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  1. To start the preview for an application: 
    Request Method and Endpoint 

    Code Block
    POST /v3/namespaces/{namespace-id}/preview
where namespace-id is the name of the namespace
Response will contain the CDAP generated unique preview-id which can be used further to get the preview data.

    Request body will contain the application configuration along with few additional configs for the preview section.

    Code Block
    {
    "artifact":{ 
      "name":"cdap-data-pipeline",
      "version":"3.5.0-SNAPSHOT",
      "scope":"SYSTEM"
    },
    "name":"MyPipeline",   
    "config":{
        "connections":[ 
         { 
            "from":"FTP",
            "to":"CSVParser"
         },
         { 
            "from":"CSVParser",
            "to":"Table"
         }
        ],
        "stages":[ 
         { 
            "name":"FTP",
            "plugin":{ 
               "name":"FTP",
               "type":"batchsource",
               "label":"FTP",
               "artifact":{ 
                  "name":"core-plugins",
                  "version":"1.4.0-SNAPSHOT",
                  "scope":"SYSTEM"
               },
               "properties":{ 
                  "referenceName":"myfile",
                  "path":"/tmp/myfile"
               }
            },
            "outputSchema":"{\"fields\":[{\"name\":\"offset\",\"type\":\"long\"},{\"name\":\"body\",\"type\":\"string\"}]}"
         },
         { 
            "name":"MyCSVParser",
            "plugin":{ 
               "name":"CSVParser",
               "type":"transform",
               "label":"CSVParser",
               "artifact":{ 
                  "name":"transform-plugins",
                  "version":"1.4.0-SNAPSHOT",
                  "scope":"SYSTEM"
               },
               "properties":{ 
                  "format":"DEFAULT",
                  "schema":"{\"type\":\"record\",\"name\":\"etlSchemaBody\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"name\",\"type\":\"string\"}]}",
                  "field":"body"
               }
            },
            "outputSchema":"{\"type\":\"record\",\"name\":\"etlSchemaBody\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"name\",\"type\":\"string\"}]}"
         },
         { 
            "name":"MyTable",
            "plugin":{ 
               "name":"Table",
               "type":"batchsink",
               "label":"Table",
               "artifact":{ 
                  "name":"core-plugins",
                  "version":"1.4.0-SNAPSHOT",
                  "scope":"SYSTEM"
               },
               "properties":{ 
                  "schema":"{\"type\":\"record\",\"name\":\"etlSchemaBody\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"name\",\"type\":\"string\"}]}",
                  "name":"mytable",
                  "schema.row.field":"id"
               }
            },
            "outputSchema":"{\"type\":\"record\",\"name\":\"etlSchemaBody\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"name\",\"type\":\"string\"}]}",
            "inputSchema":[ 
               { 
                  "name":"id",
                  "type":"int",
                  "nullable":false
               },
               { 
                  "name":"name",
                  "type":"string",
                  "nullable":false
               }
            ]
         }
      ],
       "preview": {
               "startStages": ["MyCSVParser"],
			   "endStages": ["MyTable"],
               "useSinks": ["MyTable"],
               "outputs": {
								"MockSourceFTP": {
										"data":	[
                                 			{"offset": 1, "body": "100,bob"},
                                 			{"offset": 2, "body": "200,rob"},
                                 			{"offset": 3, "body": "300,tom"}
                              			],
										"schema": {
													"type" : "record",
													"fields": [
																{"name":"offset","type":"long"},
															  	{"name":"body","type":"string"}
															 ]
												  },
										"to":
["MyCSVParser"]	
								}
						}	
			}
 
    }
}

    a.  Simple pipeline

    Code Block
    Consider simple pipeline represented by following connections.

