Kafka MongoDb Sink Connector

The MongoDB-Sink-Connector is a Kafka-Connector for scalable and reliable data streaming from a Kafka topic or number of Kafka topics to a MongoDB collection or number of MongoDB collections. It consumes Avro data from Kafka topics, converts them into Documents and inserts them into MongoDB collections.

Currently, it supports records that have an Avro schema.

Installation

This connector can be used inside a Docker stack or installed as a general Kafka Connect plugin.

Docker installation

Use the radarbase/kafka-connect-mongodb-sink Docker image to connect it inside a Docker infrastructure. For example, RADAR-Docker uses a Docker Compose file. The Kafka Connect Docker image requires environment to be set up. In RADAR-Docker, the following environment variables are set:

environment:
  CONNECT_BOOTSTRAP_SERVERS: PLAINTEXT://kafka-1:9092,PLAINTEXT://kafka-2:9092,PLAINTEXT://kafka-3:9092
  CONNECT_REST_PORT: 8083
  CONNECT_GROUP_ID: "mongodb-sink"
  CONNECT_CONFIG_STORAGE_TOPIC: "mongodb-sink.config"
  CONNECT_OFFSET_STORAGE_TOPIC: "mongodb-sink.offsets"
  CONNECT_STATUS_STORAGE_TOPIC: "mongodb-sink.status"
  CONNECT_KEY_CONVERTER: "io.confluent.connect.avro.AvroConverter"
  CONNECT_VALUE_CONVERTER: "io.confluent.connect.avro.AvroConverter"
  CONNECT_KEY_CONVERTER_SCHEMA_REGISTRY_URL: "http://schema-registry-1:8081"
  CONNECT_VALUE_CONVERTER_SCHEMA_REGISTRY_URL: "http://schema-registry-1:8081"
  CONNECT_INTERNAL_KEY_CONVERTER: "org.apache.kafka.connect.json.JsonConverter"
  CONNECT_INTERNAL_VALUE_CONVERTER: "org.apache.kafka.connect.json.JsonConverter"
  CONNECT_OFFSET_STORAGE_FILE_FILENAME: "/tmp/mongdb-sink.offset"
  CONNECT_REST_ADVERTISED_HOST_NAME: "radar-mongodb-connector"
  CONNECT_ZOOKEEPER_CONNECT: zookeeper-1:2181
  CONNECT_LOG4J_LOGGERS: "org.reflections=ERROR"
  KAFKA_BROKERS: 3

Before starting the streams, the Docker image waits for KAFKA_BROKERS number of brokers to be available as well as the schema registry.

System installation

This connector requires the following setup:

Confluent platform installed. This connector uses the Confluent 4.0.0 platform.
MongoDB instance installed and running with access credentials.

To install the connector, follow the next steps:

Build MongoDB-Sink-Connector from source or you can download the latest release from here.

git clone https://github.com/RADAR-base/MongoDb-Sink-Connector.git

cd MongoDb-Sink-Connector

./gradlew clean build

Follow Confluent platform Quick start to start zookeeper, kafka-server, schema-registry and kafka-rest to easily stream data to a Kafka topic

# Start zookeeper
zookeeper-server-start /etc/kafka/zookeeper.properties

# Start kafka-broker
kafka-server-start /etc/kafka/server.properties

# Start schema-registry
schema-registry-start /etc/schema-registry/schema-registry.properties

# Start kafka-rest
kafka-rest-start /etc/kafka-rest/kafka-rest.properties

Install and start MongoDB

Add build/libs/* and build/third-party/* to a new directory in the Connect plugin path

mkdir /usr/local/share/kafka-connect/plugins/kafka-connect-mongodb-sink
cp build/libs/* build/third-party/* /usr/local/share/kafka-connect/plugins/kafka-connect-mongodb-sink

Usage

Modify sink.properties file according your environment. The following properties are supported:

