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Comments (4)

harbby avatar harbby commented on September 15, 2024

The progress is relatively smooth, see the example below:

create source table topic1(
    key varchar,
    value varchar,
    event_time bigint
) with (
    type = 'ideal.sylph.plugins.flink.source.TestSource'
);

-- 定义数据流输出位置
create sink table print_table_sink(
    key varchar,
    cnt long,
    window_time varchar
) with (
    type = 'ideal.sylph.plugins.flink.sink.PrintSink',   -- print console
    other = 'demo001'
);

-- 定义 WATERMARK,通常您应该从kafka message中解析出event_time字段
create view TABLE foo
WATERMARK event_time FOR rowtime BY ROWMAX_OFFSET(5000)  --event_time 为您的真实数据产生时间
AS 
with tb1 as (select * from topic1)  --通常这里解析kafka message
select * from tb1;

-- 描述数据流计算过程
insert into print_table_sink
select key,
count(1),
cast(TUMBLE_START(rowtime,INTERVAL '5' SECOND) as varchar)|| '-->' 
|| cast(TUMBLE_END(rowtime,INTERVAL '5' SECOND) as varchar) AS window_time
from foo where key is not null
group by key,TUMBLE(rowtime,INTERVAL '5' SECOND)

from sylph.

harbby avatar harbby commented on September 15, 2024

How do you have any ideas for suggestions? Welcome to the discussion

from sylph.

harbby avatar harbby commented on September 15, 2024

Already supported

from sylph.

harbby avatar harbby commented on September 15, 2024

The proctime demo:

create source table topic1(
    key varchar,
    message varchar,     -- json
    event_time bigint,
    proctime as proctime()
) with (
    type = 'test'
);

from sylph.

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