqual(len(res), 2)
        self.assertTrue(all(it > 1 for it in res.values['id']))

        self.assertEqual(len(self.odps_df.name.head(2)), 2)
        self.assertEqual(len(self.odps_df.name.tail(2)), 2)

        res = self.pd_df.head(1)
        self.assertEqual(len(res), 1)

        df = self.pd_df[self.pd_df['name'] == 'name1']
        res = df.head(1)
        self.assertEqual(len(res), 1)
        self.assertTrue(context.is_cached(df))

        res = self.pd_df.tail(1)
        self.assertEqual(len(res), 1)
        self.assertEqual(res.values['id'][0], 6)

        self.assertEqual(len(self.pd_df.name.head(1)), 1)
        self.assertEqual(len(self.pd_df.name.tail(1)), 1)

        class TunnelOnlyODPSEngine(ODPSSQLEngine):
            def _do_execute(self, *args, **kwargs):
                kwargs['_force_tunnel'] = True
                return super(TunnelOnlyODPSEngine,
                             self)._do_execute(*args, **kwargs)

        engine = MixedEngine(self.odps)
        engine._odpssql_engine = TunnelOnlyODPSEngine(self.odps)

        res = engine.execute(self.odps_df['id'], head=3)
        self.assertIsNotNone(res)
        self.assertEqual(sum(res.values['id']), 6)

        table_name = tn('pyodps_df_mixed2')
        self.odps.delete_table(table_name, if_exists=True)
        table = next(self.odps_df.data_source())
        table2 = self.odps.create_table(table_name, table.schema)
        try:
            res = DataFrame(table2).head(10)
            self.assertEqual(len(res), 0)
        finally:
            table2.drop()

    def testMapReduceWithResource(self):
        pd_df2 = self.odps_df.to_pandas(wrap=True)

        @output(['name', 'id'], ['string', 'int'])
        def reducer(resources):
            d = dict()
            for r in resources[0]:
                if r.name in d:
                    d[r.name] += r.id
                else:
                    d[r.name] = r.id

            def inner(keys):
                def h(row, done):
                    if row.name in d:
                        d[row.name] += row.id
                    else:
                        d[row.name] = row.id

                    if done:
                        yield row.name, d[row.name]

                return h

            return inner

        expr = pd_df2.map_reduce(reducer=reducer,
                                 reducer_resources=[self.pd_df],
                                 group='name')
        result = expr.execute()
        self.assertEqual(result.values['id'].sum(), 17)

        odps_df2 = self.pd_df.persist(tn('pyodps_df_mixed2'), odps=self.odps)
        try:
            expr = self.odps_df.map_reduce(reducer=reducer,
                                           reducer_resources=[odps_df2],
                                           group='name')
            result = expr.execute()
            self.assertEqual(result.values['id'].sum(), 17)

            expr = self.odps_df.map_reduce(reducer=reducer,
                                           reducer_resources=[self.pd_df],
                                           group='name')
            result = expr.execute()
            self.assertEqual(result.values['id'].sum(), 17)

            expr = pd_df2.map_reduce(reducer=reducer,
                                     reducer_resources=[odps_df2],
                                     group='name')
            result = expr.execute()
            self.assertEqual(result.values['id'].sum(), 17)
        finally:
            next(odps_df2.data_source()).drop()

    def testBloomFilter(self):
        import numpy as np

        data2 = [['name1'], ['name3']]

        table_name = tn('pyodps_test_mixed_engine_bf_table2')
        self.odps.delete_table(table_name, if_exists=True)
        table2 = self.odps.create_table(name=table_name,
                                        schema=Schema.from_lists(['name'],
                                                                 ['string']))
        expr2 = DataFrame(table2)

        self.odps.write_table(table2, 0, data2)

        try:
            expr = self.odps_df.bloom_filter('name',
                                             expr2[:1].name,
                                             capacity=10)

            res = self.engine.execute(expr)

            self.assertTrue(np.all(res['name'] != 'name2'))
        finally:
            table2.drop()

    def testCachePersist(self):
        expr = self.odps_df

        data2 = [['name1', 3.2], ['name3', 2.4]]

        table_name = tn('pyodps_test_mixed_engine_cp_table2')
        self.odps.delete_table(table_name, if_exists=True)
        table2 = self.odps.create_table(
            name=table_name,
            schema=Schema.from_lists(['name', 'fid'], ['string', 'double']))
        expr2 = DataFrame(table2)
        self.odps.write_table(table2, 0, data2)

        @output(expr.schema.names, expr.schema.types)
        def h(row):
            yield row

        l = expr.filter(expr.id > 0).apply(h, axis=1).cache()
        r = expr2.filter(expr2.fid > 0)
        joined = l.join(r, on=['name', r.fid < 4])['id', 'fid'].cache()

        output_table = tn('pyodps_test_mixed_engine_cp_output_table')
        self.odps.delete_table(output_table, if_exists=True)
        schema = Schema.from_lists(['id', 'fid'], ['bigint', 'double'], ['ds'],
                                   ['string'])
        output_t = self.odps.create_table(output_table,
                                          schema,
                                          if_not_exists=True)

        t = joined.persist(output_table,
                           partition='ds=today',
                           create_partition=True)
        self.assertEqual(len(t.execute()), 2)

