How to join two Spark Structured Streams?
0
Is it possible to join two Spark Structured Streams in Spark 2.2.1? I found a lot of issues with doing very simple manipulations in Spark Structured Streaming. The documentation and number of examples seem very limited to me.
I have two sources of streaming data:
persons.json:
[
{"building_id": 70, "id": 21, "latitude": 41.20, "longitude": 2.2, "timestamp": 1532609003},
{"building_id": 70, "id": 15, "latitude": 41.24, "longitude": 2.3, "timestamp": 1532609005},
{"building_id": 71, "id": 11, "latitude": 41.28, "longitude": 2.1, "timestamp": 1532609005}
]
machines.json
[
{"building_id": 70, "mid": 222, "latitude": 42.1, "longitude": 2.11}
]
The goal is to get a merged DataFrame with latitude and longitude of persons and machines. I need it in order to estimate the distance between them in real time:
building_id id mid latitude longitude latitude_machine longitude_machine
70 21 222 41.20 2.2 42.1 2.11
# ...
If it's impossible to join two streams, then I would really appreciate some recommendation of a possible workaround.
Code:
spark = SparkSession
.builder
.appName("Test")
.master("local[2]")
.getOrCreate()
schema_persons = StructType([
StructField("building_id", IntegerType()),
StructField("id", IntegerType()),
StructField("latitude", DoubleType()),
StructField("longitude", DoubleType()),
StructField("timestamp", LongType())
])
schema_machines = StructType([
StructField("building_id", IntegerType()),
StructField("mid", IntegerType()),
StructField("latitude", DoubleType()),
StructField("longitude", DoubleType())
])
df_persons = spark
.readStream
.format("json")
.schema(schema_persons)
.load("data/persons")
df_machines = spark
.readStream
.format("json")
.schema(schema_machines)
.load("data/machines")
.withColumnRenamed("latitude", "latitude_machine")
.withColumnRenamed("longitude", "longitude_machine")
df_joined = df_persons
.join(df_machines, ["building_id"], "left")
query_persons = df_persons
.writeStream
.format('console')
.start()
query_machines = df_machines
.writeStream
.format('console')
.start()
query_persons.awaitTermination()
query_machines.awaitTermination()
python apache-spark pyspark spark-structured-streaming
asked Nov 23 '18 at 20:07
MozimakiMozimaki
374
add a comment |
0
Is it possible to join two Spark Structured Streams in Spark 2.2.1? I found a lot of issues with doing very simple manipulations in Spark Structured Streaming. The documentation and number of examples seem very limited to me.
I have two sources of streaming data:
persons.json:
[
{"building_id": 70, "id": 21, "latitude": 41.20, "longitude": 2.2, "timestamp": 1532609003},
{"building_id": 70, "id": 15, "latitude": 41.24, "longitude": 2.3, "timestamp": 1532609005},
{"building_id": 71, "id": 11, "latitude": 41.28, "longitude": 2.1, "timestamp": 1532609005}
]
machines.json
[
{"building_id": 70, "mid": 222, "latitude": 42.1, "longitude": 2.11}
]
The goal is to get a merged DataFrame with latitude and longitude of persons and machines. I need it in order to estimate the distance between them in real time:
building_id id mid latitude longitude latitude_machine longitude_machine
70 21 222 41.20 2.2 42.1 2.11
# ...
If it's impossible to join two streams, then I would really appreciate some recommendation of a possible workaround.
Code:
spark = SparkSession
.builder
.appName("Test")
.master("local[2]")
.getOrCreate()
schema_persons = StructType([
StructField("building_id", IntegerType()),
StructField("id", IntegerType()),
StructField("latitude", DoubleType()),
StructField("longitude", DoubleType()),
StructField("timestamp", LongType())
])
schema_machines = StructType([
StructField("building_id", IntegerType()),
StructField("mid", IntegerType()),
StructField("latitude", DoubleType()),
StructField("longitude", DoubleType())
])
df_persons = spark
.readStream
.format("json")
.schema(schema_persons)
.load("data/persons")
df_machines = spark
.readStream
.format("json")
.schema(schema_machines)
.load("data/machines")
.withColumnRenamed("latitude", "latitude_machine")
.withColumnRenamed("longitude", "longitude_machine")
df_joined = df_persons
.join(df_machines, ["building_id"], "left")
query_persons = df_persons
.writeStream
.format('console')
.start()
query_machines = df_machines
.writeStream
.format('console')
.start()
query_persons.awaitTermination()
query_machines.awaitTermination()
python apache-spark pyspark spark-structured-streaming
asked Nov 23 '18 at 20:07
MozimakiMozimaki
374
In general stream-to-stream joins are supported in the latest versions (2.3, 2.4), but require watermark at least at on side - see the join matrix. If you're looking for concrete examplesStreamingJoinSuitewould be the place to go. In 2.2 however, joins between streaming datasets are not supported;
– user6910411
Nov 23 '18 at 20:34
@user6910411: Thanks. But what is the workaround in Spark 2.2? Just nothing to do?
