Vectorwise Hadoop Edition (aka VectorH) has support table partitioning. Since this is the cluster edition of Vectorwise, running on different (hadoop) nodes, parallelisation of the tables is very good/required for high performance.
They difficulty is in the way the partitioning is done, and the amount of manual action required.
The syntax is:
create table tst_part (key1 int, key2 int, value int)
with partition = (HASH on key1,key2 3 partitions)
The recommended number of partitions for VectorH is:
You may always want to use 50% of maximum due to the continuous loading and the concurrent queries.
This is because Vectorwise/VectorH spawn new threads for partitions for queries, so the concurrent number of threads == number of partitions * number of concurrent queries. This is mitigated by the distribution of the query over the cluster nodes, in the case of VectorH.
A consequence is that it is impossible for HVR to automatically determine the number of partitions. The expected amount of rows or concurrent queries on a newly created table is unknown at table creation.
You can define an Environment action for varible:
This variable will ensure HVR creates n partitions for each table created while this variable is set.
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