        # test seahawks fallback
        self.assertEqual(t.input.count().execute(), 2)

        output_t.drop()

    def testBigintPartitionedCache(self):
        table = tn('pyodps_test_bigint_partitioned_cache')
        self.odps.delete_table(table, if_exists=True)
        expr = self.odps_df.persist(table, partitions=['id'])

        @output(['id', 'name'], ['int', 'string'])
        def handle(row):
            return row.id + 1, row.name

        expr = expr['tt' + expr.name, expr.id].cache()
        new_expr = expr.map_reduce(mapper=handle)

        res = self.engine.execute(new_expr)
        self.assertEqual(len(res), 3)

    def testAsync(self):
        expr = self.odps_df[self.odps_df.name == 'name1']
        future = self.engine.execute(expr, async_=True)
        self.assertFalse(future.done())
        res = future.result()
        self.assertEqual(len(res), 2)

    def testBatch(self):
        odps_expr = self.odps_df[self.odps_df.id < 4].cache()
        expr = odps_expr.join(self.pd_df, 'name').sort('id_x')

        dag = self.engine.compile(expr)
        self.assertEqual(len(dag.nodes()), 3)

        f = self.engine.execute(expr, async_=True, n_parallel=2)

        result = f.result().values

        df = DataFrame(self.odps_df.to_pandas())
        expected = self.pd_engine.execute(
            df.join(self.pd_df, 'name').sort('id_x')).values
        self.assertTrue(result.equals(expected))

    def testBatchStop(self):
        self.engine._selecter.force_odps = True

        expr1 = self.odps_df[self.odps_df.id < 3].cache()
        expr2 = self.odps_df[self.odps_df.id > 3].cache()
        expr3 = expr1.union(expr2)

        self.engine.execute([expr1, expr2, expr3], n_parallel=2, async_=True)
        time.sleep(2)

        instance_ids = self.engine._odpssql_engine._instances
        self.assertEqual(len(instance_ids), 2)

        self.engine.stop()
        instances = [self.odps.get_instance(i) for i in instance_ids]
        [i.wait_for_completion() for i in instances]
        self.assertEqual(
            list(instances[0].get_task_statuses().values())[0].status,
            Instance.Task.TaskStatus.CANCELLED)
        self.assertEqual(
            list(instances[1].get_task_statuses().values())[0].status,
            Instance.Task.TaskStatus.CANCELLED)

    def testFailure(self):
        from odps.df.backends.errors import DagDependencyError

        expr1 = self.odps_df[self.odps_df.id / 0 < 0].cache()
        expr2 = expr1.count()

        fs = self.engine.execute(expr2, async_=True)
        self.assertRaises(DagDependencyError, fs.result)

    def testAppendIDCache(self):
        options.ml.dry_run = False

        @output(['id1'] + self.odps_df.schema.names,
                ['int'] + self.odps_df.schema.types)
        def h(row):
            yield row

        expr1 = self.odps_df.append_id(id_col='id1').apply(h, axis=1)
        expr2 = self.odps_df.append_id(id_col='id2')
        expr3 = expr1.join(expr2, on='id')['id1', 'id2']
        self.assertEqual(len(expr3.execute()), 3)

    def testAppendId(self):
        options.ml.dry_run = False

        expr = self.odps_df['name', ].distinct()
        expr = expr.append_id(id_col='id2')
        expr = expr.join(self.odps_df, on=['name'])
        tablename = tn('pyodps_test_append_id_persist')
        self.odps.delete_table(tablename, if_exists=True)
        expr.persist(tablename, partitions=['name'], lifecycle=1)

    def testHorzConcat(self):
        options.ml.dry_run = False

        table_name = tn('test_horz_concat_table2_xxx_yyy')
        self.odps.delete_table(table_name, if_exists=True)

        result_table_name = tn('test_horz_concat_result')
        self.odps.delete_table(result_table_name, if_exists=True)

        self.odps_df[self.odps_df.name,
                     (self.odps_df.id *
                      2).rename('ren_id')].persist(table_name)
        df2 = self.odps.get_table(table_name).to_df()
        df2 = df2[:3]
        expr = self.odps_df.concat(df2.ren_id, axis=1)
        expr.persist(result_table_name, lifecycle=1)

    def testAsTypeMapReduce(self):
        expr = self.odps_df[self.odps_df.exclude('id'),
                            self.odps_df.id.astype('float')]
        expr = expr.filter(expr.id < 10)['id', 'name']

        @output(['id', 'name'], ['float', 'string'])
        def h(group):
            def inn(row, done):
                yield row

            return inn

        expr = expr.map_reduce(reducer=h)
        expr.execute()

        expr = self.odps_df[self.odps_df.exclude('id'),
                            self.odps_df.id.astype('float')]
        expr = expr.filter(expr.id < 10).distinct('id', 'name')

        @output(['id', 'name'], ['float', 'string'])
        def h(group):
            def inn(row, done):
                yield row

            return inn

        expr = expr.map_reduce(reducer=h)
        expr.execute()