– Mozimaki
Nov 24 '18 at 10:43
Other than upgrade? Not really.
– user6910411
Nov 24 '18 at 21:29
add a comment |
0
0
0
Is it possible to join two Spark Structured Streams in Spark 2.2.1? I found a lot of issues with doing very simple manipulations in Spark Structured Streaming. The documentation and number of examples seem very limited to me.
I have two sources of streaming data:
persons.json:
[
{"building_id": 70, "id": 21, "latitude": 41.20, "longitude": 2.2, "timestamp": 1532609003},
{"building_id": 70, "id": 15, "latitude": 41.24, "longitude": 2.3, "timestamp": 1532609005},
{"building_id": 71, "id": 11, "latitude": 41.28, "longitude": 2.1, "timestamp": 1532609005}
]
machines.json
[
{"building_id": 70, "mid": 222, "latitude": 42.1, "longitude": 2.11}
]
The goal is to get a merged DataFrame with latitude and longitude of persons and machines. I need it in order to estimate the distance between them in real time:
building_id id mid latitude longitude latitude_machine longitude_machine
70 21 222 41.20 2.2 42.1 2.11
# ...
If it's impossible to join two streams, then I would really appreciate some recommendation of a possible workaround.
Code:
spark = SparkSession
.builder
.appName("Test")
.master("local[2]")
.getOrCreate()
schema_persons = StructType([
StructField("building_id", IntegerType()),
StructField("id", IntegerType()),
StructField("latitude", DoubleType()),
StructField("longitude", DoubleType()),
StructField("timestamp", LongType())
])
schema_machines = StructType([
StructField("building_id", IntegerType()),
StructField("mid", IntegerType()),
StructField("latitude", DoubleType()),
StructField("longitude", DoubleType())
])
df_persons = spark
.readStream
.format("json")
.schema(schema_persons)
.load("data/persons")
df_machines = spark
.readStream
.format("json")
.schema(schema_machines)
.load("data/machines")
.withColumnRenamed("latitude", "latitude_machine")
.withColumnRenamed("longitude", "longitude_machine")
df_joined = df_persons
.join(df_machines, ["building_id"], "left")
query_persons = df_persons
.writeStream
.format('console')
.start()
query_machines = df_machines
.writeStream
.format('console')
.start()
query_persons.awaitTermination()
query_machines.awaitTermination()
python apache-spark pyspark spark-structured-streaming
asked Nov 23 '18 at 20:07
MozimakiMozimaki
374
Is it possible to join two Spark Structured Streams in Spark 2.2.1? I found a lot of issues with doing very simple manipulations in Spark Structured Streaming. The documentation and number of examples seem very limited to me.
I have two sources of streaming data:
persons.json:
[
{"building_id": 70, "id": 21, "latitude": 41.20, "longitude": 2.2, "timestamp": 1532609003},
{"building_id": 70, "id": 15, "latitude": 41.24, "longitude": 2.3, "timestamp": 1532609005},
{"building_id": 71, "id": 11, "latitude": 41.28, "longitude": 2.1, "timestamp": 1532609005}
]
machines.json
[
{"building_id": 70, "mid": 222, "latitude": 42.1, "longitude": 2.11}
]
The goal is to get a merged DataFrame with latitude and longitude of persons and machines. I need it in order to estimate the distance between them in real time:
building_id id mid latitude longitude latitude_machine longitude_machine
70 21 222 41.20 2.2 42.1 2.11
# ...
If it's impossible to join two streams, then I would really appreciate some recommendation of a possible workaround.
Code:
spark = SparkSession
.builder
.appName("Test")
.master("local[2]")
.getOrCreate()
schema_persons = StructType([
StructField("building_id", IntegerType()),
StructField("id", IntegerType()),
StructField("latitude", DoubleType()),
StructField("longitude", DoubleType()),
StructField("timestamp", LongType())
])
schema_machines = StructType([
StructField("building_id", IntegerType()),
StructField("mid", IntegerType()),
StructField("latitude", DoubleType()),
StructField("longitude", DoubleType())
])
df_persons = spark
.readStream
.format("json")
.schema(schema_persons)
.load("data/persons")
df_machines = spark
.readStream
.format("json")
.schema(schema_machines)
.load("data/machines")
.withColumnRenamed("latitude", "latitude_machine")
.withColumnRenamed("longitude", "longitude_machine")
df_joined = df_persons
.join(df_machines, ["building_id"], "left")
query_persons = df_persons
.writeStream
.format('console')
.start()
query_machines = df_machines
.writeStream
.format('console')
.start()
query_persons.awaitTermination()
query_machines.awaitTermination()
python apache-spark pyspark spark-structured-streaming
python apache-spark pyspark spark-structured-streaming
asked Nov 23 '18 at 20:07
MozimakiMozimaki
374
asked Nov 23 '18 at 20:07
MozimakiMozimaki
374
edited Nov 23 '18 at 20:27
Mozimaki
asked Nov 23 '18 at 20:07
MozimakiMozimaki
374
asked Nov 23 '18 at 20:07
MozimakiMozimaki
374
asked Nov 23 '18 at 20:07
MozimakiMozimaki
374
374
In general stream-to-stream joins are supported in the latest versions (2.3, 2.4), but require watermark at least at on side - see the join matrix. If you're looking for concrete examplesStreamingJoinSuitewould be the place to go. In 2.2 however, joins between streaming datasets are not supported;
– user6910411
Nov 23 '18 at 20:34
@user6910411: Thanks. But what is the workaround in Spark 2.2? Just nothing to do?
– Mozimaki
Nov 24 '18 at 10:43
Other than upgrade? Not really.
– user6910411
Nov 24 '18 at 21:29
add a comment |
In general stream-to-stream joins are supported in the latest versions (2.3, 2.4), but require watermark at least at on side - see the join matrix. If you're looking for concrete examplesStreamingJoinSuitewould be the place to go. In 2.2 however, joins between streaming datasets are not supported;
– user6910411
Nov 23 '18 at 20:34
@user6910411: Thanks. But what is the workaround in Spark 2.2? Just nothing to do?
– Mozimaki
Nov 24 '18 at 10:43
Other than upgrade? Not really.
– user6910411
Nov 24 '18 at 21:29
In general stream-to-stream joins are supported in the latest versions (2.3, 2.4), but require watermark at least at on side - see the join matrix. If you're looking for concrete examples
StreamingJoinSuite would be the place to go. In 2.2 however, joins between streaming datasets are not supported;– user6910411
Nov 23 '18 at 20:34
In general stream-to-stream joins are supported in the latest versions (2.3, 2.4), but require watermark at least at on side - see the join matrix. If you're looking for concrete examples
StreamingJoinSuite would be the place to go. In 2.2 however, joins between streaming datasets are not supported;– user6910411
Nov 23 '18 at 20:34
@user6910411: Thanks. But what is the workaround in Spark 2.2? Just nothing to do?
– Mozimaki
Nov 24 '18 at 10:43
@user6910411: Thanks. But what is the workaround in Spark 2.2? Just nothing to do?
– Mozimaki
Nov 24 '18 at 10:43
Other than upgrade? Not really.
– user6910411
Nov 24 '18 at 21:29
Other than upgrade? Not really.
– user6910411
Nov 24 '18 at 21:29
add a comment |
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In general stream-to-stream joins are supported in the latest versions (2.3, 2.4), but require watermark at least at on side - see the join matrix. If you're looking for concrete examples
StreamingJoinSuitewould be the place to go. In 2.2 however, joins between streaming datasets are not supported;– user6910411
Nov 23 '18 at 20:34
@user6910411: Thanks. But what is the workaround in Spark 2.2? Just nothing to do?
– Mozimaki
Nov 24 '18 at 10:43
Other than upgrade? Not really.
– user6910411
Nov 24 '18 at 21:29