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To start the preview for an application:
Request Method and EndpointCode Block POST /v3/namespaces/{namespace-id}/apps/{app-id}/preview where namespace-id is the name of the namespace Response will contain the CDAP generated unique apppreview-id iswhich thecan namebe ofused thefurther applicationto forget whichthe preview is to be seendata.Request body will contain the application configuration along with following few additional configs for the preview section.
Code Block "preview{ "artifact": { "name":"cdap-data-pipeline", "numRecordsversion" : "10"3.5.0-SNAPSHOT", "scope":"SYSTEM" }, "startStagename" : "stage_1MyPipeline", "config":{ "endStage"connections":[ : "stage_3", { "from":"inputDataFTP", : [ "to":"CSVParser" }, { {"name": "rob", "address": "san jose"}, "from":"CSVParser", "to":"Table" } {"name": "bob", "address": "santa clara"}], "stages":[ { {"name": "tomFTP", "address": "palo alto"} "plugin":{ ] "programTypename": "WORKFLOWFTP", // programType and programName can be optional for now. However in future if we want to preview non-hydrator application, then programType and programName can be provided to let preview system know which program to be previewed "programName": "DataPipelineWorkflow" "type":"batchsource", "label":"FTP", } Description: 1. numRecords: Number of records to preview 2. startStage: Pipeline stage from which preview need to be started. 3. endStage: Pipeline stage till which the preview need to be run. 4. inputData: Data which need to be run through preview process. Validations to be performed: 1. startStage and endStage are connected together. 2. Schema of the inputData should match with the input schema for stage_1. 3. If SOURCE plugin is specified as startStage, preview will ignore the inputData and read the records directly from the source as specified by the numRecords. 4. If startStage is other than the Source plugin then inputData is required. Preview will process the inputData ignoring numRecords.
Consider the pipeline which has FTP source, CSV parser labeled as MyCSVParser and Table sink labeled as MyTable. The configuration with preview data will look like following:
Code Block { "artifact":{ "name":"cdap-data-pipeline", "version":"3.5.0-SNAPSHOT", "scope":"SYSTEM" }, "name":"MyPipeline", "config":{ "connections":[ { "from":"FTP", "to":"CSVParser"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\"}]}" }, { "fromname":"CSVParserMyCSVParser", "toplugin":"Table"{ } ], "stages"name":["CSVParser", { "name":"FTP", "plugin":{ "name":"FTP", ""type":"batchsourcetransform", "label":"FTPCSVParser", "artifact":{ "name":"coretransform-plugins", "version":"1.4.0-SNAPSHOT", "scope":"SYSTEM" }, "properties":{ "referenceNameformat":"myfileDEFAULT", "pathschema":"/tmp/myfile" } }, "outputSchema":"{{\"type\":\"record\",\"name\":\"etlSchemaBody\",\"fields\":[{\"name\":\"offsetid\",\"type\":\"longint\"},{\"name\":\"bodyname\",\"type\":\"string\"}]}", }, "field":"body" { "name":"MyCSVParser",} "plugin":{ }, "nameoutputSchema":"CSVParser",{\"type\":\"record\",\"name\":\"etlSchemaBody\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"name\",\"type\":\"string\"}]}" }, "type":"transform", { "labelname":"CSVParserMyTable", ""plugin":{ "name":"Table", "type":"batchsink", "label":"Table", "artifact":{ "name":"transformcore-plugins", "version":"1.4.0-SNAPSHOT", "scope":"SYSTEM" }, "properties":{ "formatschema":"DEFAULT", "schema":"{\"type{\"type\":\"record\",\"name\":\"etlSchemaBody\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"name\",\"type\":\"string\"}]}", "name":"mytable", "schema.row.field":"bodyid" } }, "outputSchema":"{\"type\":\"record\",\"name\":\"etlSchemaBody\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"name\",\"type\":\"string\"}]}", }, "inputSchema":[ { { "name":"MyTable", "pluginname":{"id", "nametype":"Tableint", "typenullable":"batchsink",false "label":"Table"}, "artifact":{ "name":"core-pluginsname", "versiontype":"1.4.0-SNAPSHOTstring", "scopenullable":"SYSTEM"false }, ] "properties":{ } ], "schemapreview":"{\"type\":\"record\",\"name\":\"etlSchemaBody\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"name\",\"type\":\"string\"}]}" { "startStages": ["MyCSVParser"], "endStages": ["MyTable"], "useSinks": ["MyTable"], "outputs": "name":"mytable",{ "FTP": { "data": [ "schema.row.field":"id" {"offset": 1, "body": "100,bob"}, }, "outputSchema":"{\"type\":\"record\",\"name\":\"etlSchemaBody\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"name\",\"type\":\"string\"}]}", {"offset": 2, "body": "200,rob"}, "inputSchema":[ { {"offset": 3, "body": "300,tom"} "name":"id", ], "schema": { "type" : "intrecord", "nullable":false }, { "name":"name", "fields": [ {"name":"offset","type":"long"}, {"name":"body","type":"string", } ] } } } } } }
a. Simple pipeline
Code Block Consider simple pipeline "nullable":falserepresented by following connections. (FTP)-------->(CSV Parser)-------->(Table) CASE 1: To preview the entire pipeline: }"preview": { ] } "startStages": ["FTP"], "previewendStages": {["Table"], "numRecordsuseSinks" : ["10Table"], "numRecords": 10, "startStage" "programName": "MyCSVParserSmartWorkflow", // The program to be previewed "endStage" : "MyTable", "inputData" : ["programType": "workflow" // The program type to be previewed } CASE 2: To preview section of the pipeline: (CSV Parser)-------->(Table) "preview": { {"offset"startStages": 1["CSVParser"], "body "endStages": ["100,bobTable"}], "useSinks": ["Table"], {"offsetoutputs": 2, "body{ "FTP": "200,rob"},{ "data": [ {"offset": 31, "body": "300100,tombob"}, ] } } }
The above preview configuration will read the inputData from preview section and write the data to MyTable. If user does not want to write the data to the sink but only want to preview the MyCSVParser stage the preview configurations will look like below:
