What is Scala and what are its top alternatives?
Scala alternatives & related posts
related Kotlin posts
As the WeWork footprint continued to expand, in mid-2018 the team began to explore the next generation of identity management to handle the global scale of the business.
The team decided to vet three languages for building microservices: Go, Kotlin, and Ruby. They compared the three by building a component of an identity system in each, and assessing the performance apples-to-apples.
After building out the systems and load testing each one, the team decided to implement the new system in Go for a few reasons. In addition to better performance under heavy loads, Go, according to the team, is a simpler language that will constrain developers to simpler code. Additionally, the development lifecycle is simpler with Go, since “there is little difference between running a service directly on a dev machine, to running it in a container, to running clustered instances of the service.”
In the implementation, they the Go grpc framework to handle various common infrastructure patterns, resulting in “in a clean common server pattern that we can reuse across our microservices.”
Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.Apps
- Desktop: And Electron to ship it as a desktop application.
- Android: a mix of Java and Kotlin.
- iOS: written in a mix of Objective C and Swift.
- The core application and the API written in PHP/Hack that runs on HHVM.
- The data is stored in MySQL using Vitess.
- Caching is done using Memcached and MCRouter.
- The search service takes help from SolrCloud, with various Java services.
- The messaging system uses WebSockets with many services in Java and Go.
- Load balancing is done using HAproxy with Consul for configuration.
- Most services talk to each other over gRPC,
- Some Thrift and JSON-over-HTTP
- Voice and video calling service was built in Elixir.
- Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
related Python posts
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
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!
related Clojure posts
Stitch is run entirely on AWS. All of our transactional databases are run with Amazon RDS, and we rely on Amazon S3 for data persistence in various stages of our pipeline. Our product integrates with Amazon Redshift as a data destination, and we also use Redshift as an internal data warehouse (powered by Stitch, of course).
The majority of our services run on stateless Amazon EC2 instances that are managed by AWS OpsWorks. We recently introduced Kubernetes into our infrastructure to run the scheduled jobs that execute Singer code to extract data from various sources. Although we tend to be wary of shiny new toys, Kubernetes has proven to be a good fit for this problem, and its stability, strong community and helpful tooling have made it easy for us to incorporate into our operations.
I adopted Clojure and ClojureScript because:
- it's 1 language, multiple platforms.
- Simple syntax.
- Designed to avoid unwanted side effects and bugs.
- Immutable data-structures.
- Compact code, very expressive.
- Source code is data.
- It has super-flexible macro.
- Has metadata.