def test_join_topk(): """Tests a top k with a join :return: None """ limit = 5 query_plan = QueryPlan() # Query plan ts1 = query_plan.add_operator(SQLTableScan('supplier.csv', 'select * from S3Object;', False, 'ts1', query_plan, False)) ts1_project = query_plan.add_operator( Project([ProjectExpression(lambda t_: t_['_3'], 's_nationkey')], 'ts1_project', query_plan, False)) ts2 = query_plan.add_operator(SQLTableScan('nation.csv', 'select * from S3Object;', False, 'ts2', query_plan, False)) ts2_project = query_plan.add_operator( Project([ProjectExpression(lambda t_: t_['_0'], 'n_nationkey')], 'ts2_project', query_plan, False)) j = query_plan.add_operator(HashJoin(JoinExpression('s_nationkey', 'n_nationkey'), 'j', query_plan, False)) t = query_plan.add_operator(Limit(limit, 't', query_plan, False)) c = query_plan.add_operator(Collate('c', query_plan, False)) ts1.connect(ts1_project) ts2.connect(ts2_project) j.connect_left_producer(ts1_project) j.connect_right_producer(ts2_project) j.connect(t) t.connect(c) # Write the plan graph query_plan.write_graph(os.path.join(ROOT_DIR, "../tests-output"), gen_test_id()) # Start the query query_plan.execute() # Assert the results # num_rows = 0 # for t in c.tuples(): # num_rows += 1 # print("{}:{}".format(num_rows, t)) c.print_tuples() field_names = ['s_nationkey', 'n_nationkey'] assert len(c.tuples()) == limit + 1 assert c.tuples()[0] == field_names num_rows = 0 for t in c.tuples(): num_rows += 1 # Assert that the nation_key in table 1 has been joined with the record in table 2 with the same nation_key if num_rows > 1: lt = IndexedTuple.build(t, field_names) assert lt['s_nationkey'] == lt['n_nationkey'] # Write the metrics query_plan.print_metrics()
def test_join_empty(): """Executes a join where no records are returned. We tst this as it's somewhat peculiar with s3 select, in so much as s3 does not return column names when selecting data, meaning, unlike a traditional DBMS, no field names tuple should be present in the results. :return: None """ query_plan = QueryPlan() # Query plan supplier_scan = query_plan.add_operator( SQLTableScan('supplier.csv', 'select * from S3Object limit 0;', False, 'supplier_scan', query_plan, False)) supplier_project = query_plan.add_operator( Project([ProjectExpression(lambda t_: t_['_3'], 's_nationkey')], 'supplier_project', query_plan, False)) nation_scan = query_plan.add_operator( SQLTableScan('nation.csv', 'select * from S3Object limit 0;', False, 'nation_scan', query_plan, False)) nation_project = query_plan.add_operator( Project([ProjectExpression(lambda t_: t_['_0'], 'n_nationkey')], 'nation_project', query_plan, False)) supplier_nation_join = query_plan.add_operator( HashJoin(JoinExpression('s_nationkey', 'n_nationkey'), 'supplier_nation_join', query_plan, False)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) supplier_scan.connect(supplier_project) nation_scan.connect(nation_project) supplier_nation_join.connect_left_producer(supplier_project) supplier_nation_join.connect_right_producer(nation_project) supplier_nation_join.connect(collate) # Write the plan graph query_plan.write_graph(os.path.join(ROOT_DIR, "../tests-output"), gen_test_id()) # Start the query query_plan.execute() # Assert the results # num_rows = 0 # for t in collate.tuples(): # num_rows += 1 # print("{}:{}".format(num_rows, t)) assert len(collate.tuples()) == 0 # Write the metrics query_plan.print_metrics()
def project_partkey_avg_quantity_op(name, query_plan): """with lineitem_part_avg_group_project as ( select l_partkey, 0.2 * avg(l_quantity) as l_quantity_computed00 from lineitem_part_avg_group ) :return: """ def fn(df): # return df[['_0', '_1', '_2']] df['avg_l_quantity_computed00'] = 0.2 * ( df['sum_l_quantity_computed00'].astype(np.float) / df['cnt_l_quantity_computed00'].astype(np.float)) df = df.filter(items=['l_partkey', 'avg_l_quantity_computed00'], axis=1) # df.rename(columns={'l_partkey': 'l_partkey', 'l_quantity': 'avg_l_quantity_computed00'}, # inplace=True) return df return Project( [ # l_partkey ProjectExpression(lambda t_: t_['_0'], 'l_partkey'), # 0.2 * avg ProjectExpression(lambda t_: 0.2 * t_['_1'], 'avg_l_quantity_computed00') ], name, query_plan, False, fn)
def project_partkey_brand_container_op(name, query_plan): """with part_project as (select _0 as p_partkey from part_scan) :param query_plan: :param name: :return: """ def fn(df): # return df[['_0', '_1', '_2']] df = df.filter(items=['_0', '_3', '_6'], axis=1) df.rename(columns={ '_0': 'p_partkey', '_3': 'p_brand', '_6': 'p_container' }, inplace=True) return df return Project([ ProjectExpression(lambda t_: t_['_0'], 'p_partkey'), ProjectExpression(lambda t_: t_['_3'], 'p_brand'), ProjectExpression(lambda t_: t_['_6'], 'p_container') ], name, query_plan, False, fn)
def project_partkey_quantity_extendedprice_discount_shipinstruct_shipmode_filtered_op( name, query_plan): def fn(df): # return df[['_0', '_1', '_2']] df = df.filter(items=['_0', '_1', '_2', '_3', '_4', '_5'], axis=1) df.rename(columns={ '_0': 'l_partkey', '_1': 'l_quantity', '_2': 'l_extendedprice', '_3': 'l_discount', '_4': 'l_shipinstruct', '_5': 'l_shipmode' }, inplace=True) return df return Project([ ProjectExpression(lambda t_: t_['_0'], 'l_partkey'), ProjectExpression(lambda t_: t_['_1'], 'l_quantity'), ProjectExpression(lambda t_: t_['_2'], 'l_extendedprice'), ProjectExpression(lambda t_: t_['_3'], 'l_discount'), ProjectExpression(lambda t_: t_['_4'], 'l_shipinstruct'), ProjectExpression(lambda t_: t_['_5'], 'l_shipmode') ], name, query_plan, False, fn)
def project_lineitem_orderkey_partkey_quantity_extendedprice_op( name, query_plan): """with part_project as (select _0 as p_partkey from part_scan) :param query_plan: :param name: :return: """ def fn(df): # return df[['_0', '_1', '_2']] df = df.filter(items=['_0', '_1', '_4', '_5'], axis=1) df.rename(columns={ '_0': 'l_orderkey', '_1': 'l_partkey', '_4': 'l_quantity', '_5': 'l_extendedprice' }, inplace=True) return df return Project([ ProjectExpression(lambda t_: t_['_0'], 'l_orderkey'), ProjectExpression(lambda t_: t_['_1'], 'l_partkey'), ProjectExpression(lambda t_: t_['_4'], 'l_quantity'), ProjectExpression(lambda t_: t_['_5'], 'l_extendedprice') ], name, query_plan, False, fn)
def test_pandas_project_simple(): """Tests a projection :return: None """ query_plan = QueryPlan() # Query plan ts = query_plan.add_operator( SQLTableScan('nation.csv', 'select * from S3Object ' 'limit 3;', True, 'ts', query_plan, False)) p = query_plan.add_operator( Project([ ProjectExpression(lambda t_: t_['_2'], 'n_regionkey'), ProjectExpression(lambda t_: t_['_0'], 'n_nationkey'), ProjectExpression(lambda t_: t_['_3'], 'n_comment') ], 'p', query_plan, False)) c = query_plan.add_operator(Collate('c', query_plan, False)) ts.connect(p) p.connect(c) # Write the plan graph query_plan.write_graph(os.path.join(ROOT_DIR, "../tests-output"), gen_test_id()) # Start the query query_plan.execute() # Assert the results # num_rows = 0 # for t in c.tuples(): # num_rows += 1 # print("{}:{}".format(num_rows, t)) field_names = ['n_regionkey', 'n_nationkey', 'n_comment'] assert len(c.tuples()) == 3 + 1 assert c.tuples()[0] == field_names assert c.tuples()[1] == [ '0', '0', ' haggle. carefully final deposits detect slyly agai' ] assert c.tuples()[2] == [ '1', '1', 'al foxes promise slyly according to the regular accounts. bold requests alon' ] assert c.tuples()[3] == [ '1', '2', 'y alongside of the pending deposits. carefully special packages ' 'are about the ironic forges. slyly special ' ] # Write the metrics query_plan.print_metrics()
def test_project_perf(): """Executes a projection over many source rows to examine performance. :return: None """ num_rows = 10000 profile_file_name = os.path.join( ROOT_DIR, "../tests-output/" + gen_test_id() + ".prof") query_plan = QueryPlan(is_async=True, buffer_size=0) # Query plan random_col_defs = [ RandomIntColumnDef(0, 9), RandomStringColumnDef(10, 20), RandomDateColumnDef(datetime.strptime('2017-01-01', '%Y-%m-%d'), datetime.strptime('2018-01-01', '%Y-%m-%d')) ] random_table_scan = query_plan.add_operator( RandomTableScan(num_rows, random_col_defs, 'random_table_scan', query_plan, False)) project = query_plan.add_operator( Project([ ProjectExpression(lambda t_: t_['_0'], 'r_0'), ProjectExpression(lambda t_: t_['_1'], 'r_1'), ProjectExpression(lambda t_: t_['_2'], 'r_2') ], 'project', query_plan, False)) project.set_profiled(True, profile_file_name) collate = query_plan.add_operator(Collate('collate', query_plan, False)) random_table_scan.connect(project) project.connect(collate) # Write the plan graph query_plan.write_graph(os.path.join(ROOT_DIR, "../tests-output"), gen_test_id()) # Start the query query_plan.execute() tuples = collate.tuples() collate.print_tuples(tuples) # Write the metrics query_plan.print_metrics() query_plan.stop() # Write the metrics s = pstats.Stats(profile_file_name) s.strip_dirs().sort_stats("time").print_stats() assert len(tuples) == num_rows + 1
def aggregate_project_def(name, query_plan): def fn(df): # return df[['_0', '_1', '_2']] df = df.filter(items=['_0'], axis=1) df.rename(columns={'_0': 'revenue'}, inplace=True) return df return Project([ProjectExpression(lambda t_: t_['_0'], 'revenue')], name, query_plan, False, fn)
def project_partkey_type_operator_def(name, query_plan): # type: (str, QueryPlan) -> Project def fn(df): # return df[['_0', '_1', '_2']] df = df.filter(items=['_0', '_1'], axis=1) df.rename(columns={'_0': 'p_partkey', '_1': 'p_type'}, inplace=True) return df return Project([ ProjectExpression(lambda t_: t_['_0'], 'p_partkey'), ProjectExpression(lambda t_: t_['_1'], 'p_type') ], name, query_plan, False, fn)
