Alternatives to NCache logo

Alternatives to NCache

Redis, Memcached, Hazelcast, Couchbase, and MongoDB are the most popular alternatives and competitors to NCache.
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What is NCache and what are its top alternatives?

NCache is an open source distributed cache for .NET & .NET Core (Apache 2.0) by Alachisoft. NCache provides an extremely fast and linearly scalable distributed cache that caches application data and reduces expensive database trips.
NCache is a tool in the In-Memory Databases category of a tech stack.

Top Alternatives to NCache

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

  • Memcached

    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. ...

  • Hazelcast

    Hazelcast

    With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution. ...

  • Couchbase

    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. ...

  • MongoDB

    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. ...

  • Aerospike

    Aerospike

    Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees. ...

  • SAP HANA

    SAP HANA

    It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk. ...

  • Apache Ignite

    Apache Ignite

    It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale ...

NCache alternatives & related posts

Redis logo

Redis

42.9K
32K
3.9K
An in-memory database that persists on disk
42.9K
32K
+ 1
3.9K
PROS OF REDIS
  • 875
    Performance
  • 535
    Super fast
  • 511
    Ease of use
  • 441
    In-memory cache
  • 321
    Advanced key-value cache
  • 190
    Open source
  • 179
    Easy to deploy
  • 163
    Stable
  • 153
    Free
  • 120
    Fast
  • 40
    High-Performance
  • 39
    High Availability
  • 34
    Data Structures
  • 32
    Very Scalable
  • 23
    Replication
  • 20
    Great community
  • 19
    Pub/Sub
  • 17
    "NoSQL" key-value data store
  • 14
    Hashes
  • 12
    Sets
  • 10
    Sorted Sets
  • 9
    Lists
  • 8
    BSD licensed
  • 8
    NoSQL
  • 7
    Async replication
  • 7
    Integrates super easy with Sidekiq for Rails background
  • 7
    Bitmaps
  • 6
    Open Source
  • 6
    Keys with a limited time-to-live
  • 5
    Strings
  • 5
    Lua scripting
  • 4
    Awesomeness for Free!
  • 4
    Hyperloglogs
  • 3
    outstanding performance
  • 3
    Runs server side LUA
  • 3
    Networked
  • 3
    LRU eviction of keys
  • 3
    Written in ANSI C
  • 3
    Feature Rich
  • 3
    Transactions
  • 2
    Data structure server
  • 2
    Performance & ease of use
  • 1
    Existing Laravel Integration
  • 1
    Automatic failover
  • 1
    Easy to use
  • 1
    Object [key/value] size each 500 MB
  • 1
    Simple
  • 1
    Channels concept
  • 1
    Scalable
  • 1
    Temporarily kept on disk
  • 1
    Dont save data if no subscribers are found
  • 0
    Jk
CONS OF REDIS
  • 14
    Cannot query objects directly
  • 2
    No secondary indexes for non-numeric data types
  • 1
    No WAL

related Redis posts

Robert Zuber

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

See more

I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

See more
Memcached logo

Memcached

5.7K
4K
469
High-performance, distributed memory object caching system
5.7K
4K
+ 1
469
PROS OF MEMCACHED
  • 137
    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
CONS OF MEMCACHED
  • 2
    Only caches simple types

related Memcached posts

Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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Kir Shatrov
Engineering Lead at Shopify · | 15 upvotes · 576.6K views

At Shopify, over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, Memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.

As we grew into hundreds of shards and pods, it became clear that we needed a solution to orchestrate those deployments. Today, we use Docker, Kubernetes, and Google Kubernetes Engine to make it easy to bootstrap resources for new Shopify Pods.

See more
Hazelcast logo

Hazelcast

242
364
56
Clustering and highly scalable data distribution platform for Java
242
364
+ 1
56
PROS OF HAZELCAST
  • 10
    High Availibility
  • 6
    Distributed Locking
  • 5
    Distributed compute
  • 5
    Sharding
  • 4
    Load balancing
  • 3
    Sql query support in cluster wide
  • 3
    Map-reduce functionality
  • 3
    Written in java. runs on jvm
  • 3
    Publish-subscribe
  • 2
    Performance
  • 2
    Simple-to-use
  • 2
    Multiple client language support
  • 2
    Rest interface
  • 2
    Optimis locking for map
  • 1
    Super Fast
  • 1
    Admin Interface (Management Center)
  • 1
    Better Documentation
  • 1
    Easy to use
CONS OF HAZELCAST
  • 3
    License needed for SSL