(FTP)-------->(CSV Parser)-------->(Table)
 
CASE 1: To preview the entire pipeline:
 
"preview": {
               "startStages": ["FTP"],
			   "endStages": ["Table"],
               "useSinks": ["Table"],
			   "numRecords": 10,
			   "programName": "SmartWorkflow",  // The program to "outputs": {be previewed
								"FTP   "programType": {
										"numRecords": 10,
								}
						}"workflow"		// The program type to be previewed	
			}
 
CASE 2: To preview section of the pipeline: (CSV Parser)-------->(Table)
 
"preview": {
               "startStages": ["CSVParser"],
			   "endStages": ["Table"],
               "useSinks": ["Table"],
               "outputs": {
								"MockSourceFTP": {
										"data":	[
                                 			{"offset": 1, "body": "100,bob"},
                                 			{"offset": 2, "body": "200,rob"},
                                 			{"offset": 3, "body": "300,tom"}
                              			],
										"schema": {
													"type" : "record",
													"fields": [
																{"name":"offset","type":"long"},
															  	{"name":"body","type":"string"}
															 ]
												  },
										"to": ["MyCSVParser"]	
								}}
						}	
			}
 
CASE 3: To preview only single stage (CSV Parser) in the pipeline:
 
"preview": {
               "startStages": ["CSV Parser"],
			   "endStages": ["CSV Parser"],
               "outputs": {
								"MockSourceFTP": {
										"data":	[
                                 			{"offset": 1, "body": "100,bob"},
                                 			{"offset": 2, "body": "200,rob"},
                                 			{"offset": 3, "body": "300,tom"}
                              			],
										"schema": {
													"type" : "record",
													"fields": [
																{"name":"offset","type":"long"},
															  	{"name":"body","type":"string"}
															 ]
												  },
										"to": ["MyCSVParser"]	
								}
						}	
			}
 
CASE 4: To verify if records are read correctly from FTP:
"preview": {
               "startStages": ["FTP"],
			   "endStages": ["FTP"],
			   "numOfRecords": 10
			}
 
CASE 5: To verify the data is getting written to Table properly:
"preview": {
               "startStages": ["Table"],
			   "endStages": ["Table"],
               "useSinks": ["Table"],
               "outputs": {
								"MockSourceCSV Parser": {
										"data":	[
                                 			{"id": 1, "name": "bob"},
                                 			{"id": 2, "name": "rob"},
                                 			{"id": 3, "name": "tom"}
                              			],
										"schema": {
													"type" : "record",
													"fields": [
																{"name":"id","type":"long"},
															  	{"name":"name","type":"string"}
															 ]
												  },
										"to": ["Table"]	
								}
						}	
			}

    b. Fork in the pipeline (multiple sinks)

    Code Block
    Consider the following pipeline:
 
(S3 Source) --------->(Log Parser)--------->(Group By Aggregator)--------->(Python Evaluator)--------->(Aggregated Result)
            |
			|
			--------->(Javascript Transform)--------->(Raw Data)
 
CASE 1: To preview entire pipeline
"preview": {
               "startStages": ["S3 Source"],
			   "endStages": ["Aggregated Result", "Raw Data"],
               "useSinks": ["Aggregated Result", "Raw Data"], // useSinks seem redundant as endStages is there which can control till what point the pipeline need to run
			   "numOfRecords": 10
			}
 
CASE 2: To mock the source
"preview": {
               "startStages": ["Log Parser", "Javascript Transform"],
			   "endStages": ["Aggregated Result", "Raw Data"],
               "useSinks": ["Aggregated Result", "Raw Data"],
               "outputs": {
								"MockSourceS3 Source": {
										"data":	[
												"127.0.0.1 - frank [10/Oct/2000:13:55:36 -0800] GET /apache_pb.gif HTTP/1.0 200 2326",
												"127.0.0.1 - bob [10/Oct/2000:14:55:36 -0710] GET /apache_pb.gif HTTP/1.0 200 2326",
												"127.0.0.1 - tom [10/Oct/2000:23:55:36 -0920] GET /apache_pb.gif HTTP/1.0 200 2326"
                              			],
										"schema": {
													"type" : "record",
													"fields": [
															  	{"name":"log_line","type":"string"}
															 ]
												  },
										"to":
["Log Parser", "Javascript Transform"]	
								}
						}	
			}
 
