Pre-requisites Basic knowledge of NiFi, processors, custom properties, and configuring a NiFi flow Salesforce developer account with Bulk API (SOAP API) access Working HDF cluster General Workflow You will be performing the following steps for each API interaction after authenticating into Salesforce and getting your SessionID. The following is for an enterprise account. If you have a partner account, these instructions are still mostly valid but you may need to tweak your endpoints slightly when communicating with SFDC. Retrieve an XML request body from disk or another accessible location Modify the XML if necessary based on requirements Send your request, receive a response from the SFDC API Parse the XML result and pull out any data that is necessary to continue the interaction Authenticate into Salesforce You will first need to authenticate into Salesforce using your username and password. Create a file named login.txt that has the following information in it: <?xml version="1.0" encoding="utf-8"?> <env:Envelope xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:env="http://schemas.xmlsoap.org/soap/envelope/"> <env:Body> <n1:login xmlns:n1="urn:partner.soap.sforce.com"> <n1:username>YOUR_USERNAME@DOMAIN.COM.TEST</n1:username> <n1:password>YOURPASSWORD_YOURTOKEN</n1:password> </n1:login> </env:Body> </env:Envelope> If you have an HDP cluster, place this file in HDFS. Otherwise you will need to place it in a location where all of your NiFi nodes can access it. During my testing, my cluster only contained one NiFi node so I put this file on that node's local file system. You will need to start your flow with the FetchFile or FetchHDFS processor, specifying the location of the file you created above. Note that if you are using HDFS, you will need to reference your core-site.xml and hdfs-site.xml files. Make sure they are somewhere all your nodes can reach. Then you will need to use the InvokeHTTP processor with the following URL, passing in the file that was just fetched: https://test.salesforce.com/services/Soap/u/41.0. This is the URL for the auth server, but after authentication you will use the URL for your own instance. Configure it as a POST request. This will authenticate with the SFDC API and return XML response data. Out of this data, you will need to parse out the SessionID using the EvaluateXQuery processor. Set the destination to 'flowfile-attribute' so we can store the sessionid as an attribute. Add a custom property called 'sessionid' and assign it the value of '/*:Envelope/*:Body/*:loginResponse/*:result/*:sessionId/text()'. At this point if you run your NiFi flow, you should be authenticated into Salesforce and have sessionID stored as an attribute of the flowfile. We will use this attribute in all future communications with SFDC so it is useful to store it as an attribute and reference it when necessary. Communicating after authentication I executed the following steps after authenticating to access the data I was looking for: Create a job Create a batch inside of your job Check batch status When the batch is complete, retrieve the result ID Using the result ID, retrieve the result data. I asked SFDC to format this data as a CSV but you can choose one of several formats (specified when you create the job) We will go through one API request/response so that you have the template of how to communicate with the API. 1. Retrieve the XML request body You'll need to create a file for each XML request you plan on sending over to the server. The first one you'll need to have is creating a job. <?xml version="1.0" encoding="UTF-8"?