AllegroGraph® is a modern, high-performance, persistent graph database. AllegroGraph uses efficient memory utilization in combination with disk-based storage, enabling it to scale to billions of quads while maintaining superior performance. AllegroGraph supports SPARQL, RDFS++, and Prolog reasoning from numerous client applications.
The primary emphasis of this release has been additional enterprise functionality, efficiency and overall scalability. Please refer to the release notes for a complete list of enhancements and improvements.
AllegroGraph is designed for maximum loading speed and query speed. Loading of quads, through its highly optimized RDF/XML and N-Quads parsers, is best-of-breed, particularly with large files. The AllegroGraph product line has always pushed the performance envelope starting with version 1.0 in 2004, which was the first product to claim 1 billion triples loaded and indexed using standard x86 64-bit hardware. AllegroGraph, a purpose built (not a modified RDBMS), NoSQL Graph Database continued to drive innovation in the marketplace with the 2008 SemTech conference example of 10 billion quads loaded on Amazon’s EC2 service. The new version 4 series continues to bring performance to the forefront of Franz’s Semantic Technologies as the industry’s first OLTP semantic web database. AllegroGraph’s ability to automatically manage all available hardware resources to maximize loading, indexing and query capabilities once again raises the bar for RDF storage performance. The following table displays examples of AllegroGraph's performance in loading and indexing. Benchmark Results.
Load Rate (T/Sec)
36min, 49 sec
12 hrs, 18m, 16s
78 hrs, 9m, 23s
338 hrs, 5m
*32 core Intel E5520, 2.0 GHz, with 1 TB RAM, RedHat v6.1.
**64 core Intel x7560, 2.27 GHz, 2TB RAM, 22TB Disk, Redhat v6.1. LUBM-like data.
***240 core Intel x5650, 2.66GHz, 1.28TB RAM, 88TB Disk, Redhat v6.1. LUBM-like data.
AllegroGraph provides the broadest array of mechanisms to query and access knowledge in an RDF datastore:
Description logics or OWL-DL reasoners are good at handling complex ontologies. They tend to be complete (give all the possible answers to a query) but can be totally unpredictable with respect to execution time when the number of triples increases beyond millions. AllegroGraph offers a very fast and practical RDFS++ reasoner.
We support all the RDF and RDFS predicates and some in full OWL. The supported predicates are RDF:type, RDFS:subClassOf, range, domain, subProperty.
OWL:sameAs inverseOf, TransitiveProperty, hasValue, someValuesFrom, allValuesFrom, one of, equivalentClass, restriction, onProperty, intersectionOf.
AllegroGraph's RDFS++ engine dynamically maintains the ontological entailments required for reasoning: it has no explicit materialization phase. Materialization is the pre-computation and storage of inferred triples so that future queries run more efficiently. The central problem with materialization is its maintenance: changes to the triple-store's ontology or facts usually change the set of inferred triples. In static materialization, any change in the store requires complete re-processing before new queries can run. AllegroGraph's Dynamic Materialization simplifies store maintenance and reduces the time required between data changes and querying.
SPARQL, the W3C standard RDF query language, returns RDF, XML and other formats in responses to queries. AllegroGraph's SPARQL, one of the W3C's "interoperable implementations", includes a query optimizer, and has full support for named graphs. It can be used with the RDFS++ reasoning turned on (i.e., query over real and inferred triples). SPARQL can be used with every available AllegroGraph interface mentioned in the previous section.
AllegroGraph's RDF Prolog provides concise, powerful, industry-standard, domain-specific reasoning to build high-level concepts (that require complex rules or numerical processing) on top of RDF data. AllegroGraph Prolog is an option because many use cases are difficult (or very cumbersome) to model with only RDF/RDFS and OWL. Prolog can also be used on top of the RDFS++ reasoner as a rule based system.
Other essential Triple-Store features:
AllegroGraph stores geospatial and temporal data types as native data structures. Combined with its indexing and range query mechanisms, AllegroGraph lets you perform geospatial and temporal reasoning efficiently.
AllegroGraph includes an SNA library that treats a triple-store as a graph of relations, with functions for measuring importance and centrality as well as several families of search functions. Example algorithms are nodal-degree, nodal-neighbors, ego-group, graph-density, actor-degree-centrality, group-degree-centrality, actor-closeness-centrality, group-closeness-centrality, actor betweenness-centrality, group-betweenness-centrality, page-rank-centrality, and cliques. Geospatial and temporal primitives combined with SNA functions form an Activity Recognition framework for flexibly analyzing networks and events in large volumes of structured and unstructured data.
