By Kathleen Ting, Jarek Jarcec Cecho
Integrating facts from a number of resources is key within the age of huge facts, however it could be a tough and time-consuming activity. this useful cookbook presents dozens of ready-to-use recipes for utilizing Apache Sqoop, the command-line interface program that optimizes information transfers among relational databases and Hadoop. Sqoop is either strong and bewildering, yet with this cookbook's problem-solution-discussion structure, you will fast install after which follow Sqoop on your atmosphere. The authors offer MySQL, Oracle, and PostgreSQL database examples on GitHub for you to simply adapt for SQL Server, Netezza, Teradata, or different relational platforms.
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Extra resources for Apache Sqoop Cookbook: Unlocking Hadoop for Your Relational Database
11. Importing All Your Tables Problem You would like to import all tables from your database at once using one command rather than importing the tables one by one. Solution Rather than using the import tool for one table, you can use the import-all-tables tool. com/sqoop \ --username sqoop \ --password sqoop Discussion When using the import-all-tables tool, Sqoop will use catalog queries to retrieve a list of all tables in your database. Subsequently for each table, Sqoop will internally call the import tool to import your data to Hadoop.
3. 8. Overriding Type Mapping Problem The default type mapping that Sqoop provides between relational databases and Hadoop usually works well. You have use cases requiring you to override the mapping. Solution Use Sqoop’s ability to override default type mapping using the parameter --mapcolumn-java. info --table cities \ --map-column-java id=Long Discussion The parameter --map-column-java accepts a comma separated list where each item is a key-value pair separated by an equal sign. The exact column name is used as the key, and the target Java type is specified as the value.
First of all, the columns used to identify the row will never be updated because they are not part of the SET clause. Also, if your data in Hadoop contains some completely new rows, the WHERE clause will not match any rows on the database side. Such an operation on the database side is fully valid, but it results in no updated rows. Therefore, new rows are not exported in update mode at all. 5. Updating or Inserting at the Same Time Problem You have data in your database from a previous export, but now you need to propagate updates from Hadoop.