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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. RocksDB vs Scylla

RocksDB vs Scylla

OverviewDecisionsComparisonAlternatives

Overview

RocksDB
RocksDB
Stacks141
Followers290
Votes11
GitHub Stars30.9K
Forks6.6K
ScyllaDB
ScyllaDB
Stacks143
Followers197
Votes8

RocksDB vs Scylla: What are the differences?

Introduction:

Here we will discuss the key differences between RocksDB and Scylla. These two are popular database systems that have their own unique features and advantages. By understanding their differences, users can choose the one that best suits their requirements and needs.

  1. Storage Model: RocksDB is a key-value store that is optimized for fast storage and retrieval of key-value pairs. It is designed to efficiently handle both read and write operations. On the other hand, Scylla is a wide-column store that is based on Apache Cassandra. It is known for its ability to handle large volumes of data with high write and read performance.

  2. Consistency Model: RocksDB is a single-node database and follows strict consistency and atomicity guarantees. It ensures that all operations are performed in a serialized order and maintains strict consistency. In contrast, Scylla is a distributed database that uses a distributed consensus protocol for consistency. It provides eventual consistency and allows for high availability and fault tolerance.

  3. Replication and Scalability: RocksDB does not provide built-in support for replication and scalability, although it can be used in distributed systems through frameworks like Hadoop and Spark. On the other hand, Scylla is designed to handle large-scale deployments and provides built-in support for replication and horizontal scalability. It uses a masterless architecture that allows for automatic data distribution and replication across multiple nodes.

  4. Data Model: RocksDB is a key-value store and does not support complex data types or secondary indexes out-of-the-box. It is primarily used for simple key-value storage and retrieval. Scylla, on the other hand, supports a wide range of data types and allows for the creation of secondary indexes. It also provides support for advanced querying capabilities, including range scans and aggregations.

  5. Concurrency Control: RocksDB provides multithreaded read and write operations within a single-node environment. It utilizes multi-version concurrency control (MVCC) to provide concurrent access to data. Scylla, being a distributed database, uses a distributed concurrency control mechanism to handle concurrent operations across multiple nodes. It employs techniques like token-based partitioning and distributed locking to ensure consistency and isolation.

  6. Performance: RocksDB is known for its high performance and low-latency data access. It is optimized for fast storage and retrieval and can handle high write and read workloads efficiently. Scylla, on the other hand, is designed to provide scalable throughput and low latency for large-scale deployments. It is capable of handling millions of operations per second and can scale horizontally to handle massive amounts of data efficiently.

In Summary, RocksDB is a key-value store optimized for fast storage and retrieval, while Scylla is a wide-column store designed for high-performance handling of large volumes of data in distributed environments.

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Advice on RocksDB, ScyllaDB

Tom
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
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

RocksDB
RocksDB
ScyllaDB
ScyllaDB

RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.

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.

Designed for application servers wanting to store up to a few terabytes of data on locally attached Flash drives or in RAM;Optimized for storing small to medium size key-values on fast storage -- flash devices or in-memory;Scales linearly with number of CPUs so that it works well on ARM processors
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
30.9K
GitHub Stars
-
GitHub Forks
6.6K
GitHub Forks
-
Stacks
141
Stacks
143
Followers
290
Followers
197
Votes
11
Votes
8
Pros & Cons
Pros
  • 5
    Very fast
  • 3
    Made by Facebook
  • 2
    Consistent performance
  • 1
    Ability to add logic to the database layer where needed
Pros
  • 2
    Replication
  • 1
    High availability
  • 1
    Scale up
  • 1
    Distributed
  • 1
    Fewer nodes
Integrations
No integrations available
KairosDB
KairosDB
Wireshark
Wireshark
JanusGraph
JanusGraph
Grafana
Grafana
Hackolade
Hackolade
Prometheus
Prometheus
Kubernetes
Kubernetes
Datadog
Datadog
Kafka
Kafka
Apache Spark
Apache Spark

What are some alternatives to RocksDB, ScyllaDB?

MongoDB

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

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

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

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

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

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

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

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

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

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.

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