Introducing Badger: A fast key-value store written natively in Go
We have built an efficient and persistent log structured merge (LSM) tree based key-value store, natively 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. We also dream that graph database would no longer run as a secondary database. By building a truly robust piece of technology, we can have our users run only one database, which allows arbitrarily complex queries while providing rock solid performance.
Neo4j vs Dgraph - The numbers speak for themselves
- Loading data
- Issues faced
- Principles behind Dgraph
Releasing Dgraph v0.7.1
Dgraph team is super excited to present v0.7.1 of Dgraph . This version is the biggest step we’ve taken towards our production aim of v1.0. We’ve implemented 90% of all the features we had planned in our product roadmap, including replication and high-availability using RAFT protocol, indexing, filtering, sorting, geospatial queries, and backups.