The TableFlattener takes a nested dataset and creates an equivalent
unnested dataset. The unnested dataset is saved as Parquet.
When an array or map is encountered in the original table, the flattener
creates a new table and adds an id column to it which references the row
in the parent table. Joining on the id column should produce the
original dataset.
The flattened dataset should be loaded into Postgres in order to run the
query generator (in nested types mode) on it. There is a script that
automates generaration, flattening and loading random data into Postgres
and Impala:
testdata/bin/generate-load-nested.sh -f
Testing:
- ran ./testdata/bin/generate-load-nested.sh -f and random nested data
was generated and flattened as expected.
Change-Id: I7e7a8e53ada9274759a3e2128b97bec292c129c6
Reviewed-on: http://gerrit.cloudera.org:8080/5787
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
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