Amazon RDS for Aurora vs Citus

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Amazon RDS for Aurora
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Amazon RDS for Aurora vs Citus: What are the differences?

What is Amazon RDS for Aurora? MySQL and PostgreSQL compatible relational database with several times better performance. Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

What is Citus? Worry-free Postgres for SaaS. Built to scale out. Citus is worry-free Postgres for SaaS. Made to scale out, Citus is an extension to Postgres that distributes queries across any number of servers. Citus is available as open source, as on-prem software, and as a fully-managed service.

Amazon RDS for Aurora and Citus are primarily classified as "SQL Database as a Service" and "Databases" tools respectively.

Some of the features offered by Amazon RDS for Aurora are:

  • High Throughput with Low Jitter
  • Push-button Compute Scaling
  • Storage Auto-scaling

On the other hand, Citus provides the following key features:

  • Multi-Node Scalable PostgreSQL
  • Built-in Replication and High Availability
  • Real-time Reads/Writes On Multiple Nodes

"MySQL compatibility " is the top reason why over 11 developers like Amazon RDS for Aurora, while over 3 developers mention "Multi-core Parallel Processing" as the leading cause for choosing Citus.

Citus is an open source tool with 3.5K GitHub stars and 263 GitHub forks. Here's a link to Citus's open source repository on GitHub.

- No public GitHub repository available -

What is Amazon RDS for Aurora?

Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

What is Citus?

It's an extension to Postgres that distributes data and queries in a cluster of multiple machines. Its query engine parallelizes incoming SQL queries across these servers to enable human real-time (less than a second) responses on large datasets.
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Why do developers choose Amazon RDS for Aurora?
Why do developers choose Citus?

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    What are some alternatives to Amazon RDS for Aurora and Citus?
    Amazon RDS
    Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.
    Google Cloud SQL
    MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don’t run out of space and give your application the redundant, reliable storage it needs.
    ClearDB
    ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.
    DigitalOcean Managed Databases
    Build apps and store data in minutes with easy access to one or more databases and sleep better knowing your data is backed up and optimized.
    Azure Database for MySQL
    Azure Database for MySQL provides a managed database service for app development and deployment that allows you to stand up a MySQL database in minutes and scale on the fly – on the cloud you trust most.
    See all alternatives
    Decisions about Amazon RDS for Aurora and Citus
    Dan Robinson
    Dan Robinson
    at Heap, Inc. · | 16 upvotes · 53.4K views
    atHeapHeap
    Citus
    Citus
    PostgreSQL
    PostgreSQL
    #Databases
    #DataStores

    PostgreSQL was an easy early decision for the founding team. The relational data model fit the types of analyses they would be doing: filtering, grouping, joining, etc., and it was the database they knew best.

    Shortly after adopting PG, they discovered Citus, which is a tool that makes it easy to distribute queries. Although it was a young project and a fork of Postgres at that point, Dan says the team was very available, highly expert, and it wouldn’t be very difficult to move back to PG if they needed to.

    The stuff they forked was in query execution. You could treat the worker nodes like regular PG instances. Citus also gave them a ton of flexibility to make queries fast, and again, they felt the data model was the best fit for their application.

    #DataStores #Databases

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    Dan Robinson
    Dan Robinson
    at Heap, Inc. · | 14 upvotes · 44K views
    atHeapHeap
    Heap
    Heap
    Node.js
    Node.js
    Kafka
    Kafka
    PostgreSQL
    PostgreSQL
    Citus
    Citus
    #FrameworksFullStack
    #Databases
    #MessageQueue

    At Heap, we searched for an existing tool that would allow us to express the full range of analyses we needed, index the event definitions that made up the analyses, and was a mature, natively distributed system.

    After coming up empty on this search, we decided to compromise on the “maturity” requirement and build our own distributed system around Citus and sharded PostgreSQL. It was at this point that we also introduced Kafka as a queueing layer between the Node.js application servers and Postgres.

