RavenDB vs Scylla

Overview

RavenDB

Stacks79

Followers82

Votes9

GitHub Stars3.9K

Forks850

ScyllaDB

Stacks143

Followers197

Votes8

RavenDB vs Scylla: What are the differences?

Introduction: RavenDB and Scylla are two popular NoSQL databases known for their efficiency and scalability. However, they have key differences that set them apart in terms of functionality and performance.

Data Modeling: RavenDB is a document-oriented database, while Scylla is a wide-column store. This means that RavenDB stores data in the form of documents, which are then grouped into collections, while Scylla organizes data into rows and columns within tables. This difference determines how data is structured and queried in each database, impacting their suitability for different use cases.
Consistency Model: RavenDB offers strong consistency by default, ensuring that all reads and writes are immediately reflected across the system. On the other hand, Scylla provides eventual consistency, which allows for faster performance but may result in temporary inconsistencies that need to be resolved by the application. This difference in consistency models affects how applications handle data integrity and synchronization.
Query Language: RavenDB supports LINQ (Language Integrated Query) for querying data, making it easier for developers familiar with C# to interact with the database. In contrast, Scylla uses CQL (Cassandra Query Language), a SQL-like language specifically designed for working with wide-column stores. The choice of query language can impact developer productivity and the ease of integrating the database into existing systems.
Data Distribution: In RavenDB, data partitioning is done automatically based on document identifiers, allowing for more dynamic and flexible data distribution across nodes. In Scylla, manual configuration of partition keys is required to distribute data across partitions efficiently. This difference affects how well each database can handle large volumes of data and distribute workloads effectively.
Indexing: RavenDB supports automatic indexing of documents, simplifying the process of querying data and improving query performance. Scylla, on the other hand, requires manual configuration of secondary indexes for efficient querying of specific columns. The approach to indexing impacts how quickly queries can be executed and how effectively the database can handle complex queries.

In Summary, RavenDB and Scylla differ in their data modeling approach, consistency models, query languages, data distribution mechanisms, and indexing strategies, all of which play a crucial role in determining their suitability for various use cases in terms of scalability, performance, and developer experience.

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Advice on RavenDB, 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

RavenDB	ScyllaDB
As a document database it remains true to the core principles of these type of storage mechanisms. Somehow it managed to combine the best of relational databases with that of document databases.	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.
Multi-Platform; ACID Transactions	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
GitHub Stars 3.9K	GitHub Stars -
GitHub Forks 850	GitHub Forks -
Stacks 79	Stacks 143
Followers 82	Followers 197
Votes 9	Votes 8
Pros & Cons
Pros 4 Embedded Library 3 Easy of use 2 NoSql	Pros 2 Replication 1 Scale up 1 Distributed 1 Fewer nodes 1 High performance
Integrations
Python Windows Java Ruby Linux	KairosDB Wireshark JanusGraph Grafana Hackolade Prometheus Kubernetes Datadog Kafka Apache Spark

What are some alternatives to RavenDB, 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

RavenDB vs Scylla: What are the differences?

Data Modeling: RavenDB is a document-oriented database, while Scylla is a wide-column store. This means that RavenDB stores data in the form of documents, which are then grouped into collections, while Scylla organizes data into rows and columns within tables. This difference determines how data is structured and queried in each database, impacting their suitability for different use cases.
Consistency Model: RavenDB offers strong consistency by default, ensuring that all reads and writes are immediately reflected across the system. On the other hand, Scylla provides eventual consistency, which allows for faster performance but may result in temporary inconsistencies that need to be resolved by the application. This difference in consistency models affects how applications handle data integrity and synchronization.
Query Language: RavenDB supports LINQ (Language Integrated Query) for querying data, making it easier for developers familiar with C# to interact with the database. In contrast, Scylla uses CQL (Cassandra Query Language), a SQL-like language specifically designed for working with wide-column stores. The choice of query language can impact developer productivity and the ease of integrating the database into existing systems.
Data Distribution: In RavenDB, data partitioning is done automatically based on document identifiers, allowing for more dynamic and flexible data distribution across nodes. In Scylla, manual configuration of partition keys is required to distribute data across partitions efficiently. This difference affects how well each database can handle large volumes of data and distribute workloads effectively.
Indexing: RavenDB supports automatic indexing of documents, simplifying the process of querying data and improving query performance. Scylla, on the other hand, requires manual configuration of secondary indexes for efficient querying of specific columns. The approach to indexing impacts how quickly queries can be executed and how effectively the database can handle complex queries.

RavenDB vs Scylla

Overview