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As a user, I would like to run arbitrary queries synchronously against my datasets in BigQuery and pull those records in BigQuery and pull those records in a hydrator pipeline.

Requirements

1. User should provide the correct project id which he has access to. 
2. User should provide the SQL query against a dataset inside his project.
3. User should specify the limit time for the queryingprovide the Json Key File of the project.
4. User should provide the temporary google cloud storage bucket to store the intermediate query result. 
5. User should specify the limit time for the querying. provide the fully qualified input table in BigQuery.  If import query is not provided, this table will be downloaded to 
   temporary Google Cloud Storage directory and finally to the CDAP source. If import query is given, this table will be a intermediate 
   storage to cache the query result and then downloaded to the temporary directory in Google Cloud Storage before going to CDAP source.
6. The temporary Google Cloud Storage directory should be deleted manually to avoid unnecessary charge from Google.

Example

Following is a are two simple example examples showing how BigQuery Source would work.

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A dataset already exist in Google BigQuery:121

project Id: vernal-seasdf-123456project1

dataset name: baby_names

table name: names_2014

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example1:

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Output schema is as follows:

User run query agains dataset in BigQuery and pull the records:

Configuration is specified as follows

      ♦ project Id

         ♦ vernal-seasdf-123456

     ♦ query

        ♦  SELECT name, count FROM baby_names ORDER BY count DESC LIMIT 3

Out put is as follows

{
        "name": "BigQuery",
          "properties": {
            "referenceName": "bigquery",
            "projectId": "vernal-project1",
            "tempBuketPath": "gs://bucketName.datasetName/tableName",
            "jsonFilePath": "/path/to/jsonkeyfile",
            "InputTableId": "vernal-project1:babynames.names_2014",
            "outputSchema": "name:string,count:int"
          }
        }
      }

This source will read the vernal-project1:babynames.names_2014 table, download the whole table to gs://bucketName.datasetName/tableName, and then get the data from there.

example2:

{
        "name": "BigQuery",
          "properties": {
            "referenceName": "bigquery",
            "projectId": "vernal-project1",
            "tempBuketPath": "gs://bucketName.datasetName/tableName",
            "jsonFilePath": "/path/to/jsonkeyfile",
            "importQuery":"SELECT name as babyName, count as nameCount FROM [vernal-project1:babynames.names_2014] ORDER BY count DESC LIMIT 3",
            "InputTableId": "vernal-project1:babynames.blankTable",
            "outputSchema": "babyName:string,babyCount:int"
          }
        }
      }

Before running this source, user should create a blank table with schema : {babyname:string, babyCount:int}. File in r example, make this blank table in vernal-project1:babynames.

and the output of the source is as follows:

Implementation Tips

Design

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