1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
|
#!/usr/bin/env python2.7
# Copyright 2015 gRPC authors.
#
# Licensed 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.
import argparse
import json
import uuid
import httplib2
from apiclient import discovery
from apiclient.errors import HttpError
from oauth2client.client import GoogleCredentials
# 30 days in milliseconds
_EXPIRATION_MS = 30 * 24 * 60 * 60 * 1000
NUM_RETRIES = 3
def create_big_query():
"""Authenticates with cloud platform and gets a BiqQuery service object
"""
creds = GoogleCredentials.get_application_default()
return discovery.build(
'bigquery', 'v2', credentials=creds, cache_discovery=False)
def create_dataset(biq_query, project_id, dataset_id):
is_success = True
body = {
'datasetReference': {
'projectId': project_id,
'datasetId': dataset_id
}
}
try:
dataset_req = biq_query.datasets().insert(
projectId=project_id, body=body)
dataset_req.execute(num_retries=NUM_RETRIES)
except HttpError as http_error:
if http_error.resp.status == 409:
print 'Warning: The dataset %s already exists' % dataset_id
else:
# Note: For more debugging info, print "http_error.content"
print 'Error in creating dataset: %s. Err: %s' % (dataset_id,
http_error)
is_success = False
return is_success
def create_table(big_query, project_id, dataset_id, table_id, table_schema,
description):
fields = [{
'name': field_name,
'type': field_type,
'description': field_description
} for (field_name, field_type, field_description) in table_schema]
return create_table2(big_query, project_id, dataset_id, table_id, fields,
description)
def create_partitioned_table(big_query,
project_id,
dataset_id,
table_id,
table_schema,
description,
partition_type='DAY',
expiration_ms=_EXPIRATION_MS):
"""Creates a partitioned table. By default, a date-paritioned table is created with
each partition lasting 30 days after it was last modified.
"""
fields = [{
'name': field_name,
'type': field_type,
'description': field_description
} for (field_name, field_type, field_description) in table_schema]
return create_table2(big_query, project_id, dataset_id, table_id, fields,
description, partition_type, expiration_ms)
def create_table2(big_query,
project_id,
dataset_id,
table_id,
fields_schema,
description,
partition_type=None,
expiration_ms=None):
is_success = True
body = {
'description': description,
'schema': {
'fields': fields_schema
},
'tableReference': {
'datasetId': dataset_id,
'projectId': project_id,
'tableId': table_id
}
}
if partition_type and expiration_ms:
body["timePartitioning"] = {
"type": partition_type,
"expirationMs": expiration_ms
}
try:
table_req = big_query.tables().insert(
projectId=project_id, datasetId=dataset_id, body=body)
res = table_req.execute(num_retries=NUM_RETRIES)
print 'Successfully created %s "%s"' % (res['kind'], res['id'])
except HttpError as http_error:
if http_error.resp.status == 409:
print 'Warning: Table %s already exists' % table_id
else:
print 'Error in creating table: %s. Err: %s' % (table_id,
http_error)
is_success = False
return is_success
def patch_table(big_query, project_id, dataset_id, table_id, fields_schema):
is_success = True
body = {
'schema': {
'fields': fields_schema
},
'tableReference': {
'datasetId': dataset_id,
'projectId': project_id,
'tableId': table_id
}
}
try:
table_req = big_query.tables().patch(
projectId=project_id,
datasetId=dataset_id,
tableId=table_id,
body=body)
res = table_req.execute(num_retries=NUM_RETRIES)
print 'Successfully patched %s "%s"' % (res['kind'], res['id'])
except HttpError as http_error:
print 'Error in creating table: %s. Err: %s' % (table_id, http_error)
is_success = False
return is_success
def insert_rows(big_query, project_id, dataset_id, table_id, rows_list):
is_success = True
body = {'rows': rows_list}
try:
insert_req = big_query.tabledata().insertAll(
projectId=project_id,
datasetId=dataset_id,
tableId=table_id,
body=body)
res = insert_req.execute(num_retries=NUM_RETRIES)
if res.get('insertErrors', None):
print 'Error inserting rows! Response: %s' % res
is_success = False
except HttpError as http_error:
print 'Error inserting rows to the table %s' % table_id
is_success = False
return is_success
def sync_query_job(big_query, project_id, query, timeout=5000):
query_data = {'query': query, 'timeoutMs': timeout}
query_job = None
try:
query_job = big_query.jobs().query(
projectId=project_id,
body=query_data).execute(num_retries=NUM_RETRIES)
except HttpError as http_error:
print 'Query execute job failed with error: %s' % http_error
print http_error.content
return query_job
# List of (column name, column type, description) tuples
def make_row(unique_row_id, row_values_dict):
"""row_values_dict is a dictionary of column name and column value.
"""
return {'insertId': unique_row_id, 'json': row_values_dict}
|
