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User:AndreaWest/WDQS Testing

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This page overviews a design and specific suggestions for Wikidata SPARQL query testing. These tests will be useful to evaluate Blazegraph backend alternatives and to (possibly) establish a Wikidata SPARQL benchmark for the industry.

Goals

  • Definition of multiple test sets exercising the SPARQL functions and complexities seen in actual Wikidata queries, as well as extensions, federated query, and workloads
    • Definition of specific INSERT, DELETE, CONSTRUCT and SELECT queries for performance and capabilities analysis
    • Definition of read/write workloads for stress testing
    • Tests of system characteristics and SPARQL compliance, and to evaluate system behavior under load

Test Design

Design based on insights gathered (largely) from the following papers:

Also, the following analyses (conducted by members of the WDQS team) examined more recent data:

Testing SPARQL 1.1 and GeoSPARQL Compliance

Testing compliance to the SPARQL 1.1 specification (using the W3C test suite) will be accomplished using a modified form of the Tests for Triplestore (TFT) codebase. Details are provided on the Running TFT page.

GeoSPARQL testing will be accomplished similarly, and is also described on that same page.

Testing Wikidata-Specific Updates and Queries

This section expands on the specific SPARQL language constructs (such as FILTER, OPTIONAL, GROUP BY, ...), and query and update patterns that will be tested. Testing includes federated and geospatial queries, and support for the (evolution of the) label, GAS and MediaWiki local SERVICEs.

As regards SPARQL, tests are defined to exercise:

  • SELECT, ASK and CONSTRUCT queries, as well as INSERT and DELETE updates
    • Note that the INSERT/DELETE requests are defined from the Streaming Updater output, as discussed below
  • Language keywords
    • Solution modifiers - Distinct, Limit, Offset, Order By, Offset
    • Assignment operators - Bind, Values
    • Algebraic operators - Filter, Union, Optional, Exists, Not Exists, Minus
    • Aggregation operators - Count, Min/Max, Avg, Sum, Group By, Group_Concat, Sample, Having
  • Subqueries
  • With both constants and variables in the triples
  • With varying numbers of triples (from 1 to 50+)
  • With combinations (co-occurrences) of the above language constructs
  • Utilizing different property path lengths and structures
    • For example, property paths of the form, a*, ab*, ab*c, abc*, a|b, a*|b*, etc.
  • Using different graph patterns distinguished by the number of triples, variables and joined nodes (URIs and variables that are used in multiple triples as the subject or object) in the query, plus the largest number of joins for any of the joined entities (the "join degree") and the longest chain of triples from any subject to an object (including counting the number of sequential properties in a property path)
    • For example, this query has three triples, two variables, one join node (?q) with the longest chain = 2 (due to the property path), and the largest join degree = 2 (joining ?q in line 3 to either line 7 or line 9)
    • As another example, this query has eleven triples (not counting the comments), ten variables, five join nodes (?article, ?person, ?country, ?givenName and ?familyName) with the longest chain = 3 (from ?article to ?person, ?person to ?country, and ?coumtry to its ?countrylabel; similar result ending with ?givenName or ?familyName), and the largest join degree = 4 (joining ?person in lines 5, 8, 9 and its reference in either of the OPTIONAL clauses)
  • Cold-start and warm-start scenarios (to understand the effects of caching)
  • Mixes of highly selective, equally selective and non-selective triples (to understand optimization)
  • Small and large result sets, some with the potential for large intermediate result sets

These tests for capabilities are defined using static queries. They will be executed using the updated TFT framework to evaluate a triple store's/endpoint's support (or lack of support) for each of the Wikidata requirements, as well as the correctness and completeness of the response. In addition, the tests will be run using the modified Iguana framework to obtain an estimate of execution times.

The TFT compliance test definitions are stored in the xxx repository, which is included in the TFT code repository as another submodule. The corresponding test definitions for use in the Iguana framework are defined at yyy. Details coming.

Wikidata Triples for Compliance Testing

Since TFT (re)loads test data for every query, a small data set is used in this environment. It is available as the file, wikidata-subset.nt, located in the wikidata-tests repository, in the data directory.