> <jobInfo xmlns="http://www.force.com/2009/06/asyncapi/dataload"> <operation>query</operation> <object>OBJECT_NAME_HERE</object> <concurrencyMode>Parallel</concurrencyMode> <contentType>CSV</contentType> </jobInfo> Once you create the file and place in a location all your NiFi nodes can access, you can use the FetchFile/FetchHDFS processors to retrieve it. 2. Modify the XML If you'd like, you can dynamically replace the OBJECT_NAME_HERE text after retrieving the file using the ReplaceText processor. I did not do this as I wanted a quick example but you can. 3. Communicate with the API Using the InvokeHTTP processor, communicate with the API. In this processor you'l need to set the following: -X-SFDC-Session header variable as a custom property as I have done in the following screenshot -Content-Type to 'application/xml; charset=UTF-8'. -Remote URL to 'https://YOUR_INSTANCE.salesforce.com/services/async/41.0/job' -HTTP method to POST 4. Parse the result and incorporate it into your next request Now you will need to store the results of your communication in a flowfile-attribute so you can use it in subsequent communication. For example when creating a job, you'll need to store the JobID that comes back in the response to create a batch under that job. You'll need to use EvaluateXQuery for this with the following custom property: key: jobId value: /*:jobInfo/*:id/text() Stringing multiple requests together Now that you have the basic flow, you'll need to chain this 3/4-processor template together with itself by duplicating it and modifying the parameters. Next step is to create a batch, done with the following endpoint and request body. Endpoint: https://cs78.salesforce.com/services/async/41.0/job/${jobId}/batch request: select id from YOUR_OBJECT_NAME limit 100 From that response, you'll need to store both the batchId and the state in flowfile attributes using EvaluateXQuery. After that, you'll need to query the server using a scheduled InvokeHTTP processor (mine is every 5 seconds) until it returns a 'Completed' status. Then, you can retrieve the resultId and ultimately the CSV result. When you're done, here's what it could look like: screen-shot-2017-11-02-at-125859.png Full NiFi flow here: sfdc.xml ... View more

Ingesting HL7 Data in NiFi Pre-requisites We will assume you have the following: Basic understanding of Linux CLI HDF cluster or sandbox with NiFi, Kafka, and HDFS installed and running Basic knowledge of connecting components in NiFi and configuring settings Overview Here's the finished product: This NiFi flow accomplishes the following: Read the data from a Kafka stream Format the data to comply with HL7 specifications Extract the HL7 attributes from the formatted content block Transform the attribute list into a JSON block Impose the data's natural hierarchy onto the JSON rather than leaving it as a flat-mapped structure Write the data to HDFS Here's an example of our input data: MSH|^~\&|XXXXXX||HealthOrg01||||ORU^R01|Q1111111111111111111|P|2.3|<cr>PID|||000000001||SMITH^JOHN||19700101|M||||||||||999999999999|123456789|<cr>PD1||||1234567890^LAST^FIRST^M^^^^^NPI|<cr>OBR|1|341856649^HNAM_ORDERID|000000000000000000|648088^Basic Metabolic Panel|||20150101000100|||||||||1620^Johnson^John^R||||||20150101000100|||M|||||||||||20150101000100|<cr>OBX|1|NM|GLU^Glucose Lvl|159|mg/dL|65-99^65^99|H|||F|||20150101000100| And the output data after being written to HDFS: { "OBX_1": { "UserDefinedAccessChecks": "20150101000100", "ObservationIdentifier": { "Text": "Glucose Lvl", "Identifier": "GLU" }, "ReferencesRange": "H", "Units": { "NameOfCodingSystem": "99", "Identifier": "65-99", "Text": "65" }, "ObservationSubID": "159", "NatureOfAbnormalTest": "F", "SetIDOBX": "1", "ValueType": "NM", "ObservationValue": "mg\/dL" }, "OBR_1": { "OrderingProvider": { "FamilyName": "Johnson", "IDNumber": "1620", "GivenName": "John", "MiddleInitialOrName": "R" }, "UniversalServiceIdentifier": { "Text": "Basic Metabolic