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Apache Parquet vs Apache Spark: What are the differences?
Introduction
Apache Parquet and Apache Spark are both widely used technologies in the big data space. While Apache Parquet is a columnar storage file format, Apache Spark is a fast and general-purpose cluster computing system. In this comparison, we will highlight the key differences between the two.
Data Storage Mechanism: Parquet is a columnar storage format that stores data in columns, making it efficient for analytical workloads. On the other hand, Spark is a distributed computing framework that allows for processing large datasets across a cluster of machines. While Parquet focuses on efficient storage, Spark provides a powerful framework for distributed data processing.
File Format vs. Computing System: Parquet primarily focuses on how data is stored on disk, providing efficient compression and encoding techniques for columnar data. In contrast, Spark is a computing system that provides APIs for processing structured, semi-structured, and unstructured data. Spark can work with different file formats, including Parquet, among others.
Optimization Techniques: Parquet employs various optimization techniques such as predicate pushdown, column pruning, and dictionary encoding to achieve better query performance. It leverages the metadata stored within each file to skip unnecessary data while reading. Spark, on the other hand, offers a range of optimization techniques such as query optimization, data partitioning, and caching to optimize data processing and improve performance.
Use Cases: Parquet is commonly used in scenarios where efficient columnar storage and analytical query processing is required. It is widely used in big data analytics platforms and data warehousing systems. On the other hand, Spark is suitable for large-scale data processing tasks, including data ingestion, ETL (Extract, Transform, Load), machine learning, and real-time streaming analytics.
Language Support: Parquet provides support for multiple programming languages like Java, C++, Python, and R, enabling developers to work with Parquet files in their preferred programming language. Spark, being a distributed computing framework, offers APIs in various languages, including Scala, Java, Python, and R. It provides a unified interface for data processing across different programming languages.
Integration with Ecosystem: Parquet is designed to work well with a variety of big data processing frameworks such as Apache Hadoop, Apache Hive, Apache Pig, and Apache Impala. It seamlessly integrates with these technologies to provide efficient data storage and processing capabilities. On the other hand, Spark integrates with a wide range of big data ecosystem tools and libraries, making it highly versatile and suitable for complex data workflows.
In summary, Apache Parquet is a columnar storage file format focused on efficient data storage and retrieval, primarily used in analytics and data warehousing scenarios. Apache Spark, on the other hand, is a flexible and powerful distributed computing framework that can process large datasets across a cluster of machines, supporting a wide range of use cases from ETL to machine learning.
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 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 Apache Parquet
Cons of Apache Spark
- Speed4
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What is Apache Parquet?
What is Apache Spark?
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