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  1. Stackups
  2. Application & Data
  3. In-Memory Databases
  4. In Memory Databases
  5. Redis vs Scylla

Redis vs Scylla

OverviewDecisionsComparisonAlternatives

Overview

Redis
Redis
Stacks61.9K
Followers46.5K
Votes3.9K
GitHub Stars42
Forks6
ScyllaDB
ScyllaDB
Stacks143
Followers197
Votes8

Redis vs Scylla: What are the differences?

  1. Key Difference 1: Data Structure: Redis is a key-value store that offers various data structures such as strings, lists, sets, sorted sets, etc., making it highly flexible for different use cases. On the other hand, Scylla is a highly scalable NoSQL database that is built upon Apache Cassandra and focuses on providing wide column store functionality. It organizes data into tables with a defined schema, allowing for efficient querying and storage of large datasets.
  2. Key Difference 2: Data Distribution: Redis utilizes a master-slave replication model, where multiple slave nodes can replicate data from a single master node. This provides high availability and read scalability but limits write scalability. In contrast, Scylla follows a masterless distributed architecture using consistent hashing and gossip protocols. It allows for automatic partitioning and distribution of data across multiple nodes, ensuring high write and read scalability.
  3. Key Difference 3: Durability: Redis offers durability through a combination of techniques such as point-in-time snapshots, append-only files, and asynchronous replication. However, it is optimized for in-memory operations and may not be as suitable for scenarios with large datasets or high write frequencies. Scylla, on the other hand, is designed for durability, supporting both in-memory and on-disk storage. It integrates with distributed file systems and provides configurable levels of data persistence to meet various durability requirements.
  4. Key Difference 4: Consistency Models: Redis supports multiple consistency models, including eventual consistency and strong consistency, depending on the data structure used. It allows applications to choose the level of consistency required for their use case. Scylla follows a tunable consistency model, giving developers the ability to define their desired consistency level on a per-operation basis. This flexibility allows for a trade-off between consistency, availability, and partition tolerance.
  5. Key Difference 5: Query Language: Redis uses a simple query language that operates directly on its data structures. It supports a set of commands and pipelines, making it easy to manipulate and retrieve data. Scylla, being a wide column store, utilizes the CQL (Cassandra Query Language) which is similar to SQL but adapted for use in NoSQL databases. CQL provides powerful querying capabilities with support for filtering, data aggregation, and secondary indexes.
  6. Key Difference 6: Performance and Scaling: Redis is known for its exceptional performance as an in-memory database. It can handle high volumes of operations with low latencies. However, when it comes to scaling beyond the capacity of a single node, Redis requires additional sharding and partitioning mechanisms. Scylla, being designed for horizontal scalability from the ground up, performs exceptionally well at massive scales. It efficiently utilizes hardware resources across a cluster of nodes, making it ideal for high-performance and high-throughput applications.

In Summary, Redis and Scylla differ in terms of their data structure flexibility, data distribution models, durability options, consistency models, query languages, and performance/scaling capabilities.

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Advice on Redis, 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

Redis
Redis
ScyllaDB
ScyllaDB

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

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
42
GitHub Stars
-
GitHub Forks
6
GitHub Forks
-
Stacks
61.9K
Stacks
143
Followers
46.5K
Followers
197
Votes
3.9K
Votes
8
Pros & Cons
Pros
  • 888
    Performance
  • 542
    Super fast
  • 514
    Ease of use
  • 444
    In-memory cache
  • 324
    Advanced key-value cache
Cons
  • 15
    Cannot query objects directly
  • 3
    No secondary indexes for non-numeric data types
  • 1
    No WAL
Pros
  • 2
    Replication
  • 1
    High performance
  • 1
    High availability
  • 1
    Scale up
  • 1
    Distributed
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 Redis, 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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