Connecting ClickHouse to Kafka in Normal Mode

Scenarios

This section describes how to connect to Kafka in normal mode and consume Kafka data.

Notes and Constraints

This section applies only to MRS 3.3.0-LTS or later.

Prerequisites

• A Kafka cluster has been created and is in normal mode (Kerberos authentication is disabled).
• You have created a ClickHouse cluster and installed the ClickHouse client. The ClickHouse and Kafka clusters can communicate with each other.

Procedure

1. Log in to the FusionInsight Manager of the cluster where ClickHouse is deployed, and choose Cluster > Services > ClickHouse. Click Configurations and then All Configurations, and choose ClickHouseServer(Role) > Engine. The following table shows the parameter needs to be configured.

   Parameter	Description
   kafka_auth_mode	Authentication method for the connection between ClickHouse and Kafka. Set this parameter to NoAuth.

   

2. Choose Cluster > Services > ClickHouse. Click Configurations and then All Configurations, choose ClickHouseServer(Role) > Customization, and add the following parameters to clickhouse-config-customize.

   Name	Value
   kafka.security_protocol	plaintext

   

3. Click Save. In the displayed dialog box, click OK to save the configuration. Choose Instances, select ClickHouseServer, and click More > Instance Rolling Restart.
4. Go to the Kafka client installation directory. For details, see Using the Kafka Client.
   1. Log in to the node where the Kafka client is installed as the Kafka client installation user.
   2. Run the following command to go to the client installation directory:

      cd /opt/client

   3. Run the following command to configure environment variables:

      source bigdata_env

   4. If Kerberos authentication is enabled for the cluster, run the following command to authenticate the user. If Kerberos authentication is disabled for the cluster, skip this step.

      kinit Component service user

5. Run the following command to create a Kafka topic. For details, see Creating a Kafka Topic.

   kafka-topics.sh --topic topic1 --create --bootstrap-server <Kafka cluster IP address:21005> --command-config Kafka/kafka/config/client.properties --partitions 2 --replication-factor 1

   Table 1 Parameter description

   Parameter	Description
   --topic	Name of the topic to be created, for example, topic1.
   --bootstrap-server	IP address of the node in the Kafka cluster. To obtain the information, perform the following operations: Log in to FusionInsight Manager and choose Cluster > Services > Kafka. On the displayed the Instances tab page, click Instances to query the IP address of the Broker role instance.
   --partitions	Number of topic partitions. The value cannot be greater than the number of Kafka role instances.
   -replication-factor	Number of topic replicas. The value cannot be greater than the number of Kafka role instances.

6. Log in to the ClickHouse client node and connect it to the ClickHouse server. For details, see ClickHouse Client Practices.
7. Create a Kafka table engine. The following is an example:

   CREATE TABLE queue1 (
   key String,
   value String,
   event_date DateTime
   ) ENGINE = Kafka()
   SETTINGS kafka_broker_list = 'kafka_ip1:21005,kafka_ip2:21005,kafka_ip3:21005',
   kafka_topic_list = 'topic1',
   kafka_group_name = 'group2',
   kafka_format = 'CSV',
   kafka_row_delimiter = '\n',
   kafka_handle_error_mode='stream';

   The following table lists the related parameters.

   Parameter	Description
   kafka_broker_list	A list of IP addresses and port numbers of Kafka Broker instances. For example, :IP address 1 of Kafka broker instance:9092,IP address 2 of Kafka broker instance:9092,IP address 3 of Kafka broker instance:9092 To obtain the IP address of the Kafka broker instance, perform the following steps: Log in to FusionInsight Manager and choose Cluster > Services > Kafka. Click the Instance tab to query the IP addresses of the Kafka instances.
   kafka_topic_list	Topic where Kafka data is consumed
   kafka_group_name	Kafka consumer group
   kafka_format	Formatting type of consumed data. JSONEachRow indicates the JSON format (a piece of data in each line). CSV indicates the data is in a line but separated by commas (,). For more information, visit https://clickhouse.tech/docs/en/interfaces/formats/.
   kafka_row_delimiter	Delimiter character, which ends a message.
   kafka_handle_error_mode	If this parameter is set to stream, each message processing exception is printed. You need to create a view and query the specific exception of abnormal data through the view. The following example shows you how to create a view: CREATE MATERIALIZED VIEW default.kafka_errors2 ( `topic` String, `key` String, `partition` Int64, `offset` Int64, `timestamp` Date, `timestamp_ms` Int64, `raw` String, `error` String ) ENGINE = MergeTree ORDER BY (topic, partition, offset) SETTINGS index_granularity = 8192 AS SELECT _topic AS topic, _key AS key, _partition AS partition, _offset AS offset, _timestamp AS timestamp, _timestamp_ms AS timestamp_ms, _raw_message AS raw, _error AS error FROM default.queue1; Query the view. The following is an example: host1 :) select * from kafka_errors2; SELECT * FROM kafka_errors2 Query id: bf4d788f-bcb9-44f5-95d0-a6c83c591ddb ┌─topic──┬─key─┬─partition─┬─offset─┬──timestamp─┬─timestamp_ms─┬─raw─┬─error────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ topic1 │ │ 1 │ 8 │ 2023-06-20 │ 1687252213 │ 456 │ Cannot parse date: value is too short: (at row 1) Buffer has gone, cannot extract information about what has been parsed. │ └────────┴─────┴───────────┴────────┴────────────┴──────────────┴─────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘ 1 rows in set. Elapsed: 0.003 sec. host1 :)
   kafka_skip_broken_messages	(Optional) Number of Kafka data records where parsing exceptions are ignored. If N exceptions occur and the background thread ends, the materialized view is re-arranged to monitor the data.
   kafka_num_consumers	(Optional) Number of consumers of a single Kafka engine. You can set this parameter to a larger value to improve the data consumption throughput. But the maximum value of this parameter cannot exceed the total number of partitions of the corresponding topic.

   For details about other configurations, see https://clickhouse.com/docs/en/engines/table-engines/integrations/kafka.

8. Connect the client to ClickHouse to create a local table. The following is an example:

   CREATE TABLE daily1(
   key String,
   value String,
   event_date DateTime
   )ENGINE = MergeTree()
   ORDER BY key;

9. Connect the client to ClickHouse to create a materialized view. The following is an example:

   CREATE MATERIALIZED VIEW default.consumer1 TO default.daily1 (
   `event_date` DateTime,
   `key` String,
   `value` String
   ) AS
   SELECT
   event_date,
   key,
   value
   FROM default.queue1;

10. Perform 4 again to go to the Kafka client installation directory.
11. Run the following command to send a message to the topic created in 5:

    kafka-console-producer.sh --broker-list IP address 1 of the Kafka broker instance:9092,IP address 2 of the Kafka broker instance:9092,IP address 3 of the Kafka broker instance:9092  --topic topic1

    The result is as follows:

    >a1,b1,'2020-08-01 10:00:00'
    >a2,b2,'2020-08-02 10:00:00'
    >a3,b3,'2020-08-02 10:00:00'
    >a4,b4,'2023-09-02 10:00:00'

12. Query the consumed Kafka data and the preceding materialized view. The following is an example:

    select * from daily;