Execute ETL and vectorization in a local instance.

Local or remote execution?

Because datasets are commonly large by nature, you can decide on an execution mechanism that best suits your needs. For example, if you are vectorizing a large training dataset, you can process it in a distributed Spark cluster. However, if you need to do real-time inference, DataVec also provides a local executor that doesn't require any additional setup.

Executing a transform process

Once you've created your TransformProcess using your Schema, and you've either loaded your dataset into a Apache Spark JavaRDD or have a RecordReader that load your dataset, you can execute a transform.
Locally this looks like:
import org.datavec.local.transforms.LocalTransformExecutor;
List<List<Writable>> transformed = LocalTransformExecutor.execute(recordReader, transformProcess)
List<List<List<Writable>>> transformedSeq = LocalTransformExecutor.executeToSequence(sequenceReader, transformProcess)
List<List<Writable>> joined = LocalTransformExecutor.executeJoin(join, leftReader, rightReader)
When using Spark this looks like:
import org.datavec.spark.transforms.SparkTransformExecutor;
JavaRDD<List<Writable>> transformed = SparkTransformExecutor.execute(inputRdd, transformProcess)
JavaRDD<List<List<Writable>>> transformedSeq = SparkTransformExecutor.executeToSequence(inputSequenceRdd, transformProcess)
JavaRDD<List<Writable>> joined = SparkTransformExecutor.executeJoin(join, leftRdd, rightRdd)

Available executors


Local transform executor
public static boolean isTryCatch()
Execute the specified TransformProcess with the given input data Note: this method can only be used if the TransformProcess returns non-sequence data. For TransformProcesses that return a sequence, use {- link #executeToSequence(List, TransformProcess)}
  • param inputWritables Input data to process
  • param transformProcess TransformProcess to execute
  • return Processed data


Execute a datavec transform process on spark rdds.
public static boolean isTryCatch()
  • deprecated Use static methods instead of instance methods on SparkTransformExecutor