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Hadoop VS memcached

Compare Hadoop VS memcached and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing

memcached logo memcached

High-performance, distributed memory object caching system
  • Hadoop Landing page
    Landing page //
    2021-09-17
  • memcached Landing page
    Landing page //
    2023-07-23

Hadoop features and specs

  • Scalability
    Hadoop can easily scale from a single server to thousands of machines, each offering local computation and storage.
  • Cost-Effective
    It utilizes a distributed infrastructure, allowing you to use low-cost commodity hardware to store and process large datasets.
  • Fault Tolerance
    Hadoop automatically maintains multiple copies of all data and can automatically recover data on failure of nodes, ensuring high availability.
  • Flexibility
    It can process a wide variety of structured and unstructured data, including logs, images, audio, video, and more.
  • Parallel Processing
    Hadoop's MapReduce framework enables the parallel processing of large datasets across a distributed cluster.
  • Community Support
    As an Apache project, Hadoop has robust community support and a vast ecosystem of related tools and extensions.

Possible disadvantages of Hadoop

  • Complexity
    Setting up, maintaining, and tuning a Hadoop cluster can be complex and often requires specialized knowledge.
  • Overhead
    The MapReduce model can introduce additional overhead, particularly for tasks that require low-latency processing.
  • Security
    While improvements have been made, Hadoop's security model is considered less mature compared to some other data processing systems.
  • Hardware Requirements
    Though it can run on commodity hardware, Hadoop can still require significant computational and storage resources for larger datasets.
  • Lack of Real-Time Processing
    Hadoop is mainly designed for batch processing and is not well-suited for real-time data analytics, which can be a limitation for certain applications.
  • Data Integrity
    Distributed systems face challenges in maintaining data integrity and consistency, and Hadoop is no exception.

memcached features and specs

  • High Performance
    Memcached is incredibly fast and efficient at caching data in memory, enabling quick data retrieval and reducing the load on databases. Its in-memory nature significantly reduces latency.
  • Scalability
    Memcached can be easily scaled horizontally by adding more nodes to the caching cluster. This allows it to handle increased loads and large datasets without performance degradation.
  • Simplicity
    Memcached has a simple design and API, making it easy to implement and use. Developers can quickly integrate it into their applications without a steep learning curve.
  • Open Source
    Memcached is free and open-source software, which means it can be used and modified without any licensing fees. This makes it a cost-effective solution for caching.
  • Language Agnostic
    Memcached supports multiple programming languages through various client libraries, making it versatile and suitable for use in diverse tech stacks.

Possible disadvantages of memcached

  • Data Volatility
    Memcached stores data in RAM, so all cached data is lost if the server is restarted or crashes. This makes it unsuitable for storing critical or persistent data.
  • Limited Data Types
    Memcached primarily supports simple key-value pairs. It lacks the rich data types and more complex structures supported by some other caching solutions like Redis.
  • No Persistence
    Memcached does not offer any data persistence features. It cannot save data to disk, so all information is ephemeral and will be lost on system reset.
  • Size Limitation
    Memcached has a memory limit for each instance, thus, large-scale applications may need to manage multiple instances and ensure data is properly distributed.
  • Security
    Memcached does not provide built-in security features such as authentication or encryption. This can be a concern in environments where data privacy and security are critical.

Analysis of Hadoop

Overall verdict

  • Hadoop is a robust and powerful data processing platform that is well-suited for organizations that need to manage and analyze large-scale data. Its resilience, scalability, and open-source nature make it a popular choice for big data solutions. However, it may not be the best fit for all use cases, especially those requiring real-time processing or where ease of use is a priority.

Why this product is good

  • Hadoop is renowned for its ability to store and process large datasets using a distributed computing model. It is scalable, cost-effective, and efficient in handling massive volumes of data across clusters of computers. Its ecosystem includes a wide range of tools and technologies like HDFS, MapReduce, YARN, and Hive that enhance data processing and analysis capabilities.