 
CASE 3: To preview the section of the pipeline (Log Parser)--------->(Group By Aggregator)--------->(Python Evaluator)
"preview": {
               "startStages": ["Log Parser"],
			   "endStages": ["Aggregated Result"],
               "useSinks": ["Aggregated Result"],
               "outputs": {
								"MockSourceS3 Source": {
										"data":	[
												"127.0.0.1 - frank [10/Oct/2000:13:55:36 -0800] GET /apache_pb.gif HTTP/1.0 200 2326",
												"127.0.0.1 - bob [10/Oct/2000:14:55:36 -0710] GET /apache_pb.gif HTTP/1.0 200 2326",
												"127.0.0.1 - tom [10/Oct/2000:23:55:36 -0920] GET /apache_pb.gif HTTP/1.0 200 2326"
                              			],
										"schema": {
													"type" : "record",
													"fields": [
															  	{"name":"log_line","type":"string"}
															 ]
												  },
										"to": ["Log Parser", "Javascript Transform"]	}
								}
						}	
			}

CASE 4: To preview the single stage Python Evaluator
"preview": {
               "startStages": ["Python Evaluator"],
			   "endStages": ["Python Evaluator"],
               "outputs": {
								"MockSourceGroup By Aggregator": {
									"data": [
												{"ip":"127.0.0.1", "counts":3},
												{"ip":"127.0.0.2", "counts":4},
												{"ip":"127.0.0.3", "counts":5},
												{"ip":"127.0.0.4", "counts":6},
						 					],
									"schema": {
													"type" : "record",
													"fields": [
															  	{"name":"ip","type":"string"},
															  	{"name":"counts","type":"long"}
															 ]
									},

									"to": ["Python Evaluator"]
								}
						}	
			}

    c. Join in the pipeline (multiple sources)

    Code Block
    Consider the following pipeline:
(Database)--------->(Python Evaluator)--------->
           										| 
												|------------>(Join)-------->(Projection)------->(HBase Sink)
												|	
           (FTP)--------->(CSV Parser)--------->
 
 
CASE 1: To preview entire pipeline
 
"preview": {
               "startStages": ["Database", "FTP"],
			   "endStages": ["HBase Sink"],
			   "useSinks": ["HBase Sink"],	
			   "numOfRecords": 10
			}


CASE 2: To mock both sources
"preview": {
               "startStages": ["Python Evaluator", "CSV Parser"],
			   "endStages": ["HBase Sink"],
			   "useSinks": ["HBase Sink"],	
               "outputs": {
								"MockSource1Database": {
									"data": [
												{"name":"tom", "counts":3},
												{"name":"bob", "counts":4},
												{"name":"rob", "counts":5},
												{"name":"milo", "counts":6}
						 					],
									"schema": {
													"type" : "record",
													"fields": [
															  	{"name":"name","type":"string"},
															  	{"name":"counts","type":"long"}
															 ]
									},

									"to": ["Python Evaluator"]
								},
 
								"MockSource2FTP": {
									"data": [
												{"offset":1, "body":"tom,100"},
												{"offset":2, "body":"bob,200"},
												{"offset":3, "body":"rob,300"},
												{"offset":4, "body":"milo,400"}
						 					],
									"schema": {
													"fields": [
															  	{"name":"name","type":"string"},
															  	{"name":"offset","type":"long"}
															 ]
									},

									"to": ["CSV Parser"]
								}
 
						}	
			}
 
 
CASE 3: To preview JOIN transform only
"preview": {
               "startStages": ["JOIN"],
			   "endStages": ["JOIN"],
               "outputs": {
								"MockSource1Python Evaluator": {
									"data": [
												{"name":"tom", "counts":3},
												{"name":"bob", "counts":4},
												{"name":"rob", "counts":5},
												{"name":"milo", "counts":6}
						 					],
									"schema": {
													"type" : "record",
													"fields": [
															  	{"name":"name","type":"string"},
															  	{"name":"counts","type":"long"}
															 ]
									},

