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Apache Spark vs Kapacitor: What are the differences?
Apache Spark and Kapacitor are both powerful tools used for data processing and analysis. However, they have some distinct differences that set them apart from each other.
Nature: Apache Spark is a distributed computing system that is primarily used for big data processing and analytics, while Kapacitor is a real-time streaming data processing engine specifically designed to work with time series data.
Use Case: Apache Spark is commonly used for batch processing and iterative algorithms on large datasets, whereas Kapacitor is ideal for handling real-time streaming data and implementing time-based alerting and anomaly detection.
Architecture: Apache Spark follows a master-slave architecture with a central coordinator (Spark Master) that allocates resources and schedules tasks on worker nodes (Spark Workers). In contrast, Kapacitor uses a more streamlined pipeline architecture where the data flows sequentially through various stages of processing.
Data Processing Model: Apache Spark uses an in-memory processing model for faster data processing, making it suitable for complex analytics and machine learning tasks. Kapacitor, on the other hand, focuses on processing data as it streams in real-time, enabling quick responses to changing data patterns.
Scalability: Apache Spark is known for its scalability and can efficiently handle large datasets by distributing processing tasks across a cluster of machines. While Kapacitor can also scale horizontally, it is more optimized for handling high-velocity data streams with low latency requirements.
Integration with Other Systems: Apache Spark provides extensive integration with various data sources and libraries in the Hadoop ecosystem, making it a versatile tool for building data pipelines. In comparison, Kapacitor is tightly integrated with InfluxDB, a time series database, and is well-suited for monitoring and analyzing time-based metrics.
In Summary, Apache Spark and Kapacitor have distinct differences in their nature, use case, architecture, data processing model, scalability, and integration with other systems.
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 Kapacitor
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 Kapacitor
Cons of Apache Spark
- Speed4
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What is Kapacitor?
What is Apache Spark?
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