Cassandra vs Memcached

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Cassandra

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Memcached

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470
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Cassandra vs Memcached: What are the differences?

Developers describe Cassandra as "A partitioned row store. Rows are organized into tables with a required primary key". 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. On the other hand, Memcached is detailed as "High-performance, distributed memory object caching system". 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.

Cassandra and Memcached can be primarily classified as "Databases" tools.

"Distributed", "High performance" and "High availability" are the key factors why developers consider Cassandra; whereas "Fast object cache", "High-performance" and "Stable" are the primary reasons why Memcached is favored.

Cassandra and Memcached are both open source tools. Memcached with 8.99K GitHub stars and 2.6K forks on GitHub appears to be more popular than Cassandra with 5.27K GitHub stars and 2.35K GitHub forks.

Facebook, Instagram, and Dropbox are some of the popular companies that use Memcached, whereas Cassandra is used by Uber Technologies, Facebook, and Spotify. Memcached has a broader approval, being mentioned in 755 company stacks & 267 developers stacks; compared to Cassandra, which is listed in 342 company stacks and 239 developer stacks.

Advice on Cassandra and Memcached
Umair Iftikhar
Technical Architect at ERP Studio · | 3 upvotes · 296.5K views
Needs advice
on
CassandraCassandraDruidDruid
and
TimescaleDBTimescaleDB

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

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Replies (1)
Recommends
MongoDBMongoDB

Hi Umair, Did you try MongoDB. We are using MongoDB on a production environment and collecting data from devices like your scenario. We have a MongoDB cluster with three replicas. Data from devices are being written to the master node and real-time dashboard UI is using the secondary nodes for read operations. With this setup write operations are not affected by read operations too.

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Vinay Mehta
Needs advice
on
CassandraCassandra
and
ScyllaDBScyllaDB

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

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Replies (4)
Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 102.5K views
Recommends
CassandraCassandra

i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra

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Recommends
ScyllaDBScyllaDB

Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring

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Recommends
ScyllaDBScyllaDB

By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.

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Alex Peake
Recommends
CassandraCassandra

Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.

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Decisions about Cassandra and Memcached
Micha Mailänder
CEO & Co-Founder at Dechea · | 14 upvotes · 47.8K views

Fauna is a serverless database where you store data as JSON. Also, you have build in a HTTP GraphQL interface with a full authentication & authorization layer. That means you can skip your Backend and call it directly from the Frontend. With the power, that you can write data transformation function within Fauna with her own language called FQL, we're getting a blazing fast application.

Also, Fauna takes care about scaling and backups (All data are sharded on three different locations on the globe). That means we can fully focus on writing business logic and don't have to worry anymore about infrastructure.

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Pros of Cassandra
Pros of Memcached
  • 116
    Distributed
  • 97
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 52
    Replication
  • 26
    Reliable
  • 26
    Multi datacenter deployments
  • 9
    OLTP
  • 8
    Schema optional
  • 8
    Open source
  • 2
    Workload separation (via MDC)
  • 1
    Fast
  • 138
    Fast object cache
  • 128
    High-performance
  • 90
    Stable
  • 65
    Mature
  • 33
    Distributed caching system
  • 11
    Improved response time and throughput
  • 3
    Great for caching HTML
  • 2
    Putta

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Cons of Cassandra
Cons of Memcached
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
  • 2
    Only caches simple types

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What is 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.

What is 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.

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Jobs that mention Cassandra and Memcached as a desired skillset
CBRE
United States of America Texas Richardson
CBRE
United States of America Texas Richardson
CBRE
United States of America Texas Richardson
CBRE
United States of America Texas Richardson
Pinterest
San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US;
What companies use Cassandra?
What companies use Memcached?
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What tools integrate with Cassandra?
What tools integrate with Memcached?

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Blog Posts

Dec 22 2020 at 9:26PM

Pinterest

Amazon EC2MemcachedC lang+4
10
2483
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AppSignal

RedisRubyKafka+9
15
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GitHubDockerReact+17
35
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GitHubPythonReact+42
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GitHubPythonNode.js+47
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70936
What are some alternatives to Cassandra and Memcached?
HBase
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
Google Cloud Bigtable
Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
Hadoop
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.
Redis
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
Couchbase
Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
See all alternatives