Name	Description	Type	Default	Valid Values	Importance
topics	List of topics to be streamed.	list			high
record.converter.class	RecordConverterFactory that returns classes to convert Kafka SinkRecords to BSON documents.	class	org.radarcns.connect.mongodb.serialization.RecordConverterFactory	org.radarcns.connect.util.ValidClass@48cf768c	medium
batch.flush.ms	Flush a batch after this amount of milliseconds.	int	15000	[0,...]	low
batch.size	Batch size to initiate a MongoDB write operation. If the buffer does not reach this capacity within batch.flush.ms, it will be written anyway.	int	2500	[1,...]	low
buffer.capacity	Maximum number of items in a MongoDB writer buffer. Once the buffer becomes full, the task fails.	int	20000	[1,...]	low
mongo.host	MongoDB host name to write data to	string		org.radarcns.connect.util.NotEmptyString@59f95c5d	high
mongo.port	MongoDB port	int	27017	[1,...]	low
mongo.database	MongoDB database name	string		org.radarcns.connect.util.NotEmptyString@5ccd43c2	high
mongo.username	Username to connect to MongoDB database. If not set, no credentials are used.	string	null		medium
mongo.password	Password to connect to MongoDB database. If not set, no credentials are used.	string	null		medium
mongo.collection.format	A format string for the destination collection name, which may contain `${topic}` as a placeholder for the originating topic name. For example, `kafka_${topic}` for the topic `orders` will map to the collection name `kafka_orders`.	string	{$topic}	org.radarcns.connect.util.NotEmptyString@4aa8f0b4	medium

A sample configuration may look as below.

# Kafka consumer configuration
name=kafka-connector-mongodb-sink

# Kafka connector configuration
connector.class=org.radarcns.connect.mongodb.MongoDbSinkConnector
tasks.max=1

# Topics that will be consumed
topics=avrotest
# MongoDB server
mongo.host=localhost
mongo.port=27017

# MongoDB configuration
mongo.username=mongodb-username
mongo.password=***
mongo.database=mongodb-database

# Collection name for putting data into the MongoDB database. The {$topic} token will be replaced
# by the Kafka topic name.
#mongo.collection.format={$topic}

# Factory class to do the actual record conversion
record.converter.class=org.radarcns.connect.mongodb.serialization.RecordConverterFactory

Run the MongoDB-Sink-Connector in

standalone mode

connect-standalone /etc/schema-registry/connect-avro-standalone.properties ./sink.properties

distributed mode

connect-distributed /patht/cluster.properties ./sink.properties

Stream sample data to configured topics in sink.properties. You may use, rest-proxy to do this easily.

curl -X POST -H "Content-Type: application/vnd.kafka.avro.v2+json" \
      -H "Accept: application/vnd.kafka.v2+json" \
      --data '{"value_schema": "{\"type\": \"record\", \"name\": \"User\", \"fields\": [{\"name\": \"name\", \"type\": \"string\"}]}", "records": [{"value": {"name": "testUser"}}]}' \
      "http://localhost:8082/topics/avrotest"
      

# You should get the following response:
  {"offsets":[{"partition":0,"offset":0,"error_code":null,"error":null}],"key_schema_id":null,"value_schema_id":21}

If your all of your environment properties are configured properly, when you run the mongodb-sink-connector, you may see output as below.

SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/usr/share/java/confluent-common/slf4j-log4j12-1.7.6.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/share/java/kafka-serde-tools/slf4j-log4j12-1.7.6.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/share/java/kafka-connect-hdfs/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/share/java/kafka/slf4j-log4j12-1.7.21.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
[2017-04-14 15:23:35,626] INFO StandaloneConfig values: 
        cluster = connect
        rest.advertised.host.name = null
        task.shutdown.graceful.timeout.ms = 5000
        rest.host.name = null
        rest.advertised.port = null
        bootstrap.servers = [localhost:9092]
        offset.flush.timeout.ms = 5000
        offset.flush.interval.ms = 60000
        rest.port = 8083
        internal.key.converter = class org.apache.kafka.connect.json.JsonConverter
        access.control.allow.methods = 
        access.control.allow.origin = 
        offset.storage.file.filename = /tmp/connect.offsets
        internal.value.converter = class org.apache.kafka.connect.json.JsonConverter
        value.converter = class io.confluent.connect.avro.AvroConverter
        key.converter = class io.confluent.connect.avro.AvroConverter
 (org.apache.kafka.connect.runtime.standalone.StandaloneConfig:178)
[2017-04-14 15:23:35,767] INFO Logging initialized @509ms (org.eclipse.jetty.util.log:186)
[2017-04-14 15:23:36,094] INFO Kafka Connect starting (org.apache.kafka.connect.runtime.Connect:52)
[2017-04-14 15:23:36,094] INFO Herder starting (org.apache.kafka.connect.runtime.standalone.StandaloneHerder:71)
[2017-04-14 15:23:36,094] INFO Worker starting (org.apache.kafka.connect.runtime.Worker:102)
[2017-04-14 15:23:36,104] INFO ProducerConfig values: 
        metric.reporters = []
        metadata.max.age.ms = 300000
        ...
        ...
        partitioner.class = class org.apache.kafka.clients.producer.internals.DefaultPartitioner
        linger.ms = 0
 (org.apache.kafka.clients.producer.ProducerConfig:178)
[2017-04-14 15:23:36,138] INFO ProducerConfig values: 
        metric.reporters = []
        metadata.max.age.ms = 300000
        reconnect.backoff.ms = 50
        sasl.kerberos.ticket.renew.window.factor = 0.8
        bootstrap.servers = [localhost:9092]
        ...
        ...
        value.serializer = class org.apache.kafka.common.serialization.ByteArraySerializer
        ssl.keymanager.algorithm = SunX509
        metrics.sample.window.ms = 30000
        partitioner.class = class org.apache.kafka.clients.producer.internals.DefaultPartitioner
        linger.ms = 0
 (org.apache.kafka.clients.producer.ProducerConfig:178)
[2017-04-14 15:23:36,140] INFO Kafka version : 0.10.0.1-cp1 (org.apache.kafka.common.utils.AppInfoParser:83)
[2017-04-14 15:23:36,141] INFO Kafka commitId : e7288edd541cee03 (org.apache.kafka.common.utils.AppInfoParser:84)
[2017-04-14 15:23:36,141] INFO Starting FileOffsetBackingStore with file /tmp/connect.offsets (org.apache.kafka.connect.storage.FileOffsetBackingStore:60)
[2017-04-14 15:23:36,143] INFO Worker started (org.apache.kafka.connect.runtime.Worker:124)
[2017-04-14 15:23:36,144] INFO Herder started (org.apache.kafka.connect.runtime.standalone.StandaloneHerder:73)
[2017-04-14 15:23:36,144] INFO Starting REST server (org.apache.kafka.connect.runtime.rest.RestServer:98)
[2017-04-14 15:23:36,261] INFO jetty-9.2.15.v20160210 (org.eclipse.jetty.server.Server:327)
[2017-04-14 15:23:36,931] INFO Started o.e.j.s.ServletContextHandler@d1f74b8{/,null,AVAILABLE} (org.eclipse.jetty.server.handler.ContextHandler:744)
[2017-04-14 15:23:36,940] INFO Started ServerConnector@2ad3a1bb{HTTP/1.1}{0.0.0.0:8083} (org.eclipse.jetty.server.ServerConnector:266)
