Flink SQL Cookbook中有Chained (Event) Time Windows

該md文件舉了兩個例子，

前面一個例子是Chained Windows

后面一個例子是Non-Chained Windows

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該md文件的前半部分圖示如下:

上圖中存在兩個server_logs_window_5m

①Chained Windows

②Non-Chained Windows

所以②比①更加有計算上的效率，但是耦合性也更強。

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上圖圖示中，三條紅線就是所謂的Chained (Event) Time Windows

這個概念出自Ververcica公司的flink sql cookbook

Chained (Event) Time Windows的SQL距離

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Chained (Event) Time Windows的SQL舉例(對應上圖中的3個紅色箭頭):

Chained (Event) Time Windows的數據流向	具體對應的SQL
server_logs_window_1m->server_logs_window_5m	CREATE TEMPORARY VIEW server_logs_window_5m AS? SELECT? ? TUMBLE_START(window_end, INTERVAL '5' MINUTE) AS window_start, ? TUMBLE_ROWTIME(window_end, INTERVAL '5' MINUTE) AS window_end, ? SUM(total_size) AS total_size, ? SUM(num_requests) AS num_requests FROM server_logs_window_1m GROUP BY? ? TUMBLE(window_end, INTERVAL '5' MINUTE);
server_logs_window_1m->avg_request_size_1m	INSERT INTO avg_request_size_1m SELECT ? window_start, ? window_end,? ? total_size/num_requests AS avg_size FROM server_logs_window_1m;
server_logs_window_5m->avg_request_size_5m	INSERT INTO avg_request_size_5m SELECT ? window_start, ? window_end,? ? total_size/num_requests AS avg_size FROM server_logs_window_5m; ?

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Chained Windows與Non-Chained Windows的SQL寫法對比

Windows類型	具體對應的SQL
Chained Windows	CREATE TEMPORARY VIEW server_logs_window_5m AS? SELECT? ? TUMBLE_START(window_end, INTERVAL '5' MINUTE) AS window_start, ? TUMBLE_ROWTIME(window_end, INTERVAL '5' MINUTE) AS window_end, ? SUM(total_size) AS total_size, ? SUM(num_requests) AS num_requests FROM server_logs_window_1m GROUP BY? ? TUMBLE(window_end, INTERVAL '5' MINUTE);
Non-Chained Windows	CREATE TEMPORARY VIEW server_logs_window_5m AS? SELECT? ? TUMBLE_START ?(log_time, INTERVAL '5' MINUTE) AS window_start, ? TUMBLE_ROWTIME(log_time, INTERVAL '5' MINUTE) AS window_end, ? SUM(size) AS total_size, ? COUNT(*) AS num_requests FROM server_logs GROUP BY? ? TUMBLE(log_time, INTERVAL '5' MINUTE);

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