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Apache Spark vs Google Cloud Dataflow: What are the differences?
Introduction
Apache Spark and Google Cloud Dataflow are both popular distributed data processing frameworks used for big data analytics and processing. While they share some similarities, there are several key differences between them that set them apart in terms of their capabilities and functionalities. In this article, we will explore six key differences between Apache Spark and Google Cloud Dataflow.
Programming Language Support: Apache Spark offers support for multiple programming languages such as Scala, Java, Python, and R. This allows developers to choose the language they are most comfortable with for writing Spark applications. On the other hand, Google Cloud Dataflow primarily supports Java and Python for writing data processing pipelines.
Data Processing Model: Apache Spark uses a batch processing model, where data is processed in batches. It also provides support for real-time streaming processing through its Spark Streaming module. In contrast, Google Cloud Dataflow is designed specifically for real-time stream processing, making it well-suited for applications that require low-latency processing and near real-time insights.
Managed Service vs Open-Source: Apache Spark is an open-source framework, which means it can be deployed on various platforms and environments. It gives users more control over the deployment and management of their Spark clusters. On the other hand, Google Cloud Dataflow is a fully managed service offered by Google Cloud Platform. This means that Google takes care of the infrastructure and management of the Dataflow pipeline, allowing users to focus more on building their data processing logic.
Integration with Cloud Services: Being a part of the Google Cloud Platform, Google Cloud Dataflow integrates seamlessly with other Google Cloud services such as BigQuery, Pub/Sub, Datastore, etc. This makes it easy to build end-to-end data pipelines using these services. In comparison, while Apache Spark can also integrate with various cloud services, the level of integration and ease of use may depend on the specific cloud provider and the libraries/driver support available.
Windowing and Triggers: Google Cloud Dataflow provides more advanced windowing and triggering capabilities compared to Apache Spark. It offers flexible windowing options such as fixed windows, sliding windows, and session windows, along with various types of triggers. This allows users to define more complex window-based computations and handle late data more efficiently.
Data Parallelism: Apache Spark uses RDDs (Resilient Distributed Datasets) as the fundamental data structure, which provides a flexible and powerful abstraction for distributed data processing. RDDs allow for efficient data parallelism by splitting the data into partitions and executing computations on them in parallel. In contrast, Google Cloud Dataflow uses the concept of PCollections, which provides similar parallel processing capabilities but with a more unified and simplified programming model.
In summary, Apache Spark offers support for multiple programming languages, supports both batch and real-time processing, and gives users more control over deployment and management. Google Cloud Dataflow is a fully managed service that is designed specifically for real-time stream processing, integrates well with Google Cloud services, and offers more advanced windowing and triggering capabilities.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Pros of Google Cloud Dataflow
- Unified batch and stream processing7
- Autoscaling5
- Fully managed4
- Throughput Transparency3
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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Cons of Google Cloud Dataflow
Cons of Apache Spark
- Speed4
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