How to read the stream of changes from the table in Databricks

This recipe helps you read the stream of changes from the table in Databricks

Recipe Objective - How to read the stream of changes from the table in Databricks?

The Delta Lake table, defined as the Delta table, is both a batch table and the streaming source and sink. The Streaming data ingest, batch historic backfill, and interactive queries all work out of the box. Delta Lake provides the ability to specify the schema and also enforce it, which further helps ensure that data types are correct and the required columns are present, which also helps in building the delta tables and also preventing the insufficient data from causing data corruption in both delta lake and delta table. The Delta can write the batch and the streaming data into the same table, allowing a simpler architecture and quicker data ingestion to the query result. Also, the Delta provides the ability to infer the schema for data input which further reduces the effort required in managing the schema changes.

System Requirements

  • Scala (2.12 version)
  • Apache Spark (3.1.1 version)

This recipe explains Delta lake and how to read the stream of changes from the table Spark.

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Implementing reading of stream of changes from Table

// Importing packages import org.apache.spark.sql.{SaveMode, SparkSession} import io.delta.implicits._

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The spark SQL Savemode & Sparksession package and Delta implicit package, and Delta table package are imported in the environment to read the stream of changes from the table in Databricks.

/// Implementing reading stream changes from Delta table object DeltaTableReadStreamChanges extends App { val spark: SparkSession = SparkSession.builder() .master("local[1]") .appName("Spark Read Stream Changes Delta Table") .getOrCreate() spark.sparkContext.setLogLevel("ERROR") // Reading stream of changes from Delta table val stream_changes = spark.readStream.format("delta").load("/delta/events").writeStream.format("console").start() }

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The DeltaTableReadStreamChanges object is created in which a spark session is initiated. The Delta table from the path "/delta/events" is loaded as a stream source using the "spark.readStream.format()" function and is used as the streaming query in value "stream_changes." Further, the query processes all the data present in the table and any new data that arrives after the streaming is started. So, any changes in the Delta table will be read in the value "stream_changes" created.

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