Usage

Before you begin

RDF4J repositories represent just configured connectors to a particular RDF storage. The repositories are always created and persisted within the actual context. RDF4J Console repository configuration is persisted under the actual user home directory. Repositories created via the RDF4J Workbench exist within the actually connected RDF4J Server context only.

Halyard datasets with all the RDF data are persisted as HBase tables. The corresponding Halyard dataset can be optionally created when the repository is created.

Multiple repositories configured in various RDF4J Servers or in multiple RDF4J Consoles can share one common Halyard dataset and so point to the same HBase table.

Deleting a repository from a particular RDF4J Server or RDF4J Console does not delete the associated Halyard dataset and so it does not affect the data and other users. However clearing the repository or deleting its statements has global effect for all users.

Create repository

HBase repository settings

  • Repository ID is mandatory and may correspond to the HBase table name.
  • Repository title is optional.
  • HBase table name can be left empty when the table name corresponds to the Repository ID.
  • Create HBase table if missing: table presplit bits are ignored in case the table is not created.
  • HBase table presplit bits: keep the default value 0, unless you expect a very large dataset.
  • Use Halyard Push Evaluation Strategy: may be set to false to fallback to the default RDF4J evaluation strategy implementation.
  • Query Evaluation Timeout may be adjusted or set to 0, however, it creates a risk of resource exhaustion if long timeouts are allowed.
  • Optional ElasticSearch Index URL: URL of the supplementary ElasticSearch index with indexed literals for advanced text search capabilities.

With RDF4J Console

> create hbase
Please specify values for the following variables:
Repository ID: testRepo
Repository title:
HBase Table Name:
Create HBase Table if missing (true|false) [true]:
HBase Table presplit bits [0]:
Use Halyard Push Evaluation Strategy (true|false) [true]:
Query Evaluation Timeout [180]:
Optional ElasticSearch Index URL:
Repository created

With RDF4J Workbench

RDF4J Workbench - New Repository

Connect to the existing repository

From RDF4J Console

> open testRepo
Opened repository 'testRepo'

From RDF4J Workbench

Just select the repository from the list of repositories.

A newly created repository is connected automatically in the RDF4J Workbench.

Load RDF data

With Halyard Bulk Load

> ./bulkload /my_hdfs_path/my_triples.owl /my_hdfs_temp_path testRepo
impl.YarnClientImpl: Submitted application application_1458475483810_40875
mapreduce.Job: The url to track the job: http://my_app_master/proxy/application_1458475483810_40875/
mapreduce.Job:  map 0% reduce 0%
mapreduce.Job:  map 100% reduce 0%
mapreduce.Job:  map 100% reduce 100%
mapreduce.Job: Job job_1458475483810_40875 completed successfully
INFO: Bulk Load Completed..

Note: Before Bulk Load of very large datasets into a new HBase table it is recommended to use the Halyard PreSplit. Halyard PreSplit calculates the HBase table region splits and creates the HBase table optimized for the following Bulk Load process.

With Halyard Hive Load

> ./hiveload -Dhalyard.rdf.mime.type='application/n-triples' -Dhalyard.hive.data.column.index=3 my_hive_table /my_hdfs_temp_path testRepo
impl.YarnClientImpl: Submitted application application_1514793734614_41673
mapreduce.Job: The url to track the job: http://my_app_master/proxy/application_1514793734614_41673/
mapreduce.Job:  map 0% reduce 0%
mapreduce.Job:  map 100% reduce 0%
mapreduce.Job:  map 100% reduce 100%
mapreduce.Job: Job job_1514793734614_41673 completed successfully
INFO: Hive Load Completed..

Note: the above listing skips much debugging information from the MapReduce execution.

With RDF4J Console

testRepo> load /home/user/my_triples.owl
Loading data...
Data has been added to the repository (2622 ms)

With RDF4J Workbench

RDF4J Workbench - Add RDF

With RDF4J Server SPARQL endpoint REST APIs

PUT /rdf4j-server/repositories/testRepo/statements HTTP/1.1
Content-Type: application/rdf+xml;charset=UTF-8

[RDF/XML ENCODED RDF DATA]

SPARQL federated queries across datasets

Each Halyard dataset represents a separate RDF dataset. In order to issue SPARQL queries across multiple datasets it is possible to:

  1. Merge all the required datasets into one, as described later in this document.
  2. Use the SPARQL SERVICE clause to federate queries across multiple datasets.

Halyard resolves directly accessible HBase tables (datasets) as federation services. Halyard service URL for each dataset is constructed from the Halyard prefix http://merck.github.io/Halyard/ns# and the table name.