									"to":
["JOIN"]
								},

								"MockSource2CSV Parser": {
									"data": [
												{"offset":1, "body":"tom,100"},
												{"offset":2, "body":"bob,200"},
												{"offset":3, "body":"rob,300"},
												{"offset":4, "body":"milo,400"}
						 					],
									"schema": {
													"fields": [
															  	{"name":"name","type":"string"},
															  	{"name":"offset","type":"long"}
															 ]
									},

									"to": ["JOIN"]
								}
						}	
			}

    d. Preview for a single stage (TBD)

    Code Block
    Consider the pipeline containing only one transform which has no connections yet- 
 
(Javascript Transform)

The preview configurations can be provided as
 
"preview": {
               "startStages": ["Javascript Transform"],
			   "endStages": ["Javascript Transform"],
               "outputs": {
								"MockSource": {

									"data": [
												{"name":"tom", "id":3},
												{"name":"bob", "id":4},
												{"name":"rob", "id":5},
												{"name":"milo", "id":6}
						 					],
									"schema": {
													"type" : "record",
													"fields": [
															  	{"name":"name","type":"string"},
															  	{"name":"id","type":"long"}
															 ]
									},

									"to": ["Javascript Transform"]
								}


						}	
			}
  2. How to specify the input data: User can specify the input data for preview by inserting the data directly in table format in UI or can upload a file containing the records.
    When the data is inserted in Table format, UI will convert the data into appropriate JSON records.
    When user decides to upload a file, he can upload the JSON file conforming to the schema of the next stage. Ideally we should allow uploading the CSV file as well, however how to interpret the data will be plugin dependent. For example consider the list of CSV records. Now for CSVParser plugin, the entire record will be treated as body however for Table sink, we will have to split the record based on comma to create multiple fields as specified by the next stage's input schema.

  3. Once the preview is started, the unique preview-id will be generated for it. The runtime information (<preview-id, STATUS) for the preview will be generated and will be stored (in-memory or disk). 

  4. Once the preview execution is complete, its runtime information will be updated with the status of the preview (COMPLETED or FAILED).

  5. To get the status of the preview
    Request Method and Endpoint

    Code Block
    GET /v3/namespaces/{namespace-id}/apps/{app-id}/previews/{preview-id}/status
where namespace-id is the name of the namespace
	      apppreview-id is the nameid of the applicationpreview for which previewstatus data is to be requested
	  preview-id is the id of the preview for which status is to be requested
    Response body will contain JSON encoded preview status and 
    Response body will contain JSON encoded preview status and optional message if the preview failed.
     Code Block
    1. If preview is RUNNING
{
	"status": "RUNNING" 
}

2. If preview is COMPLETED
{
	"status": "COMPLETED" 
}

3. If preview application deployment FAILED
{
	"status": "DEPLOY_FAILED",
	"errorMessagefailureMessage": "Exception message explaining the failure"
}
 
