Class DatasetManager
Object
org.apache.spark.sql.pipelines.graph.DatasetManager
DatasetManager is responsible for materializing tables in the catalog based on the given
 graph. For each table in the graph, it will create a table if none exists (or if this is a
 full refresh), or merge the schema of an existing table to match the new flows writing to it.- 
Nested Class SummaryNested ClassesModifier and TypeClassDescriptionstatic classWraps table materialization exceptions.static class
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Constructor SummaryConstructors
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Method SummaryModifier and TypeMethodDescriptionstatic org.apache.spark.internal.Logging.LogStringContextLogStringContext(scala.StringContext sc) static DataflowGraphmaterializeDatasets(DataflowGraph resolvedDataflowGraph, PipelineUpdateContext context) Materializes the tables in the given graph.static org.slf4j.Loggerstatic voidorg$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1) 
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Constructor Details- 
DatasetManagerpublic DatasetManager()
 
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Method Details- 
materializeDatasetspublic static DataflowGraph materializeDatasets(DataflowGraph resolvedDataflowGraph, PipelineUpdateContext context) Materializes the tables in the given graph. This method will create or update the tables in the catalog based on the given graph and context.- Parameters:
- resolvedDataflowGraph- The resolved- DataflowGraphwith resolved- Flowsorted in topological order.
- context- The context for the pipeline update.
- Returns:
- The graph with materialized tables.
 
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org$apache$spark$internal$Logging$$log_public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()
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org$apache$spark$internal$Logging$$log__$eqpublic static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1) 
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LogStringContextpublic static org.apache.spark.internal.Logging.LogStringContext LogStringContext(scala.StringContext sc) 
 
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