For example, we have datasets dataset1 and dataset2. While querying dataset1, we can use the following SPARQL query to also access data from dataset2:

PREFIX halyard: <http://merck.github.io/Halyard/ns#>

SELECT *
  WHERE {
    SERVICE halyard:dataset2 {
      ?s ?p ?o .
    }
  }

This query can be used by any of the above-described ways or tools (Console, Workbench, REST API, Halyard Update, Export, or Parallel Export). No other federated service types, such as external SPARQL endpoints, are recognised.

SPARQL Update

With Halyard Update

./update -s testRepo -q 'INSERT { ?s ?p ?o . } WHERE { ?s ?p ?o . }'

With Halyard Bulk Update

> ./bulkupdate /my_hdfs_path/my_update_queries.sparql /my_hdfs_temp_path testRepo
impl.YarnClientImpl: Submitted application application_1458476924873_30975
mapreduce.Job: The url to track the job: http://my_app_master/proxy/application_1458476924873_30975/
mapreduce.Job:  map 0% reduce 0%
mapreduce.Job:  map 100% reduce 0%
mapreduce.Job:  map 100% reduce 100%
mapreduce.Job: Job job_1458476924873_30975 completed successfully
INFO: Bulk Update Load Completed..

With RDF4J Console

testRepo> sparql
enter multi-line SPARQL query (terminate with line containing single '.')
insert {?s ?p ?o} where {?s ?p ?o}
.
Executing update...
Update executed in 800 ms

With RDF4J Workbench

RDF4J Workbench - SPARQL Update

With RDF4J Server SPARQL endpoint REST APIs

POST /rdf4j-server/repositories/testRepo/statements HTTP/1.1
Content-Type: application/x-www-form-urlencoded

update=INSERT%20{?s%20?p%20?o}%20WHERE%20{?s%20?p%20?o}

SPARQL query and export data

With Halyard Export

> ./export -s testRepo -q 'select * where {?s ?p ?o}' -t file:///my_path/my_export.csv
INFO: Query execution started
INFO: Export finished

Note: additional debugging information may appear in the output of the Halyard Export execution.

With Halyard Parallel Export

> ./pexport -Dmapreduce.job.maps=10 -s testRepo -q 'PREFIX halyard: <http://merck.github.io/Halyard/ns#> select * where {?s ?p ?o . FILTER (halyard:parallelSplitBy (?s))}' -t hdfs:///my_path/my_export{0}.csv
impl.YarnClientImpl: Submitted application application_1572718538572_94727
mapreduce.Job: The url to track the job: http://my_app_master/proxy/application_1572718538572_94727/
mapreduce.Job:  map 0% reduce 0%
mapreduce.Job:  map 100% reduce 0%
mapreduce.Job: Job job_1572718538572_94727 completed successfully
INFO: Parallel Export Completed..

With RDF4J Console

testRepo> sparql
enter multi-line SPARQL query (terminate with line containing single '.')
SELECT * WHERE { ?s ?p ?o . } LIMIT 10
.
Evaluating SPARQL query...
+------------------------+------------------------+------------------------+
| s                      | p                      | o                      |
+------------------------+------------------------+------------------------+
| :complexion            | rdfs:label             | "cor da pele"@pt       |
| :complexion            | rdfs:label             | "complexion"@en        |
| :complexion            | rdfs:subPropertyOf     | dul:hasQuality         |
| :complexion            | prov:wasDerivedFrom    | <http://mappings.dbpedia.org/index.php/OntologyProperty:complexion>|
| :complexion            | rdfs:domain            | :Person                |
| :complexion            | rdf:type               | owl:ObjectProperty     |
| :complexion            | rdf:type               | rdf:Property           |
| :Document              | rdfs:comment           | "Any document"@en      |
| :Document              | rdfs:label             | "\u30C9\u30AD\u30E5\u30E1\u30F3\u30C8"@ja|
| :Document              | rdfs:label             | "document"@en          |
+------------------------+------------------------+------------------------+
10 result(s) (51 ms)

With RDF4J Workbench

RDF4J Workbench - SPARQL Query

RDF4J Workbench - Query Result

With RDF4J Server SPARQL endpoint REST APIs

GET /rdf4j-server/repositories/testRepo?query=select+*+where+%7B%3Fs+%3Fp+%3Fo%7D HTTP/1.1
Accept: application/sparql-results+xml, */*;q=0.5

Delete statements

With RDF4J Update

./update -s testRepo -q 'DELETE { ?s ?p ?o . } WHERE { ?s ?p ?o . }'

With RDF4J Workbench

RDF4J Workbench - Remove Statements

With RDF4J Server SPARQL endpoint REST APIs

DELETE /rdf4j-server/repositories/testRepo/statements?subj=&pred=&obj= HTTP/1.1

Clear repository

With RDF4J Console

testRepo> clear
Clearing repository...

With RDF4J Workbench

RDF4J Workbench - Clear All Statements

With RDF4J Server SPARQL endpoint REST APIs

DELETE /rdf4j-server/repositories/testRepo/statements HTTP/1.1

HBase shell dataset operations

Snapshot Halyard dataset

> hbase shell
HBase Shell; enter 'help<RETURN>' for list of supported commands.
Type "exit<RETURN>" to leave the HBase Shell
Version 1.1.2.2.4.2.0-258

hbase(main):001:0> snapshot 'testRepo', 'testRepo_my_snapshot'
0 row(s) in 36.3380 seconds

Clone Halyard dataset from snapshot

> hbase shell
HBase Shell; enter 'help<RETURN>' for list of supported commands.
Type "exit<RETURN>" to leave the HBase Shell
Version 1.1.2.2.4.2.0-258

hbase(main):001:0> clone_snapshot 'testRepo_my_snapshot', 'testRepo2'
0 row(s) in 31.1590 seconds

Export Halyard dataset snapshot

> hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot testRepo_my_snapshot -copy-to /my_hdfs_export_path
2016-04-28 09:01:07,019 INFO  [main] snapshot.ExportSnapshot: Loading Snapshot hfile list
2016-04-28 09:01:07,427 INFO  [main] snapshot.ExportSnapshot: Copy Snapshot Manifest
2016-04-28 09:01:11,704 INFO  [main] impl.YarnClientImpl: Submitted application application_1458475483810_41563
2016-04-28 09:01:11,826 INFO  [main] mapreduce.Job: The url to track the job: http://my_app_master/proxy/application_1458475483810_41563/
2016-04-28 09:01:19,956 INFO  [main] mapreduce.Job:  map 0% reduce 0%
2016-04-28 09:01:29,031 INFO  [main] mapreduce.Job:  map 100% reduce 0%
2016-04-28 09:01:29,039 INFO  [main] mapreduce.Job: Job job_1458475483810_41563 completed successfully
2016-04-28 09:01:29,158 INFO  [main] snapshot.ExportSnapshot: Finalize the Snapshot Export
2016-04-28 09:01:29,164 INFO  [main] snapshot.ExportSnapshot: Verify snapshot integrity
2016-04-28 09:01:29,193 INFO  [main] snapshot.ExportSnapshot: Export Completed: testRepo_my_snapshot

Note: the above listing skips much debugging information from the MapReduce execution.

Bulk merge of multiple datasets

  1. Snapshot and export all Halyard datasets you want to merge.
    (see the above-described processes)
  2. Merge the exported files
    Exported HBase files are organised under the target folder in the following structure: /archive/data/<table_namespace>/<table_name>/<region_id>/<column_family>/<region_files>. We need to merge the region files under each column family from all exports into a single structure.
    As Halyard dataset currently contains only the e column family, it can be achieved, for example, by following commands:
    > hdfs dfs -mkdir -p /my_hdfs_merged_path/e
    > hdfs dfs -mv /my_hdfs_export_path/archive/data/*/*/*/e/* /my_hdfs_merged_path/e
  3. Create a new Halyard dataset.
    (see the above-described process)
  4. Load the merged files
    > hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles /my_hdfs_merged_path new_dataset_table_name

Boost query performance by making datasets read-only

> hbase shell
HBase Shell; enter 'help<RETURN>' for list of supported commands.
Type "exit<RETURN>" to leave the HBase Shell
Version 1.1.2.2.4.2.0-258

hbase(main):001:0> alter 'testRepo', READONLY => 'true'
Updating all regions with the new schema...
0/3 regions updated.
3/3 regions updated.
Done.
0 row(s) in 2.2210 seconds

Disable/enable unused dataset to save HBase resources

> hbase shell
HBase Shell; enter 'help<RETURN>' for list of supported commands.
Type "exit<RETURN>" to leave the HBase Shell
Version 1.1.2.2.4.2.0-258

hbase(main):001:0> disable 'testRepo'
0 row(s) in 1.3040 seconds
> hbase shell
HBase Shell; enter 'help<RETURN>' for list of supported commands.
Type "exit<RETURN>" to leave the HBase Shell
Version 1.1.2.2.4.2.0-258

hbase(main):001:0> enable 'testRepo'
0 row(s) in 1.2130 seconds

Delete dataset

> hbase shell
HBase Shell; enter 'help<RETURN>' for list of supported commands.
Type "exit<RETURN>" to leave the HBase Shell
Version 1.1.2.2.4.2.0-258

hbase(main):001:0> disable 'testRepo'
0 row(s) in 1.2750 seconds

hbase(main):002:0> drop 'testRepo'
0 row(s) in 0.2070 seconds