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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings
  4. Sources
  5. Targets
  6. Transformations
  7. Data Preview
  8. Cluster Workflows
  9. Profiles
  10. Monitoring
  11. Hierarchical Data Processing
  12. Hierarchical Data Processing Configuration
  13. Hierarchical Data Processing with Schema Changes
  14. Intelligent Structure Models
  15. Stateful Computing
  16. Connections
  17. Data Type Reference
  18. Function Reference

Spark Advanced Properties

Spark Advanced Properties

Spark advanced properties are a list of advanced or custom properties that are unique to the Spark engine. Each property contains a name and a value. You can add or edit advanced properties. Each property contains a name and a value. You can add or edit advanced properties.
Configure the following properties in the
Advanced Properties
of the Spark configuration section:
To edit the property in the text box, use the following format with &: to separate each name-value pair:
<name1>=<value1>[&:<name2>=<value2>…&:<nameN>=<valueN>]
spark.authenticate
Enables authentication for the Spark service on Hadoop. Required for Spark encryption.
Set to TRUE.
For example,
spark.authenticate=TRUE
spark.authenticate.enableSaslEncryption
Enables encrypted communication when SASL authentication is enabled. Required if Spark encryption uses SASL authentication.
Set to TRUE.
For example,
spark.authenticate.enableSaslEncryption=TRUE
spark.executor.cores
Indicates the number of cores that each executor process uses to run tasklets on the Spark engine.
Set to:
spark.executor.cores=1
spark.executor.instances
Indicates the number of instances that each executor process uses to run tasklets on the Spark engine.
Set to:
spark.executor.instances=1
spark.executor.memory
Indicates the amount of memory that each executor process uses to run tasklets on the Spark engine.
Set to:
spark.executor.memory=3G
infaspark.driver.cluster.mode.extraJavaOptions
List of extra Java options for the Spark driver that runs inside the cluster. Required for streaming mappings to read from or write to a Kafka cluster that uses Kerberos authentication.
For example, set to:
infaspark.driver.cluster.mode.extraJavaOptions= -Djava.security.egd=file:/dev/./urandom -XX:MaxMetaspaceSize=256M -Djavax.security.auth.useSubjectCredsOnly=true -Djava.security.krb5.conf=/<path to keytab file>/krb5.conf -Djava.security.auth.login.config=<path to jaas config>/kafka_client_jaas.config
To configure the property for a specific user, you can include the following lines of code:
infaspark.driver.cluster.mode.extraJavaOptions = -Djava.security.egd=file:/dev/./urandom -XX:MaxMetaspaceSize=256M -XX:+UseG1GC -XX:MaxGCPauseMillis=500 -Djava.security.krb5.conf=/etc/krb5.conf
infaspark.executor.extraJavaOptions
List of extra Java options for the Spark executor. Required for streaming mappings to read from or write to a Kafka cluster that uses Kerberos authentication.
For example, set to:
infaspark.executor.extraJavaOptions= -Djava.security.egd=file:/dev/./urandom -XX:MaxMetaspaceSize=256M -Djavax.security.auth.useSubjectCredsOnly=true -Djava.security.krb5.conf=/<path to krb5.conf file>/krb5.conf -Djava.security.auth.login.config=/<path to jAAS config>/kafka_client_jaas.config
To configure the property for a specific user, you can include the following lines of code:
infaspark.executor.extraJavaOptions = -Djava.security.egd=file:/dev/./urandom -XX:MaxMetaspaceSize=256M -XX:+UseG1GC -XX:MaxGCPauseMillis=500 -Djava.security.krb5.conf=/etc/krb5.conf
infaspark.flatfile.writer.nullValue
When the Databricks Spark engine writes to a target, it converts null values to empty strings (" "). For example, 12, AB,"",23p09udj.
The Databricks Spark engine can write the empty strings to string columns, but when it tries to write an empty string to a non-string column, the mapping fails with a type mismatch.
To allow the Databricks Spark engine to convert the empty strings back to null values and write to the target, configure the following advanced property in the Databricks Spark connection:
infaspark.flatfile.writer.nullValue=true
spark.hadoop.validateOutputSpecs
Validates if the HBase table exists. Required for streaming mappings to write to a HBase target in an Amazon EMR cluster. Set the value to false.
infaspark.json.parser.mode
Specifies the parser how to handle corrupt JSON records. You can set the value to one of the following modes:
  • DROPMALFORMED. The parser ignores all corrupted records. Default mode.
  • PERMISSIVE. The parser accepts non-standard fields as nulls in corrupted records.
  • FAILFAST. The parser generates an exception when it encounters a corrupted record and the Spark application goes down.
infaspark.json.parser.multiLine
Specifies whether the parser can read a multiline record in a JSON file. You can set the value to true or false. Default is false. Applies only to non-native distributions that use Spark version 2.2.x and above.
infaspark.pythontx.exec
Required to run a Python transformation on the Spark engine for Big Data Management. The location of the Python executable binary on the worker nodes in the Hadoop cluster.
For example, set to:
infaspark.pythontx.exec=/usr/bin/python3.4
If you use the installation of Python on the Data Integration Service machine, set the value to the Python executable binary in the Informatica installation directory on the Data Integration Service machine.
For example, set to:
infaspark.pythontx.exec=INFA_HOME/services/shared/spark/python/lib/python3.4
infaspark.pythontx.executorEnv.PYTHONHOME
Required to run a Python transformation on the Spark engine for Big Data Management and Big Data Streaming. The location of the Python installation directory on the worker nodes in the Hadoop cluster.
For example, set to:
infaspark.pythontx.executorEnv.PYTHONHOME=/usr
If you use the installation of Python on the Data Integration Service machine, use the location of the Python installation directory on the Data Integration Service machine.
For example, set to:
infaspark.pythontx.executorEnv.PYTHONHOME= INFA_HOME/services/shared/spark/python/
infaspark.pythontx.executorEnv.LD_PRELOAD
Required to run a Python transformation on the Spark engine for Big Data Streaming. The location of the Python shared library in the Python installation folder on the Data Integration Service machine.
For example, set to:
infaspark.pythontx.executorEnv.LD_PRELOAD= INFA_HOME/services/shared/spark/python/lib/libpython3.6m.so
infaspark.pythontx.submit.lib.JEP_HOME
Required to run a Python transformation on the Spark engine for Big Data Streaming. The location of the Jep package in the Python installation folder on the Data Integration Service machine.
For example, set to:
infaspark.pythontx.submit.lib.JEP_HOME= INFA_HOME/services/shared/spark/python/lib/python3.6/site-packages/jep/
spark.shuffle.encryption.enabled
Enables encrypted communication when authentication is enabled. Required for Spark encryption.
Set to TRUE.
For example,
spark.shuffle.encryption.enabled=TRUE
spark.scheduler.maxRegisteredResourcesWaitingTime
The number of milliseconds to wait for resources to register before scheduling a task. Default is 30000. Decrease the value to reduce delays before starting the Spark job execution. Required to improve performance for mappings on the Spark engine.
Set to 15000.
For example,
spark.scheduler.maxRegisteredResourcesWaitingTime=15000
spark.scheduler.minRegisteredResourcesRatio
The minimum ratio of registered resources to acquire before task scheduling begins. Default is 0.8. Decrease the value to reduce any delay before starting the Spark job execution. Required to improve performance for mappings on the Spark engine.
Set to: 0.5
For example,
spark.scheduler.minRegisteredResourcesRatio=0.5


Updated January 20, 2020