There are many instances where there is a need for syncing data from source application/s to target application/s. A sample scenario is a custom mobile app/ web application developed to perform transactions on SaaS data. In this case, the mobile/web application fetches data from SaaS. User will perform transactions on this data and those transactions should be pushed to SaaS. Here source application is the custom mobile/web application and target application is SaaS. Another case could be the integration of external systems with SaaS, with a need to continuously send data from those external systems to SaaS. Regardless of what the source or target application is, it is ideal to have a middle tier using Oracle Cloud Infrastructure (OCI) native services that handles the data flow due to a number of reasons —

1. Reduced load on the source application in terms of data sync operations, retrials and error handling.
2. Ability to persist data at middle tier and perform retrials on this data in case of failure.
3. Ability to handle data sync from multiple source & target applications from a single middle tier.
4. Ability to transform or filter messages at the middle tier before sending to target application.
5. Easy monitoring/reporting of the data flow.
6. Ability to fire notifications in case of failures.
7. Ability to have a consolidated view of the data sync activities and error cases.
8. Ability to enable data syncing in a publish-subscribe asynchronous model.
9. Allow source application to continue with data syncing operation even if target application is down, say for maintenance.
10. Ability to use centralized metrics and logging features.
11. Ability to scale the middle tier based on the data load and processing requirements.

This solution shows how you can Oracle Cloud Infrastructure (OCI) cloud native services to build a serverless data syncing solution. There are various approaches to build a data sync middle tier using OCI. This one uses Streaming, API Gateway, Functions, Service Connector Hub, Vault, OCI Registry, Notifications and Object Storage.

Choosing OCI Cloud Native Services as middle tier has the following benefits,

1. They are based on open source and standards.
2. They have built-in management capabilities. So development teams can focus on building competitive features and spend less time installing, patching, and maintaining infrastructure.
3. Availability of good pricing models.
4. They are highly secure, scalable, durable and reliable.

Streaming

Streaming is a good fit for any use case in which data is produced and processed continually and sequentially in a publish-subscribe messaging model. Additionally, it can connect to Service Connector Hub which means that you can designate a stream as a data source, use Oracle Cloud Infrastructure Functions to process the stream's messages. It is also is a fully managed and scalable OCI service. Customers need to pay only for what they use, making the service attractive for workloads with large spikes in usage.

There are 2 types of streams used.

• A stream, DataSyncStream for storing the posted data from the source application/s.

• A stream or streams for storing errored data. Posting of data to target application/s can error out due to multiple reasons, like server unavailability, data inconsistency, error on the server side while processing and so forth. Some of these errors are recoverable, say an error occurred due to server unavailability is recoverable when server is available. Some of them would be unrecoverable, i.e. the processing of data will not be successful even after several retrials. It is important to categorize and re-process errored messages based on the error type to avoid data loss. In the sample code developed for this pattern, retrial is based on the REST API response code. Please note that, the error type and retrial decision is based on the business use case and using REST API response code may not be suitable for all business cases. The data will be moved from DataSyncStream to Error streams based on the error type and classification.

Functions

Functions are under an Application, DataSyncApplication . It has the following configuration variables. They are for defining the error stream OCIDs and Vault OCIDs.

3 Functions are used in this pattern. • PopulateDataStreamFunction → This Function is used to populate the DataSyncStream . It is invoked when the Source Application/s post data to the REST API exposed using API Gateway. •ReadDataStreamFunction → This Function gets the messages from the DataSyncStream from Service Connector Hub Service and calls the target application’s API. If there is a failure in target application API call, the messages are sent to error streams. The error streams to use, are configurable at the Function Application level as shown above. This gives additional flexibility in defining the error conditions and the streams to which messages are pushed based on your business case. •RetryFunction → This Function retries the messages in error streams. This Function is exposed as a public API using an API Gateway. The exposed API can be invoked as a batch process or on an ad-hoc basis, to reprocess the failed messages in any stream.

API Gateway

There is one API Gateway used, SyncDataGateway. There are 2 routes defined in API Gateway deployment. One is to map the PopulateDataStreamFunction and the other is to map the RetryFunction.

Notifications

A topic, ErrorTopic which is a subscription to an Email protocol.

Object Storage Bucket

An object storage bucket, stream-error-bucket stores errored messages.

Service Connector Hub

There are 3 Service Connectors used.

• Service Connector, DataSyncServiceConector to connect DataSyncStream to Functions, where the target of the Service Connector is set as a ReadDataStreamFunction. • Service Connector from error streams to Notifications, UnrecoverableErrorToNotificationsConnector. It connects to ErrorTopic subscription. • Service Connector from error streams to Object Storage bucket, UnrecoverableErrorToStorageConnector so that a support personnel is notified of the error and can later inspect the failed message in the Object Storage bucket.

Vault

A vault called, DataSyncVault is used to store the auth tokens as secrets.

