Welcome to the eighth episode of getting started with Dgraph. In the previous episode, we learned about building a twitter-like user-search feature using Dgraph's fuzzy search. In this episode, we'll build a graph of tourist locations around San Francisco and help our Zoologist friend, Mary, and her team in their mission to conserve birds using Dgraph's geolocation capabilities. You might have used Google to find the restaurants near you or to find the shopping centres within a mile of your current location.

Welcome to the seventh episode of getting started with Dgraph. In the previous episode, we learned about building advanced text searches on social graphs in Dgraph, by modeling tweets as an example. We queried the tweets using the full-text and trigram indices and implemented full-text and regular-expression based search on the tweets. In this tutorial, we'll continue exploring Dgraph's string querying capabilities using the twitter model from the fifth and the sixth tutorial.

We are excited to announce that the “Get Started with Dgraph” of our documentation site now includes the episodes from our getting started blog series. This blog series consists of various tutorials that are helpful for both beginners and intermediates to get started with Dgraph and to understand the product functionalities better. At the time of writing, we’ve published six tutorial episodes, and you can find all of them on the docs.

It's been a bit over three months since we released Dgraph v1.1.0 (see the announcement) but the wait and hard work were well worth it because today we announce that Dgraph v1.1.1 is released! This blog post will cover the biggest changes since v1.1.0, including new features, bug fixes, and other improvements. For those in a hurry, these are the changes covered in this article. Breaking changes Removing expand _reverse_ and _forward_ No more @type directive New features Use @cascade and @normalize anywhere Improved support for upsert blocks Changes on types Better facets Transparent data encryption Breaking changes While this is not a patch release, to fix some of the issues identified with v1.

Welcome to the sixth episode of getting started with Dgraph. In the previous episode, we learned about building social graphs in Dgraph, by modeling tweets as an example. We queried the tweets using the hash and exact indices, and implemented a keyword-based search to find your favorite tweets using the term index and its functions. In this tutorial, we'll continue from where we left off and learn about advanced text search features in Dgraph.

We took part in the recently held GraphQL Summit in San Francisco. The conference was exciting, and it had a great line up of talks. As expected, the enthusiasm in the GraphQL community was unmatchable. We took part as one of the platinum sponsors of the event, and we also unveiled the new GraphQL API for Dgraph. Read along as we share our experience at the event as it unfolded.

Welcome to the fifth episode of getting started with Dgraph. In the previous episode, we learned about using multi-language strings and operations on them using language tags. In this episode, we'll model tweets in Dgraph and, using it, we'll learn more about string indices in Dgraph. We'll specifically learn about: Modeling tweets in Dgraph. Using String indices in Dgraph Querying twitter users using the hash index. Comparing strings using the exact index.

Dgraph is an open-source, transactional, distributed, native Graph database. Dgraph is optimized for high-performance reads and writes. It can serve queries and mutations with low latency and high throughput, even when they involve deep joins and traversals. Much of Dgraph's advanced performance capabilities come from Badger. Badger is the embedded key-value store that is responsible for storing all of Dgraph's data. Badger itself is not distributed, Dgraph implements a layer on top of it to provide the distributed capabilities.