Alternatives to Neo4j logo

Alternatives to Neo4j

Titan, MongoDB, Cassandra, OrientDB, and JanusGraph are the most popular alternatives and competitors to Neo4j.
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What is Neo4j and what are its top alternatives?

Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
Neo4j is a tool in the Graph Databases category of a tech stack.
Neo4j is an open source tool with 12.6K GitHub stars and 2.3K GitHub forks. Here’s a link to Neo4j's open source repository on GitHub

Top Alternatives to Neo4j

  • Titan
    Titan

    Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. ...

  • MongoDB
    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

  • Cassandra
    Cassandra

    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. ...

  • OrientDB
    OrientDB

    It is an open source NoSQL database management system written in Java. It is a Multi-model database, supporting graph, document, key/value, and object models, but the relationships are managed as in graph databases with direct connections between records. ...

  • JanusGraph
    JanusGraph

    It is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. It is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. ...

  • Dgraph
    Dgraph

    Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP. ...

  • ArangoDB
    ArangoDB

    A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions. ...

  • Neptune
    Neptune

    It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools. ...

Neo4j alternatives & related posts

Titan logo

Titan

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Distributed Graph Database
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PROS OF TITAN
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    CONS OF TITAN
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      related Titan posts

      MongoDB logo

      MongoDB

      91.9K
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      The database for giant ideas
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      PROS OF MONGODB
      • 827
        Document-oriented storage
      • 593
        No sql
      • 553
        Ease of use
      • 464
        Fast
      • 410
        High performance
      • 257
        Free
      • 218
        Open source
      • 180
        Flexible
      • 145
        Replication & high availability
      • 112
        Easy to maintain
      • 42
        Querying
      • 39
        Easy scalability
      • 38
        Auto-sharding
      • 37
        High availability
      • 31
        Map/reduce
      • 27
        Document database
      • 25
        Easy setup
      • 25
        Full index support
      • 16
        Reliable
      • 15
        Fast in-place updates
      • 14
        Agile programming, flexible, fast
      • 12
        No database migrations
      • 8
        Easy integration with Node.Js
      • 8
        Enterprise
      • 6
        Enterprise Support
      • 5
        Great NoSQL DB
      • 4
        Support for many languages through different drivers
      • 3
        Schemaless
      • 3
        Aggregation Framework
      • 3
        Drivers support is good
      • 2
        Fast
      • 2
        Managed service
      • 2
        Easy to Scale
      • 2
        Awesome
      • 2
        Consistent
      • 1
        Good GUI
      • 1
        Acid Compliant
      CONS OF MONGODB
      • 6
        Very slowly for connected models that require joins
      • 3
        Not acid compliant
      • 1
        Proprietary query language

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      Shared insights
      on
      Node.jsNode.jsGraphQLGraphQLMongoDBMongoDB

      I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery

      For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:

      1. Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have

      2. GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.

      3. MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website

      See more
      Vaibhav Taunk
      Team Lead at Technovert · | 31 upvotes · 3.9M views

      I am starting to become a full-stack developer, by choosing and learning .NET Core for API Development, Angular CLI / React for UI Development, MongoDB for database, as it a NoSQL DB and Flutter / React Native for Mobile App Development. Using Postman, Markdown and Visual Studio Code for development.

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      Cassandra logo

      Cassandra

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      A partitioned row store. Rows are organized into tables with a required primary key.
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      PROS OF CASSANDRA
      • 119
        Distributed
      • 98
        High performance
      • 81
        High availability
      • 74
        Easy scalability
      • 53
        Replication
      • 26
        Reliable
      • 26
        Multi datacenter deployments
      • 10
        Schema optional
      • 9
        OLTP
      • 8
        Open source
      • 2
        Workload separation (via MDC)
      • 1
        Fast
      CONS OF CASSANDRA
      • 3
        Reliability of replication
      • 1
        Size
      • 1
        Updates

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      Thierry Schellenbach
      Shared insights
      on
      GolangGolangPythonPythonCassandraCassandra
      at

      After years of optimizing our existing feed technology, we decided to make a larger leap with 2.0 of Stream. While the first iteration of Stream was powered by Python and Cassandra, for Stream 2.0 of our infrastructure we switched to Go.

      The main reason why we switched from Python to Go is performance. Certain features of Stream such as aggregation, ranking and serialization were very difficult to speed up using Python.

