Citus vs Memcached

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Citus
Citus

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8
Memcached
Memcached

2.7K
1.7K
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452
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Citus vs Memcached: What are the differences?

Developers describe Citus as "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. On the other hand, Memcached is detailed as "High-performance, distributed memory object caching system". 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.

Citus and Memcached belong to "Databases" category of the tech stack.

"Multi-core Parallel Processing" is the primary reason why developers consider Citus over the competitors, whereas "Fast object cache" was stated as the key factor in picking Memcached.

Citus and Memcached are both open source tools. Memcached with 8.99K GitHub stars and 2.6K forks on GitHub appears to be more popular than Citus with 3.64K GitHub stars and 273 GitHub forks.

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.

What is 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.
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Why do developers choose Citus?
Why do developers choose Memcached?

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      What are some alternatives to Citus and Memcached?
      CockroachDB
      Cockroach Labs is the company building CockroachDB, an open source, survivable, strongly consistent, scale-out SQL database.
      TimescaleDB
      TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud.
      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.
      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.
      See all alternatives
      Decisions about Citus and Memcached
      Node.js
      Node.js
      Python
      Python
      MySQL
      MySQL
      Memcached
      Memcached
      nginx
      nginx
      RabbitMQ
      RabbitMQ
      Redis
      Redis
      Django
      Django
      Tornado
      Tornado
      Varnish
      Varnish
      HAProxy
      HAProxy

      Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.

      See more
      Kir Shatrov
      Kir Shatrov
      Production Engineer at Shopify · | 12 upvotes · 78.1K views
      atShopifyShopify
      Rails
      Rails
      MySQL
      MySQL
      Memcached
      Memcached
      Redis
      Redis

      As is common in the Rails stack, since the very beginning, we've stayed with MySQL as a relational database, Memcached for key/value storage and Redis for queues and background jobs.

      In 2014, we could no longer store all our data in a single MySQL instance - even by buying better hardware. We decided to use sharding and split all of Shopify into dozens of database partitions.

      Sharding played nicely for us because Shopify merchants are isolated from each other and we were able to put a subset of merchants on a single shard. It would have been harder if our business assumed shared data between customers.

      The sharding project bought us some time regarding database capacity, but as we soon found out, there was a huge single point of failure in our infrastructure. All those shards were still using a single Redis. At one point, the outage of that Redis took down all of Shopify, causing a major disruption we later called “Redismageddon”. This taught us an important lesson to avoid any resources that are shared across all of Shopify.

      Over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.

      See more
      Kir Shatrov
      Kir Shatrov
      Production Engineer at Shopify · | 13 upvotes · 145.4K views
      atShopifyShopify
      Docker
      Docker
      Kubernetes
      Kubernetes
      Google Kubernetes Engine
      Google Kubernetes Engine
      MySQL
      MySQL
      Redis
      Redis
      Memcached
      Memcached

      At Shopify, over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, Memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.

      As we grew into hundreds of shards and pods, it became clear that we needed a solution to orchestrate those deployments. Today, we use Docker, Kubernetes, and Google Kubernetes Engine to make it easy to bootstrap resources for new Shopify Pods.

      See more
      Dan Robinson
      Dan Robinson
      at Heap, Inc. · | 16 upvotes · 54.1K views
      atHeapHeap
      PostgreSQL
      PostgreSQL
      Citus
      Citus
      #DataStores
      #Databases

      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 · 50.2K views
      atHeapHeap
      Heap
      Heap
      Citus
      Citus
      PostgreSQL
      PostgreSQL