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Databases · head to head

Apache Flink vs Dgraph

Apache Flink logo

Apache Flink

Databases

Stateful stream processing at scale

From
Free
Rated
-
Full reviewWebsite
Dgraph logo

Dgraph

Databases

Apache 2.0 distributed graph database written in Go, maintained by Hypermode, queried through GraphQL or its own DQL language.

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Flink genuinely difficult: event time, watermarks and state backends are a real conceptual load before anything works; Dgraph sharding is by predicate, so a single very hot predicate lives entirely in one Raft group and cannot be split further; adding nodes does not relieve it and the fix is a data model change.
  • They diverge on capability: Apache Flink covers Event-time processing, Dgraph covers Apache 2.0 licence.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Flink and Dgraph actually diverge.

Attributes where Apache Flink and Dgraph differ
AttributeApache FlinkDgraph
Pricing modelOpen source, no licence fee; managed services billed separatelyfreemium
PlatformsLinux, Kubernetes, Docker, Self-hostedLinux, Mac, Docker, Web
FoundedUnknown2016

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Flink

  • Event-time processing
  • Exactly-once state
  • Batch and stream
  • SQL interface

Only in Dgraph

  • Apache 2.0 licence
  • Generated GraphQL API
  • DQL query language
  • Predicate sharding
  • Raft replication
  • Distributed ACID transactions
  • Written in Go
  • Full-text and geo indexing

What people use each for

The jobs each tool is most often brought in to do.

Apache Flink

  • Real-time aggregations and dashboards computed over an event streamnot Dgraph
  • Fraud and anomaly detection where patterns span a time windownot Dgraph
  • Joining two live streams where events arrive out of ordernot Dgraph

Dgraph

  • An application whose core data is a graph, such as permissions, social connections or product relationships, where the frontend already consumes GraphQLnot Apache Flink
  • A knowledge graph that outgrew a single machine and needs storage distributed across nodes without a per-core licence negotiationnot Apache Flink
  • Recommendation or fraud-detection features that traverse several hops at request time, where a relational join chain has become the bottlenecknot Apache Flink
  • Teams that want a graph database they can read, fork and self-host under a permissive licence rather than a source-available onenot Apache Flink

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Flink

  • Genuinely difficult: event time, watermarks and state backends are a real conceptual load before anything works
  • Operationally heavy — job managers, task managers, checkpoint storage and state size are all yours to run and tune
  • State grows with the workload, and large state changes recovery time and cost significantly
  • Overkill where a scheduled batch job would answer the same question

Dgraph

  • Sharding is by predicate, so a single very hot predicate lives entirely in one Raft group and cannot be split further; adding nodes does not relieve it and the fix is a data model change.
  • Stewardship passed from Dgraph Labs to Hypermode after the original company stopped operating independently, so anyone building on it is betting on a second custodian rather than on the original team's roadmap.
  • The GraphQL layer is generated and opinionated, so anything it does not express drops you into DQL, which is a second language your team must learn and which no other database speaks.
  • The community is a fraction of Neo4j's, so operational answers, tuning experience, hiring and third-party tooling are all thinner, and unusual failure modes in a Zero and Alpha cluster leave you reading source rather than a forum.
  • There is no portable graph standard to migrate to; Cypher, Gremlin and SPARQL are all different query models, so the schema and every query is a rewrite if you later leave, and that cost grows with the application.

Pricing, plan by plan

Apache Flink

Free
  • Apache FlinkFree
    • Full functionality
    • Self-hosted
    • No usage limits

Dgraph

Free
  • CommunityFree
    • Native GraphQL
    • Graph queries
    • Full-text search
  • Cloud$39/month
    • Managed service
    • Auto-scaling
    • Enterprise support

Which should you pick?

Choose Apache Flink if

  • You need event-time processing.
  • You want to start without paying.
  • You work on Linux, Kubernetes, Docker, Self-hosted.
  • You also want exactly-once state.
Apache Flink review

Choose Dgraph if

  • You need apache 2.0 licence.
  • You want to start without paying.
  • You work on Linux, Mac, Docker, Web.
  • You also want generated graphql api.
Dgraph review

Questions people ask

Is Apache Flink or Dgraph better?
Neither clearly leads. Apache Flink starts at Free and Dgraph at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Flink or Dgraph?
Apache Flink starts at Free and Dgraph at Free.
Does Apache Flink or Dgraph run on more platforms?
Apache Flink runs on Linux, Kubernetes, Docker, Self-hosted. Dgraph runs on Linux, Mac, Docker, Web.
Can I use Apache Flink for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Flink best used for?
Apache Flink is most often used for real-time aggregations and dashboards computed over an event stream, fraud and anomaly detection where patterns span a time window, joining two live streams where events arrive out of order. Of those, real-time aggregations and dashboards computed over an event stream and fraud and anomaly detection where patterns span a time window are not what Dgraph is typically brought in for.
What can Apache Flink do that Dgraph cannot?
Apache Flink covers Event-time processing, Exactly-once state, Batch and stream, SQL interface. Dgraph covers Apache 2.0 licence, Generated GraphQL API, DQL query language, Predicate sharding.

Answered from the vendors’ own pages

Apache Flink: Is Apache Flink free?

Yes, open source under the Apache Software Foundation. Managed services such as Amazon Managed Service for Apache Flink are billed separately.

Dgraph: Is Dgraph open source?

Yes. The current repository is Apache 2.0, which is a permissive OSI licence, and the project is at v25 under Hypermode's maintenance.

Apache Flink: Flink or Kafka?

They are complementary rather than alternatives. Kafka moves and stores events; Flink computes over them with windowing, joins and durable state.

Dgraph: Is it really GraphQL?

It serves a generated GraphQL API, which is real GraphQL for clients. Its native language, DQL, resembles GraphQL syntactically but is Dgraph's own language and is not the GraphQL specification.

Apache Flink: What is event-time processing?

Computing based on when an event actually occurred rather than when it arrived. It is what makes results correct when data is late or out of order, and it is the main reason Flink is harder than it looks.

Dgraph: How does it compare to Neo4j?

Neo4j has the larger ecosystem, Cypher, and far more operational precedent. Dgraph distributes storage across nodes by default and gives you a GraphQL endpoint without writing resolvers. The choice usually turns on whether you need horizontal scale and a GraphQL surface more than you need ecosystem depth.

Dgraph: What does a production cluster look like?

At minimum a set of Zero nodes for coordination and a replicated set of Alpha nodes for data, typically three of each for fault tolerance, which is a meaningfully larger operational footprint than a single graph server.

Dgraph: Who maintains it now?

Hypermode. Dgraph Labs, the original company, no longer operates it, and that change of custodian is the main non-technical risk to weigh.

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