griddb-kafka-connector-0.6.jar<\/code>.<\/p>\nNote: The jar file is also included in the source code provided by this repo (in the next section). If you clone the repo and run this project via docker compose, you do not need to download\/build the jar file yourself.<\/p>\n
Source Code<\/h3>\n
You can find the source code in the griddbnet github page:<\/p>\n
$ git clone https:\/\/github.com\/griddbnet\/Blogs.git --branch 7_kafka_timeseries<\/code><\/p>\nRunning Project<\/h3>\n
Once you have the source code and docker installed, you can simply run:<\/p>\n
\n
$ docker compose pull && docker compose up -d<\/code><\/pre>\n<\/div>\nAnd then once it’s done, you can start checking if the Kafka connector has the GridDB sink properties file in place by running the following:<\/p>\n
\n
$ curl http:\/\/localhost:8083\/connectors\/\n\n[\"griddb-kafka-sink\"]<\/code><\/pre>\n<\/div>\nYou can also take a look at the contents of the kafka-sink to see what it contains:<\/p>\n
\n
$ curl http:\/\/localhost:8083\/connectors\/griddb-kafka-sink<\/code><\/pre>\n<\/div>\nOnce that’s done, you can run the python script, which acts as a kafka producer.<\/p>\n
\n
$ python3 -m pip install kafka-python\n$ python3 scripts\/producer.py<\/code><\/pre>\n<\/div>\nGridDB Sink Properties<\/h3>\n
In Kafka and other stream\/event-driven architectures, the concept of sources and sinks mean to describe the direction of the flow of data. The sink is where data flows in<\/em>, or where the data ends up — in this case, we want our data payloads to persist inside of GridDB as time series data inside of a time series container. And so we set the properties file as such:<\/p>\n\n
connector.class= com.github.griddb.kafka.connect.GriddbSinkConnector\n name= griddb-kafka-sink\n cluster.name= myCluster\n user= admin\n password= admin\n notification.member= griddb-server=10001\n container.type= TIME_SERIES\n topics= meter_0,meter_1,meter_2,meter_3\n transforms= TimestampConverter\n transforms.TimestampConverter.type= org.apache.kafka.connect.transforms.TimestampConverter$Value\n transforms.TimestampConverter.format= yyyy-MM-dd hh=mm=ss\n transforms.TimestampConverter.field= timestamp\n transforms.TimestampConverter.target.type= Timestamp<\/code><\/pre>\n<\/div>\nAs compared to our previous article, the main changes are the container.type<\/code> designation and the transforms properties. The transforms properties tells our Kafka cluster which string value will be converted into timestamp, along with other useful information to help that process along. The other values are simply allowing for our broker to know where to send the data topics to, which is our GridDB docker container with a hostname of griddb-server<\/code>.<\/p>\nThe topics are the name of the data topics and will also be the names of our GridDB time series containers.<\/p>\n
Python Producer Script<\/h3>\n
There isn’t much to say here that you can’t get from simply reading the (simple) source code. The only thing I will add is that if you wished to docker-ize the docker container as well, you would change server location from localhost<\/code> to broker:9092<\/code><\/p>\n\n
#p=KafkaProducer(bootstrap_servers=['localhost:9092'])\np=KafkaProducer(bootstrap_servers=['broker:9092'])<\/code><\/pre>\n<\/div>\nOne other thing to note is that though we are making time_series data containers with time_series data as the row key, you still need to set your payload data fields as type string<\/code> (I teased this above when discussing the transform<\/code> property in the sink section).<\/p>\n\n
\"schema\": \n{\n \"fields\": [ \n { \"field\": \"timestamp\", \"optional\": False, \"type\": \"string\" },\n { \"field\": \"kwh\", \"optional\": False, \"type\": \"double\" }, \n { \"field\": \"temp\", \"optional\": False, \"type\": \"double\" } \n ], \n \"name\": \"iot\", \"optional\": False, \"type\": \"struct\" \n} <\/code><\/pre>\n<\/div>\nThe key here is that though the type is string<\/code>, we must set the first<\/em> field as our targeted timestamp type. And then in the sink for this dataset, we set the transforms.TimestampConverter.field<\/code> as the name of our field we want to convert to type timestamp. With these things in place, Kafka and GridDB will create your tables with the set schema and the proper container type.<\/p>\nRunning Kafka in Docker Containers<\/h2>\n
In our previous article about kafka, we simply ran kafka and GridDB on bare metal, meaning simply running the servers throught he CLI with commands. Though it worked well, it’s a bit confusing because you need 3-4 terminals open and need to remember to run things in sequence. For this article, we have prepared a docker compose file which allows you to run download and run everything with 2-3 commands!<\/p>\n
Confluent Docker Containers<\/h3>\n
First, let’s discuss the docker images provided by Confluent, which is a company which provides support and tools pertaining to Kafka for your large corporation. Despite this though, they provide the docker images freely which we will use in our docker compose file.<\/p>\n
Essentially what docker compose does is allow us to create a set of “services” (AKA docker containers) which we can run in unison with a simple command, with rules set in which we can set which containers rely on others. For example, we can set the various kafka containers to rely on each other so that they start up in the correct sequence.<\/p>\n
We opted for this because as explained above, running Kafka is not an easy process — it has many different parts that need to run. For example, to run this seemingly simple project where we push data from python script –> kafka topics –> GridDb it takes 5 services in our Docker compose file.<\/p>\n
Docker Compose Services<\/h3>\n
The following are all of the services.<\/p>\n
\n- GridDB<\/li>\n
- Kafka Zookeeper<\/li>\n
- Kafka Broker<\/li>\n
- Kafka Schema Registry<\/li>\n
- Kafka-Connect<\/li>\n<\/ul>\n
And another service which we omited but we could include is a kafka data producer.<\/p>\n
The Kafka zookeeper can be thought of as the brains or the main component of kafka. The Broker is the service which handles the data topics and is often run with many different brokers for failsafes, etc; when we want to point our producer of data topics to Kafka, we point to the broker.<\/p>\n
The kafka schema registry enforces schemas to be used for your topics. In our case, it’s useful for deserialization of our JSON schema of our data payloads from our python producer.<\/p>\n
The Kafka Connect container is where we add our third party libraries for use with Kafka: GridDB Kafka connect jar and our GridDB sink properties file. The connect container is a bit unique in that we need to make sure that the container is up and running first and then we push to it a json file with the GridDB sink property instructions. The GridDB Kafka Connect jar file though we push to the file system during docker image start up.<\/p>\n
Docker Compose Instructions<\/h4>\n
For GridDB there are no special instructions: we simply pull the image from griddbnet and then set some environment variables:<\/p>\n
\n
griddb-server:\n image: 'griddbnet\/griddb:5.6.0'\n container_name: griddb-server\n expose:\n - '10001'\n - '10010'\n - '10020'\n - '10040'\n - '20001'\n - '41999'\n environment:\n NOTIFICATION_MEMBER: 1\n GRIDDB_CLUSTER_NAME: myCluster<\/code><\/pre>\n<\/div>\nThe zookeeper is in a similar boat:<\/p>\n
\n
zookeeper:\n image: 'confluentinc\/cp-zookeeper:7.3.0'\n container_name: zookeeper\n environment:\n ZOOKEEPER_CLIENT_PORT: 2181\n ZOOKEEPER_TICK_TIME: 2000<\/code><\/pre>\n<\/div>\nThe broker exposes port 9092 so that we can run our python producer script outside of the context of our docker compose network environment (we just point to localhost:9092). There are also more environment variables necessary for pointing to the zookeeper and other cluster rules<\/p>\n
\n
broker:\n image: 'confluentinc\/cp-kafka:7.3.0'\n container_name: broker\n ports:\n - '9092:9092'\n depends_on:\n - zookeeper\n environment:\n KAFKA_BROKER_ID: 1\n KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'\n KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: 'PLAINTEXT:PLAINTEXT,PLAINTEXT_INTERNAL:PLAINTEXT'\n KAFKA_ADVERTISED_LISTENERS: 'PLAINTEXT:\/\/broker:9092,PLAINTEXT_INTERNAL:\/\/broker:29092'\n KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1\n KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1\n KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1<\/code><\/pre>\n<\/div>\nYou will also notice that the broker, schema registry, kafka connect all “depend” on the zookeeper. It really makes clear to us who is charge of the entire operation.<\/p>\n
\n
kafka-schema-registry:\n image: 'confluentinc\/cp-schema-registry:7.3.0'\n hostname: kafka-schema-registry\n container_name: kafka-schema-registry\n ports:\n - '8082:8082'\n environment:\n SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: 'PLAINTEXT:\/\/broker:9092'\n SCHEMA_REGISTRY_HOST_NAME: kafka-schema-registry\n SCHEMA_REGISTRY_LISTENERS: 'http:\/\/0.0.0.0:8082'\n depends_on:\n - zookeeper<\/code><\/pre>\n<\/div>\nThe kafka connect also grabs from the confluent docker hub and has tons of environment variables, but it also includes volumes with a shared filesystem with the host machine so that we can share our GridDB Kafka Connect jar file. And lastly, we have scipt at the very bottom of the service which allows us to wait until our kafka-connect HTTP endpoints are available. Once we get a 200 status code as a response, we can run our script which sends our GridDB-Sink properties file.<\/p>\n
\n
kafka-connect:\n image: confluentinc\/cp-kafka-connect:latest\n container_name: kafka-connect\n ports:\n - '8083:8083'\n environment:\n CONNECT_BOOTSTRAP_SERVERS: 'broker:9092'\n CONNECT_REST_PORT: 8083\n CONNECT_GROUP_ID: device\n CONNECT_CONFIG_STORAGE_TOPIC: device-config\n CONNECT_OFFSET_STORAGE_TOPIC: device-offsets\n CONNECT_STATUS_STORAGE_TOPIC: device-status\n CONNECT_KEY_CONVERTER: org.apache.kafka.connect.json.JsonConverter\n CONNECT_VALUE_CONVERTER: org.apache.kafka.connect.json.JsonConverter\n CONNECT_INTERNAL_KEY_CONVERTER: org.apache.kafka.connect.json.JsonConverter\n CONNECT_INTERNAL_VALUE_CONVERTER: org.apache.kafka.connect.json.JsonConverter\n CONNECT_KEY_CONVERTER_SCHEMAS_ENABLE: true\n CONNECT_VALUE_CONVERTER_SCHEMAS_ENABLE: true\n CONNECT_KEY_CONVERTER_SCHEMA_REGISTRY_URL: 'http:\/\/kafka-schema-registry:8082'\n CONNECT_VALUE_CONVERTER_SCHEMA_REGISTRY_URL: 'http:\/\/kafka-schema-registry:8082'\n CONNECT_REST_ADVERTISED_HOST_NAME: kafka-connect\n CONNECT_LOG4J_APPENDER_STDOUT_LAYOUT_CONVERSIONPATTERN: '[%d] %p %X{connector.context}%m (%c:%L)%n'\n CONNECT_CONFIG_STORAGE_REPLICATION_FACTOR: '1'\n CONNECT_OFFSET_STORAGE_REPLICATION_FACTOR: '1'\n CONNECT_STATUS_STORAGE_REPLICATION_FACTOR: '1'\n CONNECT_PLUGIN_PATH: >-\n \/usr\/share\/java,\/etc\/kafka-connect\/jars\n CLASSPATH: >-\n \/usr\/share\/java,\/etc\/kafka-connect\/jars\n volumes:\n - '.\/scripts:\/scripts'\n - '.\/kafka-connect\/connectors:\/etc\/kafka-connect\/jars\/'\n \n depends_on:\n - zookeeper\n - broker\n - kafka-schema-registry\n - griddb-server\n command:\n - bash\n - '-c'\n - >\n \/etc\/confluent\/docker\/run & \n\n echo \"Waiting for Kafka Connect to start listening on kafka-connect \u00e2\u008f\u00b3\"\n\n while [ $$(curl -s -o \/dev\/null -w %{http_code}\n http:\/\/kafka-connect:8083\/connectors) -eq 000 ] ; do \n echo -e $$(date) \" Kafka Connect listener HTTP state: \" $$(curl -s -o \/dev\/null -w %{http_code} http:\/\/kafka-connect:8083\/connectors) \" (waiting for 200)\"\n sleep 5 \n done\n\n nc -vz kafka-connect 8083\n\n echo -e \"n--n+> Creating Kafka Connect GridDB sink\"\n\n \/scripts\/create-griddb-sink.sh &&\n \/scripts\/example-sink.sh\n\n sleep infinity <\/code><\/pre>\n<\/div>\nThis properties file will give explicit instructions to Kafka that when topics with certain names are received by the broker, it should push those out to the instructions in the properties file, which in this case are our GridDB container.<\/p>\n
Conclusion<\/h2>\n
After you run the producer, you should be able to see all of your data inside of your docker griddb server through use of the GridDB CLI: $ docker exec -it griddb-server gs_sh<\/code>.<\/p>\nAnd with that, we have successfully pushed IoT-like sensor data through Kafka to a GridDB Time Series container.<\/p>\n","protected":false},"excerpt":{"rendered":"
In today’s article we will be discussing Kafka in conjunction with GridDB, which we have done before: Stream Data with GridDB and Kafka Using GridDB as a source for Kafka with JDBC Using SQL Batch Inserts with GridDB v5.5, JDBC, and Kafka Udemy course: Create a working IoT Project – Apache Kafka, Python, GridDB We […]<\/p>\n","protected":false},"author":1,"featured_media":27911,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-46827","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"\n
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