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Update documentation - add spark session and simple data #684

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Update documentation - add spark session and simple data #684

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379e80d
add spark seesion and data
yamin-msft May 7, 2024
b29f7f3
Merge branch 'master' into updateinstruction
yamin-msft May 7, 2024
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40 changes: 40 additions & 0 deletions docs/PySpark/structured-streaming-pyspark.md
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Original file line number Diff line number Diff line change
Expand Up @@ -237,6 +237,11 @@ ehConf = {
'eventhubs.connectionString' : connectionString
}

# build a spark session
spark = SparkSession.builder \
.appName("EventHubWriter") \
.getOrCreate()

df = spark \
.readStream \
.format("eventhubs") \
Expand All @@ -253,6 +258,11 @@ ehConf = {
'eventhubs.connectionString' : connectionString
}

# build a spark session
spark = SparkSession.builder \
.appName("EventHubWriter") \
.getOrCreate()

# Simple batch query
df = spark \
.read \
Expand Down Expand Up @@ -310,6 +320,21 @@ ehWriteConf = {
'eventhubs.connectionString' : writeConnectionString
}

# build a spark session
spark = SparkSession.builder \
.appName("EventHubWriter") \
.getOrCreate()

# create sample data
columns = ["body", "num"]
data = [("hello", "10"), ("azure", "10"), ("data", "10")]

for a in range(50001):
data.append(("hello", "2"))

rdd = spark.sparkContext.parallelize(data)
df = spark.createDataFrame(rdd, columns)

# Write body data from a DataFrame to EventHubs. Events are distributed across partitions using round-robin model.
ds = df \
.select("body") \
Expand Down Expand Up @@ -338,6 +363,21 @@ ehWriteConf = {
'eventhubs.connectionString' : writeConnectionString
}

# build a spark session
spark = SparkSession.builder \
.appName("EventHubWriter") \
.getOrCreate()

# create sample data
columns = ["body", "num"]
data = [("hello", "10"), ("azure", "10"), ("data", "10")]

for a in range(50001):
data.append(("hello", "2"))

rdd = spark.sparkContext.parallelize(data)
df = spark.createDataFrame(rdd, columns)

# Write body data from a DataFrame to EventHubs. Events are distributed across partitions using round-robin model.
ds = df \
.select("body") \
Expand Down
54 changes: 54 additions & 0 deletions docs/structured-streaming-eventhubs-integration.md
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Original file line number Diff line number Diff line change
Expand Up @@ -205,6 +205,14 @@ val connectionString = ConnectionStringBuilder("YOUR.EVENTHUB.COMPATIBLE.ENDPOIN
val connectionString = "Valid EventHubs connection string."
val ehConf = EventHubsConf(connectionString)

// build a spark Session
val spark =
SparkSession
.builder
.appName("Event Hub Spark App")
.config("spark.master", "local")
.getOrCreate()

val df = spark
.readStream
.format("eventhubs")
Expand Down Expand Up @@ -239,6 +247,14 @@ val df = spark
### Creating an Event Hubs Source for Batch Queries

```scala
// build a spark Session
val spark =
SparkSession
.builder
.appName("Event Hub Spark App")
.config("spark.master", "local")
.getOrCreate()

// Simple batch query
val df = spark
.read
Expand Down Expand Up @@ -315,6 +331,25 @@ Users can also provided properties via a `map[string, string]` if they would lik
### Creating an EventHubs Sink for Streaming Queries

```scala
// build a spark Session
val spark =
SparkSession
.builder
.appName("Event Hub Spark App")
.config("spark.master", "local")
.getOrCreate()

// Create sample data
val columns = Seq("body", "num")
var data = Seq(("hello", "10"), ("azure", "10"), ("data", "10"))

for (a <- 0 to 50000) {
data = data :+ ("hello", "2")
}

val rdd = spark.sparkContext.parallelize(data)
val df = spark.createDataFrame(rdd).toDF(columns: _*)

// Write body data from a DataFrame to EventHubs. Events are distributed across partitions using round-robin model.
val ds = df
.select("body")
Expand All @@ -335,6 +370,25 @@ val ds = df
### Writing the output of Batch Queries to EventHubs

```scala
// build a spark Session
val spark =
SparkSession
.builder
.appName("Event Hub Spark App")
.config("spark.master", "local")
.getOrCreate()

// Create sample data
val columns = Seq("body", "num")
var data = Seq(("hello", "10"), ("azure", "10"), ("data", "10"))

for (a <- 0 to 50000) {
data = data :+ ("hello", "2")
}

val rdd = spark.sparkContext.parallelize(data)
val df = spark.createDataFrame(rdd).toDF(columns: _*)

// Write body data from a DataFrame to EventHubs. Events are distributed across partitions using round-robin model.
df.select("body")
.write
Expand Down