So I am putting together a report on Neo4j and it's potential advantages over your average relational database. In my research, I've encountered a couple issues that might not make Neo4j the best choice at the present for a public server application. Namely:
Neo4j uses Apache Lucene, which treats all data as text. This makes purely integer data queries much slower than they need to be.
Neo4j has no user management built in. All security must be done at the application level.
My question is whether my research is outdated and these issues have solutions. I know that community and support for Neo4j and other graph databases is growing fast. Anyone with knowledge on the current state of Neo4j could really help me out.
Thanks in advance
Reply to your first bullet:
By using ValueContext you can tell Lucene to index it numerically and also to query by numeric value or numeric range. See for example https://github.com/neo4j/neo4j/blob/master/community/lucene-index/src/test/java/org/neo4j/index/impl/lucene/TestLuceneIndex.java#L622
As for the second bullet, it appears that version 2.2 (currently at M3) introduces true user management. See http://neo4j.com/blog/neo4j-2-2-milestone-1-release/ for a basic overview of the new features. Note that this is not a production ready release.
Related
I am currently building a knowledge graph for an e-commerce company, and it mainly consists of the product category hierarchies, properties, and relations among them. Besides the common relational queries, we care about the following points very much:
Master-slave cluster support. This graph database will be used for online search query processing, so high availability is crucial to us. The data volume won't be as big as millions of nodes, so we don't need a distributed cluster that can span data across multiple machines. Still, rather we may need multiple machines that can be read simultaneously, and the service won't go down even if one of the machines is offline.
Fast online query performance. Reasoning about relations can be done offline, so the performance is not that important. But we need to do a lot of online queries like "find the nodes whose property P equals to value V", so we need good performance for online query processing. This database will be read-intensive and won't be changed very much after it's initialization.
Community and documentation. Since our team is new to the field of a graph database, so we expect user-friendly documentation for deployment and development and an active community for solving problems.
Based on the requirements above, I investigated some candidates:
Neo4j. We first tried Neo4j since it's the most popular one in the field. Actually, I liked it, especially the Cypher query language. But we are about to abandon it because the community edition does not support any cluster, and currently, we don't have the budget to pay for the enterprise edition.
OrientDB. OrientDB is like the second most popular one on the market, and it seems to support cluster in its community edition. I use the word "seems" because it is not clearly stated on its website. Can anyone clear this out? Besides, I found a negative article about OrientDB which makes me hesitate: http://orientdbleaks.blogspot.jp/2015/06/the-orientdb-issues-that-made-us-give-up.html
Titan. Titan is also great, but since its original company has been acquired and its original developers are developing a different product, its future development and maintenance are in doubt.
ArangoDB. This one seems to be very fast, according to the performance report(https://www.arangodb.com/2015/10/benchmark-postgresql-mongodb-arangodb/)， but I don't know if its online query processing ability is good enough, and its support for the cluster is also unknown to me.
As for documentation and community, I really have no idea since these are the kind of things that you only get to know after you start doing it.
To sum up, based on my requirements, I think OrientDB and ArangoDB maybe my candidates, but I don't know which one to choose because of the points I stated above. Or perhaps is there any other right candidate that I'm missing?
Thanks.
Max working for ArangoDB here. ArangoDB does not only do online queries for graphs, but due to its multi-model nature you can mix graph queries with document queries (using secondary indexes), key lookups and joins. It has a sophisticated query engine with an optimizer that is fully aware of the ArangoDB cluster structure and can optimize and distribute query executions across all instances.
In a cluster, sharding, synchronous replication and self-healing are all fully automatic with configurable parameters. Deployment of an ArangoDB cluster is particularly simple (literally two clicks) on Apache Mesos or DC/OS, but is also relatively straightforward with other orchestration frameworks. ArangoDB on DC/OS additionally allows you to scale up and down via the graphical user interface or REST API calls, and failed tasks are automatically replaced.
As to the performance, all our benchmarks show a very good performance, the just released Version 3.1 even has vertex centric indexes, which is particularly important for graph queries.
We do our best to provide extensive documentation, which you find at https://www.arangodb.com/documentation/ . We have a user manual, a manual for our query language AQL as well as one for the HTTP/REST API. Furthermore, we have tutorials, frequently asked questions, a "Cookbook" for standard tasks, and we try to answer questions on StackOverflow and github issues in a timely manner.
All of this is included in the Community Edition, which is available with the Apache 2.0 open source license.
If you have more questions, do not hesitate to reach out to our team or to me personally.
OrientDB Community Edition is a free open source software, built upon by a community of developers and is constantly improving. Features such as horizontal scaling, fault tolerance, clustering, sharding and replicating aren’t disabled in OrientDB community.
For more information about cluster, take a look at the official OrientDB guide: http://orientdb.com/docs/last/Tutorial-Clusters.html
Hope it helps.
