Xin Wu created SPARK-16924: ------------------------------ Summary: DataStreamReader can not support option("inferSchema", true/false) for csv and json file source Key: SPARK-16924 URL: https://issues.apache.org/jira/browse/SPARK-16924 Project: Spark Issue Type: Bug Components: SQL Affects Versions: 2.0.0 Reporter: Xin Wu
Currently DataStreamReader can not support option("inferSchema", true|false) for csv and json file source. It only takes SQLConf setting "spark.sql.streaming.schemaInference", which needs to be set at session level. For example: {code} scala> val in = spark.readStream.format("json").option("inferSchema", true).load("/Users/xinwu/spark-test/data/json/t1") java.lang.IllegalArgumentException: Schema must be specified when creating a streaming source DataFrame. If some files already exist in the directory, then depending on the file format you may be able to create a static DataFrame on that directory with 'spark.read.load(directory)' and infer schema from it. at org.apache.spark.sql.execution.datasources.DataSource.sourceSchema(DataSource.scala:223) at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo$lzycompute(DataSource.scala:80) at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo(DataSource.scala:80) at org.apache.spark.sql.execution.streaming.StreamingRelation$.apply(StreamingRelation.scala:30) at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:142) at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:153) ... 48 elided scala> val in = spark.readStream.format("csv").option("inferSchema", true).load("/Users/xinwu/spark-test/data/csv") java.lang.IllegalArgumentException: Schema must be specified when creating a streaming source DataFrame. If some files already exist in the directory, then depending on the file format you may be able to create a static DataFrame on that directory with 'spark.read.load(directory)' and infer schema from it. at org.apache.spark.sql.execution.datasources.DataSource.sourceSchema(DataSource.scala:223) at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo$lzycompute(DataSource.scala:80) at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo(DataSource.scala:80) at org.apache.spark.sql.execution.streaming.StreamingRelation$.apply(StreamingRelation.scala:30) at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:142) at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:153) ... 48 elided {code} In the example, even though users specify the option("inferSchema", true), it does not take it. But for batch data, DataFrameReader can take it: {code} scala> val in = spark.read.format("csv").option("header", true).option("inferSchema", true).load("/Users/xinwu/spark-test/data/csv1") in: org.apache.spark.sql.DataFrame = [signal: string, flash: int] {code} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org