HashiCorp Terraform

Oracle Terraform Provider

Java

• Oracle Cloud Infrastructure SDK for Java

Step 1. Source application/s posts data to the REST API exposed by the API Gateway. The API gateway has an API deployment that invokes the Function PopulateDataStreamFunction.

The REST API call to API Gateway and sample json payload is given below. https://[hostname]/stream/sync

{
	"streamKey": "key1",
	"streamMessage": {
	   "vaultSecretName":"789",  
	    
		"targetRestApi": "https://g4kz1wyoyzrtvap-json......./....../latest/orders",
		"targetRestApiOperation": "POST",
		"targetRestApiPayload": {
			"orderid": "18jan",
			"PO": "18jan"
		},
		"targetRestApiHeaders": [{
				"key": "Content-Type",
				"value": "application/json"
			}
		]
	}

}

The json payload contains streamKey and streamMessage nodes. streamKey is the key to be sent to the DataSyncStream and streamMessage is the value to be sent to the DataSyncStream. streamKey can be empty if a key is not required while populating streams.

The streamMessage section is self-contained i.e. it contains the target application API in targetRestApi node, target application’s Rest API operation in targetRestApiOperation node and a target application’s Rest API payload in targetRestApiPayload node. Headers for target REST API call should be sent as key , value pair in targetRestApiHeaders node.

In most cases the target application API will need a security token. Usually this token is passed in the authorization header of the POST call to API Gateway. This token needs to be securely stored for target application API processing later by Functions. For this purpose, the json payload contains a node called vaultSecretName which is an id that should be unique to messages that has the same auth token passed in authorization header. The unique id will be used as a secret name in the Vault and the secret content will be the auth token passed in the authorization header. When the auth token in the authorization header changes, a new value should be passed in the vaultSecretName for those messages.

Step 2. PopulateDataStreamFunction parses the json payload and creates a new stream message with Key as streamKey and value as streamMessage and pushes it to DataSyncStream. It also reads the vaultSecretName and creates a secret in Vault with content as the authorization header token and name as vaultSecretName.

Step 3. DataSyncStream is connected to the Function, ReadDataStreamFunction through a Service Connector. Service Connector invokes this Function when DataSyncStream is populated with new messages.

Step 4. ReadDataStreamFunction processes the messages in DataSyncStream by reading the targetRestApiPayload section and then invokes the target application API. If an error occurs, say if the server is unavailable Function pushes the message to error streams defined in the Function Application configuration variables.

Step 5. Lastly there is an option to retry the messages in Error streams using an API Gateway API, that exposes the RetryFunction.

The sample REST API call and payload will look like this.

https://[host-name]/stream/retry

{
	"streamOCIDToRetry": "ocid1.stream.o...rrr",
	"noOfMessagesToProcess": 5,
	"readAfterOffset": -1,
	"readPartition": "0",
	"errormapping": [{
			"responsecode": "404",
			"stream": "ocid1.stream.oc1.iad...r"
		},
		{
			"responsecode": "503",
			"stream": "ocid1.stream.oc1.iad.am.."
		}, {
			"responsecode": "unexpectedError",
			"stream": "ocid1.stream.oc1.iad.a...q"
		}
	]


}

In the retry payload, specify the stream OCID to retry using streamOCIDToRetry and the offset from where the retry should happen. noOfMessagesToProcess is the no of Stream messages to process in a single Function call.

readAfterOffset is the offset location from where the messages are to be read. Set this to -1 to start reading from the oldest message in the Stream.

The payload also contains an errormapping section to specify the streams to which errored messages should be directed to. streamOCIDToRetry option in the retry payload gives flexibility of retrying messages in any stream.

errormapping option in the payload gives the flexibility of changing error stream mapping based on the stream which is retried and the expected error scenario.

This API's response body will have information on the last offset which was successfully processed, no. of successfully processed messages and no. of failed messages.

{"lastReadOffset":405 ,"processedmessages":0,"failedMessages":1,"endOfStream": true}

It also informs whether end of Stream has reached, so that further call for retrial can be stopped if there is no more message to process.

1. Make sure you've setup your API signing key, installed the Fn CLI, completed the CLI configuration steps and have setup the OCI Registry you want to use.

2. Ensure Terraform is installed.

3. You have the Target application's REST API, Auth token and Json Payload for loading data to it.

1. Download the files from the respository and navigate to location where you downloaded the files. Navigate to code folder.

2. Modify provider.tf , with values spefic to your OCI environment.

3. Run following Terraform commands to create all your resources in OCI. You will be asked the provide variable values.

   • terraform init

   • terraform plan

   • terraform apply

4. This step creates all the resources in OCI , including the setup of a VCN, an API Gateway, Streams, Service Connectors, Notifications, Object Storage Bucket,uploading the Oracle Cloud Functions and creating an OCI Vault.

5. Log In to OCI console and verify whether all OCI resources are created.

6. Add IAM policies related to usage of Functions, Streams, Service Connector, Object Storage and Notifications

1. To run the sample, get the API Gateway URL corresponding to sync route. It will look like following, https://[host-name]/stream/sync

A sample json payload is given below. You can have POST, PUT and DELETE operatons. Change the targetRESTApi and targetRESTApiOperation values based on your target application. Any REST API headers should be passed as key, value pairs in targetRestApiHeaders.

{
	"streamKey": "key1",
	"streamMessage": {
	   "vaultSecretName":"789",  
	    
		"targetRestApi": "https://g....../latest/orders",
		"targetRestApiOperation": "POST",
		"targetRestApiPayload": {
			"orderid": "18jan",
			"PO": "18jan"
		},
		"targetRestApiHeaders": [{
				"key": "Content-Type",
				"value": "application/json"
			}
		]
	}

}

This API call will push the streamMessage part of the payload to DataSyncStream . The Service Connector which connects DataSyncStream to Functions will get invoked and the associated Task Function ,ProcessDataStreamFunction will read the stream message and process the messages.

2. Check the target application to see the operations invoked were processed correctly.

3. To check for retry and failures, you can pass incorrect values in the payload and see whether the Error Streams got populated correctly. In case of errors, you will also receive notifications in the mail id you entered in Notifications Service. You can also see the errored messages in the Object Storage Bucket.

4. To test a retry in case of failure, call the API Gateway REST API, corresponding to retry route. It will look like this https://[host-name]/stream/retry

Sample payload is given below.

Replace the streamOCIDToRetry with the OCID of the error stream to be retried.

noOfMessagesToProcess is the no of Stream messages to process in a single Function call.

readAfterOffset is the offset location from where the messages are to be read. Set this to -1 to start reading from the oldest message in the Stream.

RetryFunction will process the messages and return the last successfully read offset. So if this API needs multiple invocation, read the response body of the API and make subsequent call by passing the last offset as the readAfterOffset value in the payload.

Also replace, stream value in the errormapping section with the error streams in your OCI environment.

{
	"streamOCIDToRetry": "ocid1.stream.o...rrr",
	"noOfMessagesToProcess": 5,
	"readAfterOffset": -1,
	"readPartition": "0",
	"errormapping": [{
			"responsecode": "404",
			"stream": "ocid1.stream.oc1.iad...r"
		},
		{
			"responsecode": "503",
			"stream": "ocid1.stream.oc1.iad.am.."
		}, {
			"responsecode": "unexpectedError",
			"stream": "ocid1.stream.oc1.iad.a...q"
		}
		
	]


}

Please note that the sample given is only to demonstrate a pattern and mostly you will need to enhance it to fit into your needs.

While enhancing the sample do consider the following.

• The function application configuration has a few error stream OCIDs defined. Add new error streams or modify the existing ones based on your requirement. Note that, the ReadDataStreamFunction code should be modified if changes are made in the configuration keys.

• Change the errormapping section of RetryFunction payload, if needed. The sample makes use of the response code for mapping streams. Change this, if a different type of mapping is required. RetryFunction code also would need change if there is a change in the payload.

• You will need a process to delete the Vault secrets once they are no longer needed. One option is to write a Function, that can do the clean-up task periodically.

• RetryFunction payload has the node noOfMessagesToProcess to set the no of messages to process in a single call. Do change this to a smaller number if processing of each message takes time and there is a possibility of Function to time out.

• Consuming messages from a stream requires you to: create a cursor, then use the cursor to read messages. A cursor is a pointer to a location in a stream. One of the option is to use a specific offset to start the reading of message. This is called an AT_OFFSET cursor. RetryFunction in the sample uses the AT_OFFSET cursor for consuming message. It accepts readAfterOffset as the starting offset to read message. It returns the last successfully read offset. To process large number of messages together, store returned offset value in a location and pass it as value of readAfterOffset in json payload and invoke RetryFunction sequentially.

• The sample function handles PUT, POST and DELETE operations. To add or remove operations, change the ReadDataStreamFunction and RetryFunction code. Also change the targetRestApiOperation section of the payload.

• The source application is responsible for sending unique value in the vaultsecretname for messages having same auth token.

• It is assumed that the authentication token to invoke the target application’s REST api is passed in the “Authorization” Header. There is a possibility that authorization token stored in Vault expires while retrying the message. This scenario is not considered in the sample.

• It is also possible to move the common methods in Functions to helper classes and reusing them.

• If things dont work, here are some troubleshooting tips for Oracle Cloud Functions you can try.

• The Oracle Cloud Functions are configured to emit logging info using standard system logging, this can be useful when debugging the functions.

• Make sure you have defined all the required IAM policies.

Oracle takes security seriously and has a dedicated response team for reporting security vulnerabilities and to answer any security and vulnerability related questions.

We welcome all contributions to this sample and have a contribution guide for you to follow if you'd like to contribute.

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If you need help with this sample, please log an issue within this repository and the code owners will help out where we can.

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