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.

Welcome to the fourth episode of getting started with Dgraph. In the previous episode, we learned about Datatypes, Indexing, Filtering, and Reverse traversals in Dgraph. In this episode, we'll learn about using multi-language strings and operations on them using the language tags. Strings and languages Strings values in Dgraph are of UTF-8 format. Dgraph also supports values for string predicate types in multiple languages. The multi-lingual capability is particularly useful to build features, which requires you to store the same information in multiple languages.

Welcome to the third episode of getting started with Dgraph. In the previous episode of the tutorial, we learned about the CRUD operations using UIDs. We also learned about traversals and recursive traversals. In this tutorial, we'll learn about Dgraph's basic types and how to query for them. Specifically, we'll learn about: Basic data types in Dgraph. Querying for predicate values. Indexing. Filtering nodes. Traversing in reverse direction. Here's the complimentary video for this blog post.

“GraphQL is not a Graph DB query language — it's a replacement for REST APIs." That's my standard opening statement whenever talking about Dgraph's choice of the query language. Most people who don't know GraphQL assume it's a graph database query language. Most people who know GraphQL wonder how a graph database is being powered by it. Our focus at Dgraph has always been on helping you build apps using an amazingly advanced graph database.

Welcome to the second episode of getting started with Dgraph. In the last episode of the tutorial, we learned some of the basics of Dgraph. Including how to run the database, add new nodes and predicates, and query them back. In this episode, we'll build the above Graph and learn more about operations using the UID (Universal Identifier) of the nodes. Specifically, we'll learn about: Querying and updating nodes, deleting predicates using their UIDs.

Welcome to getting started with Dgraph. Dgraph is an open-source, transactional, distributed, native Graph Database. Here is the first episode of the tutorial series on using Dgraph. In this tutorial, we'll learn how to build the following graph on Dgraph, In the process, we'll learn about: Running Dgraph using the dgraph/standalone docker image. Running the following basic operations using Dgraph's UI Ratel, Creating a node. Creating an edge between two nodes.

This post made it to the top of Golang subreddit and is trending in top 10 on the front page of Hacker News. Do engage in discussion there and show us love by giving us a star. With over six months of research and development, we're proud to announce the initial release of Ristretto: A High Performance, Concurrent, Memory-Bound Go cache. It is contention-proof, scales well and provides consistently high hit-ratios.