What is OrientDB and what are its top alternatives?
Top Alternatives to OrientDB
- Neo4j
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. ...
- 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. ...
- 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. ...
- PostgreSQL
PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...
- 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'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. ...
- MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...
- Microsoft SQL Server
Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions. ...
OrientDB alternatives & related posts
- Cypher – graph query language70
- Great graphdb61
- Open source33
- Rest api31
- High-Performance Native API27
- ACID24
- Easy setup21
- Great support17
- Clustering11
- Hot Backups9
- Great Web Admin UI8
- Mature7
- Powerful, flexible data model7
- Embeddable6
- Easy to Use and Model5
- Best Graphdb4
- Highly-available4
- Great onboarding process2
- It's awesome, I wanted to try it2
- Used by Crunchbase2
- Great query language and built in data browser2
- Comparably slow9
- Can't store a vertex as JSON4
- Doesn't have a managed cloud service at low cost1
related Neo4j 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.
- Is a graph database the right choice, or can we manage with RDBMS?
- If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
- 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.
I'm evaluating the use of RedisGraph vs Microsoft SQL Server 2019 graph features to build a social graph. One of the key criteria is high availability and cross data center replication of data. While Neo4j is a much-matured solution in general, I'm not accounting for it due to the cost & introduction of a new stack in the ecosystem. Also, due to the nature of data & org policies, using a cloud-based solution won't be a viable choice.
We currently use Redis as a cache & SQL server 2019 as RDBMS.
I'm inclining towards SQL server 2019 graph as we already use SQL server extensively as relational database & have all the HA and cross data center replication setup readily available. I still need to evaluate if it fulfills our need as a graph DB though, I also learned that SQL server 2019 is still a new player in the market and attempts to fit a graph-like query on top of a relational model (with node and edge tables). RedisGraph seems very promising. However, I'm not totally sure about HA, Graph data backup, cross-data center support.
- Document-oriented storage829
- No sql594
- Ease of use553
- Fast465
- High performance410
- Free257
- Open source218
- Flexible180
- Replication & high availability145
- Easy to maintain112
- Querying42
- Easy scalability39
- Auto-sharding38
- High availability37
- Map/reduce31
- Document database27
- Easy setup25
- Full index support25
- Reliable16
- Fast in-place updates15
- Agile programming, flexible, fast14
- No database migrations12
- Easy integration with Node.Js8
- Enterprise8
- Enterprise Support6
- Great NoSQL DB5
- Support for many languages through different drivers4
- Drivers support is good3
- Schemaless3
- Aggregation Framework3
- Fast2
- Managed service2
- Easy to Scale2
- Awesome2
- Consistent2
- Good GUI1
- Acid Compliant1
- Very slowly for connected models that require joins6
- Not acid compliant3
- Proprietary query language1
related MongoDB posts
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!
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.
ArangoDB
- Grahps and documents in one DB37
- Intuitive and rich query language26
- Good documentation25
- Open source25
- Joins for collections21
- Foxx is great platform15
- Great out of the box web interface with API playground14
- Good driver support6
- Low maintenance efforts6
- Clustering6
- Easy microservice creation with foxx5
- You can write true backendless apps4
- Managed solution available2
- Performance0
- Web ui has still room for improvement3
- No support for blueprints standard, using custom AQL2
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.
- Is a graph database the right choice, or can we manage with RDBMS?
- If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
- 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.
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
- Relational database756
- High availability508
- Enterprise class database436
- Sql380
- Sql + nosql303
- Great community171
- Easy to setup145
- Heroku130
- Secure by default128
- Postgis112
- Supports Key-Value48
- Great JSON support46
- Cross platform32
- Extensible30
- Replication26
- Triggers24
- Rollback22
- Multiversion concurrency control21
- Open source20
- Heroku Add-on17
- Stable, Simple and Good Performance14
- Powerful13
- Lets be serious, what other SQL DB would you go for?12
- Good documentation9
- Intelligent optimizer7
- Scalable7
- Free6
- Reliable6
- Transactional DDL6
- Modern6
- One stop solution for all things sql no matter the os5
- Relational database with MVCC4
- Full-Text Search3
- Faster Development3
- Developer friendly3
- search2
- Excellent source code2
- Great DB for Transactional system or Application2
- Free version1
- Full-text1
- Open-source1
- Text1
- Table/index bloatings9
related PostgreSQL posts
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!
We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.
We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).
And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.
I can't recommend it highly enough.
related JanusGraph posts
- Graphql as a query language is nice if you like apollo3
- Easy set up2
- Low learning curve2
- Open Source1
- High Performance1
related Dgraph posts
- Sql799
- Free678
- Easy559
- Widely used527
- Open source488
- High availability180
- Cross-platform support160
- Great community104
- Secure78
- Full-text indexing and searching75
- Fast, open, available25
- SSL support16
- Reliable15
- Robust14
- Enterprise Version8
- Easy to set up on all platforms7
- NoSQL access to JSON data type2
- Relational database1
- Easy, light, scalable1
- Sequel Pro (best SQL GUI)1
- Replica Support1
- Owned by a company with their own agenda15
- Can't roll back schema changes3
related MySQL posts
We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.
We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).
And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.
I can't recommend it highly enough.
Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:
The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:
Microsoft SQL Server
- Reliable and easy to use139
- High performance102
- Great with .net95
- Works well with .net65
- Easy to maintain56
- Azure support21
- Always on17
- Full Index Support17
- Enterprise manager is fantastic10
- In-Memory OLTP Engine9
- Security is forefront2
- Columnstore indexes1
- Great documentation1
- Faster Than Oracle1
- Decent management tools1
- Easy to setup and configure1
- Docker Delivery1
- Expensive Licensing4
- Microsoft2
related Microsoft SQL Server posts
We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.
We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.
In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.
Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache
I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:
- I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
- I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.