概述

本文将介绍如何将数据从 Kafka topic 发送到 ClickHouse 表。我们将使用 Wiki 的最近更改 feed，它提供了一个事件流，表示 Wikimedia 各个站点上的更改。步骤包括：

如何在 Ubuntu 上部署 Kafka
将数据流摄取到 Kafka topic 中
创建一个订阅该 topic 的 ClickHouse 表

1. 如何在 Ubuntu 上部署 Kafka

创建一个 Ubuntu ec2 实例，并通过 SSH 登录该实例：

ssh -i ~/training.pem ubuntu@ec2.compute.amazonaws.com

安装 Kafka (请按照此处的说明操作：https://www.linode.com/docs/guides/how-to-install-apache-kafka-on-ubuntu/) ：

sudo apt update
sudo apt install openjdk-11-jdk

mkdir /home/ubuntu/kafka
cd /home/ubuntu/kafka/

wget https://downloads.apache.org/kafka/3.7.0/kafka_2.13-3.7.0.tgz

tar -zxvf kafka_2.13-3.7.0.tgz

启动 ZooKeeper：

cd kafka_2.13-3.7.0
bin/zookeeper-server-start.sh config/zookeeper.properties

打开一个新的终端并启动 Kafka：

ssh -i ~/training.pem ubuntu@ec2.compute.amazonaws.com
cd kafka/kafka_2.13-3.7.0/
bin/kafka-server-start.sh config/server.properties

打开第三个终端，并创建名为 wikimedia 的 topic：

ssh -i ~/training.pem ubuntu@ec2.compute.amazonaws.com
cd kafka/kafka_2.13-3.7.0/

bin/kafka-topics.sh --create --topic wikimedia --bootstrap-server localhost:9092

你可以通过以下方式验证其是否已成功创建：

bin/kafka-topics.sh --list --bootstrap-server localhost:9092

2. 将 Wikimedia Stream 摄取到 Kafka

首先需要准备一些工具：

sudo apt-get install librdkafka-dev libyajl-dev
sudo apt-get install kafkacat

数据通过一条巧妙的 curl 命令发送到 Kafka：该命令会抓取最新的 Wikimedia 事件，解析其中的 JSON 数据，并将其发送到 Kafka topic：

curl -N https://stream.wikimedia.org/v2/stream/recentchange  | awk '/^data: /{gsub(/^data: /, ""); print}' | kafkacat -P -b localhost:9092 -t wikimedia

您可以“describe”这个 topic：

bin/kafka-topics.sh --describe --topic wikimedia --bootstrap-server localhost:9092

让我们通过消费一些事件来确认一切是否正常运行：

bin/kafka-console-consumer.sh --topic wikimedia --from-beginning --bootstrap-server localhost:9092

按 Ctrl+c 结束前一个命令。

3. 将数据摄取到 ClickHouse

传入的数据如下所示：

{
	"$schema": "/mediawiki/recentchange/1.0.0",
	"meta": {
		"uri": "https://www.wikidata.org/wiki/Q45791749",
		"request_id": "f64cfb17-04ba-4d09-8935-38ec6f0001c2",
		"id": "9d7d2b5a-b79b-45ea-b72c-69c3b69ae931",
		"dt": "2024-04-18T13:21:21Z",
		"domain": "www.wikidata.org",
		"stream": "mediawiki.recentchange",
		"topic": "eqiad.mediawiki.recentchange",
		"partition": 0,
		"offset": 5032636513
	},
	"id": 2196113017,
	"type": "edit",
	"namespace": 0,
	"title": "Q45791749",
	"title_url": "https://www.wikidata.org/wiki/Q45791749",
	"comment": "/* wbsetqualifier-add:1| */ [[Property:P1545]]: 20, Modify PubMed ID: 7292984 citation data from NCBI, Europe PMC and CrossRef",
	"timestamp": 1713446481,
	"user": "Cewbot",
	"bot": true,
	"notify_url": "https://www.wikidata.org/w/index.php?diff=2131981357&oldid=2131981341&rcid=2196113017",
	"minor": false,
	"patrolled": true,
	"length": {
		"old": 75618,
		"new": 75896
	},
	"revision": {
		"old": 2131981341,
		"new": 2131981357
	},
	"server_url": "https://www.wikidata.org",
	"server_name": "www.wikidata.org",
	"server_script_path": "/w",
	"wiki": "wikidatawiki",
	"parsedcomment": "<span dir=\"auto\"><span class=\"autocomment\">Added qualifier: </span></span> <a href=\"/wiki/Property:P1545\" title=\"series ordinal | position of an item in its parent series (most frequently a 1-based index), generally to be used as a qualifier (different from &quot;rank&quot; defined as a class, and from &quot;ranking&quot; defined as a property for evaluating a quality).\"><span class=\"wb-itemlink\"><span class=\"wb-itemlink-label\" lang=\"en\" dir=\"ltr\">series ordinal</span> <span class=\"wb-itemlink-id\">(P1545)</span></span></a>: 20, Modify PubMed ID: 7292984 citation data from NCBI, Europe PMC and CrossRef"
}

