SQL 第二章 (基础查询)

Kafka表引擎也是一种常见的表引擎，在很多大数据量的场景下，会从源通过Kafka将数据输送到ClickHouse，Kafka作为输送的方式，ClickHouse作为存储引擎与查询引擎，大数据量的数据可以得到快速的、高压缩的存储。

Kafka大家肯定不陌生：

• 它可以用于发布和订阅数据流，是常见的队列使用方式
• 它可以组织容错存储，是常见的容错存储的使用方式
• 它可以在流可用时对其进行处理，是常见的大数据处理的使用方式

全文概览：

• 基本语法
• 从 Kafka 写入到 ClickHouse
• 从 ClickHouse 写入到 Kafka
  • 测试1：queue->ck->queue
  • 测试2：ck->queue

基本语法

分为定义表结构和定义Kafka的接入参数，Kafka的接入参数都是常见的字段

CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
(
    name1 [type1] [ALIAS expr1],
    name2 [type2] [ALIAS expr2],
    ...
) ENGINE = Kafka()
SETTINGS
    kafka_broker_list = 'host:port',
    kafka_topic_list = 'topic1,topic2,...',
    kafka_group_name = 'group_name',
    kafka_format = 'data_format'[,]
    [kafka_schema = '',]
    [kafka_num_consumers = N,]
    [kafka_max_block_size = 0,]
    [kafka_skip_broken_messages = N,]
    [kafka_commit_every_batch = 0,]
    [kafka_client_id = '',]
    [kafka_poll_timeout_ms = 0,]
    [kafka_poll_max_batch_size = 0,]
    [kafka_flush_interval_ms = 0,]
    [kafka_thread_per_consumer = 0,]
    [kafka_handle_error_mode = 'default',]
    [kafka_commit_on_select = false,]
    [kafka_max_rows_per_message = 1];

示例：

CREATE TABLE IF NOT EXISTS test_ck_sync1
(
    `sys_time` Datetime COMMENT '',
    `num` UInt32 COMMENT ''
)
ENGINE = Kafka
SETTINGS kafka_broker_list = '127.0.0.1:9092', kafka_topic_list = 'test_ck_sync1', kafka_group_name = 'ck_test_ck_sync1', kafka_format = 'CSV', kafka_max_block_size = 200000, kafka_skip_broken_messages = 1000, kafka_row_delimiter = '\n', format_csv_delimiter = '|'

从 Kafka 写入到 ClickHouse

创建topic:

bin/kafka-topics.sh --create --bootstrap-server 127.0.0.1:9092 --replication-factor 1 --partitions 1 --topic test_ck_sync1

创建同步表：

CREATE TABLE IF NOT EXISTS test_ck_sync1
(
    `sys_time` Datetime COMMENT '',
    `num` UInt32 COMMENT ''
)
ENGINE = Kafka
SETTINGS kafka_broker_list = '127.0.0.1:9092', kafka_topic_list = 'test_ck_sync1', kafka_group_name = 'ck_test_ck_sync1', kafka_format = 'CSV', kafka_max_block_size = 200000, kafka_skip_broken_messages = 1000, kafka_row_delimiter = '\n', format_csv_delimiter = '|'

CREATE TABLE IF NOT EXISTS test_ck_sync1_res
(
    `sys_time` Datetime COMMENT '',
    `num` UInt32 COMMENT ''
)
ENGINE = MergeTree
PARTITION BY toYYYYMMDD(sys_time)
ORDER BY tuple()

创建物化视图，进行数据样式的转换:

CREATE MATERIALIZED VIEW test_ck_sync1_mv TO test_ck_sync1_res AS
SELECT
    sys_time,
    num
FROM test_ck_sync1

通过console写入数据:

[$ kafka_2.13-3.6.1]# bin/kafka-console-producer.sh --broker-list 127.0.0.1:9092 --topic test_ck_sync1
>2024-01-01 00:00:01|89  

验证数据：

$ :) select * from test_ck_sync1_res;

SELECT *
FROM test_ck_sync1_res

Query id: a666f893-5be9-4022-9327-3a1507aa5485

┌────────────sys_time─┬─num─┐
│ 2024-01-01 00:00:01 │  89 │
└─────────────────────┴─────┘
┌────────────sys_time─┬─num─┐
│ 2024-01-01 00:00:00 │  88 │
└─────────────────────┴─────┘

2 rows in set. Elapsed: 0.049 sec.

从 ClickHouse 写入到 Kafka

kafka_writers_reader --(view)--> kafka_writers_queue ---> 

创建一个队列：

bin/kafka-topics.sh --topic kafka_writers --create -bootstrap-server 127.0.0.1:9092 --partitions 1 --replication-factor 1

创建同步表：

CREATE TABLE kafka_writers_reader (     `id` Int,     `platForm` String,     `appname` String,     `time` DateTime ) 
ENGINE = Kafka SETTINGS kafka_broker_list = '127.0.0.1:9092', kafka_topic_list = 'kafka_writers_reader', kafka_group_name = 'kafka_writers_reader_group', kafka_format = 'CSV';

CREATE TABLE kafka_writers_queue (     id Int,     platForm String,     appname String,     time DateTime ) 
ENGINE = Kafka SETTINGS kafka_broker_list = '127.0.0.1:9092',        kafka_topic_list = 'kafka_writers',        kafka_group_name = 'kafka_writers_group',        kafka_format = 'CSV',       kafka_max_block_size = 1048576;

测试1：queue->ck->queue

通过写入队列kafka_writers_reader，借助ClickHouse写入队列kafka_writers

bin/kafka-topics.sh --topic kafka_writers_reader --create -bootstrap-server 127.0.0.1:9092 --partitions 1 --replication-factor 1

bin/kafka-console-producer.sh --broker-list 127.0.0.1:9092 --topic kafka_writers_reader

bin/kafka-console-consumer.sh --bootstrap-server 127.0.0.1:9092 --topic kafka_writers

测试2：ck->queue

通过写入表kafka_writers_reader，写入队列kafka_writers

$ :) INSERT INTO kafka_writers_reader (id, platForm, appname, time) 
VALUES (8,'Data','Test','2020-12-23 14:45:31'), 
(9,'Plan','Test1','2020-12-23 14:47:32'), 
(10,'Plan','Test2','2020-12-23 14:52:15'), 
(11,'Data','Test3','2020-12-23 14:54:39');

INSERT INTO kafka_writers_reader (id, platForm, appname, time) FORMAT Values

Query id: 223a63ab-97fa-488d-8ea7-c2e194155d26

Ok.

4 rows in set. Elapsed: 1.054 sec. 

[$ kafka_2.13-3.6.1]# bin/kafka-console-consumer.sh --bootstrap-server 127.0.0.1:9092 --topic kafka_writers
8,"Data","Test","1970-01-01 08:00:00"

9,"Plan","Test1","1970-01-01 08:00:00"

10,"Plan","Test2","1970-01-01 08:00:00"

11,"Data","Test3","1970-01-01 08:00:00"

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