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allegrograph: All content tagged as allegrograph in NoSQL databases and polyglot persistence

A Comparison of 7 Graph Databases

The main page of InfiniteGraph, a graph database commercialized by Objectivity, features an interesting comparison of 7 graph databases (InfiniteGraph, Neo4j, AllegroGraph, Titan, FlockDB, Dex, OrientDB) based on 16 criteria: licensing, source, scalability, graph model, schema model, API, query method, platforms, consistency, concurrency (distributed processing), partitioning, extensibility, visualizing tools, storage back end/persistency, language, backup/restore.

7 graph databases

Unfortunately the image is almost unreadable, but Peter Karussell has extracted the data in a GoogleDoc spreadsheet embedded below.

Original title and link: A Comparison of 7 Graph Databases (NoSQL database©myNoSQL)

A Survey of Graph Databases for the Java Programmers

Jasper Pei Lee provides an overview of the following graph databases from the perspective of the Java developer: Neo4j, InfiniteGraph, DEX, InfoGrid, HyperGraphDB, Trinity, AllegroGraph:

Graph Databases for the Java Programmers

His review is similar to the Quick Review of Existing Graph Databases, but stays focused on using these graph databases from a Java environment, this making it less generic than the NoSQL Graph Database Matrix.

The only part that I didn’t understand is the closing:

High-performance and distributed deploy are supposed to be supported by all products.

Without qualifying what high-performance means is difficult to assess if all reviewed products are on par[1]. And scaling graph databases is far from being a solved problem.

  1. AllegroGraph takes pride in breaking records related to the number of stored triples, while others are focused on access speed, or reliability.  

Original title and link: A Survey of Graph Databases for the Java Programmers (NoSQL database©myNoSQL)


1 Trillion RDF Triples With Franz’s AllegroGraph

Patrick Durusau mentioned on his blog a new record set by Franz’s AllegroGraph: 1 trillion RDF triples. This comes only 2 months after the previous Franz’s AllegroGraph record of 310 billion triples.

My first thought was: why is this important? It was one of the few times I’ve found the answer in the PR announcement:

A trillion RDF Statements […] is a primary interest for companies like Amdocs that use triples to represent real-time knowledge about telecom customers. Per-customer, Amdocs uses about 4,000 triples, so a large telecom like China Mobile would easily need 2 trillion triples to have detailed knowledge about each single customer.

Original title and link: 1 Trillion RDF Triples With Franz’s AllegroGraph (NoSQL database©myNoSQL)

Franz's AllegroGraph Sets New Triple Store Record

The 310 billion triple result that Franz is announcing today was achieved in only two weeks of access (actual loading time of just over 78 hours) to an 8-socket Intel Xeon E7-8870 processor-based server system configured with 2 terabytes of physical memory and 22 terabytes of physical disk.

“We’re confident that with additional time, another terabyte of memory, and a bit more storage capacity, the previously unreachable goal of 1 trillion triples can be achieved. Even double that is not out of the question,” stated Dr. Jans Aasman, CEO of Franz Inc.

I’m afraid to ask how much would this cost. But we already know that scaling graph databases is still an open question.

This next answer shows why different data and processing models are needed for different scenarios:

Dr. Aasman said, “Some people have asked, ‘Why not do this on a distributed cloud system with Hadoop?’ The quick answer: NoSQL databases like Hadoop and Cassandra fail on joins. Big Enterprise, big web companies and big government intelligence organizations are all looking into big data to work with massive amounts of semi-unstructured data. They are finding that NoSQL databases are wonderful if one needs access to a single object in an ocean of billions of objects, however, they also find that the current NoSQL databases fall short if you need to run graph database operations that require many complicated joins. A typical example would be performing a social network analysis query on a large telecom call detail record database.”

Original title and link: Franz’s AllegroGraph Sets New Triple Store Record (NoSQL databases © myNoSQL)


Release: AllegroGraph 4.0, 100% ACID

AllegroGraph RDFStore[1], a solution at the crossing of RDF stores[2] and graph databases, has released recently a new major update featuring:

  • AllegroGraph is 100 percent ACID, supporting Transactions: Commit, Rollback, and Checkpointing. See the new tutorials for the Java and Python clients
  • Full and Fast Recoverability
  • 100% Read Concurrency, Near Full Write Concurrency
  • Online Backups
  • Dynamic and Automatic Indexing – All committed triples are always indexed (7 indices)
  • Advanced Text Indexing – Lucene style but faster, text indexing per predicate. See the new tutorials for the Java and Python clients
  • Duplicate Triple deletion while indexing
  • All Clients based on http REST Protocol – Java, Sesame, Jena, and Python
  • Completely multi-processing based (SMP) – Automatic Resource Management for all processors and disks, and optimized memory use. See the new performance tuning guide here, and new server configuration guide here
  • Column-based compression of indices similar to column-based RDBMS – reduced paging, better performance
  • Dedicated and Public Sessions – In dedicated sessions users can work with their own rule sets against the same database
  • Python Client Improvements – We now provide a full Python interface. The API is based on the Java Sesame interface and includes Spatial-Temporal and Social Network support
  • LUBM Benchmarks – Updated for this release

As a side note, in the graph databases space, AllegroGraph is not the first one supporting ACID, Neo4j being fully transactional for quite a while[3].

Quick Review of Existing Graph Databases

Pere Urbón ☞ published a short review of a couple of existing graph databases. For your reference, below are the ones reviewed in the post and a couple more that we’ve previously mentioned here on myNoSQL:


☞ Neo4j is an embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs rather than in tables.


☞ DEX is a high performance library to manage very large graphs or networks


☞ HyperGraphDB: a general purpose, extensible, portable, distributed, embeddable, open-source data storage mechanism.


☞ InfoGrid: an Internet Graph Database with a many additional software components that make the development of REST-ful web applications on a graph foundation easy.


☞ vertexdb: a high performance graph database server that supports automatic garbage collection.

Note: by checking the project homepage I cannot tell if the project is still active or not.


☞ AllegroGraph RDFStore: a modern, high-performance, persistent RDF graph database.

Note: AllegroGraph seems to be positioned in the RDF stores space, which features some other solutions too.


☞ Filament: a graph persistence framework and associated toolkits based on a navigational query style.


☞ Sones GraphDS provides an inherent support for high-level data abstraction concepts (graph structures, walks, consistency, editions, revisions, copies), its own Graph Query Language, an underlying distributed file system and various interfaces like SOAP, REST or WebDAV.

And I’m not sure these are all …

Update: make sure you check the NoSQL Graph Database Matrix

Quick Review of Existing Graph Databases originally posted on the NoSQL blog: myNoSQL