Overview

ScyllaDB

Stacks143

Followers197

Votes8

DuckDB

Stacks49

Followers60

Votes0

DuckDB vs Scylla: What are the differences?

Introduction

DuckDB and Scylla are both database management systems, but they have significant differences in terms of their underlying technology, use cases, and features. In this article, we will explore the key differences between DuckDB and Scylla.

Storage Engine: DuckDB is an in-memory analytical database that relies on a columnar storage engine. It compresses and stores data in a column-wise fashion, which enables fast analytical queries. On the other hand, Scylla is a NoSQL database that uses log-structured merge (LSM) tree architecture for data storage. This allows for high write throughput and efficient storage of large amounts of data.
Data Consistency: DuckDB ensures strong consistency, which means that all data operations are immediately visible across all nodes of the database. It guarantees that a query will see the latest committed state of the data. Scylla, on the other hand, provides eventual consistency, where updates to the database may not be immediately visible, but will eventually propagate to all nodes. This enables high availability and fault tolerance.
Query Language: DuckDB supports SQL as its query language, making it compatible with a wide range of applications and tools that are SQL-based. It allows users to perform complex analytical queries using standard SQL syntax. In contrast, Scylla uses its own query language called CQL (Cassandra Query Language), which is similar to SQL but has some differences. CQL is specifically designed for NoSQL databases and provides features like eventual consistency and distributed querying.
Data Model: DuckDB follows a relational data model, where data is organized into tables and relationships are established through keys. It supports ACID transactions and provides strong data modeling capabilities. Scylla, on the other hand, follows a distributed key-value data model. It does not support ACID transactions and does not have built-in support for complex relationships. Instead, it focuses on high availability, scalability, and low latency.
Scalability: DuckDB is primarily designed for analytical workloads and is optimized for single-node performance. It can efficiently process complex analytical queries on a single machine. Scylla, on the other hand, is a distributed database that is built for scale. It can handle massive amounts of data and is designed to be deployed on a cluster of multiple nodes to achieve high throughput and scalability.
Community Support: DuckDB is an open-source project with a growing community of contributors and users. It benefits from the open-source ecosystem and the collaboration of developers worldwide. Scylla is also an open-source project with an active community, but it is more focused on providing enterprise-grade solutions and has a strong support system for commercial customers.

In summary, DuckDB and Scylla differ in their storage engine, data consistency, query language, data model, scalability, and community support. DuckDB is suited for analytical workloads with strong consistency, while Scylla is designed for high availability and scalability with eventual consistency.

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Advice on ScyllaDB, DuckDB

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	DuckDB
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.	It is an embedded database designed to execute analytical SQL queries fast while embedded in another process. It is designed to be easy to install and easy to use. DuckDB has no external dependencies. It has bindings for C/C++, Python and R.
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	Embedded database; Designed to execute analytical SQL queries fast; No external dependencies
Statistics
Stacks 143	Stacks 49
Followers 197	Followers 60
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	Python C++ R Language

What are some alternatives to ScyllaDB, DuckDB?

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.