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Apache Flume vs Apache Spark: What are the differences?
Introduction
Apache Flume and Apache Spark are both popular tools used in big data processing. While they share some similarities, there are key differences between the two.
Scalability: Apache Flume is designed for high-volume data ingestion and is well-suited for streaming data from various sources. It provides reliable and fault-tolerant data collection, but it lacks advanced processing capabilities. On the other hand, Apache Spark is a general-purpose data processing engine that offers scalability not only for ingestion but also for data transformation and analytics.
Processing Paradigm: Apache Flume follows a pull-based model, where data is collected by agents and pushed to predefined destinations. It focuses on collecting and moving data efficiently. On the contrary, Apache Spark follows a push-based model, where data is processed in-memory using RDD (Resilient Distributed Datasets) or DataFrame APIs. It provides a wide range of data transformations and analytics capabilities.
Real-time Processing: Apache Flume is primarily designed for real-time data ingestion, making it suitable for streaming scenarios. It offers low-latency data collection and supports various sinks like Hadoop Distributed File System (HDFS) and Apache Kafka. In contrast, Apache Spark also supports real-time processing but provides additional batch processing capabilities. It can process both streaming and static data efficiently.
Processing Speed: Apache Flume is optimized for high-speed data collection and delivery, ensuring low-latency data ingestion. It is built to handle data streams in real-time and optimize network bandwidth. On the other hand, Apache Spark's in-memory processing capability enables fast data transformations and analytics. It can process large datasets quickly, thanks to its ability to cache data in memory.
Data Processing Capabilities: Apache Flume is primarily focused on data ingestion and movement, lacking comprehensive data processing capabilities. It provides basic filtering and routing mechanisms but does not offer advanced analytics features like machine learning or graph processing. Apache Spark, on the other hand, provides a wide range of built-in libraries for data manipulation, machine learning, graph processing, and real-time streaming analytics.
Cluster Management: Apache Flume relies on a simple master-slave model for agent coordination, making it suitable for smaller deployments. It can be easily set up and managed. In contrast, Apache Spark comes with built-in cluster management capabilities, allowing it to run on large-scale clusters. It provides fault tolerance, automatic data partitioning, and dynamic allocation of resources.
In summary, Apache Flume is a reliable data ingestion tool with a focus on real-time streaming data, while Apache Spark is a general-purpose data processing engine that offers scalability, advanced analytics, and both real-time and batch processing 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 Apache Flume
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 Apache Flume
Cons of Apache Spark
- Speed4
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What is Apache Flume?
What is Apache Spark?
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