数据

{"user_id": "543462", "item_id":"1715", "category_id": "1464116", "behavior": "pv", "ts": "2017-11-26T01:00:00Z"}
{"user_id": "662867", "item_id":"2244074", "category_id": "1575622", "behavior": "pv", "ts": "2017-11-26T01:00:00Z"}
{"user_id": "662868", "item_id":"1784", "category_id": "54123654", "behavior": "pv", "ts": "2017-11-26T01:00:00Z"}
{"user_id": "662854", "item_id":"1456", "category_id": "12345678", "behavior": "pv", "ts": "2017-11-26T01:00:00Z"}
{"user_id": "662858", "item_id":"1457", "category_id": "12345679", "behavior": "pv", "ts": "2017-11-26T01:00:00Z"}

2.在kafka进行消费

/bin/kafka-console-consumer.sh --bootstrap-server 192.168.58.177:9092 --topic my_topic --partition 0 --offset 0

3.在Flink的sqlclient 创建表

CREATE TABLE user_log1 (
    user_id VARCHAR,
    item_id VARCHAR,
    category_id VARCHAR,
    behavior VARCHAR,
    ts VARCHAR
) WITH (
    ‘connector.type‘ = ‘kafka‘,
    ‘connector.version‘ = ‘universal‘,
    ‘connector.topic‘ = ‘my-topic-one‘,
    ‘connector.startup-mode‘ = ‘earliest-offset‘,
    ‘connector.properties.group.id‘ = ‘testGroup‘,
    ‘connector.properties.zookeeper.connect‘ = ‘192.168.58.171:2181,192.168.58.177:2181,192.168.58.178:2181‘,
    ‘connector.properties.bootstrap.servers‘ = ‘192.168.58.177:9092‘,
    ‘format.type‘ = ‘json‘
);

实时计算 

select item_id,count(*) from user_log1 group by item_id;

Flink通过SQLClinet创建kafka源表并进行实时计算

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