Cassandra vs MemSQL

Overview

MemSQL

Stacks86

Followers184

Votes44

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Cassandra vs MemSQL: What are the differences?

Introduction

In the realm of database management systems, Cassandra and MemSQL each offer unique features and benefits that cater to different needs and requirements. Understanding the key differences between these two platforms is crucial for making informed decisions on which system to implement.

Data Model: One significant difference between Cassandra and MemSQL lies in their data models. Cassandra utilizes a wide-column store NoSQL model, optimized for write-heavy workloads and distributed across multiple nodes. On the other hand, MemSQL employs a relational model with support for SQL queries, making it suitable for transactional applications that require strong consistency and real-time analytics capabilities.
Consistency Model: Another important distinction is in the consistency models employed by Cassandra and MemSQL. Cassandra emphasizes availability and partition tolerance over consistency, following the AP (Availability and Partition Tolerance) side of the CAP theorem. In contrast, MemSQL prioritizes strong consistency and ACID compliance, adhering to the CP (Consistency and Partition Tolerance) side of the CAP theorem.
Scalability: When it comes to scalability, Cassandra is known for its linear and easy scalability by adding more nodes to the cluster, allowing it to handle massive amounts of data and traffic efficiently. MemSQL, on the other hand, offers scale-out and distributed processing capabilities, enabling it to scale both horizontally and vertically to meet growing data demands.
Query Performance: In terms of query performance, MemSQL tends to excel due to its relational structure and in-memory processing capabilities. It can efficiently execute complex analytical queries and aggregations, making it ideal for real-time analytics and decision-making. Cassandra, while efficient for write-heavy workloads, may face limitations in complex query processing and aggregations.
Data Consistency and Durability: Cassandra provides high fault tolerance and eventual consistency through its distributed architecture, making it resilient to node failures and network partitions. MemSQL, with its focus on strong consistency, ensures data durability and reliability through features like replication, ensuring that data remains consistent across nodes for transactional integrity.
Deployment Flexibility: One notable difference is in the deployment flexibility offered by Cassandra and MemSQL. Cassandra is open-source, allowing users to deploy it on any infrastructure, from on-premises servers to public cloud environments. In contrast, MemSQL provides both a managed service and on-premises options, catering to different deployment preferences and requirements.

In Summary, understanding the key differences between Cassandra and MemSQL in terms of data model, consistency model, scalability, query performance, data consistency, and deployment flexibility is crucial for selecting the most suitable database management system for specific use cases and requirements.

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Advice on MemSQL, Cassandra

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

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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Detailed Comparison

MemSQL	Cassandra
MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.	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.
ANSI SQL Support;Fully-distributed Joins;Compiled Queries; ACID Compliance;In-Memory Tables;On-Disk Tables; Massively Parallel Execution;Lock Free Data Structures;JSON Support; High Availability; Online Backup and Restore;Online Replication	-
Statistics
GitHub Stars -	GitHub Stars 9.5K
GitHub Forks -	GitHub Forks 3.8K
Stacks 86	Stacks 3.6K
Followers 184	Followers 3.5K
Votes 44	Votes 507
Pros & Cons
Pros 9 Distributed 5 Realtime 4 Concurrent 4 Columnstore 4 Sql	Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates
Integrations
Google Compute Engine MySQL QlikView	No integrations available

What are some alternatives to MemSQL, Cassandra?

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.

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.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

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.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Cassandra vs MemSQL: What are the differences?

Introduction

Data Model: One significant difference between Cassandra and MemSQL lies in their data models. Cassandra utilizes a wide-column store NoSQL model, optimized for write-heavy workloads and distributed across multiple nodes. On the other hand, MemSQL employs a relational model with support for SQL queries, making it suitable for transactional applications that require strong consistency and real-time analytics capabilities.
Consistency Model: Another important distinction is in the consistency models employed by Cassandra and MemSQL. Cassandra emphasizes availability and partition tolerance over consistency, following the AP (Availability and Partition Tolerance) side of the CAP theorem. In contrast, MemSQL prioritizes strong consistency and ACID compliance, adhering to the CP (Consistency and Partition Tolerance) side of the CAP theorem.
Scalability: When it comes to scalability, Cassandra is known for its linear and easy scalability by adding more nodes to the cluster, allowing it to handle massive amounts of data and traffic efficiently. MemSQL, on the other hand, offers scale-out and distributed processing capabilities, enabling it to scale both horizontally and vertically to meet growing data demands.
Query Performance: In terms of query performance, MemSQL tends to excel due to its relational structure and in-memory processing capabilities. It can efficiently execute complex analytical queries and aggregations, making it ideal for real-time analytics and decision-making. Cassandra, while efficient for write-heavy workloads, may face limitations in complex query processing and aggregations.
Data Consistency and Durability: Cassandra provides high fault tolerance and eventual consistency through its distributed architecture, making it resilient to node failures and network partitions. MemSQL, with its focus on strong consistency, ensures data durability and reliability through features like replication, ensuring that data remains consistent across nodes for transactional integrity.
Deployment Flexibility: One notable difference is in the deployment flexibility offered by Cassandra and MemSQL. Cassandra is open-source, allowing users to deploy it on any infrastructure, from on-premises servers to public cloud environments. In contrast, MemSQL provides both a managed service and on-premises options, catering to different deployment preferences and requirements.

Cassandra vs MemSQL

Overview

Cassandra vs MemSQL: What are the differences?