Question

当我在外表上完成流连接时，当我将连续视图连接到外表时，我得到的结果不同。我期望相同的两个查询看起来是不同的。我的本地管道实例和fdw中的表之间的延迟是否会对我的连续流连接产生影响？我试图在外国表格上聚合rx_bytes和tx_bytes baesd。

我正在使用最新的

中的mysql_fdw

https://github.com/EnterpriseDB/mysql_fdw

创建外表

CREATE EXTENSION mysql_fdw;

CREATE SERVER local_mysql
    FOREIGN DATA WRAPPER mysql_fdw
    OPTIONS (host 'localhost', port '3306', secure_auth 'true');

CREATE USER MAPPING FOR pipeline
    SERVER local_mysql 
    OPTIONS(username 'username', password 'password');

CREATE FOREIGN TABLE "foo_instance" (
     "id" bigint,
     "foo_id" bigint,
)
SERVERlocal_mysql
OPTIONS(dbname 'schema', table_name 'foo_instance');

在10次插入后，我希望这两个查询产生相同的结果：

A）

    CREATE CONTINUOUS VIEW total_bytes
    AS SELECT date_trunc('minute', time_stamp::timestamp) AS minute,
        id::integer,
        sum(tx_bytes::bigint) AS tx_bytes,
        sum(rx_bytes::bigint) AS rx_bytes
     FROM byte_count_stream GROUP BY minute, id;

    SELECT minute, sum(tx_bytes) AS tx_bytes, sum(rx_bytes) AS rx_bytes, foo_id 
    FROM total_bytes JOIN foo_instance 
         ON total_bytes.id=foo_instance.id GROUP BY minute, foo_instance.foo_id;

           minute        | tx_bytes | rx_bytes | foo_id 
    ---------------------+----------+----------+---------
     2016-02-22 09:04:00 |      450 |      513 |    7939
     2016-02-22 09:04:00 |     2762 |     2210 |    7940
     2016-02-22 09:04:00 |      143 |      332 |    7941
     2016-02-22 09:04:00 |      371 |     1042 |    7942
     2016-02-22 09:04:00 |      865 |      987 |    7943
     (5 rows)

b）中

    CREATE CONTINUOUS VIEW joined_foo_total_bytes
        AS SELECT date_trunc('minute', byte_count_stream.time_stamp::timestamp) AS minute,
            sum(byte_count_stream.tx_bytes::bigint) AS tx_bytes,
            sum(byte_count_stream.rx_bytes::bigint) AS rx_bytes,
            foo_instance.foo_id
        FROM byte_count_stream JOIN foo_instance ON byte_count_stream.id::integer = foo_instance.id
        GROUP BY minute, foo_instance.foo_id;



    pipeline=# select * from joined_user_total_bytes;       
           minute        | tx_bytes | rx_bytes | foo_id 
    ---------------------+----------+----------+---------
     2016-02-22 09:04:00 |      371 |     1042 |    7942
     2016-02-22 09:04:00 |      143 |      332 |    7941
     2016-02-22 09:04:00 |      865 |      987 |    7943
     2016-02-22 09:04:00 |     2762 |     2210 |    7940
     (4 rows)

显然结果并不相同。我可以从连续视图到外表进行连接，但更喜欢使用流连接。

PIpelineDB使用mysql外部数据包装器连续查看

0 个答案: