We can disable the_common_metadataand_metadata filesusing"parquet.enable.summary-metadata=false". We can also disable the_SUCCESS fileusing"mapreduce.fileoutputcommitter.marksuccessfuljobs=false". Related documentation:https://community.databricks.com/t5/data-engineering/how-do-i-prevent-success...
(2)在spark shell执行下面命令,读取数据 val jsonDF= spark.read.json("file:///opt/bigdata/spark/examples/src/main/resources/people.json") (3)接下来就可以使用DataFrame的函数操作 4.3 读取parquet列式存储格式文件创建DataFrame (1)数据文件 使用spark安装包下的 /opt/bigdata/spark/examples/src/main/re...
load(this.concatInputPath(inputPath)); avroFile.coalesce(this.splitSize).write().format("com.databricks.spark.avro").save(outputPath); } else { System.out.println("Did not match any serialization type: text, parquet, or avro. Recieved: " + this.outputSerialization); } } 代码示例来源:ori...
/ 这里是去加载所有的META-INF/services/org.apache.spark.sql.sources.DataSourceRegister,然后返回里面的内容 // 其中有`org.apache.hudi.DefaultSource` `org.apache.hudi.Spark2DefaultSource` `org.apache.spark.sql.execution.datasources.parquet.HoodieParquetFileFormat` 等 val serviceLoader = ServiceLoader....
write().parquet(outputPath); } else if (this.outputSerialization.equals(AVRO)) { // For this to work the files must end in .avro SQLContext sqlContext = new SQLContext(sc); DataFrame avroFile = sqlContext.read().format("com.databricks.spark.avro").load(this.concatInputPath(inputPath)...
第二次“partitionby”也必须使用。也可能需要选项“hive.exec.dynamic.partition.mode”。