related Hazelcast posts

Couchbase logo

Couchbase

389
492
102
Document-Oriented NoSQL Database
389
492
+ 1
102
PROS OF COUCHBASE
  • 18
    High performance
  • 17
    Flexible data model, easy scalability, extremely fast
  • 8
    Mobile app support
  • 6
    You can query it with Ansi-92 SQL
  • 5
    All nodes can be read/write
  • 4
    Local cache capability
  • 4
    Open source, community and enterprise editions
  • 4
    Both a key-value store and document (JSON) db
  • 4
    Equal nodes in cluster, allowing fast, flexible changes
  • 3
    Automatic configuration of sharding
  • 3
    SDKs in popular programming languages
  • 3
    Elasticsearch connector
  • 3
    Easy setup
  • 3
    Web based management, query and monitoring panel
  • 3
    Linearly scalable, useful to large number of tps
  • 3
    Easy cluster administration
  • 3
    Cross data center replication
  • 2
    NoSQL
  • 2
    DBaaS available
  • 2
    Map reduce views
  • 1
    FTS + SQL together
  • 1
    Buckets, Scopes, Collections & Documents
CONS OF COUCHBASE
  • 3
    Terrible query language

related Couchbase posts

Ilias Mentzelos
Software Engineer at Plum Fintech · | 9 upvotes · 41.4K views
Shared insights
on
MongoDBMongoDBCouchbaseCouchbase

Hey, we want to build a referral campaign mechanism that will probably contain millions of records within the next few years. We want fast read access based on IDs or some indexes, and isolation is crucial as some listeners will try to update the same document at the same time. What's your suggestion between Couchbase and MongoDB? Thanks!

See more
Gabriel Pa

We implemented our first large scale EPR application from naologic.com using CouchDB .

Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

See more
MongoDB logo

MongoDB

64.2K
53.5K
4.1K
The database for giant ideas
64.2K
53.5K
+ 1
4.1K
PROS OF MONGODB
  • 824
    Document-oriented storage
  • 591
    No sql
  • 546
    Ease of use
  • 465
    Fast
  • 406
    High performance
  • 256
    Free
  • 215
    Open source
  • 179
    Flexible
  • 142
    Replication & high availability
  • 109
    Easy to maintain
  • 41
    Querying
  • 37
    Easy scalability
  • 36
    Auto-sharding
  • 35
    High availability
  • 31
    Map/reduce
  • 26
    Document database
  • 24
    Easy setup
  • 24
    Full index support
  • 15
    Reliable
  • 14
    Fast in-place updates
  • 13
    Agile programming, flexible, fast
  • 11
    No database migrations
  • 7
    Easy integration with Node.Js
  • 7
    Enterprise
  • 5
    Enterprise Support
  • 4
    Great NoSQL DB
  • 3
    Aggregation Framework
  • 3
    Support for many languages through different drivers
  • 3
    Drivers support is good
  • 2
    Schemaless
  • 2
    Easy to Scale
  • 2
    Fast
  • 2
    Awesome
  • 2
    Managed service
  • 1
    Consistent
CONS OF MONGODB
  • 5
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 1
    Proprietary query language

related MongoDB posts

Jeyabalaji Subramanian

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

See more
Robert Zuber

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

See more
Aerospike logo

Aerospike

147
207
36
Flash-optimized in-memory open source NoSQL database
147
207
+ 1
36
PROS OF AEROSPIKE
  • 11
    Ram and/or ssd persistence
  • 10
    Easy clustering support
  • 5
    Easy setup
  • 3
    Acid
  • 2
    Performance better than Redis
  • 2
    Petabyte Scale
  • 2
    Scale
  • 1
    Ease of use
CONS OF AEROSPIKE
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    related Aerospike posts

    SAP HANA logo

    SAP HANA

    119
    95
    27
    An in-memory, column-oriented, relational database management system
    119
    95
    + 1
    27
    PROS OF SAP HANA
    • 5
      In-memory
    • 5
      SQL
    • 4
      Distributed
    • 4
      Performance
    • 2
      Realtime
    • 2
      Concurrent
    • 2
      OLAP
    • 2
      OLTP
    • 1
      JSON
    CONS OF SAP HANA
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      related SAP HANA posts

      Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

      We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

      We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

      ASP.NET / Node.js / Laravel. ......?

      Please guide us

      See more
      Apache Ignite logo

      Apache Ignite

      73
      118
      20
      An open-source distributed database, caching and processing platform
      73
      118
      + 1
      20
      PROS OF APACHE IGNITE
      • 3
        Written in java. runs on jvm
      • 3
        Free
      • 2
        High Avaliability
      • 2
        Rest interface
      • 2
        Sql query support in cluster wide
      • 2
        Multiple client language support
      • 2
        Load balancing
      • 1
        Easy to use
      • 1
        Better Documentation
      • 1
        Distributed compute
      • 1
        Distributed Locking
      CONS OF APACHE IGNITE
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        related Apache Ignite posts