public void TestSignaturesV2_4_X()
{
var intMemoryStream = new MemoryStream <int>(_spark);
StreamingQuery sq = intMemoryStream
.ToDF()
.WriteStream()
.QueryName("testQuery")
.Format("console")
.Trigger(Trigger.Once())
.Start();
sq.AwaitTermination();
Assert.IsType <bool>(sq.AwaitTermination(10));
Assert.IsType <string>(sq.Name);
Assert.IsType <string>(sq.Id);
Assert.IsType <string>(sq.RunId);
Assert.IsType <bool>(sq.IsActive());
sq.Explain();
Assert.Null(sq.Exception());
sq.Stop();
}
static void Main(string[] args)
{
var host = "localhost";
var port = 9999;
SparkSession spark = SparkSession
.Builder()
.AppName("Emotion_Prediction")
.GetOrCreate();
DataFrame lines = spark
.ReadStream()
.Format("socket")
.Option("host", host)
.Option("port", port)
.Load();
Func <Column, Column> udfArray =
Udf <string, string[]>((str) => new string[] { str, " => " + Predict(str) });
DataFrame arrayDf = lines.Select(Explode(udfArray(lines["value"])));
StreamingQuery query = arrayDf
.WriteStream()
.Format("console")
.Start();
query.AwaitTermination();
}
public void Run(string[] args)
{
string servidoresKafka = args[0];
string topico = args[1];
string modelo = args[2];
// Obtém a referência ao contexto de execução do Spark
SparkSession spark = SparkSession
.Builder()
.AppName("Exemplo Streaming com Kafka")
.GetOrCreate();
// Criando um dataframe pra receber dados do Kafka
DataFrame df = spark
.ReadStream()
.Format("kafka")
.Option("kafka.bootstrap.servers", servidoresKafka)
.Option("subscribe", topico)
.Load()
.SelectExpr("CAST(value AS STRING)");
/* Criando schema pra validar o JSON que virá nas mensagens do Kafka
* Exemplo do JSON:
* {
* "cliente": "Fulano",
* "produto": "Mochila",
* "opiniao": "Muito boa!"
* }
*/
var schema = new StructType(new[]
{
new StructField("cliente", new StringType()),
new StructField("produto", new StringType()),
new StructField("opiniao", new StringType())
}); // struct<cliente:string,produto:string,valor_total:float>
// Fazendo o parse do JSON pra um array ...
df = df.WithColumn("json", Functions.FromJson(
df.Col("value"),
schema.SimpleString)
)
.Select("json.*"); // ... e retornando todas as colunas do array como um novo dataframe
//Registrando uma função personalizada pra ser usada no dataframe
spark.Udf().Register <string, float>("AnaliseDeSentimento",
(texto) => AnalisarSentimento(texto, modelo));
// Criando nova coluna nota com o resultado da análise de sentimento
df = df.WithColumn("nota", Functions.CallUDF("AnaliseDeSentimento", df.Col("opiniao")));
// Colocando o streaming pra funcionar
StreamingQuery query = df
.WriteStream()
.OutputMode(OutputMode.Append)
.Format("console")
//.Trigger(Trigger.Continuous(2000))
//.Foreach(new RedisForeachWriter())
.Start();
query.AwaitTermination(); // Necessário pra deixar a aplcação no ar para processar os dados
}
static void Main(string[] args)
{
SparkSession ss =
SparkSession
.Builder()
.AppName(".NET for Spark Streaming")
.GetOrCreate();
DataFrame stream =
ss
.ReadStream()
.Format("socket")
.Option("host", "localhost")
.Option("port", 9000)
.Load();
DataFrame grade =
stream
.Select(Col("value"));
StreamingQuery query =
grade
.WriteStream()
.OutputMode(OutputMode.Append)
.Format("console")
.Start();
query.AwaitTermination();
}
public static void RunSparkStream(string streamInputPath)
{
var foreachWriter = new TestForeachWriter();
SparkSession spark = SparkSession
.Builder()
.AppName("itur")
.GetOrCreate();
var mySchema = new Microsoft.Spark.Sql.Types.StructType(new[]
{
new StructField("IturCode", new Microsoft.Spark.Sql.Types.StringType()),
new StructField("IturERP", new Microsoft.Spark.Sql.Types.StringType()),
new StructField("QuantityEdit", new Microsoft.Spark.Sql.Types.StringType()),
new StructField("PartialQuantity", new Microsoft.Spark.Sql.Types.StringType())
});
DataFrame lines = spark
.ReadStream()
.Schema(mySchema)
.Csv(streamInputPath);
s_query = lines
.WriteStream()
.Foreach(foreachWriter)
.Trigger(Trigger.ProcessingTime(5000))
.Start();
s_query.AwaitTermination();
}
protected override Task ExecuteAsync(CancellationToken stoppingToken)
{
while (!stoppingToken.IsCancellationRequested)
{
StreamingQuery query = input.WriteStream()
.OutputMode(Microsoