Loading the Wikidata dataset 2022/12 into Virtuoso Open Source

Loading the Wikidata dataset 2022/12 into Virtuoso Open Source

Copyright © 2023 OpenLink Software


Here’s a simple guide that covers how we constructed a Wikidata instance deployed using the latest Virtuoso Open Source Edition:

Virtuoso Open Source Edition (Column Store) (multi threaded)
Version as of Oct 19 2022 (64e6ecd39)
Compiled for Linux (x86_64-generic_glibc25-linux-gnu)
Copyright (C) 1998-2022 OpenLink Software

Naturally, once loaded you end with the following:


We use the following configuration for a single host machine.

Item Value
CPU 2x Intel(R) Xeon(R) CPU E5-2630 0 @ 2.30GHz
Cores 24
Memory 378 GB
SSD 4x Crucial M4 SSD 500 GB

This system is shared by several other demo databases, so we could not use all available resources on it.

Cloud cost estimates

Although it is hard to match various cloud offerings, we used various vendor-supplied cost calculators to come up with some estimates, based on the following minimal machine specifications:

  • dedicated machine for 1 year without upfront costs
  • 128 GiB memory
  • 16 cores or more
  • 512GB SSD for the database
  • 3T outgoing internet traffic (based on our DBpedia statistics)
vendor machine type memory vCPUs monthly machine monthly disk monthly network monthly total
Amazon r5a.4xlarge 128 GiB 16 $479.61 $55.96 $276.48 $812.05
Google e2highmem-16 128 GiB 16 $594.55 $95.74 $255.00 $945.30
Azure D32a 128 GiB 32 $769.16 $38.40 $252.30 $1,060.06

Note that all prices in this table are per month.

Virtuoso INI File Settings

We updated the following settings in the virtuoso.ini before loading the data.

In the [Parameters] section:

Item Value
NumberofBuffers 30,000,000

After the database was loaded and running for a few days, we determined it was only using around 18 million buffers instead of the 30 million allocated, so we lowered the final NumberOfBuffers setting to 20,000,000 pages. This is equivalent to about 80GB memory in use for disk buffering. As we are using multiple SSD drives for the database, we could further reduce the memory footprint without too much performance loss.

In the [SPARQL] section:

Item Value
LabelInferenceName facets

Recently we added support for automatic label inferencing during the bulk-load, which uses additional threads to process the labels. This replaces the manual generation of FCT labels using the urilbl_ac_init_db() function which took around 10 hours during the previous load.

Preparing the datasets

We downloaded the Wikidata dumps from around 2022-12-28.

Filename Size (bytes)
latest-all.nt.gz 200,051,013,962
latest-lexemes.nt.gz 900,282,630

Although the file latest-all.nt.gz, which is 186 GB compressed, could in theory be loaded with a single loader, we decided to split this file into smaller fragments. This allowed us to run multiple loaders in parallel, and thereby use multiple CPU cores to load the data into the database.

Initially, we tried to used the munch.sh script from the wikidata-query-tools tree to convert the data from NTriples to Turtle, and write those in chunks of 50,000 entities into separate files that can be loaded. However, this Java-based application would have taken several days to finish, as it is a single threaded program.

Eventually we used a small Perl script that used 3 separate processes to split the file into 5639 parts, each about 42 MB in size. This took a total of 11 hours and 35 minutes.

As there are some known issues with Geodata in this dataset, we removed this bad pattern during the splitting process.

Bulk load Statistics

Description Value
Number of parallel loaders 6
Duration 13 hours 30 minutes
Total number of triples 17,888,903,520
Average triples/second 367,523
Average CPU percentage 1.792%

Additional tasks

Description Value
Setting up prefixes and labels 2 min
Loading ontologies 2 min
Loading KBpedia 1 min
FCT calculating labels calculated during the bulkload
Freetext index 24 hours

Graphs and triple counts

Main graphs

Graph Description Triple Count
http://www.wikidata.org/ the Wikidata dataset 17,742.853,944
http://www.wikidata.org/lexemes/ the Wikidata Lexemes dataset 141,960,664
urn:wikidata:labels calculated Wikidata labels 1,278,609

Ontology graphs

Graph Description Triple Count
http://wikiba.se/ontology-1.0.owl the Wikidata base ontology 288
https://schema.org/version/latest/schemaorg-current-http.nt the http://schema.org/ ontology 16,248

Knowledge Base graphs

Graph Description Triple Count
urn:kbpedia:2.50:reference:concepts:linkage:inferrence:extended the KBpedia extended graph 1,175,147
urn:kbpedia:2.50:reference:concepts:linkage the KBpedia linkage graph 850,711
urn:kbpedia:2.50:reference:concepts the KBpedia concepts graph 761,092

Link sets

At this point, we did not add any other link sets to the database.

Wikidata VoID graph

Description Value
graph name http://www.wikidata.org/void/
SPARQL endpoint https://wikidata.demo.openlinksw.com/sparql/
number of triples 17,742,853,944
number of classes 1339
number of entities 1,884,546,246
number of distinct subjects 1,888,457,596
number of properties 48,841
number of distinct objects 3,255,942,590
number of owl:sameAs links 3,910,462




I am using SPARQL interface to get statistical information about this dataset.
Unfortunately, I received timeout on some queries that are important and I would like to know if you would have this pre-calculated information to provide me.

  1. How many statements have no references?
  2. How many statements do not have qualifiers?
  3. How many statements have references but no qualifiers?
  4. How many statements have qualifiers but no references?

Thank you in advance.

1 Like

What are the queries being run, presumably against the OpenLink Wikidata SPARQL Endpoint, that are timing out ? Please provide the Query URLs such that they can be clicked on directly to see the problem agains the endpoint and Query viewed.