DB to Spark

Reads a database query/table into a Spark RDD/DataFrame. See Spark documentation for more information.

Notice: This feature requires at least Apache Spark 1.5.

Options

Driver: Upload local driver (used in this KNIME instance) or depend on cluster side provided driver.
Fetch size: Optional: The JDBC fetch size, which determines how many rows to fetch per round trip. This can help performance on JDBC drivers which default to low fetch size (eg. Oracle with 10 rows).
Partition column, lower bound, upper bound, num partitions: These options must all be specified if any of them is specified. They describe how to partition the table when reading in parallel from multiple workers. partitionColumn must be a numeric column from the table in question. Notice that lowerBound and upperBound are just used to decide the partition stride, not for filtering the rows in table. So all rows in the table will be partitioned and returned.
Query DB for upper and lower count: Fetch bounds via min/max query or use manual entered bounds.

This node has no views

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

To use this node in KNIME, install the extension KNIME Extension for Apache Spark (legacy) from the below update site following our NodePit Product and Node Installation Guide:

v5.4

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.4.0.v202411151159

On NodePit since: 2024-12-06

Last update: 2024-12-17

KNIME versions: Since v4.0

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.