Ok. Thanks.
On 7/12/18, 11:12 AM, "Thakrar, Jayesh" wrote:
Unless the tables are very small (< 1000 rows), the impact of hitting the
catalog tables is negligible.
Furthermore, normally the catalog tables (or views) are usually in memory
because they are needed for query compilation,
Unless the tables are very small (< 1000 rows), the impact of hitting the
catalog tables is negligible.
Furthermore, normally the catalog tables (or views) are usually in memory
because they are needed for query compilation, query execution (for triggers,
referential integrity, etc) and even to
Thanks Jayesh.
I was aware of the catalog table approach but I was avoiding that because I
will hit the database twice for one table, one to create DDL and other to read
the data. I have lots of table to transport from one environment to other and I
don’t want to create unnecessary load on
One option is to use plain JDBC to interrogate Postgresql catalog for the
source table and generate the DDL to create the destination table.
Then using plain JDBC again, create the table at the destination.
See the link below for some pointers…..
Hi All,
I am trying to build a spark application which will read the data from
Postgresql (source) one environment and write it to postgreSQL, Aurora
(target) on a dfiffernt environment (like to PROD to QA or QA to PROD etc)
using spark JDBC.
When I am loading the dataframe back to
