google-bigquery

问题 I'm using big query and i want to create a job which populates a table with a "record" type columns. The data will be populated by a query - so how can i write a query which returns "record" type columns. Thanks! 回答1: Somehow option proposed by Pentium10 never worked for me in GBQ UI or API Explorer. I might be missing something Meantime, the workaround I found is as in below example SELECT location.state, location.city FROM JS( ( // input table SELECT NEST(CONCAT(state, ',', city)) AS

问题 I'm using big query and i want to create a job which populates a table with a "record" type columns. The data will be populated by a query - so how can i write a query which returns "record" type columns. Thanks! 回答1: Somehow option proposed by Pentium10 never worked for me in GBQ UI or API Explorer. I might be missing something Meantime, the workaround I found is as in below example SELECT location.state, location.city FROM JS( ( // input table SELECT NEST(CONCAT(state, ',', city)) AS

问题 I am running the Query in C#.NET application using .NET API library which is added through Nu-Get package manager. My Query: Select * From DataSetID.TableID LIMIT 10000 Note: Data Processing for the above query is 1GB (get it from Web-UI). When i am running the same query in C#.NET application, i am able to get ~5600 Rows in single request, then i am passing PageToken to GetQueryResults method and get remaining records. (Through pagination). So There are 2 Query request to get 10K records.

问题 I am trying to build a Data Pipeline that will download the data from this website and push it to a BigQuery Table. def OH_Data_Pipeline(trigger='Yes'): if trigger=='Yes': import pandas as pd import pandas_gbq import datetime schema=[{'name': 'SOS_VOTERID', 'type': 'STRING'},{'name': 'COUNTY_NUMBER', 'type': 'STRING'}, {'name': 'COUNTY_ID', 'type': 'INT64'}, {'name': 'LAST_NAME', 'type': 'STRING'}, {'name': 'FIRST_NAME', 'type': 'STRING'}, {'name': 'MIDDLE_NAME', 'type': 'STRING'}, {'name':

问题 I am trying to do something which is very simple in other data services. I am trying to make a relatively simple SQL query and return it as a dataframe in python. I am on Windows 10 and using Phython 2.7 (specifically Canopy 1.7.4) Typically this would be done with pandas.read_sql_query but due to some specifics with BigQuery they require a different method pandas.io.gbq.read_gbq This method works fine unless you want to make a Big Query. If you make a Big Query on BigQuery you get the error

问题 I am trying to stream data into Big Query using templateSuffix and a date partition appended to the table name using Java API But I am getting the below exception: com.google.api.client.googleapis.json.GoogleJsonResponseException: 400 Bad Request { "code" : 400, "errors" : [ { "domain" : "global", "location" : "suffix", "locationType" : "other", "message" : "Table name should only contain _, a-z, A-Z, or 0-9.", "reason" : "invalid" } ], "message" : "Table name should only contain _, a-z, A-Z,

问题 The schema in my google table looks like this: price_datetime : DATETIME, symbol : STRING, bid_open : FLOAT, bid_high : FLOAT, bid_low : FLOAT, bid_close : FLOAT, ask_open : FLOAT, ask_high : FLOAT, ask_low : FLOAT, ask_close : FLOAT After I do a pandas.read_gbq I get a dataframe with column dtypes like this: price_datetime object symbol object bid_open float64 bid_high float64 bid_low float64 bid_close float64 ask_open float64 ask_high float64 ask_low float64 ask_close float64 dtype: object

问题 My data source is based on the events happening in the 3rd party tool. eg: - customer.created , customer.updated , customer.plan.updated . Every event has different json schema. And it is possible even the same event eg: customer.updated may have different schema from previous customer.updated event. I am planning to load this data into BigQuery but it appears that BigQuery doesn't support dynamic schema. I am building a data warehouse and want to store all the events related to customer in

问题 Given that bigquery is not meant as a platform to denormalize data, can I denormalize the data in google cloud sql prior to importing into bigquery? I have the following tables: Table1 500M rows, Table2 2M rows, Table3 800K rows, I can't denormalize in our existing relational database for various reasons. So I'd like to do a sql dump of the data base, load it into google cloud sql, then use sql join scripts to create one large flat table to be imported into bigquery. Thanks. 回答1: That should

问题 Every time I execute a query in Google bigquery in the Explanation tab, I can see that their involves an average waiting time. Is it possible to know the percentage or seconds of wait time? 回答1: Since BigQuery is a managed service, around the glob a lot of customers are using it. It has an internal scheduling system based on the billingTier (explained here https://cloud.google.com/bigquery/pricing#high-compute) and other internals of your project. Based on this the query is scheduled to be