    If we could go back in time, we probably would have started using Kafka on day one. One of the biggest benefits in adopting Kafka has been the peace of mind that it brings. In an analytics infrastructure, it’s often possible to make data ingestion idempotent.

    In Heap’s case, that means that, if anything downstream from Kafka goes down, we won’t lose any data – it’s just going to take a bit longer to get to its destination. We also learned that you want the path between data hitting your servers and your initial persistence layer (in this case, Kafka) to be as short and simple as possible, since that is the surface area where a failure means you can lose customer data. We learned that it’s a very good fit for an analytics tool, since you can handle a huge number of incoming writes with relatively low latency. Kafka also gives you the ability to “replay” the data flow: it’s like a commit log for your whole infrastructure.

    #MessageQueue #Databases #FrameworksFullStack

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    Tim Specht
    Tim Specht
    ‎Co-Founder and CTO at Dubsmash · | 13 upvotes · 56.7K views
    atDubsmashDubsmash
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    Redis
    Redis
    Amazon DynamoDB
    Amazon DynamoDB
    Amazon RDS
    Amazon RDS
    Heroku
    Heroku
    PostgreSQL
    PostgreSQL
    #PlatformAsAService
    #Databases
    #NosqlDatabaseAsAService
    #SqlDatabaseAsAService

    Over the years we have added a wide variety of different storages to our stack including PostgreSQL (some hosted by Heroku, some by Amazon RDS) for storing relational data, Amazon DynamoDB to store non-relational data like recommendations & user connections, or Redis to hold pre-aggregated data to speed up API endpoints.

    Since we started running Postgres ourselves on RDS instead of only using the managed offerings of Heroku, we've gained additional flexibility in scaling our application while reducing costs at the same time.

    We are also heavily testing Amazon RDS for Aurora in its Postgres-compatible version and will also give the new release of Aurora Serverless a try!

    #SqlDatabaseAsAService #NosqlDatabaseAsAService #Databases #PlatformAsAService

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    Julien DeFrance
    Julien DeFrance
    Principal Software Engineer at Tophatter · | 16 upvotes · 361.4K views
    atSmartZipSmartZip
    Amazon DynamoDB
    Amazon DynamoDB
    Ruby
    Ruby
    Node.js
    Node.js
    AWS Lambda
    AWS Lambda
    New Relic
    New Relic
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    Elasticsearch
    Elasticsearch
    Superset
    Superset
    Amazon Quicksight
    Amazon Quicksight
    Amazon Redshift
    Amazon Redshift
    Zapier
    Zapier
    Segment
    Segment
    Amazon CloudFront
    Amazon CloudFront
    Memcached
    Memcached
    Amazon ElastiCache
    Amazon ElastiCache
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    MySQL
    MySQL
    Amazon RDS
    Amazon RDS
    Amazon S3
    Amazon S3
    Docker
    Docker
    Capistrano
    Capistrano
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Rails API
    Rails API
    Rails
    Rails
    Algolia
    Algolia

    Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

    I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

    For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

    Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

    Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

    Future improvements / technology decisions included:

    Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

    As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

    One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

    See more
    Interest over time
    Reviews of Amazon RDS for Aurora and Citus
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    How developers use Amazon RDS for Aurora and Citus
    Avatar of Secumail
    Secumail uses Amazon RDS for AuroraAmazon RDS for Aurora

    Managed MySQL clustered database so I dont have to deal with the required infrastructure

    Avatar of RedLine13
    RedLine13 uses Amazon RDS for AuroraAmazon RDS for Aurora

    Core database for managing users, teams, tests, and result summaries

    Avatar of Yaakov Gesher
    Yaakov Gesher uses Amazon RDS for AuroraAmazon RDS for Aurora

    We moved our database from compose.io to AWS for speed and price.

    Avatar of Bùi Thanh
    Bùi Thanh uses Amazon RDS for AuroraAmazon RDS for Aurora
    • Performance, HA and Scalable.
    • AutoScale replicas.
    How much does Amazon RDS for Aurora cost?
    How much does Citus cost?