I am trying to convert CSV files to parquet and i am using Spark to accomplish this.
SparkSession spark = SparkSession
.builder()
.appName(appName)
.config("spark.master", master)
.getOrCreate();
Dataset<Row> logFile = spark.read().csv("log_file.csv");
logFile.write().parquet("log_file.parquet");
Now the problem is i don't have a schema defined and columns look like this (output displayed using printSchema() in spark)
root
|-- _c0: string (nullable = true)
|-- _c1: string (nullable = true)
|-- _c2: string (nullable = true)
....
the csv has the names on the first row but they're ignored i guess, the problem is only a few columns are strings, i also have ints and dates.
I am only using Spark, no avro or anything else basically (never used avro).
What are my options to define a schema and how? If i need to write the parquet file in another way then no problem as long as it's a quick an easy solution.
(i am using spark standalone for tests / don't know scala)
Try using the .option("inferschema","true") present Spark-csv package. This will automatically infer the schema from the data.
You can also define a custom schema for your data using struct type and use the .schema(schema_name)
to read the on the basis of a custom schema.
val sqlContext = new SQLContext(sc)
val customSchema = StructType(Array(
StructField("year", IntegerType, true),
StructField("make", StringType, true),
StructField("model", StringType, true),
StructField("comment", StringType, true),
StructField("blank", StringType, true)))
val df = sqlContext.read
.format("com.databricks.spark.csv")
.option("header", "true") // Use first line of all files as header
.schema(customSchema)
.load("cars.csv")