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Apache Flink vs Apache Parquet: What are the differences?
Introduction
Apache Flink and Apache Parquet are two popular technologies used in big data processing and analytics. While Apache Flink is a stream processing framework, Apache Parquet is a columnar storage file format. Despite their differences, both technologies play a significant role in the big data ecosystem. In this document, we will discuss the key differences between Apache Flink and Apache Parquet.
Processing Paradigm: Apache Flink is a stream processing framework that focuses on real-time data processing and low-latency analytics. It supports both batch and stream processing models, making it suitable for both real-time and batch processing tasks. On the other hand, Apache Parquet is a columnar storage format that aims at efficient data compression and query performance on large-scale datasets.
Data Storage: Apache Flink does not provide its own storage format. It can read and write data from various storage systems like Hadoop Distributed File System (HDFS), Apache Kafka, and more. It can also integrate with Apache Parquet for improved data storage and query optimization. Apache Parquet, on the other hand, is a self-contained columnar storage format that stores data in a column-wise fashion, allowing for efficient compression and fast data retrieval.
Query Optimization: Apache Flink focuses on optimizing data processing and stream analytics. The framework employs various techniques like pipelining, query optimization, and lazy evaluation to achieve high-performance data processing. On the other hand, Apache Parquet focuses more on efficient columnar storage and query performance. It leverages predicate pushdown and column pruning techniques to reduce data retrieval and processing overhead.
Data Compression: Apache Flink does not provide built-in data compression mechanisms as it primarily focuses on data processing and analytics. However, it can leverage external compression libraries like Snappy or GZIP to compress the data before storing or transferring it. Apache Parquet, on the other hand, is designed to provide efficient data compression through its columnar storage format. It uses various compression algorithms like Snappy, GZIP, and LZO to achieve high compression ratios and reduce storage costs.
Supported Use Cases: Apache Flink is well-suited for real-time streaming analytics, event-driven applications, and complex event processing. It provides stateful computations, fault-tolerance, and event-time processing capabilities out of the box. Apache Parquet, on the other hand, is more focused on efficient data storage and query performance. It is commonly used for big data processing, data warehousing, and analytics applications where read performance and space optimization are crucial.
Ecosystem Integration: Apache Flink has a rich ecosystem integration with various big data technologies, including Apache Kafka, Apache Hadoop, Apache Cassandra, and more. It serves as a processing engine for these systems, allowing developers to process and analyze data in real-time. Apache Parquet, on the other hand, is a file format that can be used with various big data processing frameworks like Apache Spark, Apache Hive, and Apache Impala for efficient data storage and query execution.
In summary, Apache Flink is a stream processing framework that focuses on real-time data processing and analytics. It provides various capabilities for stream processing, event-time processing, and fault-tolerance. Apache Parquet, on the other hand, is a columnar storage format that aims at efficient data compression and query performance. It provides column-wise data storage, data compression, and integration with other big data processing frameworks.
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 Apache Parquet
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
