1. Load to Hdfs, beware of Sqoop error handling, as its a mapreduce based framework, so if 1 mapper fails it might happen that you get partial data.
2. Create partition based on date and hour, if customer table has some date or timestamp column. 3. Think about file format also, as that will affect the load and query time. 4. Think about compression as well before hand, as that will govern the data split, and performance of your queries as well. Regards, Manish Sent from my T-Mobile 4G LTE Device -------- Original message -------- From: Raj Hadoop <hadoop...@yahoo.com> Date: 11/03/2013 7:39 AM (GMT-08:00) To: Hive <user@hive.apache.org>,Sqoop <u...@sqoop.apache.org>,User <u...@hadoop.apache.org> Subject: Oracle to HDFS through Sqoop and a Hive External Table Hi, I am sending this to the three dist-lists of Hadoop, Hive and Sqoop as this question is closely related to all the three areas. I have this requirement. I have a big table in Oracle (about 60 million rows - Primary Key Customer Id). I want to bring this to HDFS and then create a Hive external table. My requirement is running queries on this Hive table (at this time i do not know what queries i would be running). Is the following a good design for the above problem ? Any pros and cons of this. 1) Load the table to HDFS using Sqoop into multiple folders (divide Customer Id's into 100 segments). 2) Create Hive external partition table based on the above 100 HDFS directories. Thanks, Raj
