Alternatives to Scylla logo

Alternatives to Scylla

Cassandra, Redis, Aerospike, MongoDB, and Kraken.io are the most popular alternatives and competitors to Scylla.
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What is Scylla and what are its top alternatives?

Real-time big data database, with scale-up performance of 1,000,000 IOPS per node, scale-out to 100s of nodes and 99 latency of less than 1 msec.
Scylla is a tool in the Databases category of a tech stack.
Scylla is an open source tool with 7.3K GitHub stars and 850 GitHub forks. Here’s a link to Scylla's open source repository on GitHub

Top Alternatives to Scylla

  • 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. ...

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

  • Aerospike

    Aerospike

    Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees. ...

  • 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. ...

  • Kraken.io

    Kraken.io

    It supports JPEG, PNG and GIF files. You can optimize your images in two ways - by providing an URL of the image you want to optimize or by uploading an image file directly to its API. ...

  • Clickhouse

    Clickhouse

    It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query. ...

  • 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. ...

Scylla alternatives & related posts

Cassandra logo

Cassandra

3.2K
3.1K
492
A partitioned row store. Rows are organized into tables with a required primary key.
3.2K
3.1K
+ 1
492
PROS OF CASSANDRA
  • 114
    Distributed
  • 95
    High performance
  • 80
    High availability
  • 74
    Easy scalability
  • 52
    Replication
  • 26
    Multi datacenter deployments
  • 26
    Reliable
  • 8
    OLTP
  • 7
    Open source
  • 7
    Schema optional
  • 2
    Workload separation (via MDC)
  • 1
    Fast
CONS OF CASSANDRA
  • 2
    Reliability of replication
  • 1
    Updates

related Cassandra posts

Thierry Schellenbach
Shared insights
on
RedisRedisCassandraCassandraRocksDBRocksDB
at

1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

#InMemoryDatabases #DataStores #Databases

See more
Umair Iftikhar
Technical Architect at Vappar · | 3 upvotes · 136.8K views

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

See more
Redis logo

Redis

43K
32.1K
3.9K
An in-memory database that persists on disk
43K
32.1K
+ 1
3.9K
PROS OF REDIS
  • 875
    Performance
  • 535
    Super fast
  • 511
    Ease of use
  • 441
    In-memory cache
  • 321
    Advanced key-value cache
  • 190
    Open source
  • 179
    Easy to deploy
  • 163
    Stable
  • 153
    Free
  • 120
    Fast
  • 40
    High-Performance
  • 39
    High Availability
  • 34
    Data Structures
  • 32
    Very Scalable
  • 23
    Replication
  • 20
    Great community
  • 19
    Pub/Sub
  • 17
    "NoSQL" key-value data store
  • 14
    Hashes
  • 12
    Sets
  • 10
    Sorted Sets
  • 9
    Lists
  • 8
    BSD licensed
  • 8
    NoSQL
  • 7
    Async replication
  • 7
    Integrates super easy with Sidekiq for Rails background
  • 7
    Bitmaps
  • 6
    Open Source
  • 6
    Keys with a limited time-to-live
  • 5
    Strings
  • 5
    Lua scripting
  • 4
    Awesomeness for Free!
  • 4
    Hyperloglogs
  • 3
    outstanding performance
  • 3
    Runs server side LUA
  • 3
    Networked
  • 3
    LRU eviction of keys
  • 3
    Written in ANSI C
  • 3
    Feature Rich
  • 3
    Transactions
  • 2
    Data structure server
  • 2
    Performance & ease of use
  • 1
    Existing Laravel Integration
  • 1
    Automatic failover
  • 1
    Easy to use
  • 1
    Object [key/value] size each 500 MB
  • 1
    Simple
  • 1
    Channels concept
  • 1
    Scalable
  • 1
    Temporarily kept on disk
  • 1
    Dont save data if no subscribers are found
  • 0
    Jk
CONS OF REDIS
  • 14
    Cannot query objects directly
  • 2
    No secondary indexes for non-numeric data types
  • 1
    No WAL

related Redis posts

Robert Zuber

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

See more

I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

See more
Aerospike logo

Aerospike

148
208
36
Flash-optimized in-memory open source NoSQL database
148
208
+ 1
36
PROS OF AEROSPIKE
  • 11
    Ram and/or ssd persistence
  • 10
    Easy clustering support
  • 5
    Easy setup
  • 3
    Acid
  • 2
    Performance better than Redis
  • 2
    Petabyte Scale
  • 2
    Scale
  • 1
    Ease of use
CONS OF AEROSPIKE
    Be the first to leave a con

    related Aerospike posts

    MongoDB logo

    MongoDB

    64.4K
    53.7K
    4.1K
    The database for giant ideas
    64.4K
    53.7K
    + 1
    4.1K
    PROS OF MONGODB
    • 824
      Document-oriented storage
    • 591
      No sql
    • 546
      Ease of use
    • 465
      Fast
    • 406
      High performance
    • 256
      Free
    • 215
      Open source
    • 179
      Flexible
    • 142
      Replication & high availability
    • 109
      Easy to maintain
    • 41
      Querying
    • 37
      Easy scalability
    • 36
      Auto-sharding
    • 35
      High availability
    • 31
      Map/reduce
    • 26
      Document database
    • 24
      Easy setup
    • 24
      Full index support
    • 15
      Reliable
    • 14
      Fast in-place updates
    • 13
      Agile programming, flexible, fast
    • 11
      No database migrations
    • 7
      Easy integration with Node.Js
    • 7
      Enterprise
    • 5
      Enterprise Support
    • 4
      Great NoSQL DB
    • 3
      Aggregation Framework
    • 3
      Support for many languages through different drivers
    • 3
      Drivers support is good
    • 2
      Schemaless
    • 2
      Easy to Scale
    • 2
      Fast
    • 2
      Awesome
    • 2
      Managed service
    • 1
      Consistent
    CONS OF MONGODB
    • 5
      Very slowly for connected models that require joins
    • 3
      Not acid compliant
    • 1
      Proprietary query language

