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Amazon EMR vs Apache Flink: What are the differences?
Key Differences between Amazon EMR and Apache Flink
Amazon EMR and Apache Flink are both popular big data processing tools, but they have key differences that set them apart.
Data Processing Model: Amazon EMR is based on the Hadoop framework and is primarily geared towards batch processing. It supports traditional MapReduce jobs, Hive, and Pig for data processing. On the other hand, Apache Flink is a stream processing engine that is designed for low-latency and real-time processing. It supports both batch and stream processing, making it suitable for a wider range of use cases.
Fault Tolerance: Amazon EMR uses Hadoop Distributed File System (HDFS) for fault tolerance. It replicates data across multiple nodes to ensure reliability. In contrast, Apache Flink uses a distributed streaming dataflow engine with exactly-once processing semantics. It provides built-in mechanisms for fault tolerance and state management, making it more resilient to failures.
Event Time Processing: Apache Flink has built-in support for event time processing, which allows processing of events based on their actual occurrence time rather than arrival time. This is crucial for accurate analysis of time-sensitive data, such as IoT sensor readings or financial transactions. Amazon EMR does not have native support for event time processing, although it can be implemented using custom code.
Data Streaming Capabilities: While Amazon EMR can process streaming data using technologies like Spark Streaming or Storm, it is not a pure streaming processing engine like Apache Flink. Flink provides advanced features for event-driven stream processing, including event time handling, windowing, and complex event processing. It excels in use cases where real-time processing and near real-time insights are required.
Integration with Ecosystem: Amazon EMR seamlessly integrates with the entire AWS ecosystem, including services like S3 for storage, CloudWatch for monitoring, and IAM for access management. It also provides integrations with popular data processing frameworks like Apache Spark and Apache HBase. Apache Flink, on the other hand, has integrations with various storage systems and other big data tools but may require additional configuration and setup.
Development Paradigm: Amazon EMR allows developers to work with popular programming languages like Java, Python, and Scala, using frameworks like MapReduce, Hive, Pig, and Spark. Apache Flink, on the contrary, is a Java-based streaming engine that adopts a more unified and consistent programming model. Flink's APIs support both stream and batch processing and provide a higher level of abstraction for developers.
In summary, the key differences between Amazon EMR and Apache Flink lie in their data processing models (batch vs. stream processing), fault tolerance mechanisms, event time processing capabilities, integration with ecosystems, data streaming capabilities, and development paradigms.
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 EMR
- On demand processing power15
- Don't need to maintain Hadoop Cluster yourself12
- Hadoop Tools7
- Elastic6
- Backed by Amazon4
- Flexible3
- Economic - pay as you go, easy to use CLI and SDKs3
- Don't need a dedicated Ops group2
- Massive data handling1
- Great support1
Pros of Apache Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
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