CQEngine vs MemSQL

Overview

MemSQL

Stacks86

Followers184

Votes44

CQEngine

Stacks3

Followers22

Votes0

GitHub Stars1.8K

Forks255

CQEngine vs MemSQL: What are the differences?

Installation and Setup: One key difference between CQEngine and MemSQL is the installation and setup process. CQEngine is a lightweight Java library that can be easily included in Java projects via Maven or Gradle dependencies, while MemSQL is a distributed, in-memory, SQL database that requires setting up servers, nodes, and clusters for deployment.
Data Storage: CQEngine is an in-memory database that stores data in memory structures like indices for fast querying, while MemSQL can store data both in memory and on disk, providing a balance between performance and persistence.
Query Language: CQEngine provides an API to query data programmatically using Java code, while MemSQL supports SQL for querying and manipulating data, which makes it easier for users familiar with SQL to work with the database.
Scalability: MemSQL is designed to be distributed and horizontally scalable, allowing for scaling out by adding more nodes to a cluster for handling large data volumes and high query loads, whereas CQEngine is more suitable for small to medium-sized datasets that can fit in memory on a single machine.
Data Processing: MemSQL is optimized for real-time data processing and analytics, making it suitable for applications requiring low latency and high throughput, while CQEngine is more focused on providing efficient in-memory data retrieval and manipulation operations for Java applications.
Community and Support: MemSQL has a larger community and commercial support offerings compared to CQEngine, which is primarily maintained by a smaller developer community with limited support options.

In Summary, CQEngine and MemSQL differ in terms of installation, data storage, query language, scalability, data processing capabilities, and community support.

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CLI (Node.js)

Manual

Detailed Comparison

MemSQL	CQEngine
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.	It is a NoSQL indexing and Query Engine, for retrieving objects matching SQL-like queries from Java collections, with ultra-low latency
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	Ultra-fast; Query engine; No SQL
Statistics
GitHub Stars -	GitHub Stars 1.8K
GitHub Forks -	GitHub Forks 255
Stacks 86	Stacks 3
Followers 184	Followers 22
Votes 44	Votes 0
Pros & Cons
Pros 9 Distributed 5 Realtime 4 Columnstore 4 Concurrent 4 Sql	No community feedback yet
Integrations
Google Compute Engine MySQL QlikView	MongoDB PostgreSQL Fastify MySQL

What are some alternatives to MemSQL, CQEngine?

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.

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.

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.

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

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.

VoltDB

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

Tarantool

It is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications

Azure Redis Cache

It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.

KeyDB

KeyDB is a fully open source database that aims to make use of all hardware resources. KeyDB makes it possible to breach boundaries often dictated by price and complexity.

LokiJS

LokiJS is a document oriented database written in javascript, published under MIT License. Its purpose is to store javascript objects as documents in a nosql fashion and retrieve them with a similar mechanism. Runs in node (including cordova/phonegap and node-webkit), nativescript and the browser.

Related Comparisons

CQEngine vs MemSQL: What are the differences?

Installation and Setup: One key difference between CQEngine and MemSQL is the installation and setup process. CQEngine is a lightweight Java library that can be easily included in Java projects via Maven or Gradle dependencies, while MemSQL is a distributed, in-memory, SQL database that requires setting up servers, nodes, and clusters for deployment.
Data Storage: CQEngine is an in-memory database that stores data in memory structures like indices for fast querying, while MemSQL can store data both in memory and on disk, providing a balance between performance and persistence.
Query Language: CQEngine provides an API to query data programmatically using Java code, while MemSQL supports SQL for querying and manipulating data, which makes it easier for users familiar with SQL to work with the database.
Scalability: MemSQL is designed to be distributed and horizontally scalable, allowing for scaling out by adding more nodes to a cluster for handling large data volumes and high query loads, whereas CQEngine is more suitable for small to medium-sized datasets that can fit in memory on a single machine.
Data Processing: MemSQL is optimized for real-time data processing and analytics, making it suitable for applications requiring low latency and high throughput, while CQEngine is more focused on providing efficient in-memory data retrieval and manipulation operations for Java applications.
Community and Support: MemSQL has a larger community and commercial support offerings compared to CQEngine, which is primarily maintained by a smaller developer community with limited support options.

In Summary, CQEngine and MemSQL differ in terms of installation, data storage, query language, scalability, data processing capabilities, and community support.

CQEngine vs MemSQL

Overview