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 8.7K GitHub stars and 2K 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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43
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Distributed Graph Database
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+ 1
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PROS OF TITAN
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    CONS OF TITAN
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      related Titan posts

      MongoDB logo

      MongoDB

      51.8K
      41.4K
      4K
      The database for giant ideas
      51.8K
      41.4K
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      4K
      PROS OF MONGODB
      • 822
        Document-oriented storage
      • 585
        No sql
      • 544
        Ease of use
      • 462
        Fast
      • 404
        High performance
      • 251
        Free
      • 212
        Open source
      • 177
        Flexible
      • 139
        Replication & high availability
      • 107
        Easy to maintain
      • 39
        Querying
      • 35
        Easy scalability
      • 34
        Auto-sharding
      • 33
        High availability
      • 29
        Map/reduce
      • 26
        Document database
      • 24
        Easy setup
      • 24
        Full index support
      • 15
        Reliable
      • 14
        Fast in-place updates
      • 13
        Agile programming, flexible, fast
      • 11
        No database migrations
      • 7
        Enterprise
      • 7
        Easy integration with Node.Js
      • 5
        Enterprise Support
      • 4
        Great NoSQL DB
      • 3
        Aggregation Framework
      • 3
        Drivers support is good
      • 3
        Support for many languages through different drivers
      • 2
        Schemaless
      • 2
        Managed service
      • 2
        Easy to Scale
      • 2
        Fast
      • 2
        Awesome
      • 1
        Consistent
      CONS OF MONGODB
      • 5
        Very slowly for connected models that require joins
      • 3
        Not acid compliant
      • 1
        Proprietary query language

      related MongoDB posts

      Jeyabalaji Subramanian

      Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

      We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

      Based on the above criteria, we selected the following tools to perform the end to end data replication:

      We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

      We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

      In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

      Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

      In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

      See more
      Robert Zuber

      We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

      As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

      When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

      See more
      Cassandra logo

      Cassandra

      3K
      2.9K
      463
      A partitioned row store. Rows are organized into tables with a required primary key.
      3K
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      PROS OF CASSANDRA
      • 107
        Distributed
      • 90
        High performance
      • 77
        High availability
      • 71
        Easy scalability
      • 50
        Replication
      • 25
        Reliable
      • 24
        Multi datacenter deployments
      • 6
        Schema optional
      • 6
        OLTP
      • 5
        Open source
      • 2
        Workload separation (via MDC)
      CONS OF CASSANDRA
      • 1
        Reliability of replication
      • 1
        Updates

      related Cassandra posts

      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

      See more
      Umair Iftikhar
      Technical Architect at Vappar · | 3 upvotes · 14.6K views

      Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

      My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

      See more
      OrientDB logo

      OrientDB

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      60
      12
      An open source NoSQL database management system
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      60
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      PROS OF ORIENTDB
      • 3
        Great graphdb
      • 2
        Great support
      • 1
        Open source
      • 1
        Rest api
      • 1
        ACID
      • 1
        Embeddable
      • 1
        Highly-available
      • 1
        Performance
      • 1
        Multi-Model/Paradigm
      CONS OF ORIENTDB
      • 3
        Unstable

      related OrientDB 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
      JanusGraph logo

      JanusGraph

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      60
      0
      Open-source, distributed graph database
      27
      60
      + 1
      0
      PROS OF JANUSGRAPH
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        CONS OF JANUSGRAPH
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          related JanusGraph posts

          Dgraph logo

          Dgraph

          79
          137
          7
          Fast, Distributed Graph DB
          79
          137
          + 1
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          PROS OF DGRAPH
          • 3
            Graphql as a query language is nice if you like apollo
          • 1
            High Performance
          • 1
            Open Source
          • 1
            Low learning curve
          • 1
            Easy set up
          CONS OF DGRAPH
            Be the first to leave a con

            related Dgraph posts

            ArangoDB logo

            ArangoDB

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            294
            177
            A distributed open-source database with a flexible data model for documents, graphs, and key-values.
            206
            294
            + 1
            177
            PROS OF ARANGODB
            • 32
              Grahps and documents in one DB
            • 25
              Intuitive and rich query language
            • 23
              Good documentation
            • 22
              Open source
            • 18
              Joins for collections
            • 15
              Foxx is great platform
            • 14
              Great out of the box web interface with API playground
            • 6
              Low maintenance efforts
            • 6
              Clustering
            • 6
              Good driver support
            • 5
              Easy microservice creation with foxx
            • 3
              You can write true backendless apps
            • 2
              Managed solution available
            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
            Neptune logo

            Neptune

            6
            17
            0
            The most lightweight experiment tracking tool for machine learning
            6
            17
            + 1
            0
            PROS OF NEPTUNE
              Be the first to leave a pro
              CONS OF NEPTUNE
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                related Neptune posts