DalmatinerDB vs Scylla

Overview

DalmatinerDB

Stacks6

Followers10

Votes1

GitHub Stars690

Forks43

ScyllaDB

Stacks143

Followers197

Votes8

DalmatinerDB vs Scylla: What are the differences?

Introduction:
DalmatinerDB and Scylla are both database management systems that serve different purposes and have distinct features. Below are the key differences between DalmatinerDB and Scylla.

1. **Data Model**: DalmatinerDB utilizes a time series data model, geared towards storing and analyzing vast amounts of timestamped data efficiently. In contrast, Scylla is a distributed NoSQL database that supports various data models like wide-column, key-value, and document store.

2. **Consistency**: DalmatinerDB prioritizes eventual consistency, sacrificing immediate consistency for better performance and availability. On the other hand, Scylla places a higher emphasis on providing strong consistency to ensure data correctness and integrity.

3. **Query Language**: DalmatinerDB uses its own query language, Erlang-style syntax, tailored for time series data analysis and manipulation. In comparison, Scylla supports CQL, a SQL-like language, enhancing its compatibility with existing tools and applications.

4. **Use Cases**: DalmatinerDB is particularly well-suited for monitoring, metrics, and log data storage due to its efficient time series data handling capabilities. In contrast, Scylla is more versatile, capable of supporting diverse applications ranging from real-time analytics to large-scale web applications.

5. **Ease of Management**: DalmatinerDB is relatively simpler to set up and manage, designed for ease of use in time series data scenarios. On the other hand, Scylla's distributed nature and varied data models may require more expertise and resources for configuration and maintenance.

6. **Scalability**: While both systems are designed for high scalability, Scylla is known for its ability to seamlessly scale horizontally across multiple nodes, ensuring performance and reliability as the data volume grows.

In Summary, DalmatinerDB and Scylla differ in data model specialization, consistency models, query languages, use cases, ease of management, and scalability solutions.

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Advice on DalmatinerDB, 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

DalmatinerDB	ScyllaDB
DalmatinerDB is a no fluff purpose built metric database. Not a layer put on top of a general purpose database or datastore.	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
GitHub Stars 690	GitHub Stars -
GitHub Forks 43	GitHub Forks -
Stacks 6	Stacks 143
Followers 10	Followers 197
Votes 1	Votes 8
Pros & Cons
Pros 1 Light and Fast	Pros 2 Replication 1 High performance 1 Written in C++ 1 High availability 1 Scale up
Integrations
No integrations available	KairosDB Wireshark JanusGraph Grafana Hackolade Prometheus Kubernetes Datadog Kafka Apache Spark

What are some alternatives to DalmatinerDB, 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

DalmatinerDB vs Scylla: What are the differences?

Introduction:
DalmatinerDB and Scylla are both database management systems that serve different purposes and have distinct features. Below are the key differences between DalmatinerDB and Scylla.

1. **Data Model**: DalmatinerDB utilizes a time series data model, geared towards storing and analyzing vast amounts of timestamped data efficiently. In contrast, Scylla is a distributed NoSQL database that supports various data models like wide-column, key-value, and document store.

2. **Consistency**: DalmatinerDB prioritizes eventual consistency, sacrificing immediate consistency for better performance and availability. On the other hand, Scylla places a higher emphasis on providing strong consistency to ensure data correctness and integrity.

3. **Query Language**: DalmatinerDB uses its own query language, Erlang-style syntax, tailored for time series data analysis and manipulation. In comparison, Scylla supports CQL, a SQL-like language, enhancing its compatibility with existing tools and applications.

4. **Use Cases**: DalmatinerDB is particularly well-suited for monitoring, metrics, and log data storage due to its efficient time series data handling capabilities. In contrast, Scylla is more versatile, capable of supporting diverse applications ranging from real-time analytics to large-scale web applications.

5. **Ease of Management**: DalmatinerDB is relatively simpler to set up and manage, designed for ease of use in time series data scenarios. On the other hand, Scylla's distributed nature and varied data models may require more expertise and resources for configuration and maintenance.

6. **Scalability**: While both systems are designed for high scalability, Scylla is known for its ability to seamlessly scale horizontally across multiple nodes, ensuring performance and reliability as the data volume grows.

In Summary, DalmatinerDB and Scylla differ in data model specialization, consistency models, query languages, use cases, ease of management, and scalability solutions.

DalmatinerDB vs Scylla

Overview