Initially, a "small" set of Wikidata triples was created (subgraphs-5.csv). Approximately 20, well-populated items were selected at random representing entities from the human, film, gene and scholarly articles Wikidata sub-graphs. For these entities, all triples were captured, including statements and metadata triples (such as site links). This was created as a test set for the work on Phabricator ticket T303831, subgraph analysis. The information in the CSV file was manipulated using the Jupyter notebook, Create_Wikidata_Sample.ipynb, to create the query-triples.nt file. However, this data set alone was insufficient, since INSERT/DELETE data requests also would be processed during stress testing. Almost all of the deleted triples did not exist in query-triples.nt. This was not necessarily a problem, since deleting non-existent triples does not result in an error, but there was concern that the time to process a non-existent (versus existing) triple might be different.

To address this, a 15-minute capture of the WDQS Streaming Updater JSON output was created. That output is captured in this file. From the JSON, a sequence of RDF added/deleted triples was extracted and transformed into a series of SPARQL INSERT/DELETE DATA requests, using the same Jupyter notebook as noted above. The resulting SPARQL requests can be found in the file, sparql-update.txt, also in the wikidata-tests/data directory.

Although sparql-update.txt is not needed for compliance testing, it was used to create additional Wikidata triples to add to the "small" query test set. sparql-update.txt was parsed (again using the Jupyter notebook, Create_Wikidata_Sample.ipynb) to extract the first occurrence of each deleted subject-predicate pair. In this way, new triples were created and then added to the "small" set from above. This was done in order to populate the store with the actual triples that would be deleted when testing the evaluation infrastructure, when mimicking the Stream Updater processing.

The resulting data set is found in the file, wikidata-subset.nt.

Workload Testing

This evaluation utilizes combinations of the above queries/updates (and others) with the proportions of different query complexities defined based on these investigations:

The loading will be based on the:

  • Highest (+ some configurable percentage) and lowest number of "queries per second" (for a single server)
  • Highest (+ some configurable percentage) and lowest, added and deleted "triples ingestion rate" (for a single server)

Note that these workloads reflect both user and bot queries.

The tests are defined using query patterns based on the compliance queries from above with additional queries informed by the analyses and with updates as generated by the WDQS Streaming Updater. They will be executed using a modified version of the Iguana Framework across multiple systems, using multiple "client" threads/workers. The following statistics will be reported:

  • Total execution time for each query overall and by worker
  • Minimum and maximum execution times for each query (if successful) by worker and across all workers
  • Mean and geometric mean of each query (that successfully executed) by worker and across all workers
  • Mean and geometric mean of each query using its execution time (if successful) or a penalized amount (= timeout by default) for failed queries
    • By query overall and by worker
    • This adjusts for queries completing quickly due to errors (e.g., they will have a low execution time but not produce results)
  • Number of queries overall that executed and completed by worker and dataset
  • Number of queries that timed out by worker and dataset
  • Average number of queries per second (across all queries) that can be processed by a triple store for the data set

As above, the test details are defined at yyy. The stress test/workload environment assumes that the complete Wikidata RDF is loaded, and will be executed using the modified Iguana framework. More details coming.

Wikidata Triples for Stress Testing

Stress testing requires a load of the complete set of Wikidata triples, and then a capture of the streaming updates that will be applied to it. Processing of the WDQS Streaming Updater JSON output can be handled using the functionality in the Jupyter notebook discussed above, Create_Wikidata_Sample.ipynb. (See the processing in the second code block of the notebook, which produces the sparql-update.txt file.)

However, there is still a problem scenario to address. There is no need to add triples to a complete Wikidata dump before the first execution of the stress tests (the dump is, after all, "complete"). But, deletion and reload of some triples will be necessary after executing a workload test, if another execution is to be run against the same data store. The Wikidata triples are modified by the inclusion of Stream Updater INSERT/DELETE data. Those modifications need to be reversed if further tests should be run. Although the small Wikidata data set (wikidata-subset.nt) can be reloaded for TFT and Iguana compliance testing, the full data set cannot, due to its size.

Referring to the Create-Wikidata-Sample.ipynb notebook, see the fifth code block for one way to reverse the Stream Updater changes.

Testing the Evaluation Infrastructure

The full Wikidata dump will be used for evaluation testing. However, a small subset of Wikidata has been created as a test data set, to evaluate the testing infrastructure. The details of that data set are described above and the data set's evaluation using a local Stardog installation are shown on this page.