def test_filter_2(): # Let's forget about the local filter for now. The pd._expr field of the PredicateExpression class is not well documented and it is needed for the Filter class (on line 102). """ :return: """ query_plan = QueryPlan(buffer_size=64, is_async=True, use_shared_mem=False) # Query plan ''' ts = query_plan.add_operator( SQLTableScan('lineitem.csv', 'select * from S3Object limit 3;' , False, 'ts', query_plan, False)) ''' # using a 'use_native=True' argument will result in a None object being returned ts = query_plan.add_operator( SQLTableScan('random_strings_2.csv', 'select f1,f2 from S3Object limit 5;', Format.CSV, True, False, False, 'ts', query_plan, False)) def fn(df): df.columns = ["first_col", "second_col"] #We can change the types of the columns #df["first_col"] = df["first_col"].astype(np.int64) return df p = query_plan.add_operator(Project([], 'project', query_plan, False, fn)) c = query_plan.add_operator(Collate('c', query_plan, False)) ts.connect(p) p.connect(c) # Write the plan graph query_plan.write_graph(os.path.join(ROOT_DIR, "../tests-output"), gen_test_id()) # Start the query query_plan.execute() #assert 2 + 1 == len(c.tuples()) print("Tuples:") print(c.tuples()) # Write the metrics query_plan.print_metrics() print(ROOT_DIR)
def project_lineitem_from_filtered_scan_operator_def(name, query_plan): def fn(df): df.rename(columns={'_0': 'l_quantity', '_1': 'l_extendedprice', '_2': 'l_discount', '_3': 'l_tax', '_4': 'l_returnflag', '_5': 'l_linestatus', '_6': 'l_shipdate'}, inplace=True) return df return Project( [ ProjectExpression(lambda t_: t_['_0'], 'l_quantity'), ProjectExpression(lambda t_: t_['_1'], 'l_extendedprice'), ProjectExpression(lambda t_: t_['_2'], 'l_discount'), ProjectExpression(lambda t_: t_['_3'], 'l_tax'), ProjectExpression(lambda t_: t_['_4'], 'l_returnflag'), ProjectExpression(lambda t_: t_['_5'], 'l_linestatus'), ProjectExpression(lambda t_: t_['_6'], 'l_shipdate'), ], name, query_plan, False, fn)
def test_project_empty(): """Executes an projection query with no results returned. We tst this as it's somewhat peculiar with s3 select, in so much as s3 does not return column names when selecting data, meaning, unlike a traditional DBMS, no field names tuple should be present in the results. :return: None """ query_plan = QueryPlan() # Query plan ts = query_plan.add_operator( SQLTableScan('nation.csv', "select * from S3Object " "limit 0;", False, 'ts', query_plan, False)) p = query_plan.add_operator( Project([ ProjectExpression(lambda t_: t_['_2'], 'n_regionkey'), ProjectExpression(lambda t_: t_['_0'], 'n_nationkey'), ProjectExpression(lambda t_: t_['_3'], 'n_comment') ], 'p', query_plan, False)) c = query_plan.add_operator(Collate('c', query_plan, False)) ts.connect(p) p.connect(c) # Write the plan graph query_plan.write_graph(os.path.join(ROOT_DIR, "../tests-output"), gen_test_id()) # Start the query query_plan.execute() # Assert the results # num_rows = 0 # for t in c.tuples(): # num_rows += 1 # print("{}:{}".format(num_rows, t)) assert len(c.tuples()) == 0 # Write the metrics query_plan.print_metrics()
def project_lineitem_operator_def(name, query_plan): # type: (str, QueryPlan) -> Project def fn(df): df = df.filter(items=['_4', '_5', '_6', '_7', '_8', '_9', '_10'], axis=1) df.rename(columns={'_4': 'l_quantity', '_5': 'l_extendedprice', '_6': 'l_discount', '_7': 'l_tax', '_8': 'l_returnflag', '_9': 'l_linestatus', '_10': 'l_shipdate'}, inplace=True) return df return Project( [ ProjectExpression(lambda t_: t_['_4'], 'l_quantity'), ProjectExpression(lambda t_: t_['_5'], 'l_extendedprice'), ProjectExpression(lambda t_: t_['_6'], 'l_discount'), ProjectExpression(lambda t_: t_['_7'], 'l_tax'), ProjectExpression(lambda t_: t_['_8'], 'l_returnflag'), ProjectExpression(lambda t_: t_['_9'], 'l_linestatus'), ProjectExpression(lambda t_: t_['_10'], 'l_shipdate'), ], name, query_plan, False, fn)
def project_partkey_brand_size_container_filtered_op(name, query_plan): def fn(df): # return df[['_0', '_1', '_2']] df = df.filter(items=['_0', '_1', '_2', '_3'], axis=1) df.rename(columns={'_0': 'p_partkey', '_1': 'p_brand', '_2': 'p_size', '_3': 'p_container'}, inplace=True) return df return Project( [ ProjectExpression(lambda t_: t_['_0'], 'p_partkey'), ProjectExpression(lambda t_: t_['_1'], 'p_brand'), ProjectExpression(lambda t_: t_['_2'], 'p_size'), ProjectExpression(lambda t_: t_['_3'], 'p_container') ], name, query_plan, False, fn)
def project_avg_yearly_op(name, query_plan): """with extendedprice_sum_aggregate_project as ( select l_extendedprice / 7.0 as avg_yearly from extendedprice_sum_aggregate ) :return: """ def fn(df): # return df[['_0', '_1', '_2']] df['_0'] = df['_0'].astype(np.float) / 7.0 df = df.filter(items=['_0'], axis=1) df.rename(columns={'_0': 'avg_yearly'}, inplace=True) return df return Project( [ProjectExpression(lambda t_: t_['_0'] / 7.0, 'avg_yearly')], name, query_plan, False, fn)
def project_orderkey_partkey_quantity_extendedprice_op(name, query_plan): def fn(df): # return df[['_0', '_1', '_2']] df = df.filter(items=['_0', '_1', '_2', '_3'], axis=1) df.rename(columns={ '_0': 'l_orderkey', '_1': 'l_partkey', '_2': 'l_quantity', '_3': 'l_extendedprice' }, inplace=True) return df return Project([ ProjectExpression(lambda t_: t_['_0'], 'l_orderkey'), ProjectExpression(lambda t_: t_['_1'], 'l_partkey'), ProjectExpression(lambda t_: t_['_2'], 'l_quantity'), ProjectExpression(lambda t_: t_['_3'], 'l_extendedprice') ], name, query_plan, False, fn)
def project_partkey_extendedprice_discount_shipdate_operator_def( name, query_plan): def fn(df): # return df[['_0', '_1', '_2']] df = df.filter(items=['_1', '_5', '_6', '_10'], axis=1) df.rename(columns={ '_1': 'l_partkey', '_5': 'l_extendedprice', '_6': 'l_discount', '_10': 'l_shipdate' }, inplace=True) return df return Project([ ProjectExpression(lambda t_: t_['_1'], 'l_partkey'), ProjectExpression(lambda t_: t_['_5'], 'l_extendedprice'), ProjectExpression(lambda t_: t_['_6'], 'l_discount'), ProjectExpression(lambda t_: t_['_10'], 'l_shipdate') ], name, query_plan, False, fn)
def run(parallel, use_pandas, buffer_size, table_first_part, table_parts, queried_columns, queried_aliases, castable_aliases, select_str, aggregate_column, filter_str, path, format_=Format.PARQUET): secure = False use_native = False print('') print("Parquet Aggregate Benchmark") print("------------------") # Query plan query_plan = QueryPlan(is_async=parallel, buffer_size=buffer_size) # SQL scan the file scan = map( lambda p: query_plan.add_operator( SQLTableScan( "{}/lineitem.{}.parquet".format(path, p ), "select {} from S3Object " "{};".format(select_str, filter_str), format_, use_pandas, secure, use_native, 'scan_{}'.format(p), query_plan, False)), range(table_first_part, table_first_part + table_parts)) # project def fn(df): df.columns = queried_aliases df[castable_aliases] = df[castable_aliases].astype(np.double) return df project = map( lambda p: query_plan.add_operator( Project([], 'project_{}'.format(p), query_plan, False, fn)), range(table_first_part, table_first_part + table_parts)) # aggregation def agg_fun(df): if aggregate_column in df: return pd.DataFrame({'sum': [sum(df[aggregate_column])]}) else: return pd.DataFrame({'count': len(df)}, index=[0]) aggregate = query_plan.add_operator( Aggregate([], True, 'agg', query_plan, False, agg_fun)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) map(lambda (p, o): o.connect(project[p]), enumerate(scan)) map(lambda (p, o): o.connect(aggregate), enumerate(project)) aggregate.connect(collate) # Plan settings print('') print("Settings") print("--------") print('') print('use_pandas: {}'.format(use_pandas)) print("table parts: {}".format(table_parts)) print('') # Write the plan graph # query_plan.write_graph(os.path.join(ROOT_DIR, "../benchmark-output"), gen_test_id() + "-" + str(table_parts)) # Start the query query_plan.execute() print('Done') tuples = collate.tuples() collate.print_tuples(tuples) # Write the metrics query_plan.print_metrics() # Shut everything down query_plan.stop()
def run(sort_field, k, parallel, use_pandas, sort_order, buffer_size, table_first_part, table_parts, queried_columns, select_columns, path, format_): """ Executes the baseline topk query by scanning a table and keeping track of the max/min records in a heap :return: """ secure = False use_native = False print('') print("Top K Benchmark, ColumnScan. Sort Field: {}, Order: {}".format( sort_field, sort_order)) print("----------------------") # Query plan query_plan = QueryPlan(is_async=parallel, buffer_size=buffer_size) # Sampling sample_scan = map( lambda p: query_plan.add_operator( #SQLTableScan("{}/lineitem.snappy.parquet.{}".format(path, p), SQLTableScan( "{}/lineitem.typed.1RowGroup.parquet.{}".format( path, p), 'select {} from S3Object;'.format( sort_field), format_, use_pandas, secure, use_native, 'column_scan_{}'.format(p), query_plan, False)), range(table_first_part, table_first_part + table_parts)) # Sampling project def project_fn1(df): df.columns = [sort_field] df[[sort_field]] = df[[sort_field]].astype(np.float) return df project_exprs = [ProjectExpression(lambda t_: t_['_0'], sort_field)] sample_project = map( lambda p: query_plan.add_operator( Project(project_exprs, 'sample_project_{}'.format(p), query_plan, False, project_fn1)), range(table_first_part, table_first_part + table_parts)) # TopK samples sort_expr = SortExpression(sort_field, float, sort_order) sample_topk = query_plan.add_operator( Top(k, sort_expr, use_pandas, 'sample_topk', query_plan, False)) # Generate SQL command for second scan sql_gen = query_plan.add_operator( TopKFilterBuild( sort_order, 'float', 'select {} from S3object '.format(select_columns), #' CAST({} as float) '.format(sort_field), 'sql_gen', query_plan, False )) ' {} '.format(sort_field), 'sql_gen', query_plan, False)) # Scan scan = map( lambda p: query_plan.add_operator( #SQLTableScan("{}/lineitem.snappy.parquet.{}".format(path, p), SQLTableScan( "{}/lineitem.typed.1RowGroup.parquet.