我们需要使用 Kafka 表引擎从 Kafka topic 中拉取数据：

CREATE OR REPLACE TABLE wikiQueue
(
    `id` UInt32,
    `type` String,
    `title` String,
    `title_url` String,
    `comment` String,
    `timestamp` UInt64,
    `user` String,
    `bot` Bool,
    `server_url` String,
    `server_name` String,
    `wiki` String,
    `meta` Tuple(uri String, id String, stream String, topic String, domain String)
)
ENGINE = Kafka(
   'ec2.compute.amazonaws.com:9092',
   'wikimedia',
   'consumer-group-wiki',
   'JSONEachRow'
);

不知为何，Kafka 表引擎似乎会把公开的 ec2 URL 转换成私有 DNS 名称，所以我不得不把它添加到本地的 /etc/hosts 文件中：

52.14.154.92  ip.us-east-2.compute.internal

你可以从 Kafka 表中读取数据，只需启用一个设置：

SELECT *
FROM wikiQueue
LIMIT 20
FORMAT Vertical
SETTINGS stream_like_engine_allow_direct_select = 1;

返回的行应会根据 wikiQueue 表中定义的列正确解析：

id:          2473996741
type:        edit
title:       File:Père-Lachaise - Division 6 - Cassereau 05.jpg
title_url:   https://commons.wikimedia.org/wiki/File:P%C3%A8re-Lachaise_-_Division_6_-_Cassereau_05.jpg
comment:     /* wbcreateclaim-create:1| */ [[d:Special:EntityPage/P921]]: [[d:Special:EntityPage/Q112327116]], [[:toollabs:quickstatements/#/batch/228454|batch #228454]]
timestamp:   1713457283
user:        Ameisenigel
bot:         false
server_url:  https://commons.wikimedia.org
server_name: commons.wikimedia.org
wiki:        commonswiki
meta:        ('https://commons.wikimedia.org/wiki/File:P%C3%A8re-Lachaise_-_Division_6_-_Cassereau_05.jpg','01a832e2-24c5-4ccb-bd93-8e2c0e429418','mediawiki.recentchange','eqiad.mediawiki.recentchange','commons.wikimedia.org')

我们需要一个 MergeTree 表来存储这些传入的事件：

CREATE TABLE rawEvents (
    id UInt64,
    type LowCardinality(String),
    comment String,
    timestamp DateTime64(3, 'UTC'),
    title_url String,
    topic LowCardinality(String),
    user String
)
ENGINE = MergeTree
ORDER BY (type, timestamp);

让我们定义一个 materialized view：当 Kafka 表发生 insert 时，它会被触发并将数据发送到 rawEvents 表：

CREATE MATERIALIZED VIEW rawEvents_mv TO rawEvents
AS
   SELECT
       id,
       type,
       comment,
       toDateTime(timestamp) AS timestamp,
       title_url,
       tupleElement(meta, 'topic') AS topic,
       user
FROM wikiQueue
WHERE title_url <> '';

