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Amazon RDS for Aurora vs Apache Spark: What are the differences?
Introduction
Amazon RDS for Aurora and Apache Spark are two popular technologies used in data processing and analytics. While both offer solutions for handling large-scale data, there are several key differences between them.
Data Storage: Amazon RDS for Aurora uses a distributed, fault-tolerant storage system that replicates data across multiple Availability Zones for high durability and availability. On the other hand, Apache Spark does not have its own storage system but can integrate with various data storage systems like Hadoop Distributed File System (HDFS) or Amazon S3.
Processing Paradigm: Amazon RDS for Aurora is a managed relational database service, which means it follows a traditional query-based processing paradigm commonly used in SQL databases. In contrast, Apache Spark is a distributed computing system that utilizes in-memory processing and follows a more batch or streaming-oriented processing paradigm.
Scalability: Amazon RDS for Aurora provides automatic scaling capabilities, allowing it to handle a growing workload by adjusting the compute and storage resources. Apache Spark, on the other hand, is designed to scale horizontally by adding more worker nodes to the cluster, enabling it to handle large-scale data processing tasks.
Processing Speed: Due to its in-memory processing capabilities, Apache Spark can perform faster data processing operations compared to Amazon RDS for Aurora, which relies on disk-based storage. This makes Spark suitable for real-time or near-real-time processing scenarios where high-speed data analysis is required.
Data Processing Capabilities: Apache Spark offers a wide range of data processing capabilities, including batch processing, interactive queries, machine learning, and streaming analytics. Amazon RDS for Aurora primarily focuses on traditional SQL-based query processing, although it also supports some advanced analytic features like window functions and common table expressions.
Use Cases: Amazon RDS for Aurora is well-suited for applications that require a highly available and scalable relational database, such as e-commerce platforms or content management systems. Apache Spark, on the other hand, is commonly used in big data analytics, machine learning, and real-time data processing scenarios where speed and scalability are critical.
In summary, the key differences between Amazon RDS for Aurora and Apache Spark lie in their data storage, processing paradigms, scalability, processing speed, data processing capabilities, and use cases.
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 Aurora
- MySQL compatibility14
- Better performance12
- Easy read scalability10
- Speed9
- Low latency read replica7
- High IOPS cost2
- Good cost performance1
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 Aurora
- Vendor locking2
- Rigid schema1
Cons of Apache Spark
- Speed4
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What is Amazon Aurora?
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