如果列已经分区，则只扫描所需的分区。
让我们创建分区和非分区增量表并执行结构化流。
分区增量表流：

val spark = SparkSession.builder().master("local[*]").getOrCreate()
spark.sparkContext.setLogLevel("ERROR")
import spark.implicits._
//sample dataframe
val df = Seq((1,2020),(2,2021),(3,2020),(4,2020),
(5,2020),(6,2020),(7,2019),(8,2019),(9,2018),(10,2020)).toDF("id","year")
//partionBy year column and save as delta table
df.write.format("delta").partitionBy("year").save("delta-stream")
//streaming delta table
spark.readStream.format("delta").load("delta-stream")
.where('year===2020)
.writeStream.format("console").start().awaitTermination()

上述流式查询的物理计划：请注意partitionfilters

非分区增量表流：

df.write.format("delta").save("delta-stream")
spark.readStream.format("delta").load("delta-stream")
    .where('year===2020)
    .writeStream.format("console").start().awaitTermination()

上述流式查询的物理计划：请注意pushedfilters