Category: oracle-cloud

Hello guys,

Today, I’m going to discuss a new notion – living between multi-cloud environments; as you might be aware that I’ve shared a dynamic API built inside Azure in my last post. Today, I want to use that covid-19 API, originally from another third-party source & publish it as streams inside the Oracle cloud. These are the ideal case to use integration software like Tibco or Mulesoft. However, this is more useful for any start-up kind of environment or anyone who wants to build their API-based eco-system.

First, you need to register in Oracle cloud as they give a $300 trial to everyone who registers their platform for the first time.

Step 1:

You will lead the main dashboard after successfully registering in the portal by providing the essential information.

Step 2:

By default, it will show the following screen –

It would be best if you created the compartment as shown below.

Step 3:

Now, you can create the stream, as shown in the next step, by choosing the desired compartment. You need to click – “Create Stream” blue button on top of your page after selecting the desired compartment.

Also, you can test the stream by manually uploading a few sample json shown in the below step.

Step 4:

Now, we need to add API-key as follows –

This screen will prompt you to download the private key. We’ll use this in the later configuration of our python environment.

Step 5:

Now, you need to capture the content of the configuration file shown in the below figures –

You’ll get these important details under Identity marked in red-box.

Step 6:

Now, you’ll place the previously acquired private key & the content from Step-5 under the following location from where you are going to trigger the application –

You will get more details on this from these links –

• Configuration File
• Private Keys

Now, we’ve finished with the basic Oracle cloud setup.

Let’s check the Azure API one more time using postman –

Let us install some of the critical python packages –

Now, we’re ready with our environment.

Let us explore the codebase now.

1. clsConfig.py ( This is the configuration file for this demo-application)

2. clsOCIConsume.py (This will consume from the designated stream created in Oracle-cloud)

Let’s explore the key snippet from the above code –

    def get_or_create_stream(self, client, compartment_id, stream_name, partition, sac_composite):
        try:

            list_streams = client.list_streams(compartment_id=compartment_id, name=stream_name,
                                               lifecycle_state=oci.streaming.models.StreamSummary.LIFECYCLE_STATE_ACTIVE)

            if list_streams.data:
                # If we find an active stream with the correct name, we'll use it.
                print("An active stream {} has been found".format(stream_name))
                sid = list_streams.data[0].id
                return self.get_stream(sac_composite.client, sid)

            print(" No Active stream  {} has been found; Creating it now. ".format(stream_name))
            print(" Creating stream {} with {} partitions.".format(stream_name, partition))

            # Create stream_details object that need to be passed while creating stream.
            stream_details = oci.streaming.models.CreateStreamDetails(name=stream_name, partitions=partition,
                                                                      compartment_id=compartment, retention_in_hours=24)

            # Since stream creation is asynchronous; we need to wait for the stream to become active.
            response = sac_composite.create_stream_and_wait_for_state(stream_details, wait_for_states=[oci.streaming.models.StreamSummary.LIFECYCLE_STATE_ACTIVE])
            return response
        except Exception as e:
            print(str(e))

The above function will check if there is already any existing stream available or not. If not, then it will create one.

    def get_cursor_by_partition(self, client, stream_id, partition):
        print("Creating a cursor for partition {}".format(partition))
        cursor_details = oci.streaming.models.CreateCursorDetails(
            partition=partition,
            type=oci.streaming.models.CreateCursorDetails.TYPE_TRIM_HORIZON)
        response = client.create_cursor(stream_id, cursor_details)
        cursor = response.data.value
        return cursor

In Oracle cloud, you need to create a cursor to consume the streaming messages. Please refer to the following link for more details.

    def simple_message_loop(self, client, stream_id, initial_cursor):
        try:
            cursor = initial_cursor
            while True:
                get_response = client.get_messages(stream_id, cursor, limit=10)
                # No messages to process. return.
                if not get_response.data:
                    return

                # Process the messages
                print(" Read {} messages".format(len(get_response.data)))
                for message in get_response.data:
                    print("{}: {}".format(b64decode(message.key.encode()).decode(),
                                          b64decode(message.value.encode()).decode()))

                # get_messages is a throttled method; clients should retrieve sufficiently large message
                # batches, as to avoid too many http requests.
                time.sleep(1)
                # use the next-cursor for iteration
                cursor = get_response.headers["opc-next-cursor"]

            return 0
        except Exception as e:
            x = str(e)
            print('Error: ', x)

            return 1

In this case, we’re looping through the channel & consuming the messages maintaining fewer HTTP requests in mind.

3. clsOCIPublish.py (This will publish msgs from the source Azure-API to designated stream created in Oracle-cloud)

Let’s explore the key snippet from the above script –

    def publish_messages(self, client, stream_id, inputDF):
        try:
            limRec = self.limRec
            # Converting dataframe to json
            df = inputDF

            # Calculating total number of records
            cntRow = df.shape[0]
            print('Actual Record counts: ', str(cntRow))
            print('Supplied Record counts: ', str(limRec))

            # Build up a PutMessagesDetails and publish some messages to the stream
            message_list = []
            start_pos = 0
            end_pos = 0
            interval = 1

            for i in range(limRec):
                split_df = p.DataFrame()
                rJson = ''
                # Preparing Data

                # Getting Individual Element & convert them to Series
                if ((start_pos + interval) <= cntRow):
                    end_pos = start_pos + interval
                else:
                    end_pos = start_pos + (cntRow - start_pos)

                split_df = df.iloc[start_pos:end_pos]
                rJson = split_df.to_json(orient ='records')

                if ((start_pos > cntRow) | (start_pos == cntRow)):
                    pass
                else:
                    start_pos = start_pos + interval

                key = "key" + str(i)
                value = "value" + str(rJson)

                # End of data preparation

                encoded_key = b64encode(key.encode()).decode()
                encoded_value = b64encode(value.encode()).decode()
                message_list.append(oci.streaming.models.PutMessagesDetailsEntry(key=encoded_key, value=encoded_value))

            print("Publishing {} messages to the stream {} ".format(len(message_list), stream_id))
            messages = oci.streaming.models.PutMessagesDetails(messages=message_list)
            put_message_result = client.put_messages(stream_id, messages)

            # The put_message_result can contain some useful metadata for handling failures
            for entry in put_message_result.data.entries:
                if entry.error:
                    print("Error ({}) : {}".format(entry.error, entry.error_message))
                else:
                    print("Published message to partition {} , offset {}".format(entry.partition, entry.offset))

            return 0
        except Exception as e:
            x = str(e)
            print('Error: ', x)
            return 1

In the above snippet, we’re fetching data captured from our Azure-API call & then send chunk-by-chunk data to the Oracle stream for publishing.

4. clsAzureAPI.py (This will fetch msgs from the source Azure-API)

I think this one is pretty straightforward as we’re invoking the Azure-API response.

5. callAzure2OracleStreaming.py (Main calling script to invoke all the class for a end to end test)

This one is the primary calling script, invoking all the Python classes one-by-one to run our test cases together.

Let us run our application –

And, you can see the streaming data inside Oracle cloud as shown below –

You can explore my Git associated with this project & download the code from here.

So, finally, we’ve done it. 😀

I’ll bring some more exciting topic in the coming days from the Python verse.

Till then, Happy Avenging! 😀

Note: All the data & scenarios posted here are representational data & scenarios that are available over the internet & for educational purpose only. Also, I’ve used template SDK provided by Oracle & customized it to satisfy our business cases.