Dgraph vs Tigergraph

Dgraph

TigerGraph

Native GraphQL Support

Yes

Only DB to natively support GraphQL

No

Distributed ACID Transactions

• Only single server ACID transactions, not distributed.
• You cannot update the graph. Distributed queries currently (as of v2.1) can only read the graph data.
• Reference

Horizontally Scalable

• To go from a non-HA cluster to an HA-cluster, the database must be cleared and shut down first before adding new nodes.
• Reference 1
• Reference 2

Distributed Graph database

Yes

Yes

Consistent Replication

Linearizable Reads

Open Source:

• Yes, Apache 2.0.
• Proprietary code base is source-available.
• GitHub

No

Tested for correctness, fault
tolerance and durability

Suitability as a primary database to
build apps/data platform on

Query Language(s)

• GraphQL
• GraphQL Foundation founding member
• GraphQL± (Variation of GraphQL supporting advanced features)

Multiple language drivers

• Official: (Java, JavaScript, Go, Pythonk, C#)
• Unofficial (Rust, Dart, Elixir)
• Reference

Protocols

Import/export

• Using BulkLoader or LiveLoader, Dgraph can read the data as is with no modification needed
• Supported data formats are JSON and RDF
• Exporting database is explained here

• Export: None
• Import: CSV

Backups

• Binary format
• Both full and incremental backups to files, S3 and Google storage via Minio
• Live backups with no downtime
• Reference

• Proprietary tarball per node
• No live backup, must turn off writes for consistency.
• Reference

Point in time recovery

Multi-region deployments

Yes

No

Management of runaway queries

• Context cancellation which works across clients and servers. So, a context cancellation at the client level would automatically cancel the query at all involved servers
• OpenCensus integration , which allows distributed tracing all the way from app to Dgraph cluster and back.
• Open standards for query context cancellation and tracking

Pricing and Free trial

• Open source version is under Apache 2.0, so free to use and modify.
• Enterprise version pricing is based on the number of instances of Dgraph, not the number of cores / RAM / Disk, etc…
• Enterprise 30-day trial

• Developer Edition
• Enterprise
• Cloud
• Enterprise 30-day trial

Sharding

• Predicate-based sharding . Avoids N+1 problem and network broadcasts when running a query in high fanout scenarios. This ensures low-latency query execution, irrespective of the size of the cluster or the number of intermediate results. Reference
• Consistent production level latencies and consistent queries Reference
• Automatic sharding
• Sharding a single predicate on the roadmap

High availability

• Yes, HA Cluster Setup is explained here
• HA Cluster setup is available in Community Edition.

Support for graph algorithms

• Shortest k-paths
• Edge traversal limit to determine cycles in graphs
• Others requested from community listed here

Apache Spark integration

Kafka integration

Neo4j Migration tool

SQL Migration tool

Multi-database features

Graph Database As A Service (DBaaS)

Query execution plans

Authentication and authorization

• JSON web tokens
• ACLs for enterprise
• Integration with LDAP on the roadmap

• Role-Based access control and LDAP
• Graph-based access control

Transparent data encryption

• No file-level encryption.
• Encryption-at-rest relies on filesystem-level or disk-level encryption only. Reference