{}".format(path, p), "", format_, use_pandas, secure, use_native, 'scan_{}'.format( p), query_plan, False)), range(table_first_part, table_first_part + table_parts)) # Project def project_fn2(df): df.columns = queried_columns df[[sort_field]] = df[[sort_field]].astype(np.float) return df project_exprs = [ProjectExpression(lambda t_: t_['_0'], sort_field)] project = map( lambda p: query_plan.add_operator( Project(project_exprs, 'project_{}'.format(p), query_plan, False, project_fn2)), range(table_first_part, table_first_part + table_parts)) # TopK topk = map( lambda p: query_plan.add_operator( Top(k, sort_expr, use_pandas, 'topk_{}'.format(p), query_plan, False)), range(table_first_part, table_first_part + table_parts)) # TopK reduce topk_reduce = query_plan.add_operator( Top(k, sort_expr, use_pandas, 'topk_reduce', query_plan, False)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) #profile_path = '../benchmark-output/groupby/' #scan[0].set_profiled(True, os.path.join(ROOT_DIR, profile_path, gen_test_id() + "_scan_0" + ".prof")) #project[0].set_profiled(True, os.path.join(ROOT_DIR, profile_path, gen_test_id() + "_project_0" + ".prof")) #groupby[0].set_profiled(True, os.path.join(ROOT_DIR, profile_path, gen_test_id() + "_groupby_0" + ".prof")) #groupby_reduce.set_profiled(True, os.path.join(ROOT_DIR, profile_path, gen_test_id() + "_groupby_reduce" + ".prof")) #collate.set_profiled(True, os.path.join(ROOT_DIR, profile_path, gen_test_id() + "_collate" + ".prof")) map(lambda (p, o): o.connect(sample_project[p]), enumerate(sample_scan)) map(lambda (p, o): o.connect(sample_topk), enumerate(sample_project)) sample_topk.connect(sql_gen) map(lambda (p, o): sql_gen.connect(o), enumerate(scan)) map(lambda (p, o): o.connect(project[p]), enumerate(scan)) map(lambda (p, o): o.connect(topk[p]), enumerate(project)) map(lambda (p, o): o.connect(topk_reduce), enumerate(topk)) topk_reduce.connect(collate) # Plan settings print('') print("Settings") print("--------") print('') print('use_pandas: {}'.format(use_pandas)) print("table parts: {}".format(table_parts)) print('') # Write the plan graph query_plan.write_graph(os.path.join(ROOT_DIR, "../benchmark-output"), gen_test_id() + "-" + str(table_parts)) # Start the query query_plan.execute() print('Done') tuples = collate.tuples() collate.print_tuples(tuples) # Write the metrics query_plan.print_metrics() # Shut everything down query_plan.stop()
def query_plan(settings): # type: (SyntheticSemiJoinSettings) -> QueryPlan """ :return: None """ if settings.use_shared_mem: system = WorkerSystem(settings.shared_memory_size) else: system = None query_plan = QueryPlan(system, is_async=settings.parallel, buffer_size=settings.buffer_size, use_shared_mem=settings.use_shared_mem) # Define the operators scan_a = \ map(lambda p: query_plan.add_operator( SQLTableScan(get_file_key(settings.table_A_key, settings.table_A_sharded, p, settings.sf), "select " " {} " "from " " S3Object " "where " " {} " " {} " .format(settings.table_A_AB_join_key, settings.table_A_filter_sql, get_sql_suffix(settings.table_A_key, settings.table_A_parts, p, settings.table_A_sharded)), settings.format_, settings.use_pandas, settings.secure, settings.use_native, 'scan_a' + '_{}'.format(p), query_plan, False)), range(0, settings.table_A_parts)) field_names_map_a = OrderedDict( zip([ '_{}'.format(i) for i, name in enumerate(settings.table_A_field_names) ], settings.table_A_field_names)) def project_fn_a(df): df = df.rename(columns=field_names_map_a, copy=False) return df project_a = map( lambda p: query_plan.add_operator( Project([ ProjectExpression(k, v) for k, v in field_names_map_a.iteritems() ], 'project_a' + '_{}'.format(p), query_plan, False, project_fn_a) ), range(0, settings.table_A_parts)) bloom_create_ab_join_key = map( lambda p: query_plan.add_operator( BloomCreate(settings.table_A_AB_join_key, 'bloom_create_ab_join_key' + '_{}'.format(p), query_plan, False, fp_rate=settings.fp_rate)), range(0, settings.table_A_parts)) scan_b_on_ab_join_key = \ map(lambda p: query_plan.add_operator( SQLTableScanBloomUse(get_file_key(settings.table_B_key, settings.table_B_sharded, p, settings.sf), "select " " {},{} " "from " " S3Object " "where " " {} " " {} " .format(settings.table_B_BC_join_key, settings.table_B_AB_join_key, settings.table_B_filter_sql, get_sql_suffix(settings.table_B_key, settings.table_B_parts, p, settings.table_B_sharded, add_where=False)), settings.format_, settings.table_B_AB_join_key, settings.use_pandas, settings.secure, settings.use_native, 'scan_b_on_ab_join_key' + '_{}'.format(p), query_plan, False)), range(0, settings.table_B_parts)) if settings.table_C_key is None: scan_b_detail_on_b_pk = \ map(lambda p: query_plan.add_operator( SQLTableScanBloomUse(get_file_key(settings.table_B_key, settings.table_B_sharded, p, settings.sf), "select " " {},{} " "from " " S3Object " "where " " {} " " {} " .format(settings.table_B_primary_key, settings.table_B_detail_field_name, settings.table_B_filter_sql, get_sql_suffix(settings.table_B_key, settings.table_B_parts, p, settings.table_B_sharded, add_where=False)), settings.format_, settings.table_B_primary_key, settings.use_pandas, settings.secure, settings.use_native, 'scan_c_detail_on_b_pk' + '_{}'.format(p), query_plan, False)), range(0, settings.table_B_parts)) field_names_map_b_detail = OrderedDict([ ('_0', settings.table_B_primary_key), ('_1', settings.table_B_detail_field_name) ]) def project_fn_b_detail(df): df.rename(columns=field_names_map_b_detail, inplace=True) return df project_b_detail = map( lambda p: query_plan.add_operator( Project([ ProjectExpression(k, v) for k, v in field_names_map_b_detail.iteritems() ], 'project_b_detail' + '_{}'.format(p), query_plan, False, project_fn_b_detail)), range(0, settings.table_B_parts)) map_b_pk_1 = map( lambda p: query_plan.add_operator( Map(settings.table_B_primary_key, 'map_b_pk_1' + '_{}'.format( p), query_plan, False)), range(0, settings.table_B_parts)) map_b_pk_2 = map( lambda p: query_plan.add_operator( Map(settings.table_B_primary_key, 'map_b_pk_2' + '_{}'.format( p), query_plan, False)), range(0, settings.table_B_parts)) bloom_create_b_pk = map( lambda p: query_plan.add_operator( BloomCreate(settings.table_B_primary_key, 'bloom_create_b_pk' + '_{}'.format(p), query_plan, False, fp_rate=settings.fp_rate)), range(0, settings.table_B_parts)) join_probe_ab_and_b_on_b_pk = map( lambda p: query_plan.add_operator( HashJoinProbe( JoinExpression(settings.table_B_primary_key, settings. table_B_primary_key), 'join_probe_ab_and_b_on_b_pk' + '_{}'.format( p), query_plan, False)), range(0, settings.table_B_parts)) join_build_ab_and_b_on_b_pk = map( lambda p: query_plan.add_operator( HashJoinBuild(settings.table_B_primary_key, 'join_build_ab_and_b_on_b_pk' + '_{}'.format( p), query_plan, False)), range(0, settings.table_B_parts)) else: scan_c_on_bc_join_key = \ map(lambda p: query_plan.add_operator( SQLTableScanBloomUse(get_file_key(settings.table_C_key, settings.table_C_sharded, p, settings.sf), "select " " {}, {} " "from " " S3Object " "where " " {} " " {} " .format(settings.table_C_primary_key, settings.table_C_BC_join_key, settings.table_C_filter_sql, get_sql_suffix(settings.table_C_key, settings.table_C_parts, p, settings.table_C_sharded, add_where=False)), settings.table_C_BC_join_key, settings.format_, settings.use_pandas, settings.secure, settings.use_native, 'scan_c_on_bc_join_key' + '_{}'.format(p), query_plan, False)), range(0, settings.table_C_parts)) field_names_map_c = OrderedDict( zip([ '_{}'.format(i) for i, name in enumerate(settings.table_C_field_names) ], settings.table_C_field_names)) def project_fn_c(df): df.rename(columns=field_names_map_c, inplace=True) return df project_c = map( lambda p: query_plan.add_operator( Project([ ProjectExpression(k, v) for k, v in field_names_map_c.iteritems() ], 'project_c' + '_{}'.format(p), query_plan, False, project_fn_c)), range(0, settings.table_C_parts)) scan_c_detail_on_c_pk = \ map(lambda p: query_plan.add_operator( SQLTableScanBloomUse(get_file_key(settings.table_C_key, settings.table_C_sharded, p, settings.sf), "select " " {},{} " "from " " S3Object " "where " " {} " " {} " .format(settings.table_C_primary_key, settings.table_C_detail_field_name, settings.table_C_filter_sql, get_sql_suffix(settings.table_C_key, settings.table_C_parts, p, settings.table_C_sharded, add_where=False)), settings.table_C_primary_key, settings.format_, settings.use_pandas, settings.secure, settings.use_native, 'scan_c_detail_on_c_pk' + '_{}'.format(p), query_plan, False)), range(0, settings.table_C_parts)) field_names_map_c_detail = OrderedDict([ ('_0', settings.table_C_primary_key), ('_1', settings.table_C_detail_field_name) ]) def project_fn_c_detail(df): df.rename(columns=field_names_map_c_detail, inplace=True) return df project_c_detail = map( lambda p: query_plan.add_operator( Project([ ProjectExpression(k, v) for k, v in field_names_map_c_detail.iteritems() ], 'project_c_detail' + '_{}'.format(p), query_plan, False, project_fn_c_detail)), range(0, settings.table_C_parts)) map_bc_b_join_key = map( lambda p: query_plan.add_operator( Map(settings.table_B_BC_join_key, 'map_bc_b_join_key' + '_{}'. format(p), query_plan, False)), range(0, settings.table_C_parts)) map_c_pk_1 = map( lambda p: query_plan.add_operator( Map(settings.table_C_primary_key, 'map_c_pk_1' + '_{}'.format( p), query_plan, False)), range(0, settings.table_C_parts)) map_c_pk_2 = map( lambda p: query_plan.add_operator( Map(settings.table_C_primary_key, 'map_c_pk_2' + '_{}'.format( p), query_plan, False)), range(0, settings.table_C_parts)) bloom_create_c_pk = map( lambda p: query_plan.add_operator( BloomCreate(settings.table_C_primary_key, 'bloom_create_bc_b_to_c_join_key_{}'.format(p), query_plan, False, fp_rate=settings.fp_rate)), range(0, settings.table_C_parts)) join_build_ab_and_c_on_bc_join_key = map( lambda p: query_plan.add_operator( HashJoinBuild( settings.table_B_BC_join_key, 