Github user njayaram2 commented on a diff in the pull request:
https://github.com/apache/incubator-madlib/pull/141#discussion_r125089028
--- Diff: src/ports/postgres/modules/graph/bfs.py_in ---
@@ -0,0 +1,479 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# Breadth-First Search
+
+# Please refer to the bfs.sql_in file for the documentation
+
+"""
+@file bfs.py_in
+
+@namespace graph
+"""
+
+import plpy
+from graph_utils import validate_graph_coding
+from graph_utils import get_graph_usage
+from graph_utils import _grp_null_checks
+from utilities.control import MinWarning
+from utilities.utilities import _assert
+from utilities.utilities import extract_keyvalue_params
+from utilities.utilities import split_quoted_delimited_str
+from utilities.validate_args import table_exists
+from utilities.validate_args import columns_exist_in_table
+
+m4_changequote(`<!', `!>')
+
+def _validate_bfs(vertex_table, vertex_id, edge_table, edge_params,
+ source_vertex, out_table, max_distance, directed, grouping_cols_list,
**kwargs):
+
+ validate_graph_coding(vertex_table, vertex_id, edge_table, edge_params,
+ out_table,'BFS')
+
+ _assert((max_distance >= 0) and isinstance(max_distance,int),
+ """Graph BFS: Invalid max_distance type or value ({0}), must be
integer,
+ be greater than or equal to 0 and be less than max allowable
integer
+ (2147483647).""".
+ format(max_distance))
+
+ _assert(isinstance(directed,bool),
+ """Graph BFS: Invalid value for directed ({0}), must be
boolean.""".
+ format(directed))
+
+ _assert(isinstance(source_vertex,int),
+ """Graph BFS: Source vertex {source_vertex} has to be an
integer.""".
+ format(**locals()))
+ src_exists = plpy.execute("""
+ SELECT * FROM {vertex_table} WHERE {vertex_id}={source_vertex}
+ """.format(**locals()))
+ if src_exists.nrows() == 0:
+ plpy.error(
+ """Graph BFS: Source vertex {source_vertex} is not present in
the
+ vertex table {vertex_table}.""".
+ format(**locals()))
+
+ vt_error = plpy.execute(
+ """ SELECT {vertex_id}
+ FROM {vertex_table}
+ WHERE {vertex_id} IS NOT NULL
+ GROUP BY {vertex_id}
+ HAVING count(*) > 1 """.format(**locals()))
+ if vt_error.nrows() != 0:
+ plpy.error(
+ """Graph BFS: Source vertex table {vertex_table} contains
duplicate
+ vertex id's.""".
+ format(**locals()))
+
+ _assert(not table_exists(out_table+"_summary"),
+ "Graph BFS: Output summary table already exists!")
+
+ if grouping_cols_list is not None:
+ _assert(columns_exist_in_table(edge_table, grouping_cols_list),
+ """Graph BFS: Not all columns from {grouping_cols_list} are
present
+ in edge table ({edge_table}).""".
+ format(**locals()))
+
+ return None
+
+
+def graph_bfs(schema_madlib, vertex_table, vertex_id, edge_table,
+ edge_args, source_vertex, out_table, max_distance, directed,
grouping_cols,
+ **kwargs):
+
+ """
+ Breadth First Search algorithm for graphs [1].
+ Args:
+ @param vertex_table Name of the table that contains the vertex
data.
+ @param vertex_id Name of the column containing the vertex
ids.
+ @param edge_table Name of the table that contains the edge
data.
+ @param edge_args A comma-delimited string containing multiple
+ named arguments of the form "name=value".
+ @param source_vertex The source vertex id for the algorithm to
start.
+ @param out_table Name of the table to store the result of
SSSP.
+ @param max_distance Maximum distance from the source_vertex to
search for.
+ @param directed Graph will be treated as directed if this
boolean flag
+ is set to TRUE. Graph is treated as
undirected by default.
+ @param grouping_cols The list of grouping columns.
+
+ [1] https://en.wikipedia.org/wiki/Breadth-first_search
+ """
+
+ with MinWarning("warning"):
+
+ INT_MAX = 2147483647
+
+ params_types = {'src': str, 'dest': str}
+ default_args = {'src': 'src', 'dest': 'dest'}
+ edge_params = extract_keyvalue_params(edge_args,
+ params_types,
+ default_args)
+
+ # Prepare the input for recording in the summary table
+ if vertex_id is None:
+ v_st= "NULL"
+ vertex_id = "id"
+ else:
+ v_st = vertex_id
+ if edge_args is None:
+ e_st = "NULL"
+ else:
+ e_st = edge_args
+ if max_distance is None:
+ d_st= "NULL"
+ max_distance = INT_MAX
+ else:
+ d_st = max_distance
+ if directed is None:
+ dir_st= "NULL"
+ directed = False
+ else:
+ dir_st = directed
+ if grouping_cols is None:
+ g_st = "NULL"
+ glist = None
+ else:
+ g_st = grouping_cols
+ glist = split_quoted_delimited_str(grouping_cols)
+
+ src = edge_params["src"]
+ dest = edge_params["dest"]
+
+ distribution = m4_ifdef(<!__POSTGRESQL__!>, <!''!>,
+ <!"DISTRIBUTED BY ({0})".format(vertex_id)!>)
+ local_distribution = m4_ifdef(<!__POSTGRESQL__!>, <!''!>,
+ <!"DISTRIBUTED BY (id)"!>)
+
+ _validate_bfs(vertex_table, vertex_id, edge_table,
+ edge_params, source_vertex, out_table, max_distance, directed,
glist)
+
+ # Initialize grouping related variables
+ grp_comma = ""
+ and_grp_null_checks = ""
+
+ if grouping_cols is not None:
+ grp_comma = grouping_cols + ", "
+ and_grp_null_checks = " AND " + _grp_null_checks(glist)
+
+ # We keep a table of every vertex, the distance to that vertex
from source
+ # and the parent in the path to the vertex
+ # This table will be updated throughout the execution.
+ dist_col = "dist"
+ parent_col = "parent"
+ curr_dist_val = 0
+
+ # Creating the output table with the appropriate columns and data
types
+ plpy.execute("""
+ CREATE TABLE {out_table} AS (
+ SELECT
+ {grp_comma}
+ {src} AS {vertex_id},
+ {curr_dist_val}::INT AS {dist_col},
+ {src} AS {parent_col}
+ FROM {edge_table}
+ LIMIT 0
+ ) {distribution}""".format(**locals()))
--- End diff --
We don't have to distribute the output table unless it helps in faster
execution of this algorithm itself. The consumers of MADlib functions normally
use `order by` to view results, or might even redistribute the output table
based on their need.
If this is indeed required for faster retrieval/join of data in the queries
that follow, then it might be a good idea to consider `{grouping_cols}` too
apart from `{vertex_id}`.
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