dgraph

Pros of dgraph

• More active development with recent commits
• Larger community and contributor base
• Better documentation and examples

Cons of dgraph

• Potentially more complex due to additional features
• May have a steeper learning curve for beginners
• Larger codebase to maintain and update

Code Comparison

dgraph:

func (n *node) BatchSet(ctx context.Context, kvs []*bpb.KV) error {
    if len(kvs) == 0 {
        return nil
    }
    return n.store.WriteBatch(kvs)
}

dgraph>:

func (n *node) BatchSet(ctx context.Context, kvs []*pb.KV) error {
    if len(kvs) == 0 {
        return nil
    }
    return n.store.WriteBatch(kvs)
}

The code snippets show similar functionality for batch setting key-value pairs. The main difference is in the import path for the protobuf package (bpb vs pb), suggesting slightly different project structures or dependencies.

Both repositories appear to be related to graph database implementations, with dgraph being the more established and actively maintained project. The dgraph> repository might be a fork or an experimental version with potential modifications or improvements. Users should consider their specific requirements and the level of community support when choosing between the two.

Pros of Neo4j

• Mature and widely adopted graph database with extensive documentation and community support
• Powerful Cypher query language for complex graph traversals and pattern matching
• Built-in visualization tools for exploring graph data

Cons of Neo4j

• Can be resource-intensive for large-scale deployments
• Steeper learning curve for developers new to graph databases
• Limited horizontal scalability compared to distributed graph databases

Code Comparison

Neo4j (Cypher query):

MATCH (p:Person)-[:KNOWS]->(f:Person)
WHERE p.name = "Alice"
RETURN f.name

DGraph (GraphQL+-):

{
  person(func: eq(name, "Alice")) {
    name
    knows {
      name
    }
  }
}

Both examples demonstrate querying for a person named "Alice" and returning the names of people they know. Neo4j uses the Cypher query language, while DGraph uses GraphQL+- for querying. The syntax differs, but both achieve similar results in terms of graph traversal and data retrieval.

Pros of OrientDB

• Multi-model database supporting graph, document, key/value, and object models
• SQL-like query language for complex queries across different data models
• Built-in support for multi-master replication and sharding

Cons of OrientDB

• Steeper learning curve due to multiple data models and complex query language
• Less focus on distributed graph processing compared to Dgraph
• Smaller community and ecosystem compared to more specialized graph databases

Code Comparison

OrientDB query example:

SELECT FROM Person
WHERE name = 'John'
AND out('Friend').name CONTAINS 'Alice'

Dgraph query example:

{
  person(func: eq(name, "John")) @filter(has(friend)) {
    name
    friend @filter(eq(name, "Alice")) {
      name
    }
  }
}

Both databases offer query languages for graph operations, but OrientDB's SQL-like syntax may be more familiar to some developers, while Dgraph's GraphQL-like syntax is more aligned with modern graph query languages.

Pros of ArangoDB

• Multi-model database supporting key/value, document, and graph data models
• Powerful query language (AQL) for complex data operations
• Built-in full-text search capabilities

Cons of ArangoDB

• Steeper learning curve due to its multi-model nature
• Less focus on distributed systems compared to Dgraph
• Potentially higher resource consumption for smaller datasets

Code Comparison

ArangoDB query example:

FOR user IN users
  FILTER user.age > 30
  RETURN user.name

Dgraph query example:

{
  users(func: gt(age, 30)) {
    name
  }
}

Both databases offer powerful query languages, but ArangoDB's AQL is more SQL-like, while Dgraph uses a GraphQL-inspired syntax. ArangoDB's multi-model approach allows for more flexible data modeling, while Dgraph focuses on graph-native operations and scalability.

ArangoDB excels in scenarios requiring multiple data models and complex queries, while Dgraph is optimized for large-scale graph data and distributed systems. The choice between them depends on specific project requirements, data structure, and scalability needs.

Pros of JanusGraph

• Supports multiple storage backends (e.g., Apache Cassandra, HBase, Google Cloud Bigtable)
• Integrates well with Apache TinkerPop ecosystem
• Offers advanced indexing capabilities for complex graph queries

Cons of JanusGraph

• Can be more complex to set up and configure compared to Dgraph
• May have slower write performance for large-scale graph operations
• Lacks built-in GraphQL support, which Dgraph provides out of the box

Code Comparison

JanusGraph example:

GraphTraversalSource g = graph.traversal();
Vertex v1 = g.addV("person").property("name", "Alice").next();
Vertex v2 = g.addV("person").property("name", "Bob").next();
g.addE("knows").from(v1).to(v2).property("since", 2010).next();

Dgraph example:

mutation := &api.Mutation{
    SetNquads: []byte(`
        _:alice <name> "Alice" .
        _:bob <name> "Bob" .
        _:alice <knows> _:bob (since=2010) .
    `),
}
txn.Mutate(context.Background(), mutation)

Both examples demonstrate adding vertices (nodes) and edges to the graph, but Dgraph uses a more concise RDF-like syntax for mutations.

Pros of TinkerPop

• Mature and widely adopted graph computing framework with a large community
• Supports multiple graph databases and query languages
• Provides a comprehensive suite of graph computing tools and algorithms

Cons of TinkerPop

• Steeper learning curve due to its extensive feature set
• Can be more complex to set up and configure for specific use cases
• May have higher overhead for simpler graph operations

Code Comparison

TinkerPop (Gremlin):

g.V().hasLabel('person').
  has('name', 'John').
  out('knows').
  values('name')

Dgraph:

{
  people(func: eq(name, "John")) {
    name
    knows {
      name
    }
  }
}

Both examples show querying for a person named John and retrieving the names of people they know. TinkerPop uses Gremlin, a graph traversal language, while Dgraph uses GraphQL+-. Dgraph's syntax is more declarative and may be easier for developers familiar with GraphQL, while TinkerPop's Gremlin offers more flexibility for complex traversals.