A library for querying Excel files with Apache Spark, for Spark SQL and DataFrames.
This library requires Spark 2.0+
You can link against this library in your program at the following coordinates:
groupId: com.crealytics
artifactId: spark-excel_2.11
version: 0.9.12
This package can be added to Spark using the --packages command line option. For example, to include it when starting the spark shell:
$SPARK_HOME/bin/spark-shell --packages com.crealytics:spark-excel_2.11:0.9.12
This package allows querying Excel spreadsheets as Spark DataFrames.
Spark 2.0+:
import org.apache.spark.sql.SQLContext
val sqlContext = new SQLContext(sc)
val df = sqlContext.read
.format("com.crealytics.spark.excel")
.option("sheetName", "Daily") // Required
.option("useHeader", "true") // Required
.option("treatEmptyValuesAsNulls", "false") // Optional, default: true
.option("inferSchema", "false") // Optional, default: false
.option("addColorColumns", "true") // Optional, default: false
.option("startColumn", 0) // Optional, default: 0
.option("endColumn", 99) // Optional, default: Int.MaxValue
.option("timestampFormat", "MM-dd-yyyy HH:mm:ss") // Optional, default: yyyy-mm-dd hh:mm:ss[.fffffffff]
.option("maxRowsInMemory", 20) // Optional, default None. If set, uses a streaming reader which can help with big files
.option("excerptSize", 10) // Optional, default: 10. If set and if schema inferred, number of rows to infer schema from
.schema(myCustomSchema) // Optional, default: Either inferred schema, or all columns are Strings
.load("Worktime.xlsx")df.write
.format("com.crealytics.spark.excel")
.option("sheetName", "Daily")
.option("useHeader", "true")
.option("dateFormat", "yy-mmm-d") // Optional, default: yy-m-d h:mm
.option("timestampFormat", "mm-dd-yyyy hh:mm:ss") // Optional, default: yyyy-mm-dd hh:mm:ss.000
.mode("overwrite")
.save("Worktime2.xlsx")This library is built with SBT.
To build a JAR file simply run sbt assembly from the project root.
The build configuration includes support for Scala 2.11.
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