Mentat vs Scylla

Overview

ScyllaDB

Stacks143

Followers197

Votes8

Mentat

Stacks199

Followers12

Votes0

GitHub Stars1.7K

Forks115

Mentat vs Scylla: What are the differences?

*Write introduction here*

Data Model: Mentat uses the Entity-Attribute-Value (EAV) data model, which allows flexibility in data structure but can lead to complexity in querying and data manipulation. On the other hand, Scylla utilizes a wide-column data store model, providing a more structured and efficient way of managing data through tables and columns.
Consistency: Mentat prioritizes consistency in data storage by using transactions that ensure all operations are atomic and consistent across the database. Meanwhile, Scylla focuses more on partition tolerance, utilizing a distributed architecture to ensure high availability and fault tolerance, sacrificing some consistency guarantees in favor of performance and scalability.
Query Language: Mentat primarily uses Anansi Query Language (AQL) for querying data, which is a powerful but specialized language designed for working with the EAV data model. In contrast, Scylla utilizes CQL (Cassandra Query Language), offering a more familiar and SQL-like syntax for interacting with data stored in its wide-column model.
Replication Strategy: Mentat supports single-node replication and clustering for improved data durability and availability within a single data center. In contrast, Scylla provides multi-datacenter replication and built-in support for various replication strategies, allowing for greater flexibility in distributing data across geographically dispersed locations for improved resilience.
Secondary Indexes: Mentat supports secondary indexing to facilitate efficient querying of data attributes, but this can impact performance and scalability due to the nature of the EAV data model. Scylla offers limited support for secondary indexes, prioritizing alternative techniques such as materialized views and denormalization to optimize query performance within its wide-column data store.
Tuning and Optimization: Mentat requires more manual tuning and optimization efforts due to the complex nature of the EAV data model and the need to tailor database configurations to specific use cases. In contrast, Scylla automates many performance optimizations through its built-in tuning mechanisms and workload-aware design, reducing the operational burden on users for maintaining system performance.

In Summary, Mentat and Scylla differ in their data models, consistency guarantees, query languages, replication strategies, support for secondary indexes, and approaches to tuning and optimization.

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Advice on ScyllaDB, Mentat

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

ScyllaDB	Mentat
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.	Project Mentat is a persistent, embedded knowledge base. It draws heavily on DataScript and Datomic. Mentat is implemented in Rust.
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	Flexible rotational store; Makes it easy to describe, grow, and reuse your domain schema
Statistics
GitHub Stars -	GitHub Stars 1.7K
GitHub Forks -	GitHub Forks 115
Stacks 143	Stacks 199
Followers 197	Followers 12
Votes 8	Votes 0
Pros & Cons
Pros 2 Replication 1 Written in C++ 1 High availability 1 High performance 1 Distributed	No community feedback yet
Integrations
KairosDB Wireshark JanusGraph Grafana Hackolade Prometheus Kubernetes Datadog Kafka Apache Spark	No integrations available

What are some alternatives to ScyllaDB, Mentat?

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

Mentat vs Scylla: What are the differences?

*Write introduction here*

Data Model: Mentat uses the Entity-Attribute-Value (EAV) data model, which allows flexibility in data structure but can lead to complexity in querying and data manipulation. On the other hand, Scylla utilizes a wide-column data store model, providing a more structured and efficient way of managing data through tables and columns.
Consistency: Mentat prioritizes consistency in data storage by using transactions that ensure all operations are atomic and consistent across the database. Meanwhile, Scylla focuses more on partition tolerance, utilizing a distributed architecture to ensure high availability and fault tolerance, sacrificing some consistency guarantees in favor of performance and scalability.
Query Language: Mentat primarily uses Anansi Query Language (AQL) for querying data, which is a powerful but specialized language designed for working with the EAV data model. In contrast, Scylla utilizes CQL (Cassandra Query Language), offering a more familiar and SQL-like syntax for interacting with data stored in its wide-column model.
Replication Strategy: Mentat supports single-node replication and clustering for improved data durability and availability within a single data center. In contrast, Scylla provides multi-datacenter replication and built-in support for various replication strategies, allowing for greater flexibility in distributing data across geographically dispersed locations for improved resilience.
Secondary Indexes: Mentat supports secondary indexing to facilitate efficient querying of data attributes, but this can impact performance and scalability due to the nature of the EAV data model. Scylla offers limited support for secondary indexes, prioritizing alternative techniques such as materialized views and denormalization to optimize query performance within its wide-column data store.
Tuning and Optimization: Mentat requires more manual tuning and optimization efforts due to the complex nature of the EAV data model and the need to tailor database configurations to specific use cases. In contrast, Scylla automates many performance optimizations through its built-in tuning mechanisms and workload-aware design, reducing the operational burden on users for maintaining system performance.

In Summary, Mentat and Scylla differ in their data models, consistency guarantees, query languages, replication strategies, support for secondary indexes, and approaches to tuning and optimization.

Mentat vs Scylla

Overview