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Amazon Kinesis vs Apache Spark: What are the differences?
Key Differences between Amazon Kinesis and Apache Spark
1. Scalability: Amazon Kinesis is designed to handle real-time streaming data with high scalability, allowing for processing very large amounts of data efficiently. On the other hand, Apache Spark is a general-purpose distributed computing system that provides scalable processing and analytics capabilities for both batch and streaming data.
2. Architecture: Amazon Kinesis is a managed service in the cloud that makes it easy to collect, process, and analyze real-time streaming data. It provides ready-to-use components, such as Kinesis Data Streams and Kinesis Data Firehose, to ingest and process data. Apache Spark, on the other hand, is a distributed computing framework that provides a unified analytics engine for big data processing. It offers a high-level API and supports various data sources, including streaming.
3. Real-Time Processing: Amazon Kinesis is optimized for real-time data processing scenarios, allowing for near real-time ingestion and analytics of streaming data. It provides features like real-time event data streaming, data transformation, and data aggregation. Apache Spark supports real-time processing as well, but it is not specifically designed for real-time streaming data. It can process both batch and streaming data, making it a more versatile option.
4. Data Processing Capabilities: Amazon Kinesis focuses on handling data ingestion and processing at scale, with capabilities like data partitioning, record buffering, and automated scaling. It provides built-in integration with other AWS services for data storage and analytics. Apache Spark, on the other hand, offers a wide range of data processing capabilities, including batch processing, stream processing, machine learning, graph processing, and SQL queries. It provides a rich set of libraries and APIs for various data processing tasks.
5. Cost and Pricing Model: Amazon Kinesis has a pay-as-you-go pricing model, where you pay for the resources you use. The pricing is based on the amount of data ingested, stored, and processed. Apache Spark is an open-source project and can be deployed on various infrastructure options, including cloud platforms and on-premises clusters. The cost of using Apache Spark depends on the infrastructure you choose and any additional services you integrate with.
6. Development and Deployment Ease: Amazon Kinesis provides a managed service that abstracts away much of the infrastructure management and setup, making it easy to get started with real-time data processing. It integrates well with other AWS services and provides a simple API for data ingestion and processing. Apache Spark requires more setup and configuration, as it is a distributed computing framework. It offers flexibility in terms of deployment options but may require more expertise to set up and manage a Spark cluster.
In Summary, Amazon Kinesis is a managed service optimized for real-time streaming data processing, providing scalability, ease of use, and integration with AWS ecosystem. Apache Spark is a versatile distributed computing framework that supports both batch and streaming data processing with a wide range of capabilities and deployment options.
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 Amazon Kinesis
- Scalable9
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 Amazon Kinesis
- Cost3
Cons of Apache Spark
- Speed4
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