Introduced in 4.0.0
Kafka Connect is a system that is able to pull data from data sources into Kafka topics as well as write data from Kafka topics into data sinks. This project implements a Kafka Connector Sink for both Accumulo backed and Mongo backed instances of Rya.
While testing the application with both the Mongo Rya Sink and Accumulo Rya Sink uber jars installed, we were seeing ClassCastExceptions being thrown when some code was trying to cast a ContextStatement into a Statement. Normally, this wouldn't cause a problem. However, within Connect, this was caused by both uber jars containing a copy of the ContextStatement and Statement classes. Different Classloaders loaded each of those classes and the relationship between them was lost.
For now, it's important that you only deploy one of the uber jars at a time.
Applications that would like to write to a Kafka topic using the format that the sink is able to recognize must write Set<Statement> objects by using the StatementsSerializer.
Rather than using the Confluent Schema Registry and Avro to serialize Statements, we‘re going with RDF4J’s Rio Binary Format. You may read more about how that format is implemented here.
This tutorial demonstrates how to install and start the Accumulo Rya Sink and the Mongo Rya Sink by using the Open Source version of the Confluent platform. You can download it here. We're going to use the standalone version of Connect, but in a production environment you may want to use the distributed mode if there is a lot of data that needs to be inserted.
We suggest you go through the Confluent Platform Open Source Quick Start, so that you can ensure the Confluent platform is installed and ready for use. We're using Confluent 4.1.0 in this tutorial, so be aware some things may change when using newer versions of the platform. You may also find it beneficial to go through the Kafka Connect Quick Start as well to see how Kafka Connects works in general.
You can fetch the artifacts you need to follow this Quick Start from our downloads page. Click on the release of interest and follow the “Central repository for Maven and other dependency managers” URL.
Fetch the following four artifacts:
| Artifact Id | Type |
|---|---|
| rya.shell | shaded jar |
| rya.kafka.connect.client | shaded jar |
| rya.kafka.connect.accumulo | shaded jar |
| rya.kafka.connect.mongo | shaded jar |
The sink connector that we will be demonstrating reads Statements from a Kafka topic and loads them into an instance of Rya. Just to keep things simple, lets create a topic that only has a single partition and is unreplicated. Within a production environment, you will want to tune these values based on how many concurrent workers you would like to use when processing input.
kafka-topics --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic statements
Next we need to create a file that contains the statements we will load into the topic. Name the file “quickstart-statements.nt” and use a text editor to write the following lines to it:
<urn:Alice> <urn:talksTo> <urn:Bob> . <urn:Bob> <urn:talksTo> <urn:Alice> . <urn:Bob> <urn:talksTo> <urn:Charlie> . <urn:Charlie> <urn:talksTo> <urn:Alice> . <urn:David> <urn:talksTo> <urn:Eve> . <urn:Eve> <urn:listensTo> <urn:Bob> .
Use the rya.kafka.connect.client to write the file's contents to the topic we just made.
java -jar rya.kafka.connect.client-<version>-shaded.jar write -f quickstart-statements.nt -t statements
You may verify the statements were written by using the read command.
java -jar rya.kafka.connect.client-<version>-shaded.jar read -t statements
At this point you need to decide whether you are going to use an Accumulo or MongoDB backed instance of Rya. The following steps are pretty much the same for both backends, but they require different jars and commands. To follow the Accumulo set of steps, start with Accumulo Step 3 and go through Accumulo Step 5. To follow the Mongo set of steps, then just skip ahead to Mongo Step 3 and go through Mongo Step 5.
The sink needs a place to put the Statements that we just wrote to the kafka topic. We‘re going to have it write to a Rya instance named “quickstart” on your Accumulo cluster. To do this, you’ll need to use the Rya Shell. Here's roughly what an installation session should look like.
java -jar rya.shell-<version>-shaded.jar
_____ _____ _ _ _
| __ \ / ____| | | | |
| |__) | _ __ _ | (___ | |__ ___| | |
| _ / | | |/ _` | \___ \| '_ \ / _ \ | |
| | \ \ |_| | (_| | ____) | | | | __/ | |
|_| \_\__, |\__,_| |_____/|_| |_|\___|_|_|
__/ |
|___/
<version>
Welcome to the Rya Shell.
Execute one of the connect commands to start interacting with an instance of Rya.
You may press tab at any time to see which of the commands are available.
rya>connect-accumulo --zookeepers localhost --instanceName quickstart_instance --username quickstart
Password: *******
rya/quickstart_instance> install
Rya Instance Name [default: rya_]: quickstart
Use Shard Balancing (improves streamed input write speeds) [default: false]: f
Use Entity Centric Indexing [default: true]: f
Use Free Text Indexing [default: true]: f
Use Temporal Indexing [default: true]: f
Use Precomputed Join Indexing [default: true]: f
A Rya instance will be installed using the following values:
Instance Name: quickstart
Use Shard Balancing: false
Use Entity Centric Indexing: false
Use Free Text Indexing: false
Use Temporal Indexing: false
Use Precomputed Join Indexing: false
Continue with the install? (y/n) y
The Rya instance named 'quickstart' has been installed.
