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Memcached vs Oracle: What are the differences?

# Introduction
Here are the key differences between Memcached and Oracle:

1. **Data Structure Support**: Memcached is a simple key-value store that can only store data in a key-value pair format, whereas Oracle is a robust relational database management system that supports complex data structures like tables with rows and columns.

2. **Persistence**: Memcached is an in-memory caching system, which means data is stored in memory and can be lost in case of a system restart, while Oracle allows data persistence even after system shutdown through disk storage.

3. **Security Features**: Memcached lacks built-in security features such as authentication, encryption, and access control mechanisms, making it less secure compared to Oracle which offers a range of security features like user authentication, role-based access control, and data encryption.

4. **Transaction Support**: Memcached does not support transactions, making it unsuitable for applications requiring ACID properties, while Oracle supports transactions and ensures data integrity through features like commit, rollback, and isolation levels.

5. **Scalability**: Memcached is designed for horizontal scalability by adding more nodes to the cluster, making it suitable for high read/write operations, whereas Oracle can vertically scale by upgrading the hardware resources of a single server, making it more suitable for heavy transactional workloads.

6. **Query Language Support**: Memcached does not have its own query language and relies on external programming languages for data retrieval and manipulation, whereas Oracle uses SQL (Structured Query Language) for powerful and efficient database queries.

In Summary, Memcached and Oracle differ in aspects such as data structure support, persistence, security features, transaction support, scalability, and query language support.

Decisions about Memcached and Oracle
Daniel Moya
Data Engineer at Dimensigon · | 4 upvotes · 443.8K views

We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.

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We wanted a JSON datastore that could save the state of our bioinformatics visualizations without destructive normalization. As a leading NoSQL data storage technology, MongoDB has been a perfect fit for our needs. Plus it's open source, and has an enterprise SLA scale-out path, with support of hosted solutions like Atlas. Mongo has been an absolute champ. So much so that SQL and Oracle have begun shipping JSON column types as a new feature for their databases. And when Fast Healthcare Interoperability Resources (FHIR) announced support for JSON, we basically had our FHIR datalake technology.

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In the field of bioinformatics, we regularly work with hierarchical and unstructured document data. Unstructured text data from PDFs, image data from radiographs, phylogenetic trees and cladograms, network graphs, streaming ECG data... none of it fits into a traditional SQL database particularly well. As such, we prefer to use document oriented databases.

MongoDB is probably the oldest component in our stack besides Javascript, having been in it for over 5 years. At the time, we were looking for a technology that could simply cache our data visualization state (stored in JSON) in a database as-is without any destructive normalization. MongoDB was the perfect tool; and has been exceeding expectations ever since.

Trivia fact: some of the earliest electronic medical records (EMRs) used a document oriented database called MUMPS as early as the 1960s, prior to the invention of SQL. MUMPS is still in use today in systems like Epic and VistA, and stores upwards of 40% of all medical records at hospitals. So, we saw MongoDB as something as a 21st century version of the MUMPS database.

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Pros of Memcached
Pros of Oracle
  • 139
    Fast object cache
  • 129
    High-performance
  • 91
    Stable
  • 65
    Mature
  • 33
    Distributed caching system
  • 11
    Improved response time and throughput
  • 3
    Great for caching HTML
  • 2
    Putta
  • 44
    Reliable
  • 33
    Enterprise
  • 15
    High Availability
  • 5
    Hard to maintain
  • 5
    Expensive
  • 4
    Maintainable
  • 4
    Hard to use
  • 3
    High complexity

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Cons of Memcached
Cons of Oracle
  • 2
    Only caches simple types
  • 14
    Expensive

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- No public GitHub repository available -

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.

What is Oracle?

Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

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What are some alternatives to Memcached and Oracle?
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.
Ehcache
Ehcache is an open source, standards-based cache for boosting performance, offloading your database, and simplifying scalability. It's the most widely-used Java-based cache because it's robust, proven, and full-featured. Ehcache scales from in-process, with one or more nodes, all the way to mixed in-process/out-of-process configurations with terabyte-sized caches.
Varnish
Varnish Cache is a web application accelerator also known as a caching HTTP reverse proxy. You install it in front of any server that speaks HTTP and configure it to cache the contents. Varnish Cache is really, really fast. It typically speeds up delivery with a factor of 300 - 1000x, depending on your architecture.
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.
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.
See all alternatives