Recommended for

  • Organizations dealing with vast amounts of data needing efficient batch processing.
  • Businesses that require scalable storage solutions to manage their data growth.
  • Companies interested in leveraging a diverse ecosystem of data processing tools and technologies.
  • Technical teams that have the expertise to manage and optimize complex distributed systems.

Analysis of memcached

Overall verdict

  • Memcached is a solid choice for applications that require distributed caching to improve scalability and performance. It's particularly beneficial for web applications handling high traffic and needing fast, efficient data retrieval.

Why this product is good

  • Memcached is considered good due to its high performance, simplicity, and effectiveness in enhancing the speed of dynamic web applications by alleviating database load. It operates by storing data in memory, which allows for quick retrieval of cached objects and reduces the need to frequently query the database. Its distributed architecture, open-source nature, and widespread language support make it a flexible and reliable choice for caching.

Recommended for

  • Web developers looking to improve the speed and scalability of applications.
  • Organizations needing a simple and effective caching solution to reduce database load.
  • Projects that demand quick deployment of a caching solution with support across multiple programming languages.

Hadoop videos

+ Add

What is Big Data and Hadoop?

More videos:

  • Review - Product Ratings on Customer Reviews Using HADOOP.
  • Tutorial - Hadoop Tutorial For Beginners | Hadoop Ecosystem Explained in 20 min! - Frank Kane

memcached videos

+ Add

Course Preview: Using Memcached and Varnish to Speed Up Your Linux Web App

Category Popularity

0-100% (relative to Hadoop and memcached)

Hadoop

memcached

Databases
33 33%
Databases
67% 67
Big Data
100 100%
Big Data
0% 0
NoSQL Databases
10 10%
NoSQL Databases
90% 90
Relational Databases
100 100%
Relational Databases
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Hadoop and memcached

Hadoop Reviews

A List of The 16 Best ETL Tools And Why To Choose Them
Companies considering Hadoop should be aware of its costs. A significant portion of the cost of implementing Hadoop comes from the computing power required for processing and the expertise needed to maintain Hadoop ETL, rather than the tools or storage themselves.
Source: www.datacamp.com
16 Top Big Data Analytics Tools You Should Know About
Hadoop is an Apache open-source framework. Written in Java, Hadoop is an ecosystem of components that are primarily used to store, process, and analyze big data. The USP of Hadoop is it enables multiple types of analytic workloads to run on the same data, at the same time, and on a massive scale on industry-standard hardware.
Source: www.analytixlabs.co.in
5 Best-Performing Tools that Build Real-Time Data Pipeline
Hadoop is an open-source framework that allows to store and process big data in a distributed environment 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. Rather than relying on hardware to deliver high-availability, the library itself is...
Source: www.analyticsinsight.net

memcached Reviews

Redis vs. KeyDB vs. Dragonfly vs. Skytable | Hacker News
Quick ask: I donรขย€ย™t see รขย€ยœsomeรขย€ย of the other offering out there like MemCachedรขย€ยฆ what was the criteria used to select these? I donรขย€ย™t see any source of how the test where run, specs of the systems, how the DB where set up, etc. Would be very valuable to have in order to attempt to re-validate these test on our own platform. I also came back and saw some of your updates...
Source: news.ycombinator.com
Memcached vs Redis - More Different Than You Would Expect
So knowing how the difference between Redis and memcached in-memory usage, lets see what this means. Memcached slabs once assigned never change their size. This means it is possible to poison your memcached cluster and really waste memory. If you load your empty memcached cluster with lots of 1 MB items, then all of the slabs will be allocated to that size. Adding a 80 KB...
Source: engineering.kablamo.com.au
Redis vs. Memcached: In-Memory Data Storageย Systems
Memcached itself does not support distributed mode. You can only achieve the distributed storage of Memcached on the client side through distributed algorithms such as Consistent Hash. The figure below demonstrates the distributed storage implementation schema of Memcached. Before the client side sends data to the Memcached cluster, it first calculates the target node of the...
Source: medium.com
Why Redis beats Memcached for caching
Both Memcached and Redis are mature and hugely popular open source projects. Memcached was originally developed by Brad Fitzpatrick in 2003 for the LiveJournal website. Since then, Memcached has been rewritten in C (the original implementation was in Perl) and put in the public domain, where it has become a cornerstone of modern Web applications. Current development of...
Source: www.infoworld.com

