Well I don't know about postgres but you can limit the number of columns
abd rows fetched via JDBC at source rather than loading and filtering them
in Spark
val c = HiveContext.load("jdbc",
Map("url" -> _ORACLEserver,
"dbtable" -> "(SELECT to_char(CHANNEL_ID) AS CHANNEL_ID, CHANNEL_DESC FROM
sh.channels where ROWNUM <= 10000)",
"user" -> _username,
"password" -> _password))
or in your case
"dbtable" -> "(SELECT COUNT(1) FROM FROM sh.channels where ROWNUM <=
10000)",
c.show()
HTH
Dr Mich Talebzadeh
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On 14 May 2016 at 04:56, Jyun-Fan Tsai <jyun...@gmail.com> wrote:
> I try to load some rows from a big SQL table. Here is my code:
>
> ===
> jdbcDF = sqlContext.read.format("jdbc").options(
> url="jdbc:postgresql://...",
> dbtable="mytable",
> partitionColumn="t",
> lowerBound=1451577600,
> upperBound=1454256000,
> numPartitions=1).load()
> print(jdbcDF.count())
> ===
>
> The code runs very slow because Spark tries to load whole table.
> I know there is a solution that uses subquery. I can use:
>
> dbtable="(SELECT * FROM mytable WHERE t>=1451577600 AND t<= 1454256000)
> tmp".
> However, it's still slow because the subquery creates a temp table.
>
> I would like to know how can I specify where filters so I don't need
> to load the whole table?
>
> From spark source code I guess the filter in JDBCRelation is the
> solution I'm looking for. However, I don't know how to create a
> filter and pass it to jdbc driver.
> ===
>
> https://github.com/apache/spark/blob/40ed2af587cedadc6e5249031857a922b3b234ca/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRelation.scala
> ===
>
>
>
> --
> Thanks for help,
> Jyun-Fan Tsai
>
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