Need advice about which tool to choose?Ask the StackShare community!
Apache Spark vs Spring Batch: What are the differences?
Key Differences between Apache Spark and Spring Batch
Apache Spark and Spring Batch are both popular frameworks used for Big Data processing and batch processing, but they have some key differences that set them apart.
Processing Paradigm: Apache Spark is a distributed computing framework that offers in-memory processing capabilities, allowing for faster data processing, while Spring Batch is a lightweight framework that focuses on batch processing and is ideal for handling large volumes of data.
Data Processing Model: Spark operates on a data processing model called Resilient Distributed Datasets (RDD), which allows for parallel processing and fault tolerance. Spring Batch, on the other hand, follows a step-by-step approach to process data in chunks or batches, making it suitable for sequential processing.
Programming Languages: Apache Spark supports multiple programming languages such as Scala, Java, Python, and R, giving developers the flexibility to choose their preferred language. Spring Batch primarily uses Java as its programming language.
Integration with Ecosystem: Apache Spark integrates well with other Big Data tools and frameworks like Hadoop, Hive, and HBase, making it a comprehensive solution for Big Data processing. Spring Batch, on the other hand, is part of the larger Spring ecosystem, integrating seamlessly with other Spring framework components.
Real-time Vs Batch Processing: While both frameworks can handle batch processing, Spark also provides real-time stream processing capabilities through its structured streaming API. Spring Batch focuses primarily on batch processing and does not provide native support for real-time processing.
Data Manipulation: Apache Spark provides a wide range of built-in libraries and APIs for data manipulation and analysis, including SQL queries, machine learning algorithms, and graph processing. Spring Batch, on the other hand, focuses on data import/export, transformation, and business logic, without the extensive data manipulation capabilities offered by Spark.
In Summary, Apache Spark is a distributed computing framework that excels in in-memory processing and real-time stream processing, with extensive data manipulation capabilities, while Spring Batch is a lightweight framework specialized in batch processing for large volumes of data.
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 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
Pros of Spring Batch
Sign up to add or upvote prosMake informed product decisions
Cons of Apache Spark
- Speed4
Cons of Spring Batch
Sign up to add or upvote consMake informed product decisions
What is Apache Spark?
What is Spring Batch?
Need advice about which tool to choose?Ask the StackShare community!
What companies use Apache Spark?
What companies use Spring Batch?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Apache Spark?
What tools integrate with Spring Batch?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+4
+7+8
+4
+11
+6
+16
+37