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Apache Spark vs Minio: What are the differences?
Introduction:
Apache Spark and Minio are both popular tools used in the field of big data processing. While Apache Spark is a powerful open-source data processing engine, Minio is an object storage server that is compatible with Amazon S3. Despite their similarities in the big data realm, there are several key differences between these two technologies that are worth exploring.
Architecture and Functionality: Apache Spark is a distributed computing system that provides support for in-memory processing, enabling fast and efficient data analysis. It offers a variety of APIs in multiple languages and allows users to perform data operations such as filtering, aggregating, and transforming. On the other hand, Minio is an object storage server that focuses on providing scalable and highly available storage for unstructured data. It can handle large volumes of data and ensures durability and accessibility through its distributed architecture.
Data Processing Model: Apache Spark follows a data processing model known as Resilient Distributed Datasets (RDD), which allows for fault-tolerant and parallel computation on distributed data. RDDs are immutable and can be transformed and acted upon using various operations. Minio, in contrast, does not define a specific data processing model. It primarily serves as a storage layer and does not provide built-in data processing capabilities like Spark. However, it can be integrated with other tools such as Apache Hadoop or Apache Spark to process data stored in Minio.
Data Storage: Apache Spark does not have its own storage system and relies on other storage mediums such as distributed file systems or databases to store data. It can leverage data from various sources and perform computations on it. On the other hand, Minio provides its own storage system and is designed to be a standalone object storage server. It is compatible with the Amazon S3 API, allowing users to seamlessly migrate their S3-based applications to Minio without any code modifications.
Scalability and Performance: Apache Spark is known for its scalability, thanks to its distributed nature and ability to efficiently utilize available computing resources. It can handle large datasets and perform computations in parallel, leading to faster processing times. Minio, being an object storage server, is also highly scalable and can handle large volumes of data. Its distributed architecture and advanced caching mechanisms ensure good performance and low latency access to stored data.
Community and Ecosystem: Apache Spark has a large and vibrant community supporting it, with regular updates, bug fixes, and new features being contributed by developers worldwide. It has a rich ecosystem of libraries and tools built around it, making it a versatile choice for big data processing. Minio, although lesser-known compared to Spark, also has an active community and offers integration with other popular tools like Kubernetes, Docker, and Prometheus.
Use Cases: Apache Spark is widely used for big data processing and analytics tasks, such as machine learning, real-time stream processing, and batch processing. It provides a unified platform for these use cases and is used by organizations across various industries. Minio, on the other hand, is primarily used for scalable object storage and is often used as a replacement for Amazon S3. It is commonly used in scenarios where efficient storage and retrieval of unstructured data is required.
In summary, Apache Spark is a distributed data processing engine with in-memory capabilities, whereas Minio is an object storage server focused on providing scalable storage for unstructured data. Spark has its own data processing model and supports various APIs, while Minio is primarily a storage layer that can be integrated with other tools. Spark has a larger community and ecosystem, whereas Minio excels in scalable object storage 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"
Minio is a free and open source object storage system. It can be self-hosted and is S3 compatible. During the early stage it would save cost and allow us to move to a different object storage when we scale up. It is also fast and easy to set up. This is very useful during development since it can be run on localhost.
Pros of Minio
- Store and Serve Resumes & Job Description PDF, Backups10
- S3 Compatible8
- Simple4
- Open Source4
- Encryption and Tamper-Proof3
- Lambda Compute3
- Private Cloud Storage2
- Pluggable Storage Backend2
- Scalable2
- Data Protection2
- Highly Available2
- 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 Minio
- Deletion of huge buckets is not possible3
Cons of Apache Spark
- Speed4
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