'join_build_ab_and_c_on_bc_join_key' + '_{}'.format( p), query_plan, False)), range(0, settings.table_C_parts)) join_probe_ab_and_c_on_bc_join_key = map( lambda p: query_plan.add_operator( HashJoinProbe( JoinExpression(settings.table_B_BC_join_key, settings. table_C_BC_join_key), 'join_probe_ab_and_c_on_bc_join_key' + '_{}'.format( p), query_plan, False)), range(0, settings.table_C_parts)) join_build_abc_and_c_on_c_pk = map( lambda p: query_plan.add_operator( HashJoinBuild(settings.table_C_primary_key, 'join_build_abc_and_c_on_c_pk' + '_{}'.format( p), query_plan, False)), range(0, settings.table_C_parts)) join_probe_abc_and_c_on_c_pk = map( lambda p: query_plan.add_operator( HashJoinProbe( JoinExpression(settings.table_C_primary_key, settings. table_C_primary_key), 'join_probe_abc_and_c_on_c_pk' + '_{}'.format( p), query_plan, False)), range(0, settings.table_C_parts)) bloom_create_bc_join_key = map( lambda p: query_plan.add_operator( BloomCreate(settings.table_B_BC_join_key, 'bloom_create_bc_join_key' + '_{}'.format( p), query_plan, False)), range(0, settings.table_B_parts)) map_bc_c_join_key = map( lambda p: query_plan.add_operator( Map(settings.table_C_BC_join_key, 'map_bc_c_join_key' + '_{}'. format(p), query_plan, False)), range(0, settings.table_B_parts)) field_names_map_b = OrderedDict( zip([ '_{}'.format(i) for i, name in enumerate(settings.table_B_field_names) ], settings.table_B_field_names)) def project_fn_b(df): df.rename(columns=field_names_map_b, inplace=True) return df project_b = map( lambda p: query_plan.add_operator( Project([ ProjectExpression(k, v) for k, v in field_names_map_b.iteritems() ], 'project_b' + '_{}'.format(p), query_plan, False, project_fn_b) ), range(0, settings.table_B_parts)) map_ab_a_join_key = map( lambda p: query_plan.add_operator( Map(settings.table_A_AB_join_key, 'map_ab_a_join_key' + '_{}' .format(p), query_plan, False)), range(0, settings.table_A_parts)) map_ab_b_join_key = map( lambda p: query_plan.add_operator( Map(settings.table_B_AB_join_key, 'map_ab_b_join_key' + '_{}' .format(p), query_plan, False)), range(0, settings.table_B_parts)) join_build_a_and_b_on_ab_join_key = map( lambda p: query_plan.add_operator( HashJoinBuild( settings.table_A_AB_join_key, 'join_build_a_and_b_on_ab_join_key' + '_{}'.format( p), query_plan, False)), range(0, settings.table_B_parts)) join_probe_a_and_b_on_ab_join_key = map( lambda p: query_plan.add_operator( HashJoinProbe( JoinExpression(settings.table_A_AB_join_key, settings. table_B_AB_join_key), 'join_probe_a_and_b_on_ab_join_key' + '_{}'.format( p), query_plan, False)), range(0, settings.table_B_parts)) if settings.table_C_key is None: def part_aggregate_fn(df): sum_ = df[settings.table_B_detail_field_name].astype( np.float).sum() return pd.DataFrame({'_0': [sum_]}) part_aggregate = map( lambda p: query_plan.add_operator( Aggregate([ AggregateExpression( AggregateExpression.SUM, lambda t: float(t[ settings.table_B_detail_field_name])) ], settings.use_pandas, 'part_aggregate_{}'.format(p), query_plan, False, part_aggregate_fn)), range(0, settings.table_B_parts)) else: def part_aggregate_fn(df): sum_ = df[settings.table_C_detail_field_name].astype( np.float).sum() return pd.DataFrame({'_0': [sum_]}) part_aggregate = map( lambda p: query_plan.add_operator( Aggregate([ AggregateExpression( AggregateExpression.SUM, lambda t: float(t[ settings.table_C_detail_field_name])) ], settings.use_pandas, 'part_aggregate_{}'.format(p), query_plan, False, part_aggregate_fn)), range(0, settings.table_C_parts)) def aggregate_reduce_fn(df): sum_ = df['_0'].astype(np.float).sum() return pd.DataFrame({'_0': [sum_]}) aggregate_reduce = query_plan.add_operator( Aggregate([ AggregateExpression(AggregateExpression.SUM, lambda t: float(t['_0'])) ], settings.use_pandas, 'aggregate_reduce', query_plan, False, aggregate_reduce_fn)) aggregate_project = query_plan.add_operator( Project([ProjectExpression(lambda t: t['_0'], 'total_balance')], 'aggregate_project', query_plan, False)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) # Inline some of the operators map(lambda o: o.set_async(False), project_a) map(lambda o: o.set_async(False), project_b) map(lambda o: o.set_async(False), map_ab_a_join_key) map(lambda o: o.set_async(False), map_ab_b_join_key) if settings.table_C_key is None: map(lambda o: o.set_async(False), map_b_pk_1) map(lambda o: o.set_async(False), map_b_pk_2) map(lambda o: o.set_async(False), project_b_detail) else: map(lambda o: o.set_async(False), map_bc_b_join_key) map(lambda o: o.set_async(False), map_bc_c_join_key) map(lambda o: o.set_async(False), map_c_pk_1) map(lambda o: o.set_async(False), map_c_pk_2) map(lambda o: o.set_async(False), project_c) map(lambda o: o.set_async(False), project_c_detail) aggregate_project.set_async(False) # Connect the operators connect_many_to_many(scan_a, project_a) connect_many_to_many(project_a, map_ab_a_join_key) connect_all_to_all(map_ab_a_join_key, join_build_a_and_b_on_ab_join_key) connect_all_to_all(project_a, bloom_create_ab_join_key) # connect_all_to_all(map_A_to_B, join_build_a_and_b_on_ab_join_key) connect_many_to_many(join_build_a_and_b_on_ab_join_key, join_probe_a_and_b_on_ab_join_key) # connect_all_to_all(map_bloom_A_to_B, bloom_create_ab_join_key) connect_many_to_many(bloom_create_ab_join_key, scan_b_on_ab_join_key) connect_many_to_many(scan_b_on_ab_join_key, project_b) # connect_many_to_many(project_b, join_probe_a_and_b_on_ab_join_key) # connect_all_to_all(map_B_to_B, join_probe_a_and_b_on_ab_join_key) connect_many_to_many(project_b, map_ab_b_join_key) connect_all_to_all(map_ab_b_join_key, join_probe_a_and_b_on_ab_join_key) # connect_many_to_many(join_probe_a_and_b_on_ab_join_key, map_bloom_B_to_B) if settings.table_C_key is None: # connect_all_to_all(join_probe_a_and_b_on_ab_join_key, part_aggregate) connect_many_to_many(scan_b_detail_on_b_pk, project_b_detail) connect_many_to_many(project_b_detail, map_b_pk_2) connect_many_to_many(bloom_create_b_pk, scan_b_detail_on_b_pk) connect_all_to_all(join_probe_a_and_b_on_ab_join_key, bloom_create_b_pk) connect_all_to_all(map_b_pk_2, join_probe_ab_and_b_on_b_pk) connect_many_to_many(join_probe_ab_and_b_on_b_pk, part_aggregate) connect_many_to_many(join_build_ab_and_b_on_b_pk, join_probe_ab_and_b_on_b_pk) connect_many_to_many(join_probe_a_and_b_on_ab_join_key, map_b_pk_1) connect_all_to_all(map_b_pk_1, join_build_ab_and_b_on_b_pk) else: connect_all_to_all(join_probe_a_and_b_on_ab_join_key, bloom_create_bc_join_key) connect_many_to_many(bloom_create_bc_join_key, scan_c_on_bc_join_key) connect_many_to_many(scan_c_on_bc_join_key, project_c) # connect_many_to_many(project_c, join_probe_ab_and_c_on_bc_join_key) connect_all_to_all(map_bc_c_join_key, join_probe_ab_and_c_on_bc_join_key) # connect_many_to_many(join_probe_a_and_b_on_ab_join_key, join_build_ab_and_c_on_bc_join_key) connect_many_to_many(join_probe_a_and_b_on_ab_join_key, map_bc_b_join_key) connect_all_to_all(map_bc_b_join_key, join_build_ab_and_c_on_bc_join_key) connect_all_to_all(join_probe_ab_and_c_on_bc_join_key, bloom_create_c_pk) # connect_many_to_many(join_probe_ab_and_c_on_bc_join_key, join_build_abc_and_c_on_c_pk) connect_many_to_many(join_probe_ab_and_c_on_bc_join_key, map_c_pk_1) connect_all_to_all(map_c_pk_1, join_build_abc_and_c_on_c_pk) connect_many_to_many(bloom_create_c_pk, scan_c_detail_on_c_pk) # connect_all_to_all(bloom_create_bc_join_key, scan_c_detail_on_c_pk) connect_many_to_many(join_build_abc_and_c_on_c_pk, join_probe_abc_and_c_on_c_pk) # connect_many_to_many(join_probe_a_and_b_on_ab_join_key, map_B_to_C) # connect_all_to_all(join_probe_a_and_b_on_ab_join_key, join_build_abc_and_c_on_c_pk) connect_many_to_many(scan_c_detail_on_c_pk, project_c_detail) # connect_many_to_many(project_c_detail, map_C_to_C) # connect_all_to_all(project_c_detail, join_probe_abc_and_c_on_c_pk) connect_many_to_many(project_c_detail, map_c_pk_2) connect_many_to_many(project_c, map_bc_c_join_key) connect_many_to_many(join_build_ab_and_c_on_bc_join_key, join_probe_ab_and_c_on_bc_join_key) connect_all_to_all(map_c_pk_2, join_probe_abc_and_c_on_c_pk) connect_many_to_many(join_probe_abc_and_c_on_c_pk, part_aggregate) connect_many_to_one(part_aggregate, aggregate_reduce) connect_one_to_one(aggregate_reduce, aggregate_project) connect_one_to_one(aggregate_project, collate) return query_plan
def run_baseline_topk(stats, sort_field_index, sort_field, k, parallel, use_pandas, sort_order, buffer_size, table_parts_start, table_parts_end, tbl_s3key, format_, shards_path): secure = False use_native = False print('') print("Top K Benchmark, Baseline. Sort Field: {}, Order: {}, k: {}".format( sort_field, sort_order, k)) print("----------------------") stats += ['baseline', shards_path, sort_field, sort_order, k, 0, 0] # Query plan query_plan = QueryPlan(is_async=parallel, buffer_size=buffer_size) # Sampling table_parts = table_parts_end - table_parts_start + 1 per_part_samples = int(sample_size / table_parts) table_name = os.path.basename(tbl_s3key) # Scan scan = map( lambda p: query_plan.add_operator( SQLTableScan("{}.{}".format(shards_path, p ), "select * from S3Object;", format_, use_pandas, secure, use_native, 'scan_{}'.format( p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # Project def project_fn(df): df.columns = [ sort_field if x == sort_field_index else x for x in df.columns ] df[[sort_field]] = df[[sort_field]].astype(np.float) return df project_exprs = [ProjectExpression(lambda t_: t_['_0'], sort_field)] project = map( lambda p: query_plan.add_operator( Project(project_exprs, 'project_{}'.format(p), query_plan, False, project_fn)), range(table_parts_start, table_parts_end + 1)) # TopK sort_expr = [SortExpression(sort_field, float, sort_order)] topk = map( lambda p: query_plan.add_operator( Top(k, sort_expr, use_pandas, 'topk_{}'.format(p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # TopK reduce topk_reduce = query_plan.add_operator( Top(k, sort_expr, use_pandas, 'topk_reduce', query_plan, False)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) map(lambda (p, o): o.connect(project[p]), enumerate(scan)) map(lambda (p, o): o.connect(topk[p]), enumerate(project)) map(lambda (p, o): o.connect(topk_reduce), enumerate(topk)) topk_reduce.connect(collate) # Start the query query_plan.execute() print('Done') # Write the metrics query_plan.print_metrics() # Shut everything down query_plan.stop() query_time = query_plan.total_elapsed_time cost, bytes_scanned, bytes_returned, rows = query_plan.cost() computation_cost = query_plan.computation_cost() data_cost = query_plan.data_cost()[0] stats += [ 0, 0, 0, query_time, rows, bytes_scanned, bytes_returned, data_cost, computation_cost, cost ]
