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Azure Storage vs Apache Flink: What are the differences?
What is Azure Storage? Reliable, economical cloud storage for data big and small. Azure Storage provides the flexibility to store and retrieve large amounts of unstructured data, such as documents and media files with Azure Blobs; structured nosql based data with Azure Tables; reliable messages with Azure Queues, and use SMB based Azure Files for migrating on-premises applications to the cloud.
What is Apache Flink? Fast and reliable large-scale data processing engine. Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
Azure Storage and Apache Flink are primarily classified as "Cloud Storage" and "Big Data" tools respectively.
Some of the features offered by Azure Storage are:
- Blobs, Tables, Queues, and Files
- Highly scalable
- Durable & highly available
On the other hand, Apache Flink provides the following key features:
- Hybrid batch/streaming runtime that supports batch processing and data streaming programs.
- Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.
- Flexible and expressive windowing semantics for data stream programs
"All-in-one storage solution" is the top reason why over 18 developers like Azure Storage, while over 6 developers mention "Unified batch and stream processing" as the leading cause for choosing Apache Flink.
Apache Flink is an open source tool with 9.35K GitHub stars and 5K GitHub forks. Here's a link to Apache Flink's open source repository on GitHub.
According to the StackShare community, Azure Storage has a broader approval, being mentioned in 84 company stacks & 44 developers stacks; compared to Apache Flink, which is listed in 20 company stacks and 22 developer stacks.
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 Azure Storage
- All-in-one storage solution24
- Pay only for data used regardless of disk size15
- Shared drive mapping9
- Cost-effective2
- Cheapest hot and cloud storage2
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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Cons of Azure Storage
- Direct support is not provided by Azure storage2
Cons of Apache Flink
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