Celery vs Hadoop

Overview

Hadoop

Stacks2.7K

Followers2.3K

Votes56

GitHub Stars15.3K

Forks9.1K

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

Celery vs Hadoop: What are the differences?

Introduction:

Key Differences between Celery and Hadoop:

Architecture: Celery is a distributed task queue system while Hadoop is a distributed storage and processing framework. Celery is focused on offloading work to workers while Hadoop is designed for storing and analyzing large datasets.
Use Case: Celery is commonly used for running asynchronous tasks in web applications and processing time-consuming tasks in the background. On the other hand, Hadoop is ideal for processing and analyzing big data, making it suitable for data-intensive applications.
Fault Tolerance: Hadoop provides fault tolerance through data replication and job restart capabilities, ensuring that no data is lost during computation. Celery, on the other hand, relies on the message broker for task queuing and job distribution but does not have built-in fault tolerance mechanisms.
Scalability: Celery can be scaled horizontally by adding more workers to handle an increasing number of tasks. Hadoop, on the other hand, can scale both vertically by adding more resources to a single node and horizontally by adding more nodes to the cluster for increased processing power.
Programming Language: Celery is typically used with Python as the primary programming language for defining tasks and executing them. Hadoop, on the other hand, supports multiple programming languages such as Java, Python, and Scala for developing MapReduce programs and processing big data.
Real-time Processing: Celery excels in real-time processing and handling of short-lived tasks with low-latency requirements, making it suitable for interactive applications. Hadoop, on the other hand, is more geared towards batch processing of large datasets, which may not be suitable for real-time processing applications.

In Summary, the key differences between Celery and Hadoop lie in their architecture, use cases, fault tolerance mechanisms, scalability options, programming language support, and real-time processing capabilities.

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CLI (Node.js)

Manual

Detailed Comparison

Hadoop	Celery
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.	Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.
Statistics
GitHub Stars 15.3K	GitHub Stars 27.5K
GitHub Forks 9.1K	GitHub Forks 4.9K
Stacks 2.7K	Stacks 1.7K
Followers 2.3K	Followers 1.6K
Votes 56	Votes 280
Pros & Cons
Pros 39 Great ecosystem 11 One stack to rule them all 4 Great load balancer 1 Java syntax 1 Amazon aws	Pros 99 Task queue 63 Python integration 40 Django integration 30 Scheduled Task 19 Publish/subsribe Cons 4 Sometimes loses tasks 1 Depends on broker

What are some alternatives to Hadoop, Celery?

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.

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

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.

Related Comparisons

Celery vs Hadoop: What are the differences?

Introduction:

Key Differences between Celery and Hadoop:

Architecture: Celery is a distributed task queue system while Hadoop is a distributed storage and processing framework. Celery is focused on offloading work to workers while Hadoop is designed for storing and analyzing large datasets.
Use Case: Celery is commonly used for running asynchronous tasks in web applications and processing time-consuming tasks in the background. On the other hand, Hadoop is ideal for processing and analyzing big data, making it suitable for data-intensive applications.
Fault Tolerance: Hadoop provides fault tolerance through data replication and job restart capabilities, ensuring that no data is lost during computation. Celery, on the other hand, relies on the message broker for task queuing and job distribution but does not have built-in fault tolerance mechanisms.
Scalability: Celery can be scaled horizontally by adding more workers to handle an increasing number of tasks. Hadoop, on the other hand, can scale both vertically by adding more resources to a single node and horizontally by adding more nodes to the cluster for increased processing power.
Programming Language: Celery is typically used with Python as the primary programming language for defining tasks and executing them. Hadoop, on the other hand, supports multiple programming languages such as Java, Python, and Scala for developing MapReduce programs and processing big data.
Real-time Processing: Celery excels in real-time processing and handling of short-lived tasks with low-latency requirements, making it suitable for interactive applications. Hadoop, on the other hand, is more geared towards batch processing of large datasets, which may not be suitable for real-time processing applications.

Celery vs Hadoop

Overview