def run_head_table_sampling(stats, sort_field_index, sort_field, k, sample_size, parallel, use_pandas, sort_order, buffer_size, table_parts_start, table_parts_end, tbl_s3key, shards_path, format_, sampling_only=True): secure = False use_native = False print('') print( "Top K Benchmark, Head Table Sampling. Sort Field: {}, Order: {}, k: {}, Sample Size:{}" .format(sort_field, sort_order, k, sample_size)) print("----------------------") stats += [ 'sampling_{}_{}'.format('head_table', 'non-filtered'), shards_path, sort_field, sort_order, k, sample_size, 1 ] # Query plan query_plan = QueryPlan(is_async=parallel, buffer_size=buffer_size) # Sampling table_parts = table_parts_end - table_parts_start + 1 per_part_samples = int(sample_size / table_parts) table_name = os.path.basename(tbl_s3key) sample_scan = map( lambda p: query_plan.add_operator( SQLTableScan( "{}.{}".format(shards_path, p), 'select {} from S3Object limit {};'.format( sort_field, per_part_samples), format_, use_pandas, secure, use_native, 'sample_scan_{}'.format(p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # Sampling project def project_fn(df): df.columns = [sort_field] df[[sort_field]] = df[[sort_field]].astype(np.float) return df project_exprs = [ProjectExpression(lambda t_: t_['_0'], sort_field)] sample_project = map( lambda p: query_plan.add_operator( Project(project_exprs, 'sample_project_{}'.format(p), query_plan, False, project_fn)), range(table_parts_start, table_parts_end + 1)) # TopK samples sort_expr = [SortExpression(sort_field, float, sort_order)] sample_topk = map( lambda p: query_plan.add_operator( Top(k, sort_expr, use_pandas, 'sample_topk_{}'.format(p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) sample_topk_reduce = query_plan.add_operator( Top(k, sort_expr, use_pandas, 'sample_topk_reduce', query_plan, False)) # Generate SQL command for second scan sql_gen = query_plan.add_operator( TopKFilterBuild(sort_order, 'float', 'select * from S3object ', ' CAST({} as float) '.format(sort_field), 'sql_gen', query_plan, False)) if not sampling_only: # Scan scan = map( lambda p: query_plan.add_operator( SQLTableScan("{}.{}".format(shards_path, p), "", format_, use_pandas, secure, use_native, 'scan_{}'.format( p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # Project def project_fn(df): df.columns = [ sort_field if x == sort_field_index else x for x in df.columns ] df[[sort_field]] = df[[sort_field]].astype(np.float) return df project_exprs = [ProjectExpression(lambda t_: t_['_0'], sort_field)] project = map( lambda p: query_plan.add_operator( Project(project_exprs, 'project_{}'.format(p), query_plan, False, project_fn)), range(table_parts_start, table_parts_end + 1)) # TopK topk = map( lambda p: query_plan.add_operator( Top(k, sort_expr, use_pandas, 'topk_{}'.format(p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # TopK reduce topk_reduce = query_plan.add_operator( Top(k, sort_expr, use_pandas, 'topk_reduce', query_plan, False)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) map(lambda (p, o): o.connect(sample_project[p]), enumerate(sample_scan)) map(lambda (p, o): o.connect(sample_topk[p]), enumerate(sample_project)) map(lambda op: op.connect(sample_topk_reduce), sample_topk) sample_topk_reduce.connect(sql_gen) if not sampling_only: map(lambda (p, o): sql_gen.connect(o), enumerate(scan)) map(lambda (p, o): o.connect(project[p]), enumerate(scan)) map(lambda (p, o): o.connect(topk[p]), enumerate(project)) map(lambda (p, o): o.connect(topk_reduce), enumerate(topk)) topk_reduce.connect(collate) else: sql_gen.connect(collate) # Start the query query_plan.execute() print('Done') # Write the metrics query_plan.print_metrics() # Shut everything down query_plan.stop() sampling_time = query_plan.total_elapsed_time cost, bytes_scanned, bytes_returned, rows = query_plan.cost() computation_cost = query_plan.computation_cost() data_cost = query_plan.data_cost()[0] stats += [ sql_gen.threshold, sampling_time, 0, sampling_time, rows, bytes_scanned, bytes_returned, data_cost, computation_cost, cost ]
def run_local_indexed_sampling(stats, sort_field_index, sort_field, k, sample_size, batch_size, parallel, use_pandas, sort_order, buffer_size, table_parts_start, table_parts_end, tbl_s3key, shards_path, format_, sampling_only=True): """ Executes the randomly sampled topk query by firstly building a random sample, then extracting the filtering threshold Finally scanning the table to retrieve only the records beyond the threshold :return: """ secure = False use_native = False n_threads = multiprocessing.cpu_count() print('') print("Top K Benchmark, Sampling. Sort Field: {}, Order: {}".format( sort_field, sort_order)) print("----------------------") stats += [ 'sampling_{}_{}'.format('indexed', 'non-filtered'), shards_path, sort_field, sort_order, k, sample_size, batch_size ] # Query plan query_plan = QueryPlan(is_async=parallel, buffer_size=buffer_size) # Sampling tbl_smpler = query_plan.add_operator( TableRandomSampleGenerator(tbl_s3key, sample_size, batch_size, "table_sampler", query_plan, False)) sample_scan = map( lambda p: query_plan.add_operator( TableRangeAccess(tbl_s3key, use_pandas, secure, use_native, "sample_scan_{}".format(p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) map(lambda (i, op): sample_scan[i].set_nthreads(n_threads), enumerate(sample_scan)) # sample_scan = query_plan.add_operator( # TableRangeAccess(tbl_s3key, use_pandas, secure, use_native, "sample_scan_{}".format(p), # query_plan, False)) # sample_scan.set_nthreads(n_threads) # Sampling project def project_fn(df): df.columns = [ sort_field if x == sort_field_index else x for x in df.columns ] df = df[[sort_field]].astype(np.float, errors='ignore') return df project_exprs = [ProjectExpression(lambda t_: t_['_0'], sort_field)] sample_project = map( lambda p: query_plan.add_operator( Project(project_exprs, 'sample_project_{}'.format(p), query_plan, False, project_fn)), range(table_parts_start, table_parts_end + 1)) # sample_project = query_plan.add_operator( # Project(project_exprs, 'sample_project_{}'.format(p), query_plan, False, project_fn)) # TopK samples sort_expr = [SortExpression(sort_field, float, sort_order)] sample_topk = map( lambda p: query_plan.add_operator( Top(k, sort_expr, use_pandas, 'sample_topk_{}'.format(p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # sample_topk = query_plan.add_operator( # Top(k, sort_expr, use_pandas, 'sample_topk_{}'.format(p), query_plan, False)) sample_topk_reduce = query_plan.add_operator( Top(k, sort_expr, use_pandas, 'sample_topk_reduce', query_plan, False)) # Generate SQL command for second scan sql_gen = query_plan.add_operator( TopKFilterBuild(sort_order, 'float', 'select * from S3object ', ' CAST({} as float) '.format(sort_field), 'sql_gen', query_plan, False)) if not sampling_only: # Scan scan = map( lambda p: query_plan.add_operator( SQLTableScan("{}.{}".format(shards_path, p), "", format_, use_pandas, secure, use_native, 'scan_{}'.format( p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # Project def project_fn(df): df.columns = [ sort_field if x == sort_field_index else x for x in df.columns ] df[[sort_field]] = df[[sort_field]].astype(np.float) return df project_exprs = [ProjectExpression(lambda t_: t_['_0'], sort_field)] project = map( lambda p: query_plan.add_operator( Project(project_exprs, 'project_{}'.format(p), query_plan, False, project_fn)), range(table_parts_start, table_parts_end + 1)) # TopK topk = map( lambda p: query_plan.add_operator( Top(k, sort_expr, use_pandas, 'topk_{}'.format(p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # TopK reduce topk_reduce = query_plan.add_operator( Top(k, sort_expr, use_pandas, 'topk_reduce', query_plan, False)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) map(lambda o: tbl_smpler.connect(o), sample_scan) map(lambda (p, o): o.connect(sample_project[p]), enumerate(sample_scan)) map(lambda (p, o): o.connect(sample_topk[p]), enumerate(sample_project)) map(lambda o: o.connect(sample_topk_reduce), sample_topk) sample_topk_reduce.connect(sql_gen) if not sampling_only: map(lambda (p, o): sql_gen.connect(o), enumerate(scan)) map(lambda (p, o): o.connect(project[p]), enumerate(scan)) map(lambda (p, o): o.connect(topk[p]), enumerate(project)) map(lambda (p, o): o.connect(topk_reduce), enumerate(topk)) topk_reduce.connect(collate) else: sql_gen.connect(collate) # Plan settings print('') print("Settings") print("--------") print('') print('use_pandas: {}'.format(use_pandas)) print("table parts: {}".format(table_parts_end - table_parts_start)) print('') # Write the plan graph # query_plan.write_graph(os.path.join(ROOT_DIR, "../benchmark-output"), gen_test_id() + "-" + str(table_parts)) # Start the query query_plan.execute() print('Done') # tuples = collate.tuples() # collate.print_tuples(tuples) # Write the metrics query_plan.print_metrics() # Shut everything down query_plan.stop() sampling_time = query_plan.total_elapsed_time cost, bytes_scanned, bytes_returned, rows = query_plan.cost() computation_cost = query_plan.computation_cost() data_cost = query_plan.data_cost()[0] stats += [ sql_gen.threshold, sampling_time, 0, sampling_time, rows, bytes_scanned, bytes_returned, data_cost, computation_cost, cost ]
