Apache Flink vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flink vs MongoDB: What are the differences?

Introduction

In this article, we will discuss the key differences between Apache Flink and MongoDB.

Scalability: Apache Flink is designed to handle large-scale data processing and can scale horizontally across multiple machines, allowing for efficient processing of big data. On the other hand, MongoDB is a NoSQL database that can also scale horizontally by adding more servers to the cluster, allowing for increased storage and handling of high volumes of data.
Data Model: Apache Flink is primarily a stream processing and batch processing framework that operates on data streams or datasets in a distributed manner. It provides APIs for processing and analyzing data in real-time and enables complex event processing. MongoDB, on the other hand, is a document-oriented database that stores and retrieves data in flexible, JSON-like documents. It supports a rich set of querying and indexing capabilities for efficient data retrieval.
Processing Paradigm: Apache Flink follows a continuous streaming model and supports event-time and processing-time semantics for stream processing. It provides advanced windowing concepts, stateful processing, and event-time processing of data streams. MongoDB, on the other hand, is focused on document-oriented data storage and retrieval and does not provide built-in support for streaming data processing. It is primarily used for querying and manipulating documents in a database.
Fault Tolerance and Data Replication: Apache Flink is designed to provide fault tolerance by retaining the state of operations in a distributed manner across multiple machines. It also supports data replication for high availability and fault tolerance. MongoDB, on the other hand, provides automatic sharding and replication for data distribution and fault tolerance within a cluster. It ensures data availability and durability in case of failures.
Querying and Indexing: Apache Flink provides powerful querying capabilities with its SQL-like queries and expressive APIs for data processing. It supports windowing functions, aggregations, joins, and other operations on data streams and datasets. MongoDB, on the other hand, provides a flexible query language that allows for querying and indexing of documents based on various criteria. It supports a wide range of query operators, indexes, and aggregation pipelines for efficient data retrieval.
Ecosystem and Integration: Apache Flink integrates well with other big data tools and frameworks like Apache Hadoop, Apache Spark, and Apache Kafka. It also provides connectors for various data sources and sinks, allowing seamless integration with existing systems. MongoDB, on the other hand, has a rich ecosystem with support for various programming languages, frameworks, and data connectors. It can be easily integrated with popular tools and frameworks like Node.js, Python, and Java, making it suitable for a wide range of applications.

In Summary, Apache Flink is a distributed stream processing and batch processing framework designed for big data processing, while MongoDB is a document-oriented NoSQL database that provides flexible data storage and retrieval capabilities.

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Advice on MongoDB, Apache Flink

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

MongoDB	Apache Flink
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.	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.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	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
Statistics
GitHub Stars 27.7K	GitHub Stars 25.4K
GitHub Forks 5.7K	GitHub Forks 13.7K
Stacks 96.6K	Stacks 534
Followers 82.0K	Followers 879
Votes 4.1K	Votes 38
Pros & Cons
Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language	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
Integrations
No integrations available	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to MongoDB, Apache Flink?

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.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Apache Flink vs MongoDB: What are the differences?

Introduction

In this article, we will discuss the key differences between Apache Flink and MongoDB.

Scalability: Apache Flink is designed to handle large-scale data processing and can scale horizontally across multiple machines, allowing for efficient processing of big data. On the other hand, MongoDB is a NoSQL database that can also scale horizontally by adding more servers to the cluster, allowing for increased storage and handling of high volumes of data.
Data Model: Apache Flink is primarily a stream processing and batch processing framework that operates on data streams or datasets in a distributed manner. It provides APIs for processing and analyzing data in real-time and enables complex event processing. MongoDB, on the other hand, is a document-oriented database that stores and retrieves data in flexible, JSON-like documents. It supports a rich set of querying and indexing capabilities for efficient data retrieval.
Processing Paradigm: Apache Flink follows a continuous streaming model and supports event-time and processing-time semantics for stream processing. It provides advanced windowing concepts, stateful processing, and event-time processing of data streams. MongoDB, on the other hand, is focused on document-oriented data storage and retrieval and does not provide built-in support for streaming data processing. It is primarily used for querying and manipulating documents in a database.
Fault Tolerance and Data Replication: Apache Flink is designed to provide fault tolerance by retaining the state of operations in a distributed manner across multiple machines. It also supports data replication for high availability and fault tolerance. MongoDB, on the other hand, provides automatic sharding and replication for data distribution and fault tolerance within a cluster. It ensures data availability and durability in case of failures.
Querying and Indexing: Apache Flink provides powerful querying capabilities with its SQL-like queries and expressive APIs for data processing. It supports windowing functions, aggregations, joins, and other operations on data streams and datasets. MongoDB, on the other hand, provides a flexible query language that allows for querying and indexing of documents based on various criteria. It supports a wide range of query operators, indexes, and aggregation pipelines for efficient data retrieval.
Ecosystem and Integration: Apache Flink integrates well with other big data tools and frameworks like Apache Hadoop, Apache Spark, and Apache Kafka. It also provides connectors for various data sources and sinks, allowing seamless integration with existing systems. MongoDB, on the other hand, has a rich ecosystem with support for various programming languages, frameworks, and data connectors. It can be easily integrated with popular tools and frameworks like Node.js, Python, and Java, making it suitable for a wide range of applications.

Apache Flink vs MongoDB

Overview

Apache Flink vs MongoDB: What are the differences?