EPM System Release x on Oracle Exalytics In-Memory Machine (PDF) Integration Roadmap for Oracle Business Intelligence Enterprise Edition and Oracle Introduction to Hyperion System 9 Security and User Management(PDF ). Better Insight Through Integrated Business Intelligence. Presenter Name. Presenter EPM System deployment - Production Reporting to. EPM Applications. Oracle's EPM system also includes Oracle Business Intelligence applications. architecture, the unified and open Oracle Business Intelligence foundation.
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Fortunately, we can. We can create a custom Schema and then parse the source data to populate a Schema-based Structure.
But we will come to that later. First let us establish the Framework for our Source Connector.
We will put them into separate. Each Connector process will have only one SourceConnector instance. HttpSourceTask represents the Kafka task doing the actual data integration work. There can be one or many Tasks active for an active SourceConnector instance.
We will have some additional classes for config and for HTTP access. But first let us look at each of the two classes in more detail.
SourceConnector class SourceConnector is an abstract class that defines an interface that our HttpSourceConnector needs to adhere to.
HIGH, "Polling interval in milliseconds". INT, Importance. We also provide a description for each configuration parameter, that will be shown in the missing configuration error message.
These are called upon the creation and termination of a SourceConnector instance not Task instance. JavaMap here is an alias for java.
Map - a Java Map, which is not to be confused with the native Scala Map - that cannot be used here. The interface requires Java data structures, but that is fine - we can convert them from one to another.
By far the biggest problem here is the assignment of the connectorConfig variable - we cannot have a functional programming friendly immutable value here.
This does not look pretty in Scala. Hopefully somebody will write a Scala wrapper for this interface. We are almost done here.
The last function to be overridden is taskConfigs. This function is in charge of producing potentially different configurations for different Source Tasks. In our case, there is no reason for the Source Task configurations to differ.
In fact, our HTTP API will benefit little from parallelism, so, to keep things simple, we can assume the number of tasks always to be 1.
It is only slightly more complex than the Connector class. Just like for the Connector, there are start and stop functions to be overridden for the Task. Remember the taskConfigs function from above? This is where task configuration ends up - it is passed to the Task's start function.
Also, similarly to the Connector's start function, we parse the connection properties with HttpSourceTaskConfig, which is the same as HttpSourceConnectorConfig - configuration for Connector and Task in our case is the same.
We also set up the Http service that we are going to use in the poll function - we create an instance of the WeatherHttpService class.
Please note that start is executed only once, upon the creation of the task and not every time a record is polled from the data source. Now for the fun part We use the scalaj.
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Our WeatherHttpService implementation will have two functions: httpServiceResponse that will format the request and get data from the API sourceRecords that will parse the Schema and wrap the result within the Kafka SourceRecord class. Please note that error handling takes place in the fetchRecords function above. And pass inputString straight to the output, without any alteration.
Looks too easy, does it not? Schema parsing could be a big part of Source Connector implementation.
Let us implement a proper schema parser. Make sure you read the Confluent Developer Guide first.
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Our schema parser will be encapsulated into the WeatherSchemaParser object. KafkaSchemaParser is a trait with two type parameters - inbound and outbound data type. This indicates that the Parser receives data in String format and the result is a Kafka's Struct value.
Our schema is rather large, therefore I will skip most fields. The field names given are a reflection of the hierarchy structure in the source JSON. What we are aiming for is a flat, table-like structure - a likely Schema creation scenario.
If you are a Python developer, you will see that Scala JSON parsing is a bit more involved this might be an understatement , but, on the flipside, you can be sure about the result you are getting out of it.
Next we define case classes, into which we will be parsing the JSON content. Please note that the case class attribute names match one-to-one with the attribute names in JSON. You can have names like type and 3h, both of which are invalid value names in Scala. What do we do?
The corresponding JSON name was 3h. We map '3h' to the Scala attribute threeHours. Drums, please! WeatherSchema is the case class we created above. The Circle decode function returns a Scala Either monad, error on the Left , successful parsing result on the Right - nice and tidy. And safe. That in turn is called from the HttpSourceTask's poll function that is used to populate the Kafka topic.
Considering that Schema parsing in our simple example was optional, creating a Kafka Source Connector for us meant creating a Source Connector class, a Source Task class and a Source Service class. The guide mentions two options - either all the library dependencies can be added to the target JAR file, a.
Alternatively, the dependencies can be copied to the target folder. In that case they must all reside in the same folder, with no subfolder structure. For no particular reason, I went with the latter option.Let us implement a proper schema parser.
Unfortunately, most of the time it is not as simple as that: first of all Kafka supports concurrency, meaning there can be more than one Task busy processing Source records. Partitions Is our data source partitioned? That in turn is called from the HttpSourceTask's poll function that is used to populate the Kafka topic. I could cheat and implement a program that would consume OpenWeatherMap's records and produce records for Kafka.
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