[2017-04-14 15:23:36,941] INFO Started @1685ms (org.eclipse.jetty.server.Server:379)
[2017-04-14 15:23:36,942] INFO REST server listening at http://127.0.1.1:8083/, advertising URL http://127.0.1.1:8083/ (org.apache.kafka.connect.runtime.rest.RestServer:150)
[2017-04-14 15:23:36,942] INFO Kafka Connect started (org.apache.kafka.connect.runtime.Connect:58)
[2017-04-14 15:23:36,945] INFO ConnectorConfig values: 
        connector.class = org.radarcns.connect.mongodb.MongoDbSinkConnector
        tasks.max = 1
        name = kafka-connector-mongodb-sink
 (org.apache.kafka.connect.runtime.ConnectorConfig:178)
[2017-04-14 15:23:36,955] INFO Creating connector kafka-connector-mongodb-sink of type org.radarcns.connect.mongodb.MongoDbSinkConnector (org.apache.kafka.connect.runtime.Worker:168)
[2017-04-14 15:23:36,957] INFO Instantiated connector kafka-connector-mongodb-sink with version 0.10.0.1-cp1 of type org.radarcns.connect.mongodb.MongoDbSinkConnector (org.apache.kafka.connect.runtime.Worker:176)
[2017-04-14 15:23:36,959] INFO User configuration are: 
        mongo.port: 27017
        connector.class: org.radarcns.connect.mongodb.MongoDbSinkConnector
        mongo.password: ***
        mongo.database: mongodb-database
        record.converter.class: org.radarcns.connect.mongodb.serialization.RecordConverterFactory
        mongo.username: mongodb-username
        mongo.host: localhost
        topics: avrotest
        tasks.max: 1
        name: kafka-connector-mongodb-sink (org.radarcns.connect.mongodb.MongoDbSinkConnector:116)
[2017-04-14 15:23:36,961] INFO Finished creating connector kafka-connector-mongodb-sink (org.apache.kafka.connect.runtime.Worker:181)
[2017-04-14 15:23:36,961] INFO SinkConnectorConfig values: 
        connector.class = org.radarcns.connect.mongodb.MongoDbSinkConnector
        tasks.max = 1
        topics = [avrotest]
        name = kafka-connector-mongodb-sink
 (org.apache.kafka.connect.runtime.SinkConnectorConfig:178)
[2017-04-14 15:23:36,963] INFO At most 1 will be started (org.radarcns.connect.mongodb.MongoDbSinkConnector:126)
[2017-04-14 15:23:36,971] INFO TaskConfig values: 
        task.class = class org.radarcns.connect.mongodb.MongoDbSinkTask
 (org.apache.kafka.connect.runtime.TaskConfig:178)
[2017-04-14 15:23:36,972] INFO Creating task kafka-connector-mongodb-sink-0 (org.apache.kafka.connect.runtime.Worker:315)
[2017-04-14 15:23:36,972] INFO Instantiated task kafka-connector-mongodb-sink-0 with version 0.10.0.1-cp1 of type org.radarcns.connect.mongodb.MongoDbSinkTask (org.apache.kafka.connect.runtime.Worker:326)
[2017-04-14 15:23:36,987] INFO ConsumerConfig values: 
        metric.reporters = []
        ...
        ...
        send.buffer.bytes = 131072
        value.deserializer = class org.apache.kafka.common.serialization.ByteArrayDeserializer
        group.id = connect-kafka-connector-mongodb-sink
        retry.backoff.ms = 100
        ...
        ...
        key.deserializer = class org.apache.kafka.common.serialization.ByteArrayDeserializer
        ...
        ...
 (org.apache.kafka.clients.consumer.ConsumerConfig:178)
[2017-04-14 15:23:37,001] INFO ConsumerConfig values: 
        metric.reporters = []
        metadata.max.age.ms = 300000
        partition.assignment.strategy = [org.apache.kafka.clients.consumer.RangeAssignor]
        ...
        ...
        auto.offset.reset = earliest
 (org.apache.kafka.clients.consumer.ConsumerConfig:178)
[2017-04-14 15:23:37,038] INFO Kafka version : 0.10.0.1-cp1 (org.apache.kafka.common.utils.AppInfoParser:83)
[2017-04-14 15:23:37,039] INFO Kafka commitId : e7288edd541cee03 (org.apache.kafka.common.utils.AppInfoParser:84)
[2017-04-14 15:23:37,041] INFO Created connector kafka-connector-mongodb-sink (org.apache.kafka.connect.cli.ConnectStandalone:91)