4. If preview application FAILED during execution of the stages
{
	"status": "RUNTIME_FAILED",
    "failureMessage": "Failure message"
    /*
    "stages": {
		[
			"stage_1": {
				"numOfInputRecords": 10,
				"numOfOutputRecords": 10
			},
			"stage_2": {
				"numOfInputRecords": 10,
				"numOfOutputRecords": 7
			},
			"stage_3": {
				"numOfInputRecords": 7,
				"numOfOutputRecords": 4,
				"errorMessage": "Failure reason for the stage"
			}
		]
	} */
}
  6. To get the preview data for stage:
    Request Method and Endpoint
     Code Block
    GET /v3/namespaces/{namespace-id}/previews/{preview-id}/stages/{stage-nameid}
where namespace-id is the name of the namespace
	  preview-id is the id of the preview for which data is to be requested
      stage-nameid is the unique name used to identify the stageemitter
    Response body will contain JSON encoded input data and output data for the stage emitter as well as input and output schema.
     Code Block
    {
	"inputData": [
                 	{"first_name": "rob", "zipcode": 95131},
                    {"first_name": "bob", "zipcode": 95054},
                    {"first_name": "tom", "zipcode": 94306}
                 ],
	"outputData":[
					{"name": "rob", "zipcode": 95131, "age": 21},
                    {"name": "bob", "zipcode": 95054, "age": 22},
                    {"name": "tom", "zipcode": 94306, "age": 23}
				 ],
	"inputSchema": {
						"type":"record",
						"name":"etlSchemaBody",
						"fields":[
									{"name":"first_name", "type":"string"},
									{"name":"zipcode", "type":"int"}
								 ]
					},
	"outputSchema": {
						"type":"record",
						"name":"etlSchemaBody",
						"fields":[
									{"name":"name", "type":"string"},
									{"name":"zipcode", "type":"int"},
									{"name":"age", "type":"int"}
								 ]
					},
    "errorRecordSchema": {
						"type":"record",
						"name":"schemaBody",
						"fields":[
									{"name":"errCode", "type":"int"},
									{"name":"errMsg", "type":"String"},
									{"name":"invalidRecord", "type":"String"}
								 ]
					},
	"errorRecords": {
				// Error records written by the stage if any
			 },	
	"logs" : {
		"stage level logs"
	}  
}
    To get the logs/metrics for the preview:
    Request Method and Endpoint
     Code Block
    GET /v3/namespaces/{namespace-id}/previews/{preview-id}/logs
where namespace-id is the name of the namespace
	  preview-id is the id of the preview for which data is to be requested
logs end point return the entire preview logs.
GET /v3/namespaces/{namespace-id}/previews/{preview-id}/metric (TBD)
where namespace-id is the name of the namespace
	  preview-id is the id of the preview for which data is to be requested
    Response would be similar to the regular app.
    [  
      {  
         "errCode":12,
         "errMsg":"Invalid record",
         "invalidRecord":"{\"offset\":3,\"body\":\"This error record is not comma separated!\"}"
      }
   ],	
 
    // Logs per stage may not make sense will need to think more about it.
	"logs" : {
		"stage level logs"
	}
}
  7. To get the logs/metrics for the preview:
    Request Method and Endpoint
     Code Block
    GET /v3/namespaces/{namespace-id}/previews/{preview-id}/logs
where namespace-id is the name of the namespace
	  preview-id is the id of the preview for which data is to be requested
logs end point return the entire preview logs.
Sample response for the logs endpoint. Note that it is similar to the regular app.
[  
   {  
      "log":"This is sample log - 0",
      "offset":"0.1466470037680"
   },
   {  
      "log":"This is sample log - 1",
      "offset":"1.1466470037680"
   },
   {  
      "log":"This is sample log - 2",
      "offset":"2.1466470037680"
   },
   {  
      "log":"This is sample log - 3",
      "offset":"3.1466470037680"
   },
   {  
      "log":"This is sample log - 4",
      "offset":"4.1466470037680"
   }
]
 
GET /v3/namespaces/{namespace-id}/previews/{preview-id}/metrics
where namespace-id is the name of the namespace
	  preview-id is the id of the preview for which data is to be requested
 
Sample response for the metrics. Note that it is similar to the regular app.
{
    "endTime": 1466469538,
    "resolution": "2147483647s",
    "series": [
        {
            "data": [
                {
                    "time": 0,
                    "value": 4
                }
            ],
            "grouping": {},
            "metricName": "user.Projection.records.out"
        },
        {
            "data": [
                {
                    "time": 0,
                    "value": 4
                }
            ],
            "grouping": {},
            "metricName": "user.Projection.records.in"
        },
        {
            "data": [
                {
                    "time": 0,
                    "value": 4
                }
            ],
            "grouping": {},
            "metricName": "user.Stream.records.out"
        },
        {
            "data": [
                {
                    "time": 0,
                    "value": 4
                }
            ],
            "grouping": {},
            "metricName": "user.JavaScript.records.in"
        },
        {
            "data": [
                {
                    "time": 0,
                    "value": 4
                }
            ],
            "grouping": {},
            "metricName": "user.Stream.records.in"
        }
    ],
    "startTime": 0
}
    Response would be similar to the regular app.

Open Questions:
  1. How to make it easy for the user to upload the CSV file?
  2. Lookup data is the user dataset. Should we allow mocking of the look up dataset as well?