def run_memory_indexed_sampling(stats, sort_field_index, sort_field, k, sample_size, batch_size, parallel, use_pandas, sort_order, buffer_size, table_parts_start, table_parts_end, tbl_s3key, shards_path, format_, sampling_only=True): """ Executes the randomly sampled topk query by firstly building a random sample, then extracting the filtering threshold Finally scanning the table to retrieve only the records beyond the threshold :return: """ secure = False use_native = False n_threads = multiprocessing.cpu_count() print('') print( "Top K Benchmark, Memory Indexed Sampling. Sort Field: {}, Order: {}, K: {}, Sample Size: {}, Batch Size: {}" .format(sort_field, sort_order, k, sample_size, batch_size)) print("----------------------") stats += [ 'sampling_{}_{}'.format('memory_indexed', 'non-filtered'), shards_path, sort_field, sort_order, k, sample_size, batch_size ] # Query plan query_plan = QueryPlan(is_async=parallel, buffer_size=buffer_size) # Sampling tbl_smpler = query_plan.add_operator( TableRandomSampleGenerator(tbl_s3key, sample_size, batch_size, "table_sampler", query_plan, False)) sample_scanners = map( lambda p: query_plan.add_operator( TableRangeAccess(tbl_s3key, use_pandas, secure, use_native, "sample_scan_{}".format(p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) map(lambda op: op.set_nthreads(n_threads), sample_scanners) # sample_scan = query_plan.add_operator( # TableRangeAccess(tbl_s3key, use_pandas, secure, use_native, "sample_scan_{}".format(p), # query_plan, False)) # sample_scan.set_nthreads(n_threads) # Sampling project def project_fn(df): df.columns = [ sort_field if x == sort_field_index else x for x in df.columns ] df = df[[sort_field]].astype(np.float, errors='ignore') return df project_exprs = [ProjectExpression(lambda t_: t_['_0'], sort_field)] sample_project = map( lambda p: query_plan.add_operator( Project(project_exprs, 'sample_project_{}'.format(p), query_plan, False, project_fn)), range(table_parts_start, table_parts_end + 1)) # sample_project = query_plan.add_operator( # Project(project_exprs, 'sample_project_{}'.format(p), query_plan, False, project_fn)) # TopK samples sort_expr = [SortExpression(sort_field, float, sort_order)] sample_topk = map( lambda p: query_plan.add_operator( Top(k, sort_expr, use_pandas, 'sample_topk_{}'.format(p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # sample_topk = query_plan.add_operator( # Top(k, sort_expr, use_pandas, 'sample_topk_{}'.format(p), query_plan, False)) sample_topk_reduce = query_plan.add_operator( Top(k, sort_expr, use_pandas, 'sample_topk_reduce', query_plan, False)) # Generate SQL command for second scan sql_gen = query_plan.add_operator( TopKFilterBuild(sort_order, 'float', 'select * from S3object ', ' CAST({} as float) '.format(sort_field), 'sample_sql_gen', query_plan, False)) if not sampling_only: # Scan scan = map( lambda p: query_plan.add_operator( SQLTableScan("{}.{}".format(shards_path, p), "", format_, use_pandas, secure, use_native, 'scan_{}'.format( p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # Project def project_fn(df): df.columns = [ sort_field if x == sort_field_index else x for x in df.columns ] df[[sort_field]] = df[[sort_field]].astype(np.float) return df project_exprs = [ProjectExpression(lambda t_: t_['_0'], sort_field)] project = map( lambda p: query_plan.add_operator( Project(project_exprs, 'project_{}'.format(p), query_plan, False, project_fn)), range(table_parts_start, table_parts_end + 1)) # TopK topk = map( lambda p: query_plan.add_operator( Top(k, sort_expr, use_pandas, 'topk_{}'.format(p), query_plan, False)), range(table_parts_start, table_parts_end + 1)) # TopK reduce topk_reduce = query_plan.add_operator( Top(k, sort_expr, use_pandas, 'topk_reduce', query_plan, False)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) map(lambda op: tbl_smpler.connect(op), sample_scanners) map(lambda (p, o): o.connect(sample_project[p]), enumerate(sample_scanners)) map(lambda (p, o): o.connect(sample_topk[p]), enumerate(sample_project)) map(lambda o: o.connect(sample_topk_reduce), sample_topk) sample_topk_reduce.connect(sql_gen) if not sampling_only: map(lambda (p, o): sql_gen.connect(o), enumerate(scan)) map(lambda (p, o): o.connect(project[p]), enumerate(scan)) map(lambda (p, o): o.connect(topk[p]), enumerate(project)) map(lambda (p, o): o.connect(topk_reduce), enumerate(topk)) topk_reduce.connect(collate) else: sql_gen.connect(collate) # Start the query query_plan.execute() print('Done') # tuples = collate.tuples() # collate.print_tuples(tuples) # Write the metrics query_plan.print_metrics() # Shut everything down # query_plan.stop() sampling_threshold = query_plan.retrieve_sampling_threshold() sampling_runtime = query_plan.get_phase_runtime('sampl') sampling_num_http_requests, sampling_requests_cost = query_plan.requests_cost( 'sampl') sampling_returned_bytes, sampling_returned_rows, sampling_transfer_cost = query_plan.data_transfer_cost( phase_keyword='sampl') sampling_scanned_bytes, sampling_scan_cost = query_plan.data_scanning_cost( 'sampl') total_runtime = query_plan.total_elapsed_time total_http_requests, total_requests_cost = query_plan.requests_cost() total_returned_bytes, total_returned_rows, total_transfer_cost = query_plan.data_transfer_cost( ) total_scanned_bytes, total_scan_cost = query_plan.data_scanning_cost() total_data_cost = query_plan.data_cost()[0] total_computation_cost = query_plan.computation_cost() total_cost = query_plan.cost()[0] stats += [ sampling_threshold, sampling_runtime, total_runtime - sampling_runtime, total_runtime, sampling_returned_rows, sampling_scanned_bytes * BYTE_TO_MB, sampling_returned_bytes * BYTE_TO_MB, sampling_num_http_requests, sampling_requests_cost, sampling_transfer_cost, sampling_scan_cost, total_returned_rows, total_scanned_bytes * BYTE_TO_MB, total_returned_bytes * BYTE_TO_MB, total_http_requests, total_requests_cost, total_transfer_cost, total_scan_cost, total_data_cost, total_computation_cost, total_cost ]
def run(parallel, use_pandas, secure, use_native, buffer_size, format_, customer_parts, order_parts, lineitem_parts, customer_sharded, order_sharded, lineitem_sharded, other_parts, fp_rate, sf, expected_result, customer_filter_sql=None, order_filter_sql=None, lineitem_filter_sql=None): """ :return: None """ print('') print("TPCH Q3 Bloom Join") print("------------------") query_plan = QueryPlan(is_async=parallel, buffer_size=buffer_size) customer_scan = map(lambda p: query_plan.add_operator( SQLTableScan(get_file_key('customer', customer_sharded, p, sf, format_), "select " " c_custkey " "from " " S3Object " "where " " c_mktsegment = 'BUILDING' " " {} " " {} " .format( ' and ' + customer_filter_sql if customer_filter_sql is not None else '', get_sql_suffix('customer', customer_parts, p, customer_sharded, add_where=False)), format_, use_pandas, secure, use_native, 'customer_scan' + '_{}'.format(p), query_plan, False)), range(0, customer_parts)) def customer_project_fn(df): df = df.filter(items=['_0'], axis=1) df.rename(columns={'_0': 'c_custkey'}, inplace=True) return df customer_project = map(lambda p: query_plan.add_operator( Project([], 'customer_project' + '_{}'.format(p), query_plan, False, customer_project_fn)), range(0, customer_parts)) customer_bloom_create = query_plan.add_operator( BloomCreate('c_custkey', 'customer_bloom_create', query_plan, False, fp_rate)) customer_map = map(lambda p: query_plan.add_operator(Map('c_custkey', 'customer_map' + '_' + str(p), query_plan, False)), range(0, customer_parts)) order_scan = map(lambda p: query_plan.add_operator( SQLTableScanBloomUse(get_file_key('orders', order_sharded, p, sf, format_), "select " " o_custkey, o_orderkey, o_orderdate, o_shippriority " "from " " S3Object " "where " " cast(o_orderdate as timestamp) < cast('1995-03-01' as timestamp) " " {} " " {} " .format( ' and ' + order_filter_sql if order_filter_sql is not None else '', get_sql_suffix('orders', order_parts, p, order_sharded, add_where=False)), 'o_custkey', format_, use_pandas, secure, use_native, 'order_scan' + '_{}'.format(p), query_plan, False)), range(0, order_parts)) def order_project_fn(df): df = df.filter(items=['_0', '_1', '_2', '_3'], axis=1) df.rename(columns={'_0': 'o_custkey', '_1': 'o_orderkey', '_2': 'o_orderdate', '_3': 'o_shippriority'}, inplace=True) return df order_project = map(lambda p: query_plan.add_operator( Project([], 'order_project' + '_{}'.format(p), query_plan, False, order_project_fn)), range(0, customer_parts)) order_map_1 = map(lambda p: query_plan.add_operator(Map('o_custkey', 'order_map_1' + '_' + str(p), query_plan, False)), range(0, order_parts)) customer_order_join_build = map(lambda p: query_plan.add_operator( HashJoinBuild('c_custkey', 'customer_order_join_build' + '_' + str(p), query_plan, False)), range(0, other_parts)) customer_order_join_probe = map(lambda p: query_plan.add_operator( HashJoinProbe(JoinExpression('c_custkey', 'o_custkey'), 'customer_order_join_probe' + '_' + str(p), query_plan, False)), range(0, other_parts)) order_bloom_create = query_plan.add_operator( BloomCreate('o_orderkey', 'order_bloom_create', query_plan, False, fp_rate)) lineitem_scan = map(lambda p: query_plan.add_operator( SQLTableScanBloomUse(get_file_key('lineitem', lineitem_sharded, p, sf, format_), "select " " l_orderkey, l_extendedprice, l_discount " "from " " S3Object " "where " " cast(l_shipdate as timestamp) > cast('1995-03-01' as timestamp) " " {} " " {} " .format( ' and ' + lineitem_filter_sql if lineitem_filter_sql is not None else '', get_sql_suffix('lineitem', lineitem_parts, p, lineitem_sharded, add_where=False)), 'l_orderkey', format_, use_pandas, secure, use_native, 'lineitem_scan' + '_{}'.format(p), query_plan, False)), range(0, lineitem_parts)) def lineitem_project_fn(df): df = df.filter(items=['_0', '_1', '_2'], axis=1) df.rename(columns={'_0': 'l_orderkey', '_1': 'l_extendedprice', '_2': 'l_discount'}, inplace=True) return df lineitem_project = map(lambda p: query_plan.add_operator( Project([], 'lineitem_project' + '_{}'.format(p), query_plan, False, lineitem_project_fn)), range(0, lineitem_parts)) lineitem_map = map(lambda p: query_plan.add_operator(Map('l_orderkey', 'lineitem_map' + '_' + str(p), query_plan, False)), range(0, lineitem_parts)) order_map_2 = map(lambda p: query_plan.add_operator(Map('o_orderkey', 'order_map_2' + '_' + str(p), query_plan, False)), range(0, other_parts)) customer_order_lineitem_join_build = map(lambda p: query_plan.add_operator( HashJoinBuild('o_orderkey', 'customer_order_lineitem_join_build' + '_' + str(p), query_plan, False)), range(0, other_parts)) customer_order_lineitem_join_probe = map(lambda p: query_plan.add_operator( HashJoinProbe(JoinExpression('o_orderkey', 'l_orderkey'), 'customer_order_lineitem_join_probe' + '_' + str(p), query_plan, False)), range(0, other_parts)) def groupby_fn(df): df['l_extendedprice'] = df['l_extendedprice'].astype(np.float) df['l_discount'] = df['l_discount'].astype(np.float) df['revenue'] = df['l_extendedprice'] * (1 - df['l_discount']) grouped = df.groupby(['l_orderkey', 'o_orderdate', 'o_shippriority']) agg_df = grouped['revenue'].sum() return agg_df.reset_index() group = map(lambda p: query_plan.add_operator( Group( ['l_orderkey', 'o_orderdate', 'o_shippriority'], # l_partkey [ AggregateExpression(AggregateExpression.SUM, lambda t_: float(t_['l_extendedprice'] * (1 - t_['l_discount']))) ], 'group' + '_{}'.format(p), query_plan, False, groupby_fn)), range(0, other_parts)) def group_reduce_fn(df): grouped = df.groupby(['l_orderkey', 'o_orderdate', 'o_shippriority']) agg_df = grouped['revenue'].sum() return agg_df.reset_index() group_reduce = query_plan.add_operator( Group( ['l_orderkey', 'o_orderdate', 'o_shippriority'], # l_partkey [ AggregateExpression(AggregateExpression.SUM, lambda t_: float(t_['l_extendedprice'] * (1 - t_['l_discount']))) ], 'group_reduce', query_plan, False, group_reduce_fn)) top = query_plan.add_operator( Top(10, [SortExpression('revenue', float, 'DESC'), SortExpression('o_orderdate', date, 'ASC')], use_pandas, 'top', query_plan, False)) collate = query_plan.add_operator(tpch_q19.collate_op('collate', query_plan)) # Inline what we can map(lambda o: o.set_async(False), lineitem_project) map(lambda o: o.set_async(False), customer_project) map(lambda o: o.set_async(False), order_project) map(lambda o: o.set_async(False), lineitem_map) map(lambda o: o.set_async(False), customer_map) map(lambda o: o.set_async(False), order_map_1) map(lambda o: o.set_async(False), order_map_2) # Connect the operators connect_many_to_many(customer_scan, customer_project) connect_many_to_many(customer_project, customer_map) connect_many_to_one(customer_project, customer_bloom_create) connect_one_to_many(customer_bloom_create, order_scan) connect_many_to_many(order_scan, order_project) connect_many_to_many(order_project, order_map_1) connect_all_to_all(customer_map, customer_order_join_build) connect_many_to_many(customer_order_join_build, customer_order_join_probe) connect_all_to_all(order_map_1, customer_order_join_probe) # connect_many_to_one(customer_order_join_probe, collate) connect_many_to_one(order_project, order_bloom_create) connect_one_to_many(order_bloom_create, lineitem_scan) connect_many_to_many(lineitem_scan, lineitem_project) connect_many_to_many(lineitem_project, lineitem_map) connect_many_to_many(customer_order_join_probe, order_map_2) connect_all_to_all(order_map_2, customer_order_lineitem_join_build) connect_many_to_many(customer_order_lineitem_join_build, customer_order_lineitem_join_probe) connect_all_to_all(lineitem_map, customer_order_lineitem_join_probe) # connect_many_to_one(customer_order_lineitem_join_probe, collate) connect_many_to_many(customer_order_lineitem_join_probe, group) # connect_many_to_one(group, collate) connect_many_to_one(group, group_reduce) connect_one_to_one(group_reduce, top) connect_one_to_one(top, collate) # Plan settings print('') print("Settings") print("--------") print('') print('use_pandas: {}'.format(use_pandas)) print('secure: {}'.format(secure)) print('use_native: {}'.format(use_native)) print("customer_parts: {}".format(customer_parts)) print("order_parts: {}".format(order_parts)) print("lineitem_parts: {}".format(lineitem_parts)) print("customer_sharded: {}".format(customer_sharded)) print("order_sharded: {}".format(order_sharded)) print("lineitem_sharded: {}".format(lineitem_sharded)) print("other_parts: {}".format(other_parts)) print("fp_rate: {}".format(fp_rate)) print("format: {}".format(format_)) print('') # Write the plan graph query_plan.write_graph(os.path.join(ROOT_DIR, "../benchmark-output"), gen_test_id()) # Start the query query_plan.execute() tuples = collate.tuples() collate.print_tuples(tuples) # Write the metrics query_plan.print_metrics() # Shut everything down query_plan.stop() field_names = ['l_orderkey', 'o_orderdate', 'o_shippriority', 'revenue'] assert len(tuples) == 10 + 1 assert tuples[0] == field_names # NOTE: This result has been verified with the equivalent data and query on PostgreSQL test_util.assert_tuples(expected_result, tuples)
def query_plan(settings): # type: (SyntheticFilteredJoinSettings) -> QueryPlan """ :return: None """ if settings.use_shared_mem: system = WorkerSystem(settings.shared_memory_size) else: system = None query_plan = QueryPlan(system, is_async=settings.parallel, buffer_size=settings.buffer_size, use_shared_mem=settings.use_shared_mem) # Define the operators scan_A = \ map(lambda p: query_plan.add_operator( SQLTableScan(get_file_key(settings.table_A_key, settings.table_A_sharded, p, settings.sf), "select " " {} " "from " " S3Object " " {} " " {} " .format(','.join(settings.table_A_field_names), ' where {} '.format( settings.table_A_filter_sql) if settings.table_A_filter_sql is not None else '', get_sql_suffix(settings.table_A_key, settings.table_A_parts, p, settings.table_A_sharded, add_where=settings.table_A_filter_sql is None)), settings.format_, settings.use_pandas, settings.secure, settings.use_native, 'scan_A_{}'.format(p), query_plan, False)), range(0, settings.table_A_parts)) field_names_map_A = OrderedDict( zip([ '_{}'.format(i) for i, name in enumerate(settings.table_A_field_names) ], settings.table_A_field_names)) def project_fn_A(df): df = df.rename(columns=field_names_map_A, copy=False) return df project_A = map( lambda p: query_plan.add_operator( Project([ ProjectExpression(k, v) for k, v in field_names_map_A.iteritems() ], 'project_A_{}'.format(p), query_plan, False, project_fn_A)), range(0, settings.table_A_parts)) scan_B = \ map(lambda p: query_plan.add_operator( SQLTableScan( get_file_key(settings.table_B_key, settings.table_B_sharded, p, settings.sf), "select " " {} " "from " " S3Object " " {} " " {} " .format(','.join(settings.table_B_field_names), ' where {} '.format( settings.table_B_filter_sql) if settings.table_B_filter_sql is not None else '', get_sql_suffix(settings.table_B_key, settings.table_B_parts, p, settings.table_B_sharded, add_where=settings.table_B_filter_sql is None)), settings.format_, settings.use_pandas, settings.secure, settings.use_native, 'scan_B_{}'.format(p), query_plan, False)), range(0, settings.table_B_parts)) field_names_map_B = OrderedDict( zip([ '_{}'.format(i) for i, name in enumerate(settings.table_B_field_names) ], settings.table_B_field_names)) def project_fn_B(df): df.rename(columns=field_names_map_B, inplace=True) return df project_B = map( lambda p: query_plan.add_operator( Project([ ProjectExpression(k, v) for k, v in field_names_map_B.iteritems() ], 'project_B_{}'.format(p), query_plan, False, project_fn_B)), range(0, settings.table_B_parts)) if settings.table_C_key is not None: scan_C = \ map(lambda p: query_plan.add_operator( SQLTableScan( get_file_key(settings.table_C_key, settings.table_C_sharded, p, settings.sf), "select " " {} " "from " " S3Object " "where " " {} " " {} " .format(','.join(settings.table_C_field_names), settings.table_C_filter_sql, get_sql_suffix(settings.table_C_key, settings.table_C_parts, p, settings.table_C_sharded, add_where=False)), settings.format_, settings.use_pandas, settings.secure, settings.use_native, 'scan_C_{}'.format(p), query_plan, False)), range(0, settings.table_C_parts)) field_names_map_C = OrderedDict( zip([ '_{}'.format(i) for i, name in enumerate(settings.table_C_field_names) ], settings.table_C_field_names)) def project_fn_C(df): df = df.rename(columns=field_names_map_C, copy=False) return df project_C = map( lambda p: query_plan.add_operator( Project([ ProjectExpression(k, v) for k, v in field_names_map_C.iteritems() ], 'project_C_{}'.format(p), query_plan, False, project_fn_C)), range(0, settings.table_C_parts)) map_B_to_C = map( lambda p: query_plan.add_operator( Map(settings.table_B_BC_join_key, 'map_B_to_C_{}'.format(p), query_plan, False)), range(0, settings.table_B_parts)) map_C_to_C = map( lambda p: query_plan.add_operator( Map(settings.table_C_BC_join_key, 'map_C_to_C_{}'.format(p), query_plan, False)), range(0, settings.table_C_parts)) join_build_AB_C = map( lambda p: query_plan.add_operator( HashJoinBuild(settings.table_B_BC_join_key, 'join_build_AB_C_{}'.format( p), query_plan, False)), range(0, settings.table_C_parts)) join_probe_AB_C = map( lambda p: query_plan.add_operator( HashJoinProbe( JoinExpression(settings.table_B_BC_join_key, settings. table_C_BC_join_key), 'join_probe_AB_C_{}'. format(p), query_plan, False)), range(0, settings.table_C_parts)) map_A_to_B = map( lambda p: query_plan.add_operator( Map(settings.table_A_AB_join_key, 'map_A_to_B_{}'.format(p), query_plan, False)), range(0, settings.table_A_parts)) map_B_to_B = map( lambda p: query_plan.add_operator( Map(settings.table_B_AB_join_key, 