[2017-04-14 15:23:37,042] INFO 0 have been processed (org.radarcns.connect.mongodb.MongoDbSinkTask:56)
[2017-04-14 15:23:37,115] INFO Cluster created with settings {hosts=[localhost:27017], mode=SINGLE, requiredClusterType=UNKNOWN, serverSelectionTimeout='30000 ms', maxWaitQueueSize=500} (org.mongodb.driver.cluster:71)
[2017-04-14 15:23:37,132] INFO 0 have been written in MongoDB 0 records need to be processed. (org.radarcns.connect.mongodb.MongoDbWriter:58)
[2017-04-14 15:23:37,142] INFO No server chosen by ReadPreferenceServerSelector{readPreference=primary} from cluster description ClusterDescription{type=UNKNOWN, connectionMode=SINGLE, serverDescriptions=[ServerDescription{address=localhost:27017, type=UNKNOWN, state=CONNECTING}]}. Waiting for 30000 ms before timing out (org.mongodb.driver.cluster:71)
[2017-04-14 15:23:37,413] INFO Opened connection [connectionId{localValue:1, serverValue:5}] to localhost:27017 (org.mongodb.driver.connection:71)
[2017-04-14 15:23:37,415] INFO Monitor thread successfully connected to server with description ServerDescription{address=localhost:27017, type=STANDALONE, state=CONNECTED, ok=true, version=ServerVersion{versionList=[3, 2, 10]}, minWireVersion=0, maxWireVersion=4, maxDocumentSize=16777216, roundTripTimeNanos=722850} (org.mongodb.driver.cluster:71)
[2017-04-14 15:23:37,510] INFO Opened connection [connectionId{localValue:2, serverValue:6}] to localhost:27017 (org.mongodb.driver.connection:71)
[2017-04-14 15:23:37,534] INFO Sink task WorkerSinkTask{id=kafka-connector-mongodb-sink-0} finished initialization and start (org.apache.kafka.connect.runtime.WorkerSinkTask:208)
[2017-04-14 15:23:37,534] INFO Started MongoDbWriter (org.radarcns.connect.mongodb.MongoDbWriter:97)
[2017-04-14 15:23:37,687] INFO Discovered coordinator nivethika-XPS-15-9550:9092 (id: 2147483647 rack: null) for group connect-kafka-connector-mongodb-sink. (org.apache.kafka.clients.consumer.internals.AbstractCoordinator:528)
[2017-04-14 15:23:37,688] INFO Revoking previously assigned partitions [] for group connect-kafka-connector-mongodb-sink (org.apache.kafka.clients.consumer.internals.ConsumerCoordinator:292)
[2017-04-14 15:23:37,688] INFO (Re-)joining group connect-kafka-connector-mongodb-sink (org.apache.kafka.clients.consumer.internals.AbstractCoordinator:349)
[2017-04-14 15:23:37,731] INFO Successfully joined group connect-kafka-connector-mongodb-sink with generation 1 (org.apache.kafka.clients.consumer.internals.AbstractCoordinator:457)
[2017-04-14 15:23:37,732] INFO Setting newly assigned partitions [avrotest-0] for group connect-kafka-connector-mongodb-sink (org.apache.kafka.clients.consumer.internals.ConsumerCoordinator:231)
[2017-04-14 15:23:42,197] INFO Reflections took 5994 ms to scan 254 urls, producing 12617 keys and 82584 values  (org.reflections.Reflections:229)
[2017-04-14 15:24:07,043] INFO 4 have been processed (org.radarcns.connect.mongodb.MongoDbSinkTask:56)
[2017-04-14 15:24:07,131] INFO 4 have been written in MongoDB 0 records need to be processed. (org.radarcns.connect.mongodb.MongoDbWriter:58)
[2017-04-14 15:24:36,975] INFO [FLUSH-WRITER] Time-elapsed: 3.6946E-5 s (org.radarcns.connect.mongodb.MongoDbWriter:205)
[2017-04-14 15:24:36,996] INFO [FLUSH] Time elapsed: 0.020898882 s (org.radarcns.connect.mongodb.MongoDbSinkTask:153)
[2017-04-14 15:24:36,996] INFO WorkerSinkTask{id=kafka-connector-mongodb-sink-0} Committing offsets (org.apache.kafka.connect.runtime.WorkerSinkTask:261)
[2017-04-14 15:24:37,043] INFO 2 have been processed (org.radarcns.connect.mongodb.MongoDbSinkTask:56)
[2017-04-14 15:24:37,131] INFO 2 have been written in MongoDB 0 records need to be processed. (org.radarcns.connect.mongodb.MongoDbWriter:58)

You can view the data in MongoDB. For the above example you may see results as follows.

# log-in to mongo cli
mongo
MongoDB shell version: 3.2.12
connecting to: test
Server has startup warnings: 
2017-04-14T13:14:29.177+0000 I CONTROL  [initandlisten] ** WARNING: You are running this process as the root user, which is not recommended.
2017-04-14T13:14:29.178+0000 I CONTROL  [initandlisten] 
2017-04-14T13:14:29.178+0000 I CONTROL  [initandlisten] 
2017-04-14T13:14:29.178+0000 I CONTROL  [initandlisten] ** WARNING: /sys/kernel/mm/transparent_hugepage/enabled is 'always'.
2017-04-14T13:14:29.178+0000 I CONTROL  [initandlisten] **        We suggest setting it to 'never'
2017-04-14T13:14:29.178+0000 I CONTROL  [initandlisten] 
2017-04-14T13:14:29.178+0000 I CONTROL  [initandlisten] ** WARNING: /sys/kernel/mm/transparent_hugepage/defrag is 'always'.
2017-04-14T13:14:29.178+0000 I CONTROL  [initandlisten] **        We suggest setting it to 'never'
2017-04-14T13:14:29.178+0000 I CONTROL  [initandlisten] 

# view available databases
> show dbs
admin       0.000GB
mongodb-database  0.000GB
local       0.000GB

# switch to your database
> use mongodb-database
switched to db mongodb-database

# view available collections. You should see collections with configured topic names and additional `OFFSETS` collection
> show collections
OFFSETS
avrotest

# query the data inside collections
> db.avrotest.find()
{ "_id" : null, "name" : "somee" }
> db.OFFSETS.find()
{ "_id" : { "topic" : "avrotest", "partition" : 0 }, "offset" : NumberLong(5) }
>

To stop your connector press CTRL-C

Developer guide

This MongoDB-Sink-Connector works based on RecordConverters to convert a SinkRecord to a Document. The default RecordConverter is GenericRecordConverter, which converts a record-key as _id and adds a field for every field-name from record-value. The GenericRecordConverter supports conversion of most of the primitive types and collections.

For Avro records with complex schemas, or for custom collection format it is recommended to write your own RecordConverter and register it to an extended RecordConverterFactory. Writing a custom RecordConverter is relatively straight forward. The interface requires two methods to be implemented.

/**
 * Converts Kafka records to MongoDB documents.
 */
public interface RecordConverter {
    /**
     * <p>The schema names used are the fully qualified (including namespace) and case-sensitive
     * names. If the converter requires records with both a key and a value schema, the  returned
     * format is "KeySchemaName-ValueSchemaName". If the key is not required, only "ValueSchemaName"
     * may be returned. KeySchemaName and ValueSchemaName may be substituted by the Object class
     * that it supports. If the converter supports all types of data, return null.
     */
    Collection<String> supportedSchemaNames();

    /**
     * Convert a Kafka record to a BSON document.
     *
     * @param record record to convert
     * @return BSON document
     * @throws DataException if the record cannot be converted by the current converter.
     */
    Document convert(SinkRecord record) throws DataException;
}

Sample RecordConverter Implementation

Implement a custom RecordConverter. An example is given below. Consider a record consisting key-schema

{
  "namespace": "org.radarcns.key",
  "type": "record",
  "name": "MeasurementKey",
  "doc": "Measurement key in the RADAR-base project",
  "fields": [
    {"name": "userId", "type": "string", "doc": "user ID"},
    {"name": "sourceId", "type": "string", "doc": "device source ID"}
  ]
}

and a value-schema as below.

{
  "namespace": "org.radarcns.application",
  "type": "record",
  "name": "ApplicationRecordCounts",
  "doc": "Number of records cached or created.",
  "fields": [
    {"name": "time", "type": "double", "doc": "device timestamp in UTC (s)"},
    {"name": "timeReceived", "type": "double", "doc": "device receiver timestamp in UTC (s)"},
    {"name": "recordsCached", "type": "int", "doc": "number of records currently being cached", "default": -1},
    {"name": "recordsSent", "type": "int", "doc": "number of records sent since application start"},
    {"name": "recordsUnsent", "type": "int", "doc": "number of unsent records", "default": -1}
  ]
}

These samples would give us the KeySchemaName as org.radarcns.key.MeasurementKey and ValueSchemaName as org.radarcns.application.ApplicationRecordCounts. Lets call our custom RecordConverter as CountsStatusRecordConverter. The implementation can be as simple as below.

/**
 * RecordConverter to convert a StatusCounts record to a MongoDB Document.
 */
public class CountsStatusRecordConverter implements RecordConverter {

    /**
     * Returns the list of supported schemas, which behaves as the id to select suitable
     * RecordConverter for a SinkRecord.
     *
     * @return a list of supported Schemas
     */
    @Override
    public Collection<String> supportedSchemaNames() {
        return Collections.singleton("org.radarcns.key.MeasurementKey" + "-"
                + "org.radarcns.application.ApplicationRecordCounts");
    }

    /**
     * Converts a ServerStatus SinkRecord into a MongoDB Document.
     *
     * @param sinkRecord record to be converted
     * @return converted MongoDB Document to write
     */
    @Override
    public Document convert(SinkRecord sinkRecord) throws DataException {

        Struct key = (Struct) sinkRecord.key();
        Struct value = (Struct) sinkRecord.value();

        return new Document("_id", key.get("userId") + "-" + key.get("sourceId"))
                .append("user", key.getString("userId"))
                .append("source", key.getString("sourceId"))
                .append("recordsCached", value.getInt32("recordsCached"))
                .append("recordsSent", value.getInt32("recordsSent"))
                .append("timestamp", Converter.toDateTime(value.get("timeReceived")));
    }
}

Register implemented RecordConverter to an extended RecordConverterFactory.

package org.radarcns.connect.mongodb.example;
/**
 * Extended RecordConverterFactory to allow customized RecordConverter class that are needed
 */
public class RecordConverterFactoryExample extends RecordConverterFactory {

    /**
     * Overrides genericConverter to append custom RecordConverter class to RecordConverterFactory
     *
     * @return list of RecordConverters available
     */
    protected List<RecordConverter> genericConverters() {
        List<RecordConverter> recordConverters = new ArrayList<RecordConverter>();
        recordConverters.addAll(super.genericConverters());
        recordConverters.add(new CountsStatusRecordConverter());
        return recordConverters;
    }

}

Use extended RecordConverterFactoryExample in sink.properties

# Factory class to do the actual record conversion
record.converter.class=org.radarcns.connect.mongodb.example.RecordConverterFactoryExample

Notes

The only available setting is the number of records returned in a single call to poll() (i.e. consumer.max.poll.records param inside standalone.properties)

Connectors can be run inside any machine where Kafka has been installed. Therefore, you can fire them also inside a machine that does not host a Kafka broker.

To reset a connector running in standalone mode you have to stop it and then modify name and offset.storage.file.filename respectively inside sink.properties and standalone.properties

Javadoc

More info and Javadocs of the connector are available at -

Javadoc

Contributing

All of the contribution code should be formatted using the Google Java Code Style Guide. If you want to contribute a feature or fix browse our issues, and please make a pull request.

Name		Name	Last commit message	Last commit date
Latest commit History 156 Commits
config		config
gradle		gradle
src		src
.dockerignore		.dockerignore
.gitignore		.gitignore
.travis.yml		.travis.yml
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
build.gradle		build.gradle
gradle.properties		gradle.properties
gradlew		gradlew
gradlew.bat		gradlew.bat
settings.gradle		settings.gradle
sink.properties		sink.properties
update-gh-pages-docs.sh		update-gh-pages-docs.sh

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Kafka MongoDb Sink Connector

Installation

Docker installation

System installation

Usage

Developer guide

Sample RecordConverter Implementation

Notes

Javadoc

Contributing

About

Releases

Packages

Languages

License

foodchain-technologies/MongoDb-Sink-Connector

Folders and files

Latest commit

History

Repository files navigation

Kafka MongoDb Sink Connector

Installation

Docker installation

System installation

Usage

Developer guide

Sample RecordConverter Implementation

Notes

Javadoc

Contributing

About

Resources

License

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages