GraphDB-Enterprise Release Notes

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h1. GraphDB version 6.1


- *Varios stability fixes to the cluster*, including proper master shutdown sequence error handling, error-resilient synchronization threads, safe saving of configuration properties of the cluster config. The repository fingerprint now also reflects the number of statements and there is better handling of stress events which happen during transactions & dirty shutdowns (e.g. out of disk space)

- *Much faster write transactions* for small insert/update/delete operations on large repositories. Results on LDBC Semantic Publishing Benchmark (SPB) at 50M went up from 32 read and 12 write queries per second in ver. 6.0 to 37 read/sec and 28 write/sec in ver. 6.1. The improvement gets even more visible and SPB at 1B scale: from 10 read/sec and 2 write/sec in ver. 6.0 to 11 read/sec and 10 write/sec in ver. 6.1. In summary, GraphDB 6.1 is able to handle twice more updates at 50M scale and 5 times more updates at scale of 1 billion statements. This way GraphDB 6.1 is already capable to deal with true Dynamic Semantic Publishing scenario, like the one of BBC, at a scale of 1 billion statements.

- *Improved load capabilities for large new datasets in live databases* instances. The scenario is this: we have a cluster with average load - running LDBC-50m (SPB) with 4 Reading threads (doing Select queries) and 1 Writing threads (doing update queries). We need to add a large dataset (e.g. DBPedia) with ~100M statements. We need to this is as fast as possible without disrupting the cluster speed too much (not introducing write latency of more than 1-2s). The data set is loaded with an empty rule set, so there is no inference.
Our implementation introduces a new "magic" statement (u, u, u), where u=<>. If this statement is inserted in the beggining of the transaction, then the data will be loaded faster (it reuses parts of the load-chain from LoadRDF tool) and also the engine will temporarily switch the ruleset to 'emtpy'. We found that splitting the data set into 50k chunks is a good compromise between loading speed and lower latency.

- Small *improvements in the bulk loading tools (LoadRDF)*. It is possible to load different files into different contexts now, as well as provide Statements programmatically to it. See the page for the LoadRDF for the details. [GraphDB6:GraphDB-SE LoadRDF tool]
These improvements, combined with the increased update speed, allow us to load the English part of DBPedia 2014, which consists of 1.6B statements, in a bit more than one hour at speed of 400 000 statements/sec.

- Improvements in *GraphDB Workbench*: The focus with this release was on *security* (users, roles) as well as stability and usability improvements.

This is an integrated release that includes:
- GraphDB Engine 6.0.8264
- GraphDB Workbench 6.2.3
- GraphDB Connectors 3.1.0

h1. GraphDB version 6.0-RC6
This is an integrated release that includes: