MonetDB vs Scylla

Overview

MonetDB

Stacks13

Followers35

Votes2

ScyllaDB

Stacks143

Followers197

Votes8

MonetDB vs Scylla: What are the differences?

## Introduction

MonetDB and Scylla are two popular databases used for different use cases. They have key differences that make them suitable for specific types of applications.

1. **Query Language**: MonetDB uses a SQL-like query language called MonetDB/SQL, while Scylla uses CQL (Cassandra Query Language) for querying the database. This means that developers and users familiar with SQL may find MonetDB easier to work with, while those with experience in Cassandra will prefer Scylla.
   
2. **Data Model**: MonetDB is a column-oriented database, optimized for analytical workloads where columns of data are frequently accessed together. On the other hand, Scylla is a distributed NoSQL database based on Apache Cassandra, which is designed for high availability and scalability across multiple nodes. This makes Scylla more suitable for distributed applications that require high availability.

3. **Consistency and Availability**: MonetDB focuses on strong consistency and ACID transactions, ensuring that data integrity is maintained at all times. In contrast, Scylla prioritizes availability and partition tolerance over consistency, making it more suitable for use cases where high availability is critical, even at the cost of potentially having eventual consistency.

4. **Data Partitioning**: Scylla uses consistent hashing to partition data evenly across nodes in a cluster, ensuring that data is distributed efficiently. MonetDB does not have built-in data partitioning mechanisms, making it less suitable for distributed environments where data sharding is required for scalability.

5. **Tuning and Optimization**: MonetDB provides a rich set of optimization techniques and tools for fine-tuning query performance, making it suitable for analytical workloads with complex queries. In comparison, Scylla offers built-in performance optimizations for write-heavy workloads, such as using a log-structured storage engine and configurable compaction strategies to improve write throughput.

6. **Deployment and Scalability**: MonetDB can be deployed as a standalone server or a cluster of servers for distributed processing, but it may require additional configuration for high availability and scalability. On the other hand, Scylla is designed for seamless deployment in distributed environments and offers easy scalability by adding more nodes to the cluster without downtime.

In Summary, MonetDB and Scylla differ in query language, data model, consistency, data partitioning, tuning, and optimization, and deployment and scalability, making them suitable for distinct use cases.

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Advice on MonetDB, ScyllaDB

Tom

CEO at Gentlent

Jun 9, 2020

Decided

The Gentlent Tech Team made lots of updates within the past year. The biggest one being our database:

We decided to migrate our #PostgreSQL -based database systems to a custom implementation of #Cassandra . This allows us to integrate our product data perfectly in a system that just makes sense. High availability and scalability are supported out of the box.

387k views387k

Comments

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.

174k views174k

Comments

Detailed Comparison

MonetDB	ScyllaDB
MonetDB innovates at all layers of a DBMS, e.g. a storage model based on vertical fragmentation, a modern CPU-tuned query execution architecture, automatic and self-tuning indexes, run-time query optimization, and a modular software architecture.	ScyllaDB is the database for data-intensive apps that require high performance and low latency. It enables teams to harness the ever-increasing computing power of modern infrastructures – eliminating barriers to scale as data grows.
-	High availability; horizontal scalability; vertical scalability; Cassandra compatible; DynamoDB compatible; wide column; NoSQL; lightweight transactions; change data capture; workload prioritization; shard-per-core; IO scheduler; self-tuning
Statistics
Stacks 13	Stacks 143
Followers 35	Followers 197
Votes 2	Votes 8
Pros & Cons
Pros 2 High Performance	Pros 2 Replication 1 Written in C++ 1 High availability 1 Scale up 1 Distributed
Integrations
No integrations available	KairosDB Wireshark JanusGraph Grafana Hackolade Prometheus Kubernetes Datadog Kafka Apache Spark

What are some alternatives to MonetDB, ScyllaDB?

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.

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.

Cassandra

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.

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

MonetDB vs Scylla: What are the differences?

## Introduction

MonetDB and Scylla are two popular databases used for different use cases. They have key differences that make them suitable for specific types of applications.

1. **Query Language**: MonetDB uses a SQL-like query language called MonetDB/SQL, while Scylla uses CQL (Cassandra Query Language) for querying the database. This means that developers and users familiar with SQL may find MonetDB easier to work with, while those with experience in Cassandra will prefer Scylla.
   
2. **Data Model**: MonetDB is a column-oriented database, optimized for analytical workloads where columns of data are frequently accessed together. On the other hand, Scylla is a distributed NoSQL database based on Apache Cassandra, which is designed for high availability and scalability across multiple nodes. This makes Scylla more suitable for distributed applications that require high availability.

3. **Consistency and Availability**: MonetDB focuses on strong consistency and ACID transactions, ensuring that data integrity is maintained at all times. In contrast, Scylla prioritizes availability and partition tolerance over consistency, making it more suitable for use cases where high availability is critical, even at the cost of potentially having eventual consistency.

4. **Data Partitioning**: Scylla uses consistent hashing to partition data evenly across nodes in a cluster, ensuring that data is distributed efficiently. MonetDB does not have built-in data partitioning mechanisms, making it less suitable for distributed environments where data sharding is required for scalability.

5. **Tuning and Optimization**: MonetDB provides a rich set of optimization techniques and tools for fine-tuning query performance, making it suitable for analytical workloads with complex queries. In comparison, Scylla offers built-in performance optimizations for write-heavy workloads, such as using a log-structured storage engine and configurable compaction strategies to improve write throughput.

6. **Deployment and Scalability**: MonetDB can be deployed as a standalone server or a cluster of servers for distributed processing, but it may require additional configuration for high availability and scalability. On the other hand, Scylla is designed for seamless deployment in distributed environments and offers easy scalability by adding more nodes to the cluster without downtime.

MonetDB vs Scylla

Overview