Regards
Neo4j enterprise edition can be used under the AGPL license. I am surprised a lot of people arn't aware this. If you are using Neo4j Enterprise as a server and communicating with it via REST or bolt protocol (Apache Licensed), then you don't have to worry about releasing the code of the system connecting to it under AGPL.
If you are using it embedded, then you to adhere to AGPL, but then why would you need Neo4j enterprise in that situation?
Remember to clone and compile Neo4j Enterprise from github if you plan on using it under AGPL, don't download trial.
Neo Technology gives great support and that is what you are essentially paying for for the enterprise subscription.
I've been using MySQL happily for many years but have now come across an issue using it and wondered where I should go from here. My issue is full-text indexing. MySQL just doesn't perform well using this feature with large tables, unless you use a third-party plugin like Lucene etc.
I don't mind paying for the database but would prefer a free service. I don't have a DB administration team that can maintain it, it's just me, so it has to be simple to maintain, develop and scale. I develop on a Windows IIS7 environment, usually in ASP.NET and Classic ASP. My application will probably have a maximum of 10 million rows in the full-text table, so not huge but fairly hefty.
I could quite easily grab Lucene and use that with MySQL, but I would really like to know which DB performs best using full-text indexing, straight out of the box, so-to-speak?
Any suggestions or experiences would be marvelous.
cant help a lot other than saying that full text on sql server is amazing. It seems complicated at first (because of catalogs, indexes end everything) but once you give it a go you'll see thats quite simple to implement. This website shows an example with screens
you also have several functions to manipulate (search) the data (the saurus, stoplists, etc..)
Postgres.
In addition, there are Ruby gems to take advantage of it easily.
I worked at a place that used Oracle for full-text search, and they were happy with that until they found Lucene -- now they are switching to Lucene.
I've heard good things about Postgres' full-text search, but I've never seen it in action.
Lucene.NET is a straight .NET port of Lucene, and performs well.
With the rising of non-sql database usage in high traffic website, I'm interested to use it for my project. Now I've heard several names like Voldermort, MongoDB and CouchDB. But which are among these NonSQL database that is production ready? I've seen the download pages and it seems that none of them is production ready because is not version 1.0 yet. Is there any other names other than these 3 that is recommendable to be used in production?
What do you mean by production ready? As far as I know, all of them are being used on live systems.
You should make your choice based on how the features they provide fit your needs.
You can also add Tokyo Cabinet to the list as well as the mnesia database provided by the Erlang VM.
I think you need to start out from your project requirements to see what kind of database you really need. There are many non-relational DBMS:s out there and they differ a lot in what kind of problems they are good at solving. I think the article Should you go Beyond Relational Databases? by Martin Kleppmann is a good starting point for finding out what you need. There's also a lot of stackoverflow threads on similar topics, these are my favorites:
The Next-gen Databases
Non-Relational Database Design
When shouldn’t you use a relational
database?
Good reasons NOT to use a relational
database?
When you have narrowed down what you actually need you can take a deeper look into the alternatives to see which DBMS are production ready for your use case. Production readiness isn't a yes/no thing: people may successfully deploy some solution that for example lacks in tool support - in another project this could be a no-go.
As for version numbers different projects have a different take on this, so you can't just compare the version numbers. I'm involved in the graph database project Neo4j and even if it has been in production use for 5+ years by now we still haven't released a version 1.0 final yet.
I'm tempted to answer "use SIRA_PRISE".
It's definitely non-SQL.
And its current version is 1.2, meaning that someone like you must definitely assume it's "production-ready".
But perhaps I shouldn't be answering at all.
Nice article comparing rdbms with 'next gen' and listing some providers:
Is the Relational Database Doomed?
http://readwrite.com/2009/02/12/is-the-relational-database-doomed
I will suggest you to use Arangodb.
ArangoDB is a multi-model mostly-memory database with a flexible data model for documents and graphs. It is designed as a “general purpose database”, offering all the features you typically need for modern web applications.
ArangoDB is supposed to grow with the application—the project may start as a simple single-server prototype, nothing you couldn’t do with a relational database equally well. After some time, some geo-location features are needed and a shopping cart requires transactions. ArangoDB’s graph data model is useful for the recommendation system. The smartphone app needs a lean API to the back-end—this is where Foxx, ArangoDB’s integrated Javascript application framework, comes into play.
Another unique feature is ArangoDB’s query language AQL — it makes querying powerful and convenient. AQL enables you to describe complex filter conditions and joins in a readable format, much in the same way as SQL.
You can model your data in several ways:
in key/value pairs
as collections of documents
as graphs with nodes, edges, and properties for both
You can access data in ArangoDB:
using the general HTTP REST API via curl/wget, or your browser
via the ArangoDB shell (“arangosh”)
using a programming language specific client library
Server requirements for ArangoDB:
ArangoDB runs on Linux, OS X and Microsoft Windows.
