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Apache NiFi vs Apache Spark: What are the differences?
Introduction
Apache NiFi and Apache Spark are both open-source data processing frameworks used for big data analysis and processing. While they both offer powerful features, there are several key differences between the two.
Data Flow vs Data Processing: One of the major differences between Apache NiFi and Apache Spark is their primary focus. NiFi focuses on data flow management, enabling users to easily design and manage data pipelines with a visually intuitive interface. On the other hand, Spark focuses on data processing and analytics, providing a distributed computing framework that can handle complex computations efficiently.
Real-Time vs Batch Processing: Apache NiFi is designed for real-time data processing, allowing users to collect, transform, and route data in real-time. It provides capabilities for handling streaming data and supports continuous data ingestion. In contrast, Apache Spark is primarily designed for batch processing, although it also offers support for real-time processing using its Structured Streaming API.
Ease of Use: Apache NiFi aims to provide a user-friendly interface for data engineers and non-technical users to easily design and manage data flows. It offers a drag-and-drop GUI and a powerful visual representation of data flows, making it easy to understand and maintain complex data pipelines. Apache Spark, on the other hand, has a steeper learning curve and requires knowledge of programming languages like Scala, Java, or Python.
Processing Speed: Apache Spark is known for its high processing speed and in-memory computing capabilities. It utilizes distributed computing across a cluster of machines, allowing it to process large datasets in parallel. NiFi, on the other hand, may not offer the same level of speed and efficiency for large-scale data processing, as it focuses more on data flow management and real-time ingestion.
Data Transformation and Integration: NiFi provides extensive support for data transformation and integration, with a wide range of processors and connectors for various data sources and systems. It allows users to perform tasks such as enrichment, filtering, and joining of data streams. Apache Spark also offers data transformation capabilities but primarily focuses on processing and analytics, with a rich library of functions for data manipulation.
Scalability and Fault Tolerance: Both Apache NiFi and Apache Spark are designed to scale horizontally and handle large volumes of data. However, Spark's underlying architecture and execution model make it more suitable for big data processing and analytics at scale. Spark also provides built-in fault tolerance mechanisms, ensuring that computations continue even in the event of failures.
In summary, Apache NiFi is a data flow management tool that excels in real-time data processing and provides an easy-to-use interface, while Apache Spark is a distributed computing framework primarily focused on data processing and analytics, offering high performance and scalability.
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 NiFi
- Visual Data Flows using Directed Acyclic Graphs (DAGs)17
- Free (Open Source)8
- Simple-to-use7
- Scalable horizontally as well as vertically5
- Reactive with back-pressure5
- Fast prototyping4
- Bi-directional channels3
- End-to-end security between all nodes3
- Built-in graphical user interface2
- Can handle messages up to gigabytes in size2
- Data provenance2
- Lots of documentation1
- Hbase support1
- Support for custom Processor in Java1
- Hive support1
- Kudu support1
- Slack integration1
- Lot of articles1
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 NiFi
- HA support is not full fledge2
- Memory-intensive2
- Kkk1
Cons of Apache Spark
- Speed4
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