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Apache Spark vs Samza: What are the differences?
Introduction
Apache Spark and Samza are both powerful distributed computing frameworks commonly used in big data processing. They differ in various aspects that cater to different use cases and requirements.
Processing Model: Apache Spark is primarily designed for in-memory processing of data, making it suitable for iterative algorithms and interactive data analysis. On the other hand, Samza focuses on stream processing, enabling real-time processing of large volumes of data with low latency.
Fault Tolerance: Spark provides fault tolerance through resilient distributed datasets (RDDs), offering fault recovery at the granularity of RDDs. Samza, on the other hand, provides fault tolerance through checkpoints, allowing it to resume processing from the last checkpoint in case of failures.
Batch vs. Streaming: While Spark can handle both batch and streaming data processing, it is more optimized for batch processing compared to streaming. Samza, on the other hand, is specifically designed for stream processing, making it more efficient for real-time data processing requirements.
Programming Language Support: Apache Spark supports multiple programming languages like Scala, Java, Python, and R, making it versatile for developers with different preferences. Samza is heavily focused on Java and allows for seamless integration with existing Java-based applications and infrastructure.
State Management: Spark does not provide built-in state management capabilities for stream processing, requiring users to integrate external systems for state handling. In contrast, Samza comes with built-in state management capabilities, making it easier for users to manage and store state information within the framework.
Use Cases: Apache Spark is suitable for a wide range of use cases, including machine learning, interactive data analysis, and batch processing, making it a versatile choice for various data processing requirements. Samza, on the other hand, is more specialized for stream processing use cases that require low-latency real-time processing of data streams.
In Summary, Apache Spark and Samza differ in their processing models, fault tolerance mechanisms, focus on batch vs. streaming processing, programming language support, state management capabilities, and specific use cases they cater to in the realm of distributed computing.
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 Samza
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 Samza
Cons of Apache Spark
- Speed4