'map_B_to_B_{}'.format(p), query_plan, False)), range(0, settings.table_B_parts)) join_build_A_B = map( lambda p: query_plan.add_operator( HashJoinBuild(settings.table_A_AB_join_key, 'join_build_A_B_{}'. format(p), query_plan, False)), range(0, settings.other_parts)) join_probe_A_B = map( lambda p: query_plan.add_operator( HashJoinProbe( JoinExpression(settings.table_A_AB_join_key, settings. table_B_AB_join_key), 'join_probe_A_B_{}'. format(p), query_plan, False)), range(0, settings.other_parts)) if settings.table_C_key is None: def part_aggregate_fn(df): sum_ = df[settings.table_B_detail_field_name].astype( np.float).sum() return pd.DataFrame({'_0': [sum_]}) part_aggregate = map( lambda p: query_plan.add_operator( Aggregate([ AggregateExpression( AggregateExpression.SUM, lambda t: float(t[ settings.table_B_detail_field_name])) ], settings.use_pandas, 'part_aggregate_{}'.format(p), query_plan, False, part_aggregate_fn)), range(0, settings.other_parts)) else: def part_aggregate_fn(df): sum_ = df[settings.table_C_detail_field_name].astype( np.float).sum() return pd.DataFrame({'_0': [sum_]}) part_aggregate = map( lambda p: query_plan.add_operator( Aggregate([ AggregateExpression( AggregateExpression.SUM, lambda t: float(t[ settings.table_C_detail_field_name])) ], settings.use_pandas, 'part_aggregate_{}'.format(p), query_plan, False, part_aggregate_fn)), range(0, settings.table_C_parts)) def aggregate_reduce_fn(df): sum_ = df['_0'].astype(np.float).sum() return pd.DataFrame({'_0': [sum_]}) aggregate_reduce = query_plan.add_operator( Aggregate([ AggregateExpression(AggregateExpression.SUM, lambda t: float(t['_0'])) ], settings.use_pandas, 'aggregate_reduce', query_plan, False, aggregate_reduce_fn)) aggregate_project = query_plan.add_operator( Project([ProjectExpression(lambda t: t['_0'], 'total_balance')], 'aggregate_project', query_plan, False)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) # Inline some of the operators map(lambda o: o.set_async(False), project_A) map(lambda o: o.set_async(False), project_B) map(lambda o: o.set_async(False), map_A_to_B) map(lambda o: o.set_async(False), map_B_to_B) if settings.table_C_key is not None: map(lambda o: o.set_async(False), map_B_to_C) map(lambda o: o.set_async(False), map_C_to_C) map(lambda o: o.set_async(False), project_C) map(lambda o: o.set_async(False), part_aggregate) aggregate_project.set_async(False) # Connect the operators connect_many_to_many(scan_A, project_A) connect_many_to_many(project_A, map_A_to_B) connect_all_to_all(map_A_to_B, join_build_A_B) connect_many_to_many(join_build_A_B, join_probe_A_B) connect_many_to_many(scan_B, project_B) connect_many_to_many(project_B, map_B_to_B) connect_all_to_all(map_B_to_B, join_probe_A_B) if settings.table_C_key is None: connect_many_to_many(join_probe_A_B, part_aggregate) else: connect_many_to_many(join_probe_A_B, map_B_to_C) connect_all_to_all(map_B_to_C, join_build_AB_C) connect_many_to_many(join_build_AB_C, join_probe_AB_C) connect_many_to_many(scan_C, project_C) connect_many_to_many(project_C, map_C_to_C) connect_all_to_all(map_C_to_C, join_probe_AB_C) connect_many_to_many(join_probe_AB_C, part_aggregate) connect_many_to_one(part_aggregate, aggregate_reduce) connect_one_to_one(aggregate_reduce, aggregate_project) connect_one_to_one(aggregate_project, collate) return query_plan
def run(parallel, use_pandas, buffer_size, table_parts, lower, upper, sf, format_=Format.CSV): secure = False use_native = False print('') print("Indexing Benchmark") print("------------------") # Query plan query_plan = QueryPlan(is_async=parallel, buffer_size=buffer_size) # SQL scan the file scan = map( lambda p: query_plan.add_operator( SQLTableScan( get_file_key('lineitem', True, p, sf=sf, format_=format_ ), "select * from S3Object " "where cast(l_extendedprice as float) >= {} and cast(l_extendedprice as float) <= {};" .format(lower, upper), format_, use_pandas, secure, use_native, 'scan_{}'.format(p), query_plan, False)), range(0, table_parts)) ''' scan = map(lambda p: query_plan.add_operator( SQLTableScan(get_file_key('lineitem', True, p, sf=sf, format_=format_), "select * from S3Object " "where l_extendedprice >= {} and l_extendedprice <= {};".format( lower, upper), format_, use_pandas, secure, use_native, 'scan_{}'.format(p), query_plan, False)), range(0, table_parts)) ''' # project def fn(df): df.columns = [ 'l_orderkey', 'l_partkey', 'l_suppkey', 'l_linenumber', 'l_quantity', 'l_extendedprice', 'l_discount', 'l_tax', 'l_returnflag', 'l_linestatus', 'l_shipdate', 'l_commitdate', 'l_receiptdate', 'l_shipinstruct', 'l_shipmode', 'l_comment' ] df[['l_extendedprice']] = df[['l_extendedprice']].astype(np.float) return df project = map( lambda p: query_plan.add_operator( Project([], 'project_{}'.format(p), query_plan, False, fn)), range(0, table_parts)) # aggregation def agg_fun(df): return pd.DataFrame({'count': [len(df)]}) aggregate = query_plan.add_operator( Aggregate([], True, 'agg', query_plan, False, agg_fun)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) map(lambda (p, o): o.connect(project[p]), enumerate(scan)) map(lambda (p, o): o.connect(aggregate), enumerate(project)) aggregate.connect(collate) # Plan settings print('') print("Settings") print("--------") print('') print('use_pandas: {}'.format(use_pandas)) print("table parts: {}".format(table_parts)) print('') # Write the plan graph query_plan.write_graph(os.path.join(ROOT_DIR, "../benchmark-output"), gen_test_id() + "-" + str(table_parts)) # Start the query query_plan.execute() print('Done') tuples = collate.tuples() collate.print_tuples(tuples) # Write the metrics query_plan.print_metrics() # Shut everything down query_plan.stop()
def run(parallel, use_pandas, buffer_size, selectivity1not0, typed, path, format_): secure = False use_native = False print('') print("CSV/Parquet Filter Return 1 Column Benchmark") print("------------------") # Query plan query_plan = QueryPlan(is_async=parallel, buffer_size=buffer_size) if selectivity1not0: if typed: # SQL scan the file scan = map(lambda p: query_plan.add_operator( SQLTableScan(path, "select F0 from S3Object " "where F0 >= 0;", format_, use_pandas, secure, use_native, 'scan_{}'.format(p), query_plan, False)), range(0, 1)) else: # SQL scan the file scan = map(lambda p: query_plan.add_operator( SQLTableScan(path, "select F0 from S3Object " "where cast(F0 as int) >= 0;", format_, use_pandas, secure, use_native, 'scan_{}'.format(p), query_plan, False)), range(0, 1)) else: # selectivity = 0 if typed: scan = map(lambda p: query_plan.add_operator( SQLTableScan(path, "select F0 from S3Object " "where F0 < 0;", format_, use_pandas, secure, use_native, 'scan_{}'.format(p), query_plan, False)), range(0, 1)) else: scan = map(lambda p: query_plan.add_operator( SQLTableScan(path, "select F0 from S3Object " "where cast(F0 as int) < 0;", format_, use_pandas, secure, use_native, 'scan_{}'.format(p), query_plan, False)), range(0, 1)) # project def fn(df): df.columns = ["F0"] return df project = map(lambda p: query_plan.add_operator( Project([], 'project_{}'.format(p), query_plan, False, fn)), range(0, 1)) # aggregation def agg_fun(df): return pd.DataFrame({'count': [len(df)]}) aggregate = query_plan.add_operator( Aggregate([], True, 'agg', query_plan, False, agg_fun)) collate = query_plan.add_operator( Collate('collate', query_plan, False)) map(lambda (p, o): o.connect(project[p]), enumerate(scan)) map(lambda (p, o): o.connect(aggregate), enumerate(project)) aggregate.connect(collate) # Plan settings print('') print("Settings") print("--------") print('') print('use_pandas: {}'.format(use_pandas)) print("table parts: {}".format(1)) print('') # Write the plan graph # query_plan.write_graph(os.path.join(ROOT_DIR, "../benchmark-output"), gen_test_id() + "-" + str(table_parts)) # Start the query query_plan.execute() print('Done') tuples = collate.tuples() collate.print_tuples(tuples) # Write the metrics query_plan.print_metrics() # Shut everything down query_plan.stop()
def run(parallel, use_pandas, buffer_size, table_first_part, table_parts, queried_columns, select_columns, lower, upper, path, format_=Format.CSV): secure = False use_native = False print('') print("CSV Filter+Project Benchmark") print("------------------") # Query plan query_plan = QueryPlan(is_async=parallel, buffer_size=buffer_size) # SQL scan the file scan = map( lambda p: query_plan.add_operator( SQLTableScan( "{}/lineitem.tbl.{}".format(path, p ), "select {} from S3Object " "where cast(l_orderkey as int) >= {} and cast(l_orderkey as int) <= {};" .format(select_columns, lower, upper), format_, use_pandas, secure, use_native, 'scan_{}'.format(p), query_plan, False)), range(table_first_part, table_first_part + table_parts)) # project def fn(df): df.columns = queried_columns df[['l_orderkey']] = df[['l_orderkey']].astype(np.int64) return df project = map( lambda p: query_plan.add_operator( Project([], 'project_{}'.format(p), query_plan, False, fn)), range(table_first_part, table_first_part + table_parts)) # aggregation def agg_fun(df): return pd.DataFrame({'count': [len(df)]}) aggregate = query_plan.add_operator( Aggregate([], True, 'agg', query_plan, False, agg_fun)) collate = query_plan.add_operator(Collate('collate', query_plan, False)) map(lambda (p, o): o.connect(project[p]), enumerate(scan)) map(lambda (p, o): o.connect(aggregate), enumerate(project)) aggregate.connect(collate) # Plan settings print('') print("Settings") print("--------") print('') print('use_pandas: {}'.format(use_pandas)) print("table parts: {}".format(table_parts)) print('') # Write the plan graph query_plan.write_graph(os.path.join(ROOT_DIR, "../benchmark-output"), gen_test_id() + "-" + str(table_parts)) # Start the query query_plan.execute() print('Done') tuples = collate.tuples() collate.print_tuples(tuples) # Write the metrics query_plan.print_metrics() # Shut everything down query_plan.stop()