We also want to ensure the instance we just installed does not have any Statements yet. We can do this using that same shell instance.
# 2. Verify no data has been inserted yet.
rya/quickstart_instance> connect-rya --instance quickstart
rya/quickstart_instance:quickstart> sparql-query
Enter a SPARQL Query.
Type '\e' to execute the current query.
Type '\c' to clear the current query.
SPARQL> select * where { ?s ?p ?o .}\e
Executing Query...
Query Results:
rya/quickstart_instance:quickstart>
At this point we have a kafka topic that is filled with RDF Statements that need to be loaded into Rya. We also have a Rya instance for them to be written to. All that is left is to install the Accumulo Rya Sink, configure it to use those two endpoints, and then load it.
The version of the Confluent platform we used for this quick start doesn't seem to be able to find new connector installs dynamically, so start by shutting everything down.
confluent stop
Install the shaded jar that contains the Accumulo Rya Sink connector.
mkdir confluent-4.1.0/share/java/kafka-connect-rya-accumulo cp rya.kafka.connect.accumulo-<version>-shaded.jar confluent-4.1.0/share/java/kafka-connect-rya-accumulo
Then we need to configure the connector to read from the “statements” topic, specify which Accumulo cluster is hosting the Rya Instance, which Rya Instance to write to, and specify the classes that define the Connector and the Converter to use. This file has clear text passwords in it, so ensure it has appropriate access restrictions applied to it.
touch confluent-4.1.0/etc/kafka/connect-rya-accumulo-sink.properties
And then use your favorite text editor to fill in the following values:
name=rya-accumulo-sink connector.class=org.apache.rya.kafka.connect.accumulo.AccumuloRyaSinkConnector tasks.max=1 value.converter=org.apache.rya.kafka.connect.api.StatementsConverter topics=statements accumulo.zookeepers=127.0.0.1 accumulo.cluster.name=<your cluster name here> accumulo.username=<your username here> accumulo.password=<your password here> rya.instance.name=quickstart
Start the Confluent platform:
confluent start
Even after the start command says everything is started, it may take a moment for the load command to work. Rerun this command until you get a response from the REST service printed to the screen and confluent reports it as loaded:
confluent load rya-accumulo-sink -d confluent-4.1.0/etc/kafka/connect-rya-accumulo-sink.properties
The connector will automatically start workers that load the data from the configured topic into the configured Rya instance.
At this point you should be able to rerun the query from Accumulo Step 3 and see that Statements have been added to the Rya instance.
rya/quickstart_instance:quickstart> sparql-query
Enter a SPARQL Query.
Type '\e' to execute the current query.
Type '\c' to clear the current query.
SPARQL> select * where { ?s ?p ?o . }\e
Executing Query...
Query Results:
p,s,o
urn:talksTo,urn:Alice,urn:Bob
urn:talksTo,urn:Bob,urn:Alice
urn:talksTo,urn:Bob,urn:Charlie
urn:talksTo,urn:Charlie,urn:Alice
urn:talksTo,urn:David,urn:Eve
urn:listensTo,urn:Eve,urn:Bob
Done.
The sink needs a place to put the Statements that we just wrote to the kafka topic. We‘re going to have it write to a Rya instance named “quickstart” within your Mongo database. To do this, you’ll need to use the Rya Shell. Here's roughly what an installation session should look like.
[root@localhost ~]# java -jar rya.shell-<version>-shaded.jar
_____ _____ _ _ _
| __ \ / ____| | | | |
| |__) | _ __ _ | (___ | |__ ___| | |
| _ / | | |/ _` | \___ \| '_ \ / _ \ | |
| | \ \ |_| | (_| | ____) | | | | __/ | |
|_| \_\__, |\__,_| |_____/|_| |_|\___|_|_|
__/ |
|___/
<version>
Welcome to the Rya Shell.
Execute one of the connect commands to start interacting with an instance of Rya.
You may press tab at any time to see which of the commands are available.
rya> connect-mongo --hostname localhost --port 27017
Connected. You must select a Rya instance to interact with next.
rya/localhost> install
Rya Instance Name [default: rya_]: quickstart
Use Free Text Indexing [default: true]: f
Use Temporal Indexing [default: true]: f
Use PCJ Indexing [default: true]: f
A Rya instance will be installed using the following values:
Instance Name: quickstart
Use Free Text Indexing: false
Use Temporal Indexing: false
Use PCJ Indexing: false
Continue with the install? (y/n) y
The Rya instance named 'quickstart' has been installed.
We also want to ensure the instance we just installed does not have any Statements yet. We can do this using that same shell instance.
rya/localhost> connect-rya --instance quickstart
rya/localhost:quickstart> select * where { ?s ?p ?o .}\e
Command 'select * where { ?s ?p ?o .}\e' not found (for assistance press TAB)
rya/localhost:quickstart> sparql-query
Enter a SPARQL Query.
Type '\e' to execute the current query.
Type '\c' to clear the current query.
SPARQL> select * where { ?s ?p ?o .}\e
Executing Query...
No Results Found.
Done.
rya/localhost:quickstart>
At this point we have a kafka topic that is filled with RDF Statements that need to be loaded into Rya. We also have a Rya instance for them to be written to. All that is left is to install the Mongo Rya Sink, configure it to use those two endpoints, and then load it.
The version of the Confluent platform we used for this quick start doesn't seem to be able to find new connector installs dynamically, so start but shutting everything down.
confluent stop
Install the shaded jar that contains the Mongo Rya Sink connector.
mkdir confluent-4.1.0/share/java/kafka-connect-rya-mongo cp rya.kafka.connect.mongo-<version>-shaded.jar confluent-4.1.0/share/java/kafka-connect-rya-mongo
Then we need to configure the connector to read from the “statements” topic, specify which Mongo database is hosting the Rya Instance, which Rya Instance to write to, and specify the classes that define the Connector and the Converter to use. This file has clear text passwords in it, so ensure it has appropriate access restrictions applied to it.
touch confluent-4.1.0/etc/kafka/connect-rya-mongo-sink.properties
And then use your favorite text editor to fill in the following values:
name=rya-mongo-sink connector.class=org.apache.rya.kafka.connect.mongo.MongoRyaSinkConnector tasks.max=1 value.converter=org.apache.rya.kafka.connect.api.StatementsConverter topics=statements mongo.hostname=127.0.0.1 mongo.port=27017 mongo.username= mongo.password= rya.instance.name=quickstart
Start the Confluent platform:
confluent start
Even after the start command says everything is started, it may take a moment for the load command to work. Rerun this command until you get a response from the REST service printed to the screen and confluent reports it as loaded:
confluent load rya-mongo-sink -d confluent-4.1.0/etc/kafka/connect-rya-mongo-sink.properties
The connector will automatically start workers that load the data from the configured topic into the configured Rya instance.
At this point you should be able to rerun the query from Mongo Step 3 and see that statements have been added to the Rya instance.
rya/localhost:quickstart> sparql-query
Enter a SPARQL Query.
Type '\e' to execute the current query.
Type '\c' to clear the current query.
SPARQL> select * where { ?s ?p ?o . }\e
Executing Query...
Query Results:
p,s,o
urn:talksTo,urn:Alice,urn:Bob
urn:talksTo,urn:Bob,urn:Charlie
urn:talksTo,urn:Charlie,urn:Alice
urn:talksTo,urn:Bob,urn:Alice
urn:talksTo,urn:David,urn:Eve
urn:listensTo,urn:Eve,urn:Bob
Done.
It‘s a security flaw that the connector’s passwords for connecting to Accumulo and Mongo are in clear text within the configuration files. The log files hide them when they log the configuration, but it's still written to standard out when the use the confluent command to load the connector. There should be another way for the connector to receive the credentials required to connect.
Currently, you can only use the Mongo Rya Sink or the Accumulo Rya Sink because of the problem mentioned in An Important Note About Deploying the Plugins.
We could have a single uber jar plugin that supports both backends. We could also just not use uber jars and include all of the jars that the plugins depend on in a single folder.
The sinks are able to write Statement objects, but that means none of those statements are allowed to have visibility expressions. If they do, then the visibilities will be dropped when the statement is inserted.
It would be nice if the Rya implementation of the RDF4J RepositoryConnection were able to figure out when a Statement is actually a VisibilityStatement. It could then retain the visibility expression when it is persisted.
The Visibility Binding Sets that are stored within the Precomputed Join index could be generated by an external system (such as Rya Streams) and written to a Kafka topic. It would be convenient to also have a connector that is able to write those values to the index.
Rya Streams would benefit from having a Kafka Connect Source that is able to determine when new Statements have been added to the core tables, and then write those Visibility Statements to a Kafka topic.
It‘s difficult to get the Geo indexing into the Sail object that represents Rya because the geo project is optional. While optional, we don’t use dependency injection to get the GeoRyaSailFactory into the application instead of the normal Rya Sail Factory. An improvement to this project would be to resolve that problem so that it may do geo indexing while inserting statements.