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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Convert the SQLite DB
The OSM data is fundamentally quite relational, and so should fit well with a relational DB (though we can expect disk usage to be high).  Python has SQLite built-in, so it's a natural choice.  (Of course, enterprise levels DBs and GIS addons would make more sense for very heavily lifting).
Here I use the Isle of Man data, from http
Step1:  Similarly for relations.  It is worth noting something a little surprising about the input data here.  The first relation returned is "Scotland".  This is because, in OSM data, the boundary for Scotland forms part of the boundary of the Isle of Man.  If you investigate further, you'll find that some of the nodes and ways in this relation are not included in the XML file, because e.g. the label node for Scotland is not part of the Isle of Man.
Step2:  The library also supports reading full way and relation information, basically doing the necessary "joins" for you...  For relations, as noted above, some of the members may not be documented in the XML file extract.
Step3:  Experiment with file sizes
The following is not so interesting from a usage perspective...
One problem with this approach is that the resulting .db files are rather large.  Given that this format should be considered an intermediate format, to aid data exploration, but not for any form of distribution, my view is that this doesn't matter.  However, for academic interest, here we explore the file size, and show that we could store the data in a somewhat more efficient schema, if we really wanted to.
Step4:  Let's look at the average number of tags etc.
Step5:  Size of each DB component
Here we create a single DB for each component (nodes, tags, ways, ...)  Some thoughts
Step6:  Change the schema for ways
Step7:  Storing around 10 to 15 node references in each row would be more efficient...
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Python Code:
import sys
sys.path.insert(0, "..")
import sqlite3
import datetime
import os
import osmdigest.sqlite as sq
# Delete the database if it already exists.
try:
    os.remove("demo.db")
except FileNotFoundError:
    pass
import os
filename = os.path.join("//media", "disk", "OSM_Data", "isle-of-man-latest.osm.bz2")
#filename = os.path.join("..", "..", "..", "Data", "isle-of-man-latest.osm.bz2")
start = datetime.datetime.now()
sq.convert(filename, "demo.db")
print(datetime.datetime.now() - start)
db = sq.OSM_SQLite("demo.db")
for i, node in zip(range(10), db.nodes()):
    print(node)
for i, node in zip(range(10), db.ways()):
    print(node)
Explanation: Convert the SQLite DB
The OSM data is fundamentally quite relational, and so should fit well with a relational DB (though we can expect disk usage to be high).  Python has SQLite built-in, so it's a natural choice.  (Of course, enterprise levels DBs and GIS addons would make more sense for very heavily lifting).
Here I use the Isle of Man data, from http://download.geofabrik.de/europe.html, to experiment on.
End of explanation
for i, node in zip(range(2), db.relations()):
    print(node)
Explanation: Similarly for relations.  It is worth noting something a little surprising about the input data here.  The first relation returned is "Scotland".  This is because, in OSM data, the boundary for Scotland forms part of the boundary of the Isle of Man.  If you investigate further, you'll find that some of the nodes and ways in this relation are not included in the XML file, because e.g. the label node for Scotland is not part of the Isle of Man.
End of explanation
way = next(db.ways())
print("Normal way:")
print(way)
print("And with full node details:")
print(db.complete_way(way))
all_relations = db.relations()
for _ in range(4):
    relation = next(all_relations)
relation = db.complete_relation(relation)
relation.members[0], relation.complete_members[0]
Explanation: The library also supports reading full way and relation information, basically doing the necessary "joins" for you...  For relations, as noted above, some of the members may not be documented in the XML file extract.
End of explanation
os.stat("demo.db").st_size / 1024 / 1024
Explanation: Experiment with file sizes
The following is not so interesting from a usage perspective...
One problem with this approach is that the resulting .db files are rather large.  Given that this format should be considered an intermediate format, to aid data exploration, but not for any form of distribution, my view is that this doesn't matter.  However, for academic interest, here we explore the file size, and show that we could store the data in a somewhat more efficient schema, if we really wanted to.
End of explanation
import collections
collections.Counter(len(node.tags) for node in db.nodes())
# Sanity check: this agrees, and is (a bit) slower
import osmdigest.digest as digest
gen = digest.parse(filename)
next(gen), next(gen)
collections.Counter(len(element.tags) for element in gen if element.name == "node")
collections.Counter(len(node.tags) for node in db.ways())
collections.Counter(len(node.tags) for node in db.relations())
collections.Counter(len(way.nodes) for way in db.ways())
Explanation: Let's look at the average number of tags etc.
