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How to ingest data from Kafka into ClickHouse

· 6 min read

Overview: This article walks through the process of sending data from a Kafka topic to a ClickHouse table. We’ll use the Wiki recent changes feed, which provides a stream of events that represent changes made to various Wikimedia properties. The steps include:

  1. How to setup Kafka on Ubuntu
  2. Ingest a stream of data into a Kakfa topic
  3. Create a ClickHouse table that subscribes to the topic

1. Setup Kafka on Ubuntu

  1. Create an Ubuntu ec2 instance and SSH on to it:
ssh -i ~/training.pem ubuntu@ec2.compute.amazonaws.com
  1. Install Kafka (based on the instructions here: 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
  1. Start ZooKeeper:
cd kafka_2.13-3.7.0
bin/zookeeper-server-start.sh config/zookeeper.properties
  1. Open a new console and launch 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
  1. Open a third console and create a topic named wikimedia:
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
  1. You can verify it was created successfully by:
bin/kafka-topics.sh --list --bootstrap-server localhost:9092

2. Ingest the Wikimedia Stream into Kafka

  1. We need some utilities first:
sudo apt-get install librdkafka-dev libyajl-dev
sudo apt-get install kafkacat
  1. The data is sent to Kafka using a clever curl command that grabs the latest Wikimedia events, parses out the JSON data and sends that to the Kafka topic:
curl -N https://stream.wikimedia.org/v2/stream/recentchange  | awk '/^data: /{gsub(/^data: /, ""); print}' | kafkacat -P -b localhost:9092 -t wikimedia
  1. You can "describe" the topic:
bin/kafka-topics.sh --describe --topic wikimedia --bootstrap-server localhost:9092
  1. Let's verify everything is working by consuming some events:
bin/kafka-console-consumer.sh --topic wikimedia --from-beginning --bootstrap-server localhost:9092
  1. Hit Ctrl+c to kill the previous command.

3. Ingest the Data into ClickHouse

  1. Here is what the incoming data looks like:
{
    "$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"
}
  1. We will need the Kafka table engine to pull the data from the Kafka topic:
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'
);
  1. For some reason the Kafka table engine seems to take the public ec2 URL and convert it to the private DNS name, so I had to add that to my local /etc/hosts file:
52.14.154.92  ip.us-east-2.compute.internal
  1. You can read from a Kafka table, you just have to enable a setting:
SELECT *
FROM wikiQueue
LIMIT 20
FORMAT Vertical
SETTINGS stream_like_engine_allow_direct_select = 1;

The rows should come back nicely parsed based on the columns defined in the wikiQueue table:

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')
  1. We need a MergeTree table to store these incoming events:
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);
  1. Let's define a materialized view that gets triggered when an insert occurs on the Kafka table and sends the data to our rawEvents table:
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 <> '';
  1. You should start seeing data going into rawEvents almost immediately:
SELECT count()
FROM rawEvents;
  1. Let's view some of the rows:
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
  1. Let's see what types of events are coming in:
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 │
   └────────────┴─────────┘

Let's define a materialized view chained to our current materialized view. We will keep track of some aggregated stats per minute:

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;
  1. We will need -Merge functions to view the results:
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;