It runs on 32bit and 64bit systems, though using a 32bit system will limit you to using only approximately 2 to 3 GB of data with ArangoDB.
We have a new django powered project which have a potential heavy-traffic characteristic(means a heavy db interaction). So we need to consider the database scalability in advance. With some researches, the following questions are still not clear to us:
coarse-grained: how to specify one db table(a django model) to a specific db(maybe in another server)?
fine-grained: how to specify a group of table rows to a specific db(so-called sharding, also can in another db server)?
how to specify write and read to different db?(which will be helpful for future mysql master/slave replication)
We are finding the solution with:
be transparent to application program(means we don't need to have additional codes in views.py)
should be in ORM level(means only needs to specify in models.py)
compatible with the current(or future) django release(to keep a minimal change for future's upgrading of django)
I'm still doing the research. And will share in this thread later if I've got some fruits.
Hope anyone with the experience can answer. Thanks.
Don't forget about caching either. Using memcached to relieve your DB of load is key to building a high performance site.
As alex said, django-core doesn't support your specific requests for those features, though they are definitely on the todo list.
If you don't do this in the application layer, you're basically asking for performance trouble. There aren't any really good open source automation layers for this sort of task, since it tends to break SQL axioms. If you're really concerned about it, you should be coding the entire application for it, not simply hoping that your ORM will take care of it.
There is the GSoC project by Alex Gaynor that in future will allow to use multiple databases in one Django project. But now there is no cross-RDBMS working solution.
There is no solution right now too.
And again - there is no cross-RDBMS solution. But if you are using MySQL you can try excellent third-party Django application called - mysql_replicated. It allows to setup master-slave replication scenario easily.
here for some reason we r using django with sqlalchemy. maybe combination of django and sqlalchemy also works for your needs.
I am looking into mechanisms for better search capabilities against our database. It is currently a huge bottleneck (causing long-lasting queries that are hurting our database performance).
My boss wanted me to look into Solr, but on closer inspection, it seems we actually want some kind of DB integration mechanism with Lucene itself.
From the Lucene FAQ, they recommend Hibernate Search, Compass, and DBSight.
As a background of our current technology stack, we are using straight JSPs on Tomcat, no Hibernate, no other frameworks on top of it... just straight Java, JSP, and JDBC against a DB2 database.
Given that, it seems Hibernate Search might be a bit more difficult to integrate into our system, though it might be nice to have the option of using Hibernate after such an integration.
Does anyone have any experiences they can share with using one of these tools (or other similar Lucene based solutions) that might help in picking the right tool?
It needs to be a FOSS solution, and ideally will manage updating Lucene with changes from the database automagicly (though efficiently), without extra effort to notify the tool when changes have been made (otherwise, it seems rolling my own Lucene solution would be just as good). Also, we have multiple application servers with just 1 database (+failover), so it would be nice if it is easy to use the solution from all application servers seamlessly.
I am continuing to inspect the options now, but it would be really helpful to utilize other people's experiences.
When you say "search against a DB", what do you mean?
Relational databases and information retrieval systems use very different approaches for good reason. What kind of data are you searching? What kind of queries do you perform?
If I were going to implement an inverted index on top of a database, as Compass does, I would not use their approach, which is to implement Lucene's Directory abstraction with BLOBs. Rather, I'd implement Lucene's IndexReader abstraction.
Relational databases are quite capable of maintaining indexes. The value that Lucene brings in this context is its analysis capabilities, which are most useful for unstructured text records. A good approach would leverage the strengths of each tool.
As updates are made to the index, Lucene creates more segments (additional files or BLOBs), which degrade performance until a costly "optimize" procedure is used. Most databases will amortize this cost over each index update, giving you more stable performance.
I have had good experiences with Compass. It has really good integration with hibernate and can mirror data changes made through hibernate and jdbc directly to the Lucene indexes though its GPS devices http://www.compass-project.org/docs/1.2.2/reference/html/gps-jdbc.html.
Maintaining the Lucene indexes on all your application servers may be an issue. If you have multiple App servers updating the db, then you may hit some issues with keeping the index in sync with all the changes. Compass may have an alternate mechanism for handling this now.
The Alfresco Project (CMS) also uses Lucene and have a mechanism for replicating Lucene index changes between servers that may be useful in handling these issues.
We started using Compass before Hibernate Search was really off the ground so I cannot offer any comparison with it.
LuSql http://code.google.com/p/lusql/ allows you to load the contents of a JDBC-accessible database into Lucene, making it searchable. It is highly optimized and multi-threaded. I am the author of LuSql and will be coming out with a new version (re-architected with a new plugable architecture) in the next month.
For a pure performance boost with searching Lucene will certainly help out a lot. Only index what you care about/need and you should be good. You could use Hibernate or some other piece if you like but I don't think it is required.
Well, it seems DBSight doesn't meet the FOSS requirement, so unless it is an absolutely stellar solution, it is not an option for me right now...