End of explanation
def make_new_db(name):
    try:
        os.remove(name)
    except Exception as e:
        print("Failed to delete {} because {}".format(name, e))
    return sqlite3.connect(name)
def close_connection(connection, name):
    connection.commit()
    connection.close()
    connection = sqlite3.connect(name, isolation_level=None)
    connection.execute("vacuum")
    connection.close()
    return os.stat(name).st_size / 1024 / 1024
connection = make_new_db("nodes.db")
connection.execute("create table nodes(osm_id integer primary key, longitude integer, latitude integer)")
for res in db.connection.execute("select * from nodes"):
    connection.execute("insert into nodes values(?,?,?)", tuple(res))
close_connection(connection, "nodes.db")
connection = make_new_db("node_tags.db")
connection.execute("create table node_tags(osm_id integer, key text, value text)")
connection.execute("create index node_tags_osm_id_idx on node_tags(osm_id)")
for res in db.connection.execute("select * from node_tags"):
    connection.execute("insert into node_tags values(?,?,?)", tuple(res))
close_connection(connection, "node_tags.db")
connection = make_new_db("ways.db")
connection.execute("create table ways(osm_id integer, position integer, noderef integer)")
connection.execute("create index ways_idx on ways(osm_id, position)")
for res in db.connection.execute("select * from ways"):
    connection.execute("insert into ways values(?,?,?)", tuple(res))
close_connection(connection, "ways.db")
connection = make_new_db("way_tags.db")
connection.execute("create table way_tags(osm_id integer, key text, value text)")
connection.execute("create index way_tags_osm_id_idx on way_tags(osm_id)")
for res in db.connection.execute("select * from way_tags"):
    connection.execute("insert into way_tags values(?,?,?)", tuple(res))
close_connection(connection, "way_tags.db")
connection = make_new_db("relations.db")
connection.execute("create table relations(osm_id integer, member text, memberref integer, role text)")
connection.execute("create index relations_idx on relations(osm_id)")
for res in db.connection.execute("select * from relations"):
    connection.execute("insert into relations values(?,?,?,?)", tuple(res))
close_connection(connection, "relations.db")
connection = make_new_db("relation_tags.db")
connection.execute("create table relation_tags(osm_id integer, key text, value text)")
connection.execute("create index relation_tags_osm_id_idx on relation_tags(osm_id)")
for res in db.connection.execute("select * from relation_tags"):
    connection.execute("insert into relation_tags values(?,?,?)", tuple(res))
close_connection(connection, "relation_tags.db")
Explanation: Size of each DB component
Here we create a single DB for each component (nodes, tags, ways, ...)  Some thoughts:
There is not much we can about nodes in terms of improving the schema.
Tags on nodes, and everything to do with relations, form a small part of the total.
So focus on ways and their tags.
End of explanation
def chunk(nodes, length):
    i = 0
    out = []
    part = []
    while i < len(nodes):
        part.append(nodes[i])
        i += 1
        if i == len(nodes):
            while len(part) < length:
                part.append(None)
            out.append(part)
            break
        if len(part) == length:
            out.append(part)
            part = []
    return out
    
assert(chunk([1,2,3,4,5,6], 2) == [[1,2],[3,4],[5,6]])
assert(chunk([1,2,3,4,5], 2) == [[1,2],[3,4],[5,None]])
Explanation: Change the schema for ways: list more than one node per row.  As you can see, we can reduce the disk usage quite a bit.  But probably not enough to justify making the code more complicated.
End of explanation
for length in range(1,20):
    connection = make_new_db("ways_trial.db")
    create_string = ("create table ways(osm_id integer, position integer,"
        + ", ".join(["noderef"+str(i)+" integer" for i in range(length)])
        + ")" )
    insert_string = ("insert into ways values (?,?,"
        + ",".join(["?"]*length) + ")" )
    connection.execute(create_string)
    connection.execute("create index ways_idx on ways(osm_id, position)")
    for way in db.ways():
        for pos, part in enumerate(chunk(way.nodes, length)):
            data = (way.osm_id, pos, *part)
            connection.execute(insert_string, data)
    size = close_connection(connection, "ways_trial.db")
    print("{} -> {}".format(length, size))
Explanation: Storing around 10 to 15 node references in each row would be more efficient...
End of explanation
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