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Apache Flink vs Delta Lake: What are the differences?
Introduction
Apache Flink and Delta Lake are two popular technologies used in the field of big data processing. While both of them have similar goals of improving data processing and analytics, they have distinct differences that set them apart.
Data Processing Model: Apache Flink is a distributed processing framework that supports both batch and stream processing. It provides a unified API and execution engine for processing large-scale data sets in real-time. Delta Lake, on the other hand, is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to data lakes. It enables batch and streaming workloads to read and write data in an ACID compliant manner.
Data Storage Format: Apache Flink supports various data storage formats, including Apache Parquet, Apache Avro, and more. It can read and write data in different formats based on the use case. In contrast, Delta Lake uses a specialized open file format optimized for data lakes called the Delta format. This format provides several performance optimizations, including data skipping, Z-ordering, and statistics collection, to enhance query performance.
Data Lake Functionality: Apache Flink primarily focuses on data processing and analytics, offering features like stateful processing, event time processing, and advanced windowing capabilities. It provides a rich set of APIs for building data processing pipelines. Delta Lake, on the other hand, aims to solve data reliability, scalability, and performance issues in data lakes. It brings features like transactional rights, schema enforcement, time travel, and data versioning to the table.
Data Consistency: Apache Flink ensures exactly-once processing guarantees through its checkpointing mechanism and state management. It allows for fault-tolerance and guarantees that each event is processed exactly once. Delta Lake, on the other hand, brings ACID transactions to data lakes, providing strong consistency guarantees. It ensures that concurrent writes and reads are handled correctly and consistently, even in the presence of failures.
Data Operations: Apache Flink focuses on data transformations and computations through its powerful stream and batch processing capabilities. It allows for complex data operations like joins, aggregations, and windowing operations. Delta Lake, in addition to processing operations, also provides data management capabilities. It supports operations like insert, update, delete, and merge on data stored in the Delta format, making it suitable for data lake use cases with evolving data requirements.
Integration and Ecosystem: Apache Flink has a vibrant and active community, with strong integration with popular data processing frameworks and systems like Apache Kafka, Apache Hadoop, and more. It supports seamless integration with other open-source tools in the big data ecosystem. Delta Lake is also gaining popularity and has integrations with various data processing engines, including Apache Spark, for reading and writing data in the Delta format.
In summary, Apache Flink is a powerful data processing framework that supports both batch and stream processing, while Delta Lake is a storage layer that brings ACID transactions to data lakes. Apache Flink focuses on data processing and analytics, while Delta Lake solves data reliability and performance issues in data lakes.
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 Delta Lake
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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What is Delta Lake?
What is Apache Flink?
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What companies use Apache Flink?
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