你应该几乎立刻就能看到数据开始进入 rawEvents：

SELECT count()
FROM rawEvents;

我们来查看其中几行数据：

SELECT *
FROM rawEvents
LIMIT 5
FORMAT Vertical

Row 1:
──────
id:        124842852
type:      142
comment:   Pere prlpz commented on "Plantilles Enciclopèdia Catalana" (Diria que no cal fer res als articles. Es pot actualitzar els enllaços que es facin servir a les referències (tot i que l'antic encara ha...)
timestamp: 2024-04-18 16:22:29.000
title_url: https://ca.wikipedia.org/wiki/Tema:Wu36d6vfsiuu4jsi
topic:     eqiad.mediawiki.recentchange
user:      Pere prlpz

Row 2:
──────
id:        2473996748
type:      categorize
comment:   [[:File:Ruïne van een poortgebouw, RP-T-1976-29-6(R).jpg]] removed from category
timestamp: 2024-04-18 16:21:20.000
title_url: https://commons.wikimedia.org/wiki/Category:Pieter_Moninckx
topic:     eqiad.mediawiki.recentchange
user:      Warburg1866

Row 3:
──────
id:        311828596
type:      categorize
comment:   [[:Cujo (película)]] añadida a la categoría
timestamp: 2024-04-18 16:21:21.000
title_url: https://es.wikipedia.org/wiki/Categor%C3%ADa:Pel%C3%ADculas_basadas_en_obras_de_Stephen_King
topic:     eqiad.mediawiki.recentchange
user:      Beta15

Row 4:
──────
id:        311828597
type:      categorize
comment:   [[:Cujo (película)]] eliminada de la categoría
timestamp: 2024-04-18 16:21:21.000
title_url: https://es.wikipedia.org/wiki/Categor%C3%ADa:Trabajos_basados_en_obras_de_Stephen_King
topic:     eqiad.mediawiki.recentchange
user:      Beta15

Row 5:
──────
id:        48494536
type:      categorize
comment:   [[:braiteremmo]] ajoutée à la catégorie
timestamp: 2024-04-18 16:21:21.000
title_url: https://fr.wiktionary.org/wiki/Cat%C3%A9gorie:Wiktionnaire:Exemples_manquants_en_italien
topic:     eqiad.mediawiki.recentchange
user:      Àncilu bot

来看看传入了哪些类型的事件：

SELECT
    type,
    count()
FROM rawEvents
GROUP BY type

   ┌─type───────┬─count()─┐
1. │ 142        │       1 │
2. │ new        │    1003 │
3. │ categorize │   12228 │
4. │ log        │    1799 │
5. │ edit       │   17142 │
   └────────────┴─────────┘

让我们基于当前的 materialized view 再定义一个 materialized view。我们将按分钟跟踪一些聚合统计信息：

CREATE TABLE byMinute
(
    `dateTime` DateTime64(3, 'UTC') NOT NULL,
    `users` AggregateFunction(uniq, String),
    `pages` AggregateFunction(uniq, String),
    `updates` AggregateFunction(sum, UInt32)
)
ENGINE = AggregatingMergeTree
ORDER BY dateTime;

CREATE MATERIALIZED VIEW byMinute_mv TO byMinute
AS SELECT
    toStartOfMinute(timestamp) AS dateTime,
    uniqState(user) AS users,
    uniqState(title_url) AS pages,
    sumState(toUInt32(1)) AS updates
FROM rawEvents
GROUP BY dateTime;

我们需要使用 -Merge 函数来查看结果：

SELECT
    dateTime AS dateTime,
    uniqMerge(users) AS users,
    uniqMerge(pages) AS pages,
    sumMerge(updates) AS updates
FROM byMinute
GROUP BY dateTime
ORDER BY dateTime DESC
LIMIT 10;

如何将数据从 Kafka 摄取到 ClickHouse

概述

1. 如何在 Ubuntu 上部署 Kafka

2. 将 Wikimedia Stream 摄取到 Kafka

3. 将数据摄取到 ClickHouse