def test_nested_array():
schema = StructType([
StructField("id", IntegerType()),
StructField("scores", ArrayType(LongType())),
])
assert schema == parse_schema(schema.simpleString())
assert schema == parse_schema("STRUCT<id:int,scores:ARRAY<bigint>>")
assert schema == parse_schema("STRUCT<id:int,scores:ARRAY<long>>")
def build_schema(self, metadata, fileconfig):
data_types = metadata.data_types
derived_columns = fileconfig.get('derived_columns')
if derived_columns:
col_num = len(data_types) - len(derived_columns)
else:
col_num = len(data_types)
columns_to_drop = fileconfig.get('columns_to_drop')
if columns_to_drop:
col_num = col_num + len(columns_to_drop.split(','))
schema = StructType()
for i in range(col_num):
schema.add(StructField("_c" + str(i), StringType(), True))
self.logger.info('schema=%s' % schema.simpleString())
return schema
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType
spark = SparkSession \
.builder \
.appName("KConsumer") \
.getOrCreate()
temperatureSchema = StructType().add("day",
"string").add("tempInCelsius", "string")
temperatureSchema.simpleString()
temperature_streaming_df = spark \
.readStream \
.option("sep", ",") \
.schema(temperatureSchema) \
.csv("E://PycharmProjects//pythonProject//data//tempratureData.csv")
temperature_streaming_df.isStreaming
temperature_streaming_df.printSchema()
query = temperature_streaming_df \
.writeStream \
.format("console") \
.outputMode("append") \
.start()
