def _online_fg_to_avro(self, feature_group, dataframe):
"""Packs all features into named struct to be serialized to single avro/binary
column. And packs primary key into arry to be serialized for partitioning.
"""
return dataframe.select([
# be aware: primary_key array should always be sorted
to_avro(
concat(*[
col(f).cast("string")
for f in sorted(feature_group.primary_key)
])).alias("key"),
to_avro(
struct([
field["name"] for field in json.loads(
feature_group.avro_schema)["fields"]
]),
feature_group._get_encoded_avro_schema(),
).alias("value"),
])
def _encode_complex_features(self, feature_group, dataframe):
"""Encodes all complex type features to binary using their avro type as schema."""
return dataframe.select([
field["name"] if field["name"]
not in feature_group.get_complex_features() else to_avro(
field["name"],
feature_group._get_feature_avro_schema(field["name"])).alias(
field["name"])
for field in json.loads(feature_group.avro_schema)["fields"]
])
"header",
True).load("dbfs:/databricks-datasets/nyctaxi/tripdata/green/*2019*.csv.gz"
).orderBy(col("lpep_pickup_datetime").asc()))
# COMMAND ----------
green_2019_df.count()
# COMMAND ----------
display(green_2019_df)
# COMMAND ----------
eh_green_avro_df = green_2019_df.select(
to_avro(struct(col("*"))).alias("body"))
# COMMAND ----------
display(eh_green_avro_df)
# COMMAND ----------
green_avro_schema = """
{
"type":"record",
"name":"topLevelRecord",
"fields":
[
{"name":"VendorID","type":["int","null"]},
{"name":"lpep_pickup_datetime","type":["string","null"]},
explode_df = value_df.selectExpr("value.InvoiceNumber", "value.CreatedTime", "value.StoreID",
"value.PosID", "value.CustomerType", "value.CustomerCardNo", "value.DeliveryType",
"value.DeliveryAddress.City",
"value.DeliveryAddress.State", "value.DeliveryAddress.PinCode",
"explode(value.InvoiceLineItems) as LineItem")
flattened_df = explode_df \
.withColumn("ItemCode", expr("LineItem.ItemCode")) \
.withColumn("ItemDescription", expr("LineItem.ItemDescription")) \
.withColumn("ItemPrice", expr("LineItem.ItemPrice")) \
.withColumn("ItemQty", expr("LineItem.ItemQty")) \
.withColumn("TotalValue", expr("LineItem.TotalValue")) \
.drop("LineItem")
kafka_target_df = flattened_df.select(expr("InvoiceNumber as key"),
to_avro(struct("*")).alias("value"))
invoice_writer_query = kafka_target_df \
.writeStream \
.queryName("Flattened Invoice Writer") \
.format("kafka") \
.option("kafka.bootstrap.servers", "localhost:9092") \
.option("topic", "invoice-items") \
.outputMode("append") \
.option("checkpointLocation", "chk-point-dir") \
.start()
logger.info("Start Writer Query")
invoice_writer_query.awaitTermination()
# flatten the struct to a normal DataFrame
df3 = df2.select(*(df2.columns), col("value2.*")).drop('value2')
print('df3', df3)
# df3 = df2.withColumn('value', to_avro("value", stock_schema))
# print('df2', df2)
df3.createOrReplaceTempView("data")
df4 = spark.sql("""
SELECT key, cast(NAMED_STRUCT('event_time', event_time, 'symbol', symbol, 'price', price, 'quantity', quantity) as string) AS value
FROM data
""")
print('df4', df4)
df5 = df4.selectExpr("key", to_avro("value", stock_schema))
print('df5', df5)
# query = (df1.writeStream
# .outputMode("append")
# .format("console")
# .option("truncate", False)
# .start()
# )
# query.awaitTermination()
query = (df5.writeStream.format("kafka").option(
"kafka.bootstrap.servers",
brokers).option("topic", "stocks-avro").option("checkpointLocation",
"/tmp").start())
sqlContext = SQLContext(spark.sparkContext)
spark.sparkContext.setLogLevel('WARN')
staging_data = spark \
.readStream \
.format("parquet") \
.schema(spark.read.parquet("D:\\s3\\bkt-staging-data").schema) \
.option("path", "D:\\s3\\bkt-staging-data") \
.load()
staging_data.createOrReplaceTempView("evento")
aggregated_data = sqlContext.sql("SELECT payload.data.codigo_produto_operacional, COUNT(*) as quantidade_eventos_transmitidos, COUNT(case when payload.data.codigo_empresa = 341 then 1 else null end) as quantidade_eventos_transmitidos_sucesso, COUNT(case when payload.data.codigo_empresa = 350 then 1 else null end) as quantidade_eventos_transmitidos_erro FROM evento GROUP BY payload.data.codigo_produto_operacional") \
.withColumn("data", struct("*")) \
.withColumn("value", concat(lit(magic_byte), lit(id_bytes), to_avro(struct("data"), str(latest_schema)))) \
.withColumn("headers ",
array(
struct(lit("specversion").alias("key"), lit("1").cast("binary").alias("value")),
struct(lit("type").alias("key"), lit("").cast("binary").alias("value")),
struct(lit("source").alias("key"), lit("urn:sigla:gerar-relatorio-transmissao-job").cast("binary").alias("value")),
struct(lit("id").alias("key"), expr("uuid()").cast("binary").alias("value")),
struct(lit("time").alias("key"), date_format(current_timestamp(), "yyyy-MM-dd'T'HH:mm:ss.SSS'Z'").cast("binary").alias("value")),
struct(lit("messageversion").alias("key"), lit("1").cast("binary").alias("value")),
struct(lit("transactionid").alias("key"), lit("").cast("binary").alias("value")),
struct(lit("correlationid").alias("key"), lit("").cast("binary").alias("value")),
struct(lit("datacontenttype").alias("key"), lit("application/avro").cast("binary").alias("value"))
)
) \
.writeStream \
.format("console") \
{"name":"tip_amount","type":["double","null"]},
{"name":"tolls_amount","type":["double","null"]},
{"name":"ehail_fee","type":["double","null"]},
{"name":"improvement_surcharge","type":["double","null"]},
{"name":"total_amount","type":["double","null"]},
{"name":"payment_type","type":["int","null"]},
{"name":"trip_type","type":["int","null"]},
{"name":"congestion_surcharge","type":["double","null"]}
]
}
"""
# COMMAND ----------
eh_green_kafka_df = green_2019_df.select(
to_avro(struct(col("lpep_pickup_datetime"))).alias("key"),
to_avro(struct(col("*"))).alias("value")
)
# COMMAND ----------
display(eh_green_kafka_df)
# COMMAND ----------
test_read_kafka_df = eh_green_kafka_df.select(from_avro(col("value"),green_avro_schema).alias("value"))
display(test_read_kafka_df)
# COMMAND ----------
(