Redis

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I needed to implement the "Top Contributors" feature of StackShare, that gets updated whenever someone posts a decision. I hesitated between 2 solutions for data storage:

1. Store real time updates in a Redis sorted set. It's like having a big in-memory hash, but in Redis.

   • Pros: Best for pure real time feedback. Also, Redis sorted sets are cool!
   • Cons: Web devs may not be familiar with Redis sorted sets. Also, we still have to query our traditional DB to enrich Redis results.

2. Add a cached_score column for the PostgreSQL records shown in the leaderboard. This column is recalculated in our app whenever a record is updated. Make that column indexable for sorting, sort the records by descending order, and voilà, you have leaderboards. Cache the result for X seconds to keep DB queries low while still keeping a more or less "real time" feel.

   • Pros: Web devs are more familiar with traditional DB sorting code.
   • Cons: Every X seconds, you still have to do a DB sort on a big table.

We ended up choosing solution 2 with PostgreSQL for a couple reasons. 1) We currently don't treat Redis as persistent storage and don't have redundancy in place, so data could disappear. 2) Up-to-the-second real time updates weren't mandatory. #Leaderboards #StackDecisionsLaunch

I'm building a new process management tool. I decided to build with Rails as my backend, using Sidekiq for background jobs. I chose to work with these tools because I've worked with them before and know that they're able to get the job done. They may not be the sexiest tools, but they work and are reliable, which is what I was optimizing for. For data stores, I opted for PostgreSQL and Redis. Because I'm planning on offering dashboards, I wanted a SQL database instead of something like MongoDB that might work early on, but be difficult to use as soon as I want to facilitate aggregate queries.

On the front-end I'm using Vue.js and vuex in combination with #Turbolinks. In effect, I want to render most pages on the server side without key interactions being managed by Vue.js . This is the first project I'm working on where I've explicitly decided not to include jQuery . I have found React and Redux.js more confusing to setup. I appreciate the opinionated approach from the Vue.js community and that things just work together the way that I'd expect. To manage my javascript dependencies, I'm using Yarn .

For CSS frameworks, I'm using #Bulma.io. I really appreciate it's minimal nature and that there are no hard javascript dependencies. And to add a little spice, I'm using #font-awesome.

Uploadcare has built an infinitely scalable infrastructure by leveraging AWS. Building on top of AWS allows us to process 350M daily requests for file uploads, manipulations, and deliveries. When we started in 2011 the only cloud alternative to AWS was Google App Engine which was a no-go for a rather complex solution we wanted to build. We also didn’t want to buy any hardware or use co-locations.

Our stack handles receiving files, communicating with external file sources, managing file storage, managing user and file data, processing files, file caching and delivery, and managing user interface dashboards.

At its core, Uploadcare runs on Python. The Europython 2011 conference in Florence really inspired us, coupled with the fact that it was general enough to solve all of our challenges informed this decision. Additionally we had prior experience working in Python.

We chose to build the main application with Django because of its feature completeness and large footprint within the Python ecosystem.

All the communications within our ecosystem occur via several HTTP APIs, Redis, Amazon S3, and Amazon DynamoDB. We decided on this architecture so that our our system could be scalable in terms of storage and database throughput. This way we only need Django running on top of our database cluster. We use PostgreSQL as our database because it is considered an industry standard when it comes to clustering and scaling.

Sentry started as (and remains) an open-source project, growing out of an error logging tool built in 2008. That original build nine years ago was Django and Celery (Python’s asynchronous task codebase), with PostgreSQL as the database and Redis as the power behind Celery.

We displayed a truly shrewd notion of branding even then, giving the project a catchy name that companies the world over remain jealous of to this day: django-db-log. For the longest time, Sentry’s subtitle on GitHub was “A simple Django app, built with love.” A slightly more accurate description probably would have included Starcraft and Soylent alongside love; regardless, this captured what Sentry was all about.

#MessageQueue #InMemoryDatabases

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

Our real time infrastructure is based on Go , Redis and the excellent gorilla websocket library. It implements the Bayeux protocol.

In terms of architecture it’s very similar to the node based Faye library. It was interesting to read the “Ditching Go for Node.js” post on Hacker News. The author moves from Go to Node to improve performance. We actually did the exact opposite and moved from Node to Go for our real time system. The new Go-based infrastructure handles 8x the traffic per node. #InMemoryDatabases #RealtimeBackendApi #ApplicationHosting #Languages #DataStores

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

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.