Panel", "Identifier": "648088" }, "FillerOrderNumber": { "EntityIdentifier": "000000000000000000" }, "PlacerOrderNumber": { "NamespaceID": "HNAM_ORDERID", "EntityIdentifier": "341856649" }, "ResultStatus": "M", "ObservationDateTime": "20150101000100", "ScheduledDateTime": "20150101000100", "SetIDObservationRequest": "1", "ResultsRptStatusChngDateTime": "20150101000100" }, "MSH": { "MessageControlID": "Q1111111111111111111", "SendingApplication": { "NamespaceID": "XXXXXX" }, "ReceivingApplication": { "NamespaceID": "HealthOrg01" }, "ProcessingID": { "ProcessingID": "P" }, "MessageType": { "MessageType": "ORU", "TriggerEvent": "R01" }, "EncodingCharacters": "^~\&", "VersionID": "2.3", "FieldSeparator": "|" }, "uuid": "de394ca2-cbaf-4703-9e25-4ea280a8c691", "PID": { "SSNNumberPatient": "123456789", "PatientAccountNumber": { "ID": "999999999999" }, "DateOfBirth": "19700101", "Sex": "M", "PatientName": { "GivenName": "JOHN", "FamilyName": "SMITH" }, "PatientIDInternalID": { "ID": "000000001" } }, "path": ".\/", "PD1": { "PatientPrimaryCareProviderNameIDNo": { "IDNumber": "1234567890", "FamilyName": "LAST", "GivenName": "FIRST", "AssigningAuthority": "NPI", "MiddleInitialOrName": "M" } }, "filename": "279877223850444", "kafka": { "partition": "0", "offset": "221", "topic": "test" } }   Reading from Kafka To read from Kafka, we will need to first create a topic. Execute the following to create a topic: ./kafka-topics.sh --create --zookeeper <ZOOKEEPER_HOSTNAME>:2181 --replication-factor 1 --partitions 1 --topic test You should have a topic named test available to you. We can now create the ConsumeKafka processor with this configuration: You can test that this topic is functioning correctly by using the command-line kafka-console-producer and kafka-console-consumer tools in two different command prompts. Formatting to Spec The text data we are using had to be compressed into one line since we are using Kafka to read it (Kafka needs each entry as a single line without any carriage returns), so we now need to de-compress our FlowFile into the appropriate number of lines. We will be replacing all '<cr>' strings with a carriage return ('\r', 0x0D). Use the following configuration on the ReplaceText processor to do so: Extracting HL7 attributes We will use the built-in ExtractHL7Attributes processor to transform our formatted text into "Attributes" that are native to NiFi and understood by it. Transforming into JSON We can now do a simple conversion of those attributes into JSON. Note that our data is destined for the flowfile-content, so we will now overwrite the formatted data that we used to extract our attributes from. Up until now, we had both the content and attributes which are duplicates of the content. Formatting the JSON To format the JSON, we will use a Jolt Shift specification. This specification will fully depend on the data you are using and expecting. The sample spec below is based on my sample input data. { "OBX_1.UserDefinedAccessChecks": "OBX_1.UserDefinedAccessChecks", "OBR_1.OrderingProvider.FamilyName": "OBR_1.OrderingProvider.FamilyName", "MSH.MessageControlID": "MSH.MessageControlID", "OBX_1.ObservationIdentifier.Text": "OBX_1.ObservationIdentifier.Text", "MSH.SendingApplication.NamespaceID": "MSH.SendingApplication.NamespaceID", "OBR_1.UniversalServiceIdentifier.Text": "OBR_1.UniversalServiceIdentifier.Text", "MSH.ReceivingApplication.NamespaceID": "MSH.ReceivingApplication.NamespaceID", "MSH.ProcessingID.ProcessingID": "MSH.ProcessingID.ProcessingID", "uuid": "uuid", "PID.SSNNumberPatient": "PID.SSNNumberPatient", "OBR_1.FillerOrderNumber.EntityIdentifier": "OBR_1.FillerOrderNumber.EntityIdentifier", "path": "path", "PID.PatientAccountNumber.ID": "PID.PatientAccountNumber.ID", "PID.DateOfBirth": "PID.DateOfBirth", "PD1.PatientPrimaryCareProviderNameIDNo.IDNumber": "PD1.PatientPrimaryCareProviderNameIDNo.IDNumber", "PID.Sex": "PID.Sex", "MSH.MessageType.MessageType": "MSH.MessageType.MessageType", "OBX_1.ReferencesRange": "OBX_1.ReferencesRange", "OBR_1.OrderingProvider.IDNumber