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Amazon Redshift Spectrum vs Apache Spark: What are the differences?
Introduction
Here, we will discuss the key differences between Amazon Redshift Spectrum and Apache Spark in terms of their functionalities and capabilities.
Data Processing Paradigm: Amazon Redshift Spectrum is a fully managed data lake query service that allows querying data directly from an Amazon S3 data lake. It leverages the massive parallel processing (MPP) architecture of Amazon Redshift to optimize and execute queries efficiently on large volumes of data stored in S3. On the other hand, Apache Spark is an open-source distributed data processing framework that enables processing and analyzing large datasets in a distributed and fault-tolerant manner. It provides a unified programming model and supports various data processing operations like batch processing, streaming, machine learning, and graph processing.
Data Storage: In Redshift Spectrum, the data is stored in Amazon S3, which allows for cost-effective storage of large volumes of data without the need for managing infrastructure. Spark, on the other hand, can work with various storage systems like Hadoop Distributed File System (HDFS), Amazon S3, Apache Cassandra, and more. It offers flexibility in terms of choosing the storage system that best suits the requirements.
Query Optimization: Redshift Spectrum leverages the MPP architecture of Amazon Redshift to execute queries efficiently by distributing the workload across multiple compute resources. It optimizes query plans based on data statistics to minimize the amount of data scanned from S3. In contrast, Apache Spark optimizes query execution using a cost-based optimizer that considers factors like data distribution, join types, and transformations. It also provides support for data caching and lazy evaluation to optimize query performance.
Data Processing Capabilities: Redshift Spectrum supports SQL queries and is optimized for querying structured and semi-structured data stored in S3. It provides advanced analytical functions and supports complex aggregations, window functions, and data type conversions. Spark, on the other hand, offers a wide range of data processing capabilities, including SQL queries, batch processing, real-time stream processing, machine learning, and graph processing. It provides a rich set of APIs and libraries to perform various data processing tasks.
Processing Speed: Redshift Spectrum is designed for high-performance query execution on large datasets stored in S3. It utilizes the distributed computing power of Amazon Redshift to achieve fast query performance. Spark, on the other hand, provides in-memory processing capability, which can significantly improve the processing speed for iterative algorithms and interactive queries. Moreover, Spark allows for data parallelism and can scale horizontally by adding more worker nodes to the cluster.
Integration with Other Ecosystems: Redshift Spectrum integrates seamlessly with other services in the AWS ecosystem, such as Amazon Redshift, AWS Glue, AWS Lambda, and Amazon Athena. It enables data movement, data transformation, and serverless query processing across these services. Spark also offers integration with a wide range of data sources and systems, including Hadoop, Hive, Kafka, Cassandra, and more. It can easily interoperate with various tools and frameworks within the big data ecosystem.
In summary, Amazon Redshift Spectrum is optimized for querying data stored in S3 using SQL and leverages the MPP architecture of Amazon Redshift. Apache Spark, on the other hand, is a distributed data processing framework that supports various data processing operations and provides in-memory processing capability. Both offer different strengths and can be chosen based on the specific requirements of the use case.
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 Redshift Spectrum
- Good Performance1
- Great Documentation1
- Economical1
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 Redshift Spectrum
Cons of Apache Spark
- Speed4
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What is Amazon Redshift Spectrum?
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