Social recommendations and mentions

memcached might be a bit more popular than Hadoop. We know about 37 links to it since March 2021 and only 26 links to Hadoop. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Hadoop mentions (26)

  • JuiceFS 1.3 Beta 2 Integrates Apache Ranger for Fine-Grained Access Control
    To simplify โ€‹โ€‹fine-grained permission managementโ€‹โ€‹ and enable centralized โ€‹โ€‹web-based administrationโ€‹โ€‹, JuiceFS now supports โ€‹โ€‹Apache Rangerโ€‹โ€‹, a widely adopted security framework in the Hadoop ecosystem. - Source: dev.to / 4 months ago
  • Apache Hadoop: Open Source Business Model, Funding, and Community
    This post provides an inโ€depth look at Apache Hadoop, a transformative distributed computing framework built on an open source business model. We explore its history, innovative open funding strategies, the influence of the Apache License 2.0, and the vibrant community that drives its continuous evolution. Additionally, we examine practical use cases, upcoming challenges in scaling big data processing, and future... - Source: dev.to / 5 months ago
  • What is Apache Kafka? The Open Source Business Model, Funding, and Community
    Modular Integration: Thanks to its modular approach, Kafka integrates seamlessly with other systems including container orchestration platforms like Kubernetes and third-party tools such as Apache Hadoop. - Source: dev.to / 5 months ago
  • India Open Source Development: Harnessing Collaborative Innovation for Global Impact
    Over the years, Indian developers have played increasingly vital roles in many international projects. From contributions to frameworks such as Kubernetes and Apache Hadoop to the emergence of homegrown platforms like OpenStack India, India has steadily carved out a global reputation as a powerhouse of open source talent. - Source: dev.to / 5 months ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 7 months ago
View more

memcached mentions (37)

  • Redis vs. Memcached: How to Choose Your NoSQL Champion
    Memcached has a single, focused goal: to be a high-performance, distributed, in-memory object caching system. It stores all data in RAM, which means reads and writes are incredibly fast. But its main weakness is just as clear: data is completely lost when the service restarts, as it offers no persistence. Its data model is a simple key-value store, limited to basic get, set, and delete operations. - Source: dev.to / about 2 months ago
  • MySQL Performance Tuning Techniques
    Memcached can help when lightning-fast performance is needed. These tools store frequently accessed data, such as session details, API responses, or product prices, in RAM. This reduces the laid on your primary database, so you can deliver microsecond response times. - Source: dev.to / 7 months ago
  • 10 Best Practices for API Rate Limiting in 2025
    In-memory tools like Redis or Memcached for fast Data retrieval. - Source: dev.to / 8 months ago
  • Outgrowing Postgres: Handling increased user concurrency
    A caching layer using popular in-memory databases like Redis or Memcached can go a long way in addressing Postgres connection overload issues by being able to handle a much larger concurrent request load. Adding a cache lets you serve frequent reads from memory instead, taking pressure off Postgres. - Source: dev.to / 8 months ago
  • API Caching: Techniques for Better Performance
    Memcached โ€” Free and well-known for its simplicity, Memcached is a distributed and powerful memory object caching system. It uses key-value pairs to store small data chunks from database calls, API calls, and page rendering. It is available on Windows. Strings are the only supported data type. Its client-server architecture distributes the cache logic, with half of the logic implemented on the server and the other... - Source: dev.to / 12 months ago
View more

What are some alternatives?

When comparing Hadoop and memcached, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.

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