Rdflib-appengine

Latest version: v1.2.0

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1.2

The library is now better at handling larger graphs. By increasing the following configuration from their defaults, more shards will be created. This addresses the potential issue that each shard cannot be larger than 1MB (because it is an Entity in NDB).

no_of_subject_shards = 16,
no_of_shards_per_predicate_default = 1,

The first option defines how many shards subjects are divided into (default is 16). A subject is assigned to a shard based on its SHA1 digest. Allowed values for this option is 1, 16, 256, 4096.

The second option defines how many shards each predicate is divided into (default is 1). Triples will be written to a random one of these. Allowed values for this option is 1, 16, 256, 4096.

Predicates will often vary considerable in sizes, so it is also possible to divide a name predicates into more shards than the number set above. This is done by setting the option

no_of_shards_per_predicate_dict = {}

The keys must be URIRefs. Allowed values are 1, 16, 256, 4096. Example configuration:

{'no_of_shards_per_predicate_dict': {URIRef(''http://www.w3.org/2000/01/rdf-schemalabel): 16}

1.1

Added NDBStore.destroy() overriding Store.destroy()

This method can be used for efficiently removing a graph from NDB.

1.0

The original implementation has been scrapped in favour of the new, battle-tested and far superior one based on GraphShards.

The Makefile now creates a package that can be installed by pip.

The documentation has been improved.

0.5

For this release, we have added the CoarseStore which stores a Graph by sharding it into smaller graphs, then storing each of these as N3 text in NDB.


blog-post
This version of the code was used to write two blog posts - here's part 1:

https://semanticwebrecipes.wordpress.com/2014/10/01/deploying-a-sparql-endpoint-on-google-app-engine-using-rdflib/

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