3 posts tagged with "cross organization data collaboration"

The third part of this article series is an activity log. Motivated by the learnings from last time, I stated implementing a Kafka Source component and a Kafka Sink component for the Apache Spark platform in Apache Wayang. In our previous article we shared the results of the work on the frist Apache Kafka integration using the Java Platform.

Let's see how it goes this time with Apache Spark.

The goal of this implementation​

We want to process data from Apache Kafka topics, which are hosted on Confluent cloud. In our example scenario, data is available in multiple different clusters, in different regions and owned by different organizations. Each organization uses the "stream sharing" feature provided by Confluent cloud.

This way, the operator of our central processing job has been granted appropriate permissions. The plaftorm provided the necessary configuration properties, including access coordinates and credentials in the name of the topic owner to us.

The following illustration has already been introduced in part one of the blog series, but for clarity we repeat it here.

Today, we focus on Job 4 in the image. We implement a program which uses data federation based on multiple sources. Each source allows us to read the data from that particular topic so that we can process it in a different governance context. In this example it is a public processing context, in which data from multiple private processing contexts are used together.

This use case is already prepared for high processing loads We can utilize the scalability capabilities of Apache Spark or simply use a Java program for initial tests of the solution. Switching between both is done in one line of code in Apache Wayang.

Again, we start with a WayangContext, as shown by examples in the Wayang code repository.

WayangContext wayangContext = new WayangContext().with(Spark.basicPlugin());

We simply switched the backend system towards Apache Spark by using the WayangContext with Spark.basicPlugin(). The JavaPlanBuilder and all other logic of our example job won't be touched.

In order to make this working we will now implement the Mappings and the Operators for the Apache Spark platform module.

Implementation of Input- and Output Operators​

We reuse the Kafka Source and Kafka Sink components which have been created for the JavaKafkaSource and JavaKafkaSink. Hence we work with Wayang's Java API.

Level 1 – Wayang execution plan with abstract operators

Since the JavaPlanBuilder already exposes the function for selecting a Kafka topic as source and the DataQuantaBuilder class exposes the writeKafkaTopic function we can move on quickly.

Remember, in this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below.

Level 2 – Wiring between Platform Abstraction and Implementation

As in the case with the Java Platform, in the second layer we build a bridge between the WayangContext and the PlanBuilders, which work together with DataQuanta and the DataQuantaBuilder.

We must provide the mapping between the abstract components and the specific implementations in this layer.

Therefore, the mappings package in project wayang-platforms/wayang-spark has a class Mappings in which our KafkaTopicSinkMapping and KafkaTopicSourceMapping will be registered.

Again, these classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively).

While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. In this case this leads to an Apache Spark job, running on a Spark cluster which is set up by the Apache Wayang framework using the logical components of the execution plan, and the Apache Spark configuration provided at runtime.

A mapping links an operator implementation to the abstraction used in an execution plan. We define two new mappings for our purpose, namely KafkaTopicSourceMapping, and KafkaTopicSinkMapping, both could be reused from last round.

For the Spark platform we simply replace the occurences of JavaPlatform with SparkPlatform.

Furthermore, we create an implementation of the SparkKafkaTopicSource and SparkKafkaTopicSink.

Layer 3 – Input/Output Connector Layer

Let's quickly recap, how does Apache Spark interacts with Apache Kafka?

There is already an integration which gives us a DataSet using the Spark SQL framework. For Spark Streaming, there is also a Kafka integration using the SparkSession's readStream() function. Kafka client properties are provided as key value pairs k and v by using the option( k, v ) function. For writing into a topic, we can use the writeStream() function. But from a first look, it seems to be not the best fit.

Another approach is possible. We can use simple RDDs to process data previously consumed from Apache Kafka. This is a more low-level approach compared to using Datasets with Spark Structured Streaming, and it typically involves using the Kafka RDD API provided by Spark.

This approach is less common with newer versions of Spark, as Structured Streaming provides a higher-level abstraction that simplifies stream processing. However, we might need that approach for the integration with Apache Wayang.

For now, we will focus on the lower level approach and plan to consume data from Kafka using a Kafka client, and then we parallelize the records in an RDD.

This allows us to reuse KafkaTopicSource and KafkaTopicSink classes we built last time. Those were made specifically for a simple non parallel Java program, using one Consumer and one Producer.

The selected approach does not yet fully take advantage from Spark's parallelism at load time. For higher loads and especially for streaming processing we would have to investigate another approache, using a SparkStreamingContext, but this is out of scope for now.

Since we can't reuse the JavaKafkaTopicSource and JavaKafkaTopicSink we rather implement SparkKafkaTopicSource and SparkKafkaTopicSink based on given SparkTextFileSource and SparkTextFileSink which both cary all needed RDD specific logic.

Summary​

As expected, the integration of Apache Spark with Apache Wayang was no magic, thanks to a fluent API design and a well structured architecture of Apache Wayang. We could easily follow the pattern we have worked out in the previous exercise.

But a bunch of much more interesting work will follow next. More testing, more serialization schemes, and Kafka Schema Registry support should follow, and full parallelization as well.

The code has been submitted to the Apache Wayang repository.

Outlook​

The next part of the article series will cover the real world example as described in image 1. We will show how analysts and developers can use the Apache Kafka integration for Apache Wayang to solve cross organizational collaboration issues. Therefore, we will bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case.

In the second part of the article series we describe the implementation of the Kafka Source and Kafka Sink component for Apache Wayang. We look into the “Read- and Write-Path” for our data items, called DataQuanta.

Apache Wayang’s Read & Write Path for Kafka topics​

To describe the read and write paths for data in the context of the created Apache Wayang code snippet, the primary classes and interfaces we need to understand are as follows:

WayangContext: This class is essential for initializing the Wayang processing environment. It allows you to configure the execution environment and register plugins that define which platforms Wayang can use for data processing tasks, such as Java.basicPlugin() for local Java execution.

JavaPlanBuilder: This class is used to build and define the data processing pipeline (or plan) in Wayang. It provides a fluent API to specify the operations to be performed on the data, from reading the input to processing it and writing the output.

Read Path​

The read path describes how data is ingested from a source into the Wayang processing pipeline:

Reading from Kafka Topic: The method readKafkaTopic(topicName) is used to ingest data from a specified Kafka topic. This is the starting point of the data processing pipeline, where topicName represents the name of the Kafka topic from which data is read.

Data Tokenization and Preparation: Once the data is read from Kafka, it undergoes several transformations such as Splitting, Filtering, and Mapping. What follows are the procedures known as Reducing, Grouping, Co-Grouping, and Counting.

Write Path​

Writing to Kafka Topic: The final step in the pipeline involves writing the processed data back to a Kafka topic using .writeKafkaTopic(...). This method takes parameters that specify the target Kafka topic, a serialization function to format the data as strings, and additional configuration for load profile estimation, which optimizes the writing process.

This read-write path provides a comprehensive flow of data from ingestion from Kafka, through various processing steps, and finally back to Kafka, showcasing a full cycle of data processing within Apache Wayang's abstracted environment and is implemented in our example program shown in listing 1.

Implementation of Input- and Output Operators​

The next section shows how a new pair of operators can be implemented to extend Apache Wayang’s capabilities on the input and output side. We created the Kafka Source and Kafka Sink components so that our cross organizational data collaboration scenario can be implemented using data streaming infrastructure.

Level 1 – Wayang execution plan with abstract operators

The implementation of our Kafka Source and Kafka Sink components for Apache Wayang requires new methods and classes on three layers. First of all in the API package. Here we use the JavaPlanBuilder to expose the function for selecting a Kafka topic as the source to be used by client. The class JavaPlanBuilder in package org.apache.wayang.api in the project wayang-api/wayang-api-scala-java exposes our new functionality to our external client. An instance of the JavaPlanBuilder is used to define the data processing pipeline. We use its readKafkaTopic() which specifies the source Kafka topic to read from, and for the write path we use the writeKafkaTopic() method. Both Methods do only trigger activities in the background.

For the output side, we use the DataQuantaBuilder class, which offers an implementation of the writeKafkaTopic function. This function is designed to send processed data, referred to as DataQuanta, to a specified Kafka topic. Essentially, it marks the final step in a data processing sequence constructed using the Apache Wayang framework.

In the DataQuanta class we implemented the methods writeKafkaTopic and writeKafkaTopicJava which use the KafkaTopicSink class. In this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below.

Level 2 – Wiring between Platform Abstraction and Implementation

The second layer builds the bridge between the WayangContext and PlanBuilders which work together with DataQuanta and the DataQuantaBuilder.

Also, the mapping between the abstract components and the specific implementations are defined in this layer.

Therefore, the mappings package has a class Mappings in which all relevant input and output operators are listed. We use it to register the KafkaSourceMapping and a KafkaSinkMapping for the particular platform, Java in our case. These classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively). While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. In our case this leads to a Java program running on a JVM which is set up by the Apache Wayang framework using the logical components of the execution plan.

Those mappings link the real implementation of our operators the ones used in an execution plan. The JavaKafkaTopicSource and the JavaKafkaTopicSink extend the KafkaTopicSource and KafkaTopicSink so that the lower level implementation of those classes become available within Wayang’s Java Platform context.

In this layer, the KafkaConsumer class and the KafkaProducer class are used, but both are configured and instantiated in the next layer underneath. All this is done in the project wayang-plarforms/wayang-java.

Layer 3 – Input/Output Connector Layer

The KafkaTopicSource and KafkaTopicSink classes build the third layer of our implementation. Both are implemented in Java programming language. In this layer, the real Kafka-Client logic is defined. Details about consumer and producers, client configuration, and schema handling have to be handled here.

Summary​

Both classes in the third layer implement the Kafka client logic which is needed by the Wayang-execution plan when external data flows should be established. The layer above handles the mapping of the components at startup time. All this wiring is needed to keep Wayang open and flexible so that multiple external systems can be used in a variety of combinations and using multiple target platforms in combinations.

Outlook​

The next part of the article series will cover the creation of an Kafka Source and Sink component for the Apache Spark platform, which allows our use case to scale. Finally, in part four we bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case.

Intro​

This article is the first of a four part series about federated data analysis using Apache Wayang. The first article starts with an introduction of a typical data colaboration scenario which will emerge in our digital future.

In part two and three we will share a summary of our Apache Kafka client implementation for Apache Wayang. We started with the Java Platform (part 2) and the Apache Spark implementation follows (W.I.P.) in part three.

The use case behind this work is an imaginary data collaboration scenario. We see this example and the demand for a solution already in many places. For us this is motivation enough to propose a solution. This would also allow us to do more local data processing, and businesses can stop moving data around the world, but rather care about data locality while they expose and share specific information to others by using data federation. This reduces complexity of data management and cost dramatically.

For this purpose, we illustrate a cross organizational data sharing scenario from the finance sector soon. This analysis pattern will also be relevant in the context of data analysis along supply chains, another typical example where data from many stakeholder together is needed but never managed in one place, for good reasons.

Data federation can help us to unlock the hidden value of all those isolated data lakes.

A cross organizational data sharing scenario​

Our goal is the implementation of a cross organization decentralized data processing scenario, in which protected local data should be processed in combination with public data from public sources in a collaborative manner. Instead of copying all data into a central data lake or a central data platform we decided to use federated analytics. Apache Wayang is the tool we work with. In our case, the public data is hosted on publicly available websites or data pods. A client can use the HTTP(S) protocol to read the data which is given in a well defined format. For simplicity we decided to use CSV format. When we look into the data of each participant we have a different perspective.

Our processing procedure should calculate a particular metric on the local data of each participant. An example of such a metric is the average spending of all users on a particular product category per month. This can vary from partner to partner, hence, we want to be able to calculate a peer-group comparison so that each partner can see its own metric compared with a global average calculated from contributions by all partners. Such a process requires global averaging and local averaging. And due to governance constraints, we can’t bring all raw data together in one place.

Instead, we want to use Apache Wayang for this purpose. We simplify the procedure and split it into two phases. Phase one is the process, which allows each participant to calculate the local metrics. This requires only local data. The second phase requires data from all collaborating partners. The monthly sum and counter values per partner and category are needed in one place by all other parties. Hence, the algorithm of the first phase stores the local results locally, and the contributions to the global results in an externally accessible Kafka topic. We assume this is done by each of the partners.

Now we have a scenario, in which an Apache Wayang process must be able to read data from multiple Apache Kafka topics from multiple Apache Kafka clusters but finally writes into a single Kafka topic, which then can be accessed by all the participating clients.

The illustration shows the data flows in such a scenario. Jobs with red border are executed by the participants in isolation within their own data processing environments. But they share some of the data, using publicly accessible Kafka topics, marked by A. Job 4 is the Apache Wayang job in our focus: here we intent to read data from 3 different source systems, and write results into a fourth system (marked as B), which can be accesses by all participants again.

With this in mind we want to implement an Apache Wayang application which implements the illustrated Job 4. Since as of today, there is now KafkaSource and KafkaSink available in Apache Wayang, an implementation of both will be our first step. Our assumption is, that in the beginning, there won’t be much data.

Apache Spark is not required to cope with the load, but we expect, that in the future, a single Java application would not be able to handle our workload. Hence, we want to utilize the Apache Wayang abstraction over multiple processing platforms, starting with Java. Later, we want to switch to Apache Spark.