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MemSQL vs Oracle: What are the differences?
## MemSQL vs Oracle
MemSQL is a distributed, in-memory, SQL database designed for high performance analytics and real-time business intelligence. On the other hand, Oracle is a relational database management system that offers a wide range of database services and features.
1. **Architecture**: MemSQL is designed as a distributed system that can scale out horizontally, allowing it to handle large volumes of data and provide high availability. In contrast, Oracle follows a traditional architecture with a master-slave setup, which may limit its scalability for massive data processing.
2. **In-Memory Processing**: MemSQL primarily operates in-memory, enabling faster data processing and analytics. Oracle, although capable of in-memory processing, may not offer the same level of performance optimization for real-time analytics as MemSQL does.
3. **SQL Compatibility**: Both MemSQL and Oracle support SQL queries, but MemSQL often provides better compatibility with standard SQL queries and syntax. Oracle, on the other hand, may have its own proprietary SQL extensions and functionalities that differ from traditional SQL standards.
4. **Data Storage**: MemSQL's design focuses on keeping data in memory for faster access, while also providing disk-based storage options. Oracle traditionally relies on disk-based storage, with the option for in-memory storage that may not be as optimized as MemSQL for real-time processing.
5. **Integration with Big Data Technologies**: MemSQL is built to integrate seamlessly with various big data technologies such as Apache Kafka and Spark, offering easier integration with modern data processing frameworks. Oracle, while also providing integration options, may not offer the same level of compatibility and ease of integration with the latest big data technologies.
6. **License and Cost**: MemSQL may be more cost-effective for certain use cases, as it offers different licensing models, including open source options. Oracle, as a commercial database system, may have higher licensing costs for enterprise deployments.
In Summary, MemSQL and Oracle differ in terms of architecture, in-memory processing, SQL compatibility, data storage approaches, integration with big data technologies, and licensing costs.
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.
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.
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.
Pros of MemSQL
- Distributed9
- Realtime5
- Columnstore4
- Sql4
- Concurrent4
- JSON4
- Ultra fast3
- Scalable3
- Unlimited Storage Database2
- Pipeline2
- Mixed workload2
- Availability Group2
Pros of Oracle
- Reliable44
- Enterprise33
- High Availability15
- Hard to maintain5
- Expensive5
- Maintainable4
- Hard to use4
- High complexity3
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Cons of MemSQL
Cons of Oracle
- Expensive14