Once the preview is started, the unique preview id will be generated for it. Preview id could be of the form: namespace_id.app_id.preview. The runtime information (<Preview_id, STATUS) for the preview will be generated and will be stored (in-memory or disk). While preview is running if user again sends the preview request for the same application and the STATUS is RUNNING, user will get 403 status code with "Preview already running for application." error message. This enforces only one preview is running at any instance of time for a given application in a given namespace.Code Block "preview": {{"offset": 2, "body": "200,rob"}, "numRecords" : "10", {"startStageoffset": 3, "body": "MyCSVParser",300,tom"} "endStage" : "MyCSVParser", // In order to execute single stage start stage is same as end stage "inputData" : [ {"offset": 1, "body": "100,bob"}, {"offset": 2, "body": "200,rob"}, ], "schema": { "type" : "record", "fields": [ {"name":"offset","type":"long"}, {"name":"body","type":"string"} ] } } } } CASE 3: To preview only single stage (CSV Parser) in the pipeline: "preview": { "startStages": ["CSV Parser"], "endStages": ["CSV Parser"], "outputs": { "FTP": { "data": [ {"offset": 31, "body": "300100,tombob"}, ] }
If the startStage in the preview configurations is not SOURCE plugin, then the preview system will generate the MOCK source in the pipeline which will read the JSON records specified in the inputData field and convert them into the StructureRecord.
Once the preview execution is complete, its runtime information will be updated with the status of the preview (COMPLETED or FAILED).To get the status of the preview
Request Method and EndpointCode Block GET /v3/namespaces/{namespace-id}/apps/{app-id}/preview/status where namespace-id is the name of the namespace app-id is the name of the application for which preview data is to be requestedResponse 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 FAILED { "status": "FAILED" "errorMessage": "Preview failure root cause message." }To get the preview data for stage:
Request Method and EndpointCode Block GET /v3/namespaces/{namespace-id}/apps/{app-id}/preview/stages/{stage-name} where namespace-id is the name of the namespace app-id is the name of the application for which preview data is to be requested stage-name is the unique name used to identify the stageResponse body will contain JSON encoded input data and output data for the stage as well as input and output schema.
Code Block { "inputData": [{"offset": 2, "body": "200,rob"}, {"offset": 3, "body": "300,tom"} ], "schema": { "type" : "record", "fields": [ {"name":"offset","type":"long"}, {"name":"body","type":"string"} ] } } } } 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": { "CSV 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"} ] } } } }
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": { "S3 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"} ] } } } } 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": { "S3 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"} ] } } } } CASE 4: To preview the single stage Python Evaluator "preview": { "startStages": ["Python Evaluator"], "endStages": ["Python Evaluator"], "outputs": { "Group 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"} ] } } } }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": { "Database": { "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"} ] } }, "FTP": { "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"} ] } } } } CASE 3: To preview JOIN transform only "preview": { "startStages": ["JOIN"], "endStages": ["JOIN"], "outputs": { "Python 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"} ] } }, "CSV 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"} ] } } } }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"} ] } } } }- 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. - 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).
- Once the preview execution is complete, its runtime information will be updated with the status of the preview (COMPLETED or FAILED).
To get the status of the preview
Request Method and EndpointCode Block GET /v3/namespaces/{namespace-id}/previews/{preview-id}/status where namespace-id is the name of the namespace preview-id is the id of the preview for which status is to be requestedResponse 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", "failureMessage": "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" } ] } */ }To get the preview data for stage:
Request Method and EndpointCode Block GET /v3/namespaces/{namespace-id}/previews/{preview-id}/stages/{stage-id} 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-id is the unique name used to identify the emitterResponse body will contain JSON encoded input data and output data for the 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": [ { "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" } }To get the logs/metrics for the preview:
Request Method and EndpointCode 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, {"first_name": "rob", "zipcodevalue": 95131}, 4 {"first_name": "bob", "zipcode": 95054},} ], {"first_name": "tom", "zipcodegrouping": 94306} {}, ], "outputDatametricName":[ {"name": "rob", "zipcode": 95131, "age": 21"user.Stream.records.out" }, { { "namedata": "bob", "zipcode": 95054, "age": 22}, [ { {"nametime": "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"} ] } }
To get the logs for the preview:
Request Method and EndpointCode Block GET /v3/namespaces/{namespace-id}/apps/{app-id}/preview/logs where namespace-id is the name of the namespace app-id is the name of the application for which preview data is to be requested0, "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:
- How to make it easy for the user to upload the CSV file?
- Lookup data is the user dataset. Should we allow mocking of the look up dataset as well?