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Batch scoring configuration and usage

You can use H2O eScorer to perform batch scoring (database scoring). The batch scoring process can be summarized as follows:

  • Read the data from the source database
  • Make predictions on the data
  • Write the results back to the database

For more in-depth information on database scoring in H2O eScorer, download the H2O eScorer Database Scoring PDF.


Batch scoring tasks require JDBC drivers to connect to the data source to ingest data and record predictions. The following is a comprehensive list of the JDBC drivers and their supported versions for the most common batch scoring use cases, AWS S3 and Snowflake.


  • mojo2-runtime.jar
  • aws-java-sdk-core-1.12.311.jar
  • aws-java-sdk-s3-1.12.311.jar
  • jackson-annotations-2.12.6.jar
  • httpclient-4.5.13.jar
  • httpcore-4.4.15.jar
  • jackson-databind-
  • jackson-core-2.12.6.jar
  • commons-io-2.4.jar


  • mojo2-runtime.jar
  • ibatis-core-3.0.jar (Optional - Required only if you want to do the post-processing)
  • 1.4.2-1.0.jar (Optional - Required only if you want to run MLI)
  • aws-java-sdk-core-1.12.311.jar
  • snowflake-jdbc-3.9.2.jar

The following sections describe how to auto-generate and use a Properties file to configure your Batch Scorer.

Auto-generate a properties file

The H2O eScorer Wave app lets you automatically generate deployment code for you model with the AutoGen feature.

To generate the model deployment code, click Deploy -> AutoGen and specify a Model and Deployment Type. To confirm your selection, click the Generate button. In this example, a batch scorer properties file called is generated to score the riskmodel.mojo model.


In this example, riskmodel.mojo had already been deployed to the eScorer and made available in the model registry.


What do the properties mean?


Notice how the model features are auto-generated for the riskmodel.mojo file.

  1. JDBC connection string for the database:

  2. A description for the model:

    Description = A Machine Learning model
  3. Use Environment Variables to set values for the parameters. For example, SQLUSER='$USER' sets the SQLUSER parameter with the value from the environment variable.

    EnvVariables = true
  4. Name and path of the Model. Do not change for H2O eScorer.

    ModelName = '$MODELNAME'
  5. SELECT SQL statement to get the data that will be scored:

    SQLSelect = SELECT <add-table-key-or-unique-field>, loan_amnt, term, int_rate, installment, emp_length, annual_inc, verification_status, addr_state, dti, inq_last_6mths, revol_bal, revol_util, total_acc FROM <add-table-name>.<add-table-name>
  6. Unique key of the table

    SQLKey = <add-table-key-or-unique-field> 
  7. Prediction class to return. One of: 0, 1, __blank__ (All)

    SQLPrediction =  
  8. SQL statement to write the results back to the database

    SQLWriteBatch=insert into <results-table> ("<SQLKey>", "bad_loan_0", "bad_loan_1")  values(?, ?, ? )
  9. SQL friendly model target field. For ex: removed spaces and dot predictions

    SQLWriteFriendlyNames = false
  10. Skip features with empty string

    IgnoreEmptyStrings = true
  11. Ignore logic checks (enable with caution)

    IgnoreChecks = False
  12. Ignore SQL statement logic checks (enable with caution)

    IgnoreSQLChecks = False
  13. Change NULL in SQL results to NA. This depends on the model training and should match it.

    SetNullAs = NA
  14. Return Shapley contributions with the Predictions. (SQLWriteBatch must be set)

    ShapPredictContrib = false