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memcached VS Google Cloud Dataflow

Compare memcached VS Google Cloud Dataflow and see what are their differences

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memcached logo memcached

High-performance, distributed memory object caching system

Google Cloud Dataflow logo Google Cloud Dataflow

Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
  • memcached Landing page
    Landing page //
    2023-07-23
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03

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.

Google Cloud Dataflow features and specs

  • Scalability
    Google Cloud Dataflow can automatically scale up or down depending on your data processing needs, handling massive datasets with ease.
  • Fully Managed
    Dataflow is a fully managed service, which means you don't have to worry about managing the underlying infrastructure.
  • Unified Programming Model
    It provides a single programming model for both batch and streaming data processing using Apache Beam, simplifying the development process.
  • Integration
    Seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Bigtable.
  • Real-time Analytics
    Supports real-time data processing, enabling quicker insights and facilitating faster decision-making.
  • Cost Efficiency
    Pay-as-you-go pricing model ensures you only pay for resources you actually use, which can be cost-effective.
  • Global Availability
    Cloud Dataflow is available globally, which allows for regionalized data processing.
  • Fault Tolerance
    Built-in fault tolerance mechanisms help ensure uninterrupted data processing.

Possible disadvantages of Google Cloud Dataflow

  • Steep Learning Curve
    The complexity of using Apache Beam and understanding its model can be challenging for beginners.
  • Debugging Difficulties
    Debugging data processing pipelines can be complex and time-consuming, especially for large-scale data flows.
  • Cost Management
    While it can be cost-efficient, the costs can rise quickly if not monitored properly, particularly with real-time data processing.
  • Vendor Lock-in
    Using Google Cloud Dataflow can lead to vendor lock-in, making it challenging to migrate to another cloud provider.
  • Limited Support for Non-Google Services
    While it integrates well within Google Cloud, support for non-Google services may not be as robust.
  • Latency
    There can be some latency in data processing, especially when dealing with high volumes of data.
  • Complexity in Pipeline Design
    Designing pipelines to be efficient and cost-effective can be complex, requiring significant expertise.

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.

Analysis of Google Cloud Dataflow

Overall verdict

  • Google Cloud Dataflow is a strong choice for users who need a flexible and scalable data processing solution. It is particularly well-suited for real-time and large-scale data processing tasks. However, the best choice ultimately depends on your specific requirements, including cost considerations, existing infrastructure, and technical skills.

Why this product is good

  • Google Cloud Dataflow is a fully managed service for stream and batch data processing. It is based on the Apache Beam model, allowing for a unified data processing approach. It is highly scalable, offers robust integration with other Google Cloud services, and provides powerful data processing capabilities. Its serverless nature means that users do not have to worry about infrastructure management, and it dynamically allocates resources based on the data processing needs.

Recommended for

  • Organizations that require real-time data processing.
  • Projects involving complex data transformations.
  • Users who already utilize Google Cloud Platform and need seamless integration with other Google services.
  • Developers and data engineers familiar with Apache Beam or those willing to learn.

memcached videos

+ Add

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

Google Cloud Dataflow videos

+ Add

Introduction to Google Cloud Dataflow - Course Introduction

More videos:

  • Review - Serverless data processing with Google Cloud Dataflow (Google Cloud Next '17)
  • Review - Apache Beam and Google Cloud Dataflow

Category Popularity

0-100% (relative to memcached and Google Cloud Dataflow)

memcached

Google Cloud Dataflow

Databases
100 100%
Databases
0% 0
Big Data
0 0%
Big Data
100% 100
NoSQL Databases
100 100%
NoSQL Databases
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

Share your experience with using memcached and Google Cloud Dataflow. For example, how are they different and which one is better?
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Reviews

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

memcached Reviews

Redis vs. KeyDB vs. Dragonfly vs. Skytable | Hacker News
Quick ask: I don’t see “some” of the other offering out there like MemCached… what was the criteria used to select these? I don’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 Storage 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

Google Cloud Dataflow Reviews

Top 8 Apache Airflow Alternatives in 2024
Google Cloud Dataflow is highly focused on real-time streaming data and batch data processing from web resources, IoT devices, etc. Data gets cleansed and filtered as Dataflow implements Apache Beam to simplify large-scale data processing. Such prepared data is ready for analysis for Google BigQuery or other analytics tools for prediction, personalization, and other purposes.
Source: blog.skyvia.com

Social recommendations and mentions

Based on our record, memcached should be more popular than Google Cloud Dataflow. It has been mentiond 36 times since March 2021. 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.

memcached mentions (36)

  • 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 / 3 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 / 4 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 / 4 months ago
  • API Caching: Techniques for Better Performance
    Memcached — 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 / 8 months ago
  • story of upgrading rails 5.x to 7.x
    The app depends on several packages to run, so I need to install them locally too. I used a combination of brew and orbstack / docker for installing packages. Some dependencies for this project are redis, mongodb and memcache. - Source: dev.to / 9 months ago
View more

Google Cloud Dataflow mentions (14)

  • How do you implement CDC in your organization
    Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 2 years ago
  • Here’s a playlist of 7 hours of music I use to focus when I’m coding/developing. Post yours as well if you also have one!
    This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 2 years ago
  • How are view/listen counts rolled up on something like Spotify/YouTube?
    I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 2 years ago
  • Best way to export several GCP datasets to AWS?
    You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 2 years ago
  • Why we don’t use Spark
    It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 3 years ago
View more

What are some alternatives?

When comparing memcached and Google Cloud Dataflow, you can also consider the following products

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

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Aerospike - Aerospike is a high-performing NoSQL database supporting high transaction volumes with low latency.

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

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