我试图完成一个where子句，但是失败了。我想检查用户的当前日期是否等于created_at，第二个子句是用户是否具有按用户ID的条目。我正在开发一个健身应用程序，用户可以在其中跟踪他跑步的公里数。而要在数据库表中创建新条目，只需将它们添加到现有条目中即可。
我的问题专注于if子句的问题，因为变量$hasUserEntries不等于null，但是数据库表中没有条目。它是空的。
我尝试使用get()而不是使用first()。但是问题是我无法使用Carbon::today()，或者可能是我在需要的where子句中使用了3个值，因为我无法仅通过Date < strong> YYYY-MM-DD 。在created_at语句中，我使用了一个硬编码的DateTime来与first()进行检查，并且可以正常工作。但是我认为我不能解释为什么硬编码不是最佳的。
我在Stackoverflow上进行了搜索，发现大多数答案都与使用created_at有关。很好，但是为什么触发我的get()是因为从我的角度来看数据库是空的（Null），所以应该触发else。
if($hasUserEntries==null) 预期结果应该是if语句的触发原因，因为如果数据库表为空，用户标识不存在或者created_at的日期与当前日期不同，则应该为触发了public function add_km_to_progressbar(Request $request, TrackKM $trackKM)
{
$track_dailies = new TrackDaily();
$track_dailies->user_id = \Auth::user()->id;
$amount_runned_km = $request->input('amount_runned_km');
$amount_runned_km = (int)$amount_runned_km;
$track_dailies->amount = (int)$amount_runned_km;
$track_dailies->type = 1;
$check_requirements = [
'user_id'=>\Auth::user()->id,
'created_at'=>'Carbon::today()'
];
$hasUserEntries = DB::table('track_dailies')
->where('user_id','=',\Auth::user())
->where('created_at','>=',Carbon::today())
->get();
if ($hasUserEntries == null) {
return dd('does not work');
} else {
return dd('does work');
}
}
。如果数据库中此条件== null，我想在那里创建新行。
实际结果 if($hasUserEntries==null)是正确的，即使数据库表为空。我认为方法if($hasUserEntries==null)保存的值与数据库无关。
我希望你能帮助我。
答案 0 :(得分：1)
我认为您应该做的是在继续操作之前检查数据库中是否存在该记录...
ipfs有了它，它不会抛出错误！
答案 1 :(得分：0)
如果需要，请尝试以下操作：
if (is_empty($hasUserEntries))
答案 2 :(得分：0)
import logging
import os
import json
from google.cloud import storage
from apache_beam import Pipeline, ParDo, DoFn
from apache_beam.io import ReadFromPubSub, WriteToBigQuery, BigQueryDisposition
from apache_beam.options.pipeline_options import PipelineOptions, StandardOptions, WorkerOptions, GoogleCloudOptions, \
SetupOptions
def _get_storage_service():
storage_client = storage.Client \
.from_service_account_json(
json_credentials_path='C:\Users\dneema\PycharmProjects\iot_dataflow\df_stm_iot_pubsub_bq\service_account_credentials.json')
print('storage service fetched')
return storage_client
class RuntimeOptions(PipelineOptions):
def __init__(self, flags=None, **kwargs):
super(RuntimeOptions, self).__init__(flags, **kwargs)
@classmethod
def _add_argparse_args(cls, parser):
parser.add_value_provider_argument('--bucket_name', type=str)
parser.add_value_provider_argument('--config_json_path', type=str,)
class PipelineCreator:
def __init__(self):
self.options = PipelineOptions()
storage_client = storage.Client.from_service_account_json(
'service_account_credentials_updated.json')
runtime_options = self.options.view_as(RuntimeOptions)
bucket_name = str(runtime_options.bucket_name)
config_json_path = str(runtime_options.config_json_path)
# get the bucket with name
bucket = storage_client.get_bucket(bucket_name)
# get bucket file as blob
blob = bucket.get_blob(config_json_path)
# convert to string and load config
json_data = blob.download_as_string()
self.configData = json.loads(json_data)
dataflow_config = self.configData['dataflow_config']
self.options.view_as(StandardOptions).streaming = bool(dataflow_config['streaming'])
self.options.view_as(SetupOptions).save_main_session = True
worker_options = self.options.view_as(WorkerOptions)
worker_options.max_num_workers = int(dataflow_config['max_num_worker'])
worker_options.autoscaling_algorithm = str(dataflow_config['autoscaling_algorithm'])
#worker_options.machine_type = str(dataflow_config['machine_type'])
#worker_options.zone = str(dataflow_config['zone'])
#worker_options.network = str(dataflow_config['network'])
#worker_options.subnetwork = str(dataflow_config['subnetwork'])
def run(self):
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'dataflow-service-account.json'
project_id = self.configData['project_id']
dataset_id = self.configData['dataset_id']
topics = self.configData['topics']
table_ids = self.configData['bq_table_ids']
error_table_id = self.configData['error_table_id']
logger = logging.getLogger(project_id)
logger.info(self.options.display_data())
pipeline = Pipeline(options=self.options)
size = len(topics)
for index in range(size):
print(topics[index])
pipeline_name = "pipeline_"+str(index)
logger.info("Launch pipeline :: "+pipeline_name)
messages = pipeline | 'Read PubSub Message in ' + pipeline_name >> ReadFromPubSub(topic=topics[index])
logger.info("Read PubSub Message")
valid_messages, invalid_messages = messages | 'Convert Messages to TableRows in ' + pipeline_name >> ParDo(TransformMessageToTableRow()).with_outputs('invalid', main='valid')
valid_messages | 'Write Messages to BigQuery in ' + pipeline_name >> WriteToBigQuery(table=table_ids[index],
dataset=dataset_id,
project=project_id,
write_disposition=BigQueryDisposition.WRITE_APPEND)
pipeline.run().wait_until_finish()
class TransformMessageToTableRow(DoFn):
def process(self, element, *args, **kwargs):
logging.getLogger('dataflow').log(logging.INFO, element)
print element
print("element type ", type(element))
print("inside bq pardo")
import json
try:
message_rows = json.loads(element)
# if using emulator, uncomment below line
message_rows = json.loads(message_rows)
print 'loaded element'
except:
try:
element = "[" + element + "]"
message_rows = json.loads(element)
except Exception as e:
print(e)
from apache_beam import pvalue
yield [pvalue.TaggedOutput('invalid', [element, str(e)])]
print(message_rows)
print("message rows", type(message_rows))
if not isinstance(message_rows, list):
message_rows = [message_rows]
#rows = list()
if isinstance(message_rows, list):
for row in message_rows:
try:
new_row = dict()
for k, v in row.items():
new_row[str(k)] = v
#rows.append(new_row)
print(new_row)
yield new_row
except Exception as e:
print(e)
from apache_beam import pvalue
yield pvalue.TaggedOutput('invalid', [row, str(e)])
if __name__ == '__main__':
PipelineCreator().run()
请记住，user_id的记录不可能为空，因为首先必须在db内部进行记录