Dgraph

Dgraph shards the data to horizontally scale to hundreds of servers. It is designed to minimize the number of disk seeks and network calls. Dgraph is built like a search engine. Queries are broken into sub-queries, which run concurrently to achieve low-latency and high throughput. Dgraph can easily scale to multiple machines, or datacenters. Its sharded storage and query processing were specifically designed to minimize the number of network calls.

Languages supported:

9.6/10 (Expert Score) ★★★★★
Product is rated as #3 in category Graph Databases
Ease of use
9.2
Support
9.6
Ease of Setup
9.2

Images

Check Software Images

Dgraph is the world’s most advanced GraphQL database with a graph backend. The number one graph database on GitHub and over 500,000 downloads every month, Dgraph is built for performance and scalability.

Jepsen tested, it has the best performance, returning millisecond query responses on terabytes of data. Dgraph is ideal for a range of use cases, from customer 360 and fraud detection to complicated queries with multi-hops and arbitrary-depth joins. Strong performance and memory management make the graph database ideal for enterprises while Dgraph Cloud makes it quick and easy for app developers to launch a project over the weekend.

Scale from zero to billions of records effortlessly. Available in open source and hosted versions (Dgraph Cloud) and enterprise license.

Show more categories

Customer Reviews

Dgraph Reviews

aditya g.

Advanced user of Dgraph
★★★★★
review on Dgrpah

What do you like best?

I like high hop query performance with optimal indexing and schema. One hop query is equivalent to fetching all connected edges of a node. A two-hop query is equivalent to fetching all connected nodes of the result of the one-hop query.

We are able to store a very complex data of addresses. The data is unbounded in nature and Dgraph has been tested on 2X size of current data.

There is one other thing to highlight, which is query on reverse edge. These queries are very fast compared to other databases with same indexing and schema.

What do you dislike?

Dgraph requires high RAM to store a graph compare to other graph offerings. The managed offering dosen't provide all functionality which are possible using own kubernates cluster. The management has to be done by yourself or the devops team of the organization.

Recommendations to others considering the product:

I would recommend Dgraph if you can manage it yourself and have a huge data size.

What problems are you solving with the product? What benefits have you realized?

We are storing a vast database of addresses in India and querying the same for new addresses. It is helping us to fetch data for nearby addresses based on the historical data available.

The benifits include

query performance: Based on response time of same query on different databases with same data and schema.

scaling: Self managed kubernates cluster is easy to scale and it takes care of data being replicated from old nodes and new nodes.

loading time of data: The bulk load is way faster in nature to load one time complete data. Live load can be used to add incremental data.

Review source: G2.com

Leave a reply

Your total score

B2B Software Guide