AllegroGraph stores a wide range of data types directly in its low level triple representation. This allows for very efficient range queries and significant reduction in triple-store data size. With other triple-stores that only store strings, the only way to do a range query is to go through all the values for a particular predicate. This works well if everything fits in memory; but if the predicate has millions of triples, it will need costly machines with huge amounts of RAM. AllegroGraph supports most XML Schema types (native numeric types, dates, times, longitudes, latitudes, durations and telephone numbers).
AllegroGraph supports free-text indexing on the objects of triples whose predicates have been registered for indexing. Once indexed, triples can be found using a simple but robust query language. Free-text indexing support includes functions to register predicates and see which predicates are registered. Support for Solr was added in AllegroGrpah version 4.5
AllegroGraph actually stores quints. A triple in AllegroGraph contains 5 slots, the first three being subject (s), predicate (p), and object (o). The remaining two are a named-graph slot (g) and a unique id assigned by AllegroGraph. The id slot is used for internal administrative purposes, but can also be referred to by other triples directly.
The W3C proposal is to use the 'named-graph' slot for clustering triples. So for example, you load a file with triples into AllegroGraph and you use the filename as the named-graph. This way, if there are changes to the triple file, you just update those triples in the named graph that came from the original file. However, with AllegroGraph, you can also put other attributes such as weights, trust factors, times, latitudes, longitudes, etc, into the named graph slot.
AllegroGraph allows triple-ids to be the subject or object of another triple. This is beyond the scope of pure RDF. The advantage of this approach is that you can reduce the total number of triples in the store to a more manageable size, and, even more importantly, dramatically reduce query time because a single query can retrieve more data.
The AllegroGraph architecture is designed to maximize hardware resources for all data management procedures (Loading, Indexing, Query, etc.). The hardware utilization can be managed through the AllegroGraph configuration file as necessary.
Triple-indices are user configurable, or index management can be taken care of entirely by AllegroGraph. By default, all committed triples are always indexed (default: 7 indices). AllegroGraph now supports any index combination of S, P, O, G. The default indices are:
AllegroGraph supports queries with distributed databases. You can group multiple triple-stores, both local and remote into a single virtual store. It allows thread-safe opening of multiple triple-databases from one application (for the read only parts of the database). Queries over multiple databases are easy with direct data access from applications. It also supports physical merging of databases.
Production AllegroGraph databases can now be paired with transactionally consistent Warm Standby databases, co-located in the same data center or across the globe. Whether for planned maintenance or a hardware failure, your enterprise application never needs to be down.
Provides a user the option to advance the state of a restored database forward to any later commit that was made to the original (and perhaps still running) database. This functionality performs as if the user performed a backup after every commit thus providing complete data integrity.
Allows multiple AllegroGraph databases to be kept synchronized and transactionally consistent in real-time with the master. These replicates can provide scalability and load balancing for applications by offering numerous clients the ability to read data that reflects content in the master database. Replication occurs across the network so any set of AllegroGraph databases connected by a network can participate in replication.
Make the most of your use of semantic technologies by utilizing our consulting services. We provide:
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The AllegroGraph v4 Database Server runs natively on Linux x86-64 bit. To run AllegroGraph v4 on other operating systems (i.e. Windows, Mac) we suggest you set up a Linux Virtual Machine or EC2. We provide a Virtual Machine and EC2 AMIs to help facilitate installation. Clients to an AllegroGraph server may be any OS and either 32-bit or 64-bit. franz.com/downloads
|AllegroGraph v4.x||Virtual Machine Appliance (Requires 64-bit hardware)|
|Linux (x86-64), glibc 2.4 and above||Apple Mac OSX 10.6 and newer|
|Amazon EC2 (Linux x86-64)||32 or 64 bit Microsoft Windows|
The Virtual Machine Appliance will let you run the AllegroGraph Linux version on a Windows or Mac operating system. Performance will be slower than running natively, so we encourage you to install AllegroGraph natively for performance evaluation.
Installation details for the Virtual Machine Appliance.
For more info, send email to AllegroGraph@franz.com or call (510) 452-2000, Option 3-Sales and Marketing.
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