Apache Flink vs Apache Parquet

Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Parquet

Stacks97

Followers190

Votes0

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.

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Advice on Apache Flink, Apache Parquet

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

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Detailed Comparison

Apache Flink	Apache Parquet
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.	It is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.
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;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance	Columnar storage format;Type-specific encoding; Pig integration; Cascading integration; Crunch integration; Apache Arrow integration; Apache Scrooge integration;Adaptive dictionary encoding; Predicate pushdown; Column stats
Statistics
GitHub Stars 25.4K	GitHub Stars -
GitHub Forks 13.7K	GitHub Forks -
Stacks 534	Stacks 97
Followers 879	Followers 190
Votes 38	Votes 0
Pros & Cons
Pros 16 Unified batch and stream processing 8 Out-of-the box connector to kinesis,s3,hdfs 8 Easy to use streaming apis 4 Open Source 2 Low latency	No community feedback yet
Integrations
YARN Hadoop Hadoop HBase Kafka	Hadoop Java Apache Impala Apache Thrift Apache Hive Pig

What are some alternatives to Apache Flink, Apache Parquet?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Apache Flink vs Apache Parquet: What are the differences?

Introduction

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.

Apache Flink vs Apache Parquet

Overview

Apache Flink vs Apache Parquet: What are the differences?