new_field = 'my_1st_column' # name of the column field_type = 'INTEGER' # column data type cur.execute('''CREATE TABLE {tn} ({nf} {ft} PRIMARY KEY AUTOINCREMENT UNIQUE)'''\ .format(tn=table_name, nf=new_field, ft=field_type)) # create table 插入列: table_name = 'my_table...
airflow 是能进行数据pipeline的管理,甚至是可以当做更高级的cron job 来使用。现在一般的大厂都说自己的数据处理是ETL,美其名曰 data pipeline,可能跟google倡导的有关。airbnb的airflow是用python写的,它能进行工作流的调度,提供更可靠的流程,而且它还有自带的UI(可能是跟airbnb设计主导有关)。话不多说,先放两...
self.broker.append(content)definput_pipeline(self,content,use=False):""" pipelineofinputforcontent stashArgs:use:is use,defaul Falsecontent:dictReturns:"""ifnot use:return# input filterifself.input_filter_fn:_filter=self.input_filter_fn(content)# insert to queueifnot _filter:self.insert_queue...
from pyspark.sql import SparkSession import pyspark.pandas as ps spark = SparkSession.builder.appName('testpyspark').getOrCreate() ps_data = ps.read_csv(data_file, names=header_name) 运行apply函数,记录耗时: for col in ps_data.columns: ps_data[col] = ps_data[col].apply(apply_md5) ...
api_url = "https://example-api.com/sales"headers = {'Authorization': 'Bearer your_token'} # Extract data def extract_sales():response = requests.get(api_url, headers=headers)sales_data = response.json()return pd.DataFrame(sales_data)# Transform data def transform_sales(df):# Convert ...
items import DangdangItem class ExampleSpider(scrapy.Spider): name = 'dangdangSpider' allowed_domains = ['www.bang.dangdang.com'] start_urls = [] for i in range(1, 26): start_urls.append('http://bang.dangdang.com/books/bestsellers/01.00.00.00.00.00-24hours-0-0-1-1' + str(i)) ...
Created database "postgresql+psycopg2://root@localhost/example_etl_mara" CREATE TABLE data_integration_file_dependency ( node_path TEXT[] NOT NULL, dependency_type VARCHAR NOT NULL, hash VARCHAR, timestamp TIMESTAMP WITHOUT TIME ZONE, PRIMARY KEY (node_path, dependency_type) ...
azureml-datadrift azureml-interpret azureml-mlflow azureml-monitoring azureml-opendatasets azureml-pipeline-core azureml-pipeline-steps azureml-synapse azureml-tensorboard azureml-train-automl-client azureml-train-automl-runtime azureml-train-core azureml-training-tabular azureml-widgets azureml-contri...
After you enable the HTTP streaming feature, you can create functions that stream data over HTTP. This example is an HTTP triggered function that streams HTTP response data. You might use these capabilities to support scenarios like sending event data through a pipeline for real time visualization...
Example: fromank.components.pipe_appimportPipeAppclassExampleApp(PipeApp):defstart(self):foriinrange(100):self.chain_process(i)defprocess(self,message=None):'''Args:message: {'content': (*) 'content of message','flags': (list|tuple) 'define next process will be use'}raise TypeError if...