": "OBR_1.OrderingProvider.IDNumber", "PD1.PatientPrimaryCareProviderNameIDNo.FamilyName": "PD1.PatientPrimaryCareProviderNameIDNo.FamilyName", "OBX_1.Units.NameOfCodingSystem": "OBX_1.Units.NameOfCodingSystem", "OBX_1.Units.Identifier": "OBX_1.Units.Identifier", "filename": "filename", "PID.PatientName.GivenName": "PID.PatientName.GivenName", "OBX_1.ObservationSubID": "OBX_1.ObservationSubID", "PD1.PatientPrimaryCareProviderNameIDNo.GivenName": "PD1.PatientPrimaryCareProviderNameIDNo.GivenName", "OBR_1.PlacerOrderNumber.NamespaceID": "OBR_1.PlacerOrderNumber.NamespaceID", "MSH.MessageType.TriggerEvent": "MSH.MessageType.TriggerEvent", "PD1.PatientPrimaryCareProviderNameIDNo.AssigningAuthority": "PD1.PatientPrimaryCareProviderNameIDNo.AssigningAuthority", "OBR_1.ResultStatus": "OBR_1.ResultStatus", "PID.PatientName.FamilyName": "PID.PatientName.FamilyName", "MSH.EncodingCharacters": "MSH.EncodingCharacters", "MSH.VersionID": "MSH.VersionID", "kafka.partition": "kafka.partition", "OBR_1.UniversalServiceIdentifier.Identifier": "OBR_1.UniversalServiceIdentifier.Identifier", "OBR_1.ObservationDateTime": "OBR_1.ObservationDateTime", "OBR_1.ScheduledDateTime": "OBR_1.ScheduledDateTime", "OBX_1.ObservationIdentifier.Identifier": "OBX_1.ObservationIdentifier.Identifier", "OBR_1.OrderingProvider.GivenName": "OBR_1.OrderingProvider.GivenName", "OBR_1.SetIDObservationRequest": "OBR_1.SetIDObservationRequest", "OBR_1.ResultsRptStatusChngDateTime": "OBR_1.ResultsRptStatusChngDateTime", "OBR_1.PlacerOrderNumber.EntityIdentifier": "OBR_1.PlacerOrderNumber.EntityIdentifier", "OBX_1.NatureOfAbnormalTest": "OBX_1.NatureOfAbnormalTest", "OBX_1.SetIDOBX": "OBX_1.SetIDOBX", "MSH.FieldSeparator": "MSH.FieldSeparator", "PD1.PatientPrimaryCareProviderNameIDNo.MiddleInitialOrName": "PD1.PatientPrimaryCareProviderNameIDNo.MiddleInitialOrName", "OBX_1.Units.Text": "OBX_1.Units.Text", "OBX_1.ValueType": "OBX_1.ValueType", "kafka.offset": "kafka.offset", "PID.PatientIDInternalID.ID": "PID.PatientIDInternalID.ID", "kafka.topic": "kafka.topic", "OBX_1.ObservationValue": "OBX_1.ObservationValue", "OBR_1.OrderingProvider.MiddleInitialOrName": "OBR_1.OrderingProvider.MiddleInitialOrName" } Here's the associated NiFi configuration: Writing to HDFS Finally, we will persist this JSON-transformed data to HDFS for further analysis and storage. Configure your PutHDFS processor as follows Note that the 'Hadoop Configuration Resources' is a required field for your connection from NiFi to HDFS to work properly, so be sure to fill that in with wherever your core-site.xml and hdfs-site.xml HDFS configuration files are on disk. Be sure to create the directory /tmp/tst/helloworld in hdfs and change ownership to the nifi user: hdfs dfs -mkdir -p /tmp/tst/helloworld hdfs dfs -chown -R nifi /tmp/tst Putting it all together Now you should be able to connect all of your processors, start them, and see your data move through. To send the sample data through our new flow, do the following: 1. SSH into a kafka broker in your cluster and cd to the binaries folder - in my environment, that is located at /usr/hdp/current/kafka-broker/bin. 2. Create a file in your home directory and paste the contents of your sample input data (from the Overview section above). This file should be exactly 1 line, no more. 3. Feed your sample data into the kafka producer with the following command: /usr/hdp/current/kafka-broker/bin/kafka-console-producer.sh --broker-list <KAFKA_BROKER_HOSTNAME>:6667 --topic test < hl7data.txt If all goes well, when you NiFi flow refreshes you should see it go all the way through. At this point you can check HDFS with the following command: hdfs dfs -ls /tmp/tst/helloworld You should see a file there. You can read it with the following: hdfs dfs -cat /tmp/tst/helloworld/<FILENAME> Thanks for reading! Full NiFi template: hl7flow-template.xml ... View more