| D toc |
|---|
The
| D s platform | ||
|---|---|---|
|
| Tip |
|---|
Tip: In general, you should accept the default environment that is presented for job execution. The application attempts to match the scope of your job to the most appropriate running environment. |
This section applies to execution of transform jobs. For more information on options for profiling jobs, see Profiling Options.
Available Environments
| D s photon |
|---|
This running environment is available through the
| D s node |
|---|
| Info |
|---|
NOTE: This running environment is enabled by default. |
Suitable for small to medium jobs.
Required Installation: None.
Required Configuration: See Configure Photon Running Environment.
Supported Output Formats: CSV, JSON, Avro, Parquet
Notes and Limitations:
| Info | |
|---|---|
NOTE: When a recipe containing a user-defined function is applied to text data, any null characters cause records to be truncated during
|
Spark Running Environment
This running environment is the new default running environment. The Spark running environment deploys Spark libraries from the
| D s node |
|---|
Suitable for jobs of all sizes.
Required Installation: None.
Required Configuration: See Configure Spark Running Environment.
Supported Output Formats: CSV, JSON, Avro, Parquet
Notes and Limitations:
| Info |
|---|
NOTE: When executing a job on the Spark running environment using a relational source, the job fails if one or more columns has been dropped from the underlying source table. As a workaround, the recipe panel may show steps referencing the missing columns, which can be used to fix to either fix the recipe or the source data. |
| Info |
|---|
NOTE: The Spark running environment does not support use of multi-character delimiters for CSV outputs. You can switch your job to a different running environment or use single-character delimiters. For more information on this issue, see https://issues.apache.org/jira/browse/SPARK-24540. |
EMR Running Environment
If you have deployed the
| D s platform |
|---|
Required Installation: None.
Required Configuration: See Configure for EMR.
Supported Output Formats: CSV, JSON, Avro, Parquet
Notes and Limitations:
| Info |
|---|
NOTE: Job cancellation is not supported on an EMR cluster. |
Azure Databricks Running Environment
The Azure Databricks running environment is an Apache Spark implementation that has been tuned specifically for deployment on Microsoft Azure.
This running environment deploys Spark libraries from the
| D s node |
|---|
Suitable for jobs of all sizes.
Required Installation: None.
Required Configuration: See below.
Supported Output Formats: CSV, JSON, Avro, Parquet
Notes and Limitations:
| Info |
|---|
NOTE: Use of Azure Databricks is not supported on Marketplace installs. |
| Info |
|---|
NOTE: When executing a job on the Azure Databricks running environment using a relational source, the job fails if one or more columns has been dropped from the underlying source table. As a workaround, the recipe panel may show steps referencing the missing columns, which can be used to either fix the recipe or the source data. |
Configure
Available Running Environments
| D s config | ||
|---|---|---|
|
The following parameters define the available running environments:
| Code Block |
|---|
"webapp.runInTrifactaServer": true, "webapp.runWithSparkSubmit": true, "webapp.runinEMR": false, "webapp.runInDataflow": false, |
For more information on configuring the running environment for EMR, see Configure for EMR.Below, you can see the configuration settings required to enable each running environment.
- The Spark running environment requires a Hadoop cluster as the backend job execution environment.
- In the Run Job page, select Spark.
- The
running environment executes on theD s photon
and provide processing to the front-end client and at execution time.D s node In the Run Job page, select Photon.
For more information on disabling the
running environment, see Configure Photon Running Environment.D s photon
| Type | Running Environment | Configuration Parameters | Notes | ||
|---|---|---|---|---|---|
| Hadoop Backend | Spark |
| The Spark running environment is the default configuration. | ||
| Client Front-end and non-Hadoop Backend |
| |
|
| Info |
|---|
NOTE: Do not modify the |
Configure Default Running Environment
When you specify a job, the default running environment is pre-configured for you, based on the following parameter:
| Info | |
|---|---|
NOTE: If your environment has no running environment such as Spark for running large-scale jobs, this parameter is not used. All jobs are run on the
|
| Code Block |
|---|
"webapp.client.maxExecutionBytes.photon": 1000000000, |
The default environment presented to you is based on the size of the primary datasource. For the above setting of 1 GB:
| Running Environment | Default Condition | |
|---|---|---|
| Size of primary datasource is less than or equal to the above value in bytes. | |
| Spark | Size of primary datasource is greater than the above value in bytes. |
| Info |
|---|
NOTE: This setting defines only the environment that is recommended to you as a predefined selection. If a second running environment is available, you can choose to select it, although it is not recommended to choose an environment other than the default. See Run Job Page. |
| Warning | |
|---|---|
Setting this value too high forces more jobs onto the |
| Tip | |
|---|---|
Tip: To force the default setting to always be a Hadoop or bulk running environment, set this value to
|