    related MongoDB posts

    Jeyabalaji Subramanian

    Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

    We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

    Based on the above criteria, we selected the following tools to perform the end to end data replication:

    We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

    We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

    In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

    Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

    In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

    See more
    Robert Zuber

    We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

    As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

    When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

    See more
    Kraken.io logo

    Kraken.io

    13
    43
    6
    Image optimization and compression API
    13
    43
    + 1
    6
    PROS OF KRAKEN.IO
    • 5
      Free
    • 1
      Magento plugin
    CONS OF KRAKEN.IO
      Be the first to leave a con

      related Kraken.io posts

      Clickhouse logo

      Clickhouse

      243
      320
      58
      A column-oriented database management system
      243
      320
      + 1
      58
      PROS OF CLICKHOUSE
      • 15
        Fast, very very fast
      • 10
        Good compression ratio
      • 5
        Horizontally scalable
      • 4
        RESTful
      • 4
        Utilizes all CPU resources
      • 4
        Great CLI
      • 3
        Has no transactions
      • 3
        Great number of SQL functions
      • 2
        Buggy
      • 2
        Open-source
      • 1
        In IDEA data import via HTTP interface not working
      • 1
        Server crashes its normal :(
      • 1
        Highly available
      • 1
        Flexible compression options
      • 1
        Flexible connection options
      • 1
        ODBC
      CONS OF CLICKHOUSE
      • 2
        Slow insert operations

      related Clickhouse posts

      MySQL logo

      MySQL

      85.1K
      68.8K
      3.7K
      The world's most popular open source database
      85.1K
      68.8K
      + 1
      3.7K
      PROS OF MYSQL
      • 793
        Sql
      • 672
        Free
      • 555
        Easy
      • 526
        Widely used
      • 485
        Open source
      • 180
        High availability
      • 160
        Cross-platform support
      • 103
        Great community
      • 78
        Secure
      • 75
        Full-text indexing and searching
      • 25
        Fast, open, available
      • 14
        SSL support
      • 13
        Reliable
      • 13
        Robust
      • 8
        Enterprise Version
      • 7
        Easy to set up on all platforms
      • 2
        NoSQL access to JSON data type
      • 1
        Relational database
      • 1
        Easy, light, scalable
      • 1
        Sequel Pro (best SQL GUI)
      • 1
        Replica Support
      CONS OF MYSQL
      • 14
        Owned by a company with their own agenda
      • 1
        Can't roll back schema changes

      related MySQL posts

      Tim Abbott

      We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

      We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

      And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

      I can't recommend it highly enough.

      See more
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 21 upvotes · 1.1M views

      Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

      The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

      https://eng.uber.com/mysql-migration/

      See more
      PostgreSQL logo

      PostgreSQL

      65K
      52.2K
      3.5K
      A powerful, open source object-relational database system
      65K
      52.2K
      + 1
      3.5K
      PROS OF POSTGRESQL
      • 755
        Relational database
      • 508
        High availability
      • 436
        Enterprise class database
      • 380
        Sql
      • 302
        Sql + nosql
      • 171
        Great community
      • 145
        Easy to setup
      • 129
        Heroku
      • 128
        Secure by default
      • 111
        Postgis
      • 48
        Supports Key-Value
      • 46
        Great JSON support
      • 32
        Cross platform
      • 30
        Extensible
      • 26
        Replication
      • 24
        Triggers
      • 22
        Rollback
      • 21
        Multiversion concurrency control
      • 20
        Open source
      • 17
        Heroku Add-on
      • 14
        Stable, Simple and Good Performance
      • 13
        Powerful
      • 12
        Lets be serious, what other SQL DB would you go for?
      • 9
        Good documentation
      • 7
        Intelligent optimizer
      • 7
        Scalable
      • 6
        Reliable
      • 6
        Transactional DDL
      • 6
        Modern
      • 5
        Free
      • 5
        One stop solution for all things sql no matter the os
      • 4
        Relational database with MVCC
      • 3
        Faster Development
      • 3
        Full-Text Search
      • 3
        Developer friendly
      • 2
        Excellent source code
      • 2
        Great DB for Transactional system or Application
      • 2
        search
      • 1
        Free version
      • 1
        Open-source
      • 1
        Full-text
      • 1
        Text
      CONS OF POSTGRESQL
      • 9
        Table/index bloatings

      related PostgreSQL posts

      Jeyabalaji Subramanian

      Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

      We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

      Based on the above criteria, we selected the following tools to perform the end to end data replication:

      We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

      We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

      In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

      Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

      In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

      See more
      Tim Abbott

      We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

      We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

      And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

      I can't recommend it highly enough.

      See more