Build a Realtime Recommendation Engine: Part 1
Preface In today’s world, user experience is paramount. It’s no longer about basic CRUD, just serving user data; it’s about mining the data to generate interesting predictions and suggesting actions to the user. That’s the field of recommendations. They’re everywhere. In fact, they happen so frequently that you don’t even realize them. You wake up and open Facebook, which shows you a feed of articles that it has chosen for you based on your viewing history.
go get github.com/dgraph-io/dgraph/...
Thank you Go community for all the love that you showered on Badger. Within 8 hours of announcing Badger, the blog post made it to the first page of Hacker News. And within three days, the Github repo received 1250 stars, having crossed 1500 by the time of this post. We have already merged contributions and received feedback that we need to work on. All this goes to show how much people enjoy Go native libraries.
Introducing Badger: A fast key-value store written purely in Go
We have built an efficient and persistent log structured merge (LSM) tree based key-value store, purely in Go language. It is based upon WiscKey paper included in USENIX FAST 2016. This design is highly SSD-optimized and separates keys from values to minimize I/O amplification; leveraging both the sequential and the random performance of SSDs. We call it Badger. Based on benchmarks, Badger is at least 3.5x faster than RocksDB when doing random reads.
String matching in Dgraph v0.7.5
The recent release of Dgraph is packed with new features and improvements. Many of them are related to strings - full text search (with support for 15 languages!) and regular expression matching have been added, and handling of string values in multiple languages was greatly improved. All of these changes make Dgraph an excellent tool for working with multilingual applications.
Building a long lasting company around open-source
Dgraph started with the idea that every startup should be able to have the same level of technology as run by big giants. We designed Dgraph from ground-up to allow data sharding, horizontal scalability, consistent replication, and a fast and distributed architecture. Update (Apr 2018): Dgraph has switched back to Apache 2.0, with a Commons clause restriction. See new post here. We also dream that graph database would no longer run as a secondary database.