      We’ve been using Go since March 2017 and it’s been a great experience so far. Go has greatly increased the productivity of our development team. Not only has it improved the speed at which we develop, it’s also 30x faster for many components of Stream. Initially we struggled a bit with package management for Go. However, using Dep together with the VG package contributed to creating a great workflow.

      Go as a language is heavily focused on performance. The built-in PPROF tool is amazing for finding performance issues. Uber’s Go-Torch library is great for visualizing data from PPROF and will be bundled in PPROF in Go 1.10.

      The performance of Go greatly influenced our architecture in a positive way. With Python we often found ourselves delegating logic to the database layer purely for performance reasons. The high performance of Go gave us more flexibility in terms of architecture. This led to a huge simplification of our infrastructure and a dramatic improvement of latency. For instance, we saw a 10 to 1 reduction in web-server count thanks to the lower memory and CPU usage for the same number of requests.

      #DataStores #Databases

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      Thierry Schellenbach
      Shared insights
      on
      RedisRedisCassandraCassandraRocksDBRocksDB
      at

      1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

      Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

      RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

      This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

      #InMemoryDatabases #DataStores #Databases

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      OrientDB logo

      OrientDB

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      An open source NoSQL database management system
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      PROS OF ORIENTDB
      • 4
        Great graphdb
      • 2
        Great support
      • 2
        Open source
      • 1
        Multi-Model/Paradigm
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        ACID
      • 1
        Highly-available
      • 1
        Performance
      • 1
        Embeddable
      • 1
        Rest api
      CONS OF ORIENTDB
      • 4
        Unstable

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      We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.

      1. Is a graph database the right choice, or can we manage with RDBMS?
      2. If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
      3. If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?

      I am sorry that this might be a loaded question.

      See more
      JanusGraph logo

      JanusGraph

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      Open-source, distributed graph database
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      + 1
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      PROS OF JANUSGRAPH
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        CONS OF JANUSGRAPH
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          related JanusGraph posts

          Dgraph logo

          Dgraph

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          219
          9
          Fast, Distributed Graph DB
          124
          219
          + 1
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          PROS OF DGRAPH
          • 3
            Graphql as a query language is nice if you like apollo
          • 2
            Easy set up
          • 2
            Low learning curve
          • 1
            Open Source
          • 1
            High Performance
          CONS OF DGRAPH
            Be the first to leave a con

            related Dgraph posts

            ArangoDB logo

            ArangoDB

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            442
            192
            A distributed open-source database with a flexible data model for documents, graphs, and key-values.
            274
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            + 1
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            PROS OF ARANGODB
            • 37
              Grahps and documents in one DB
            • 26
              Intuitive and rich query language
            • 25
              Good documentation
            • 25
              Open source
            • 21
              Joins for collections
            • 15
              Foxx is great platform
            • 14
              Great out of the box web interface with API playground
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              Good driver support
            • 6
              Low maintenance efforts
            • 6
              Clustering
            • 5
              Easy microservice creation with foxx
            • 4
              You can write true backendless apps
            • 2
              Managed solution available
            • 0
              Performance
            CONS OF ARANGODB
            • 3
              Web ui has still room for improvement
            • 2
              No support for blueprints standard, using custom AQL

            related ArangoDB posts

            We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.

            1. Is a graph database the right choice, or can we manage with RDBMS?
            2. If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
            3. If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?

            I am sorry that this might be a loaded question.

            See more

            Hello All, I'm building an app that will enable users to create documents using ckeditor or TinyMCE editor. The data is then stored in a database and retrieved to display to the user, these docs can contain image data also. The number of pages generated for a single document can go up to 1000. Therefore by design, each page is stored in a separate JSON. I'm wondering which database is the right one to choose between ArangoDB and PostgreSQL. Your thoughts, advice please. Thanks, Kashyap

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            Neptune logo

            Neptune

            14
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            2
            The most lightweight experiment tracking tool for machine learning
            14
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            + 1
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            PROS OF NEPTUNE
            • 1
              Aws managed services
            • 1
              Supports both gremlin and openCypher query languages
            CONS OF NEPTUNE
            • 1
              Doesn't have much support for openCypher clients
            • 1
              Doesn't have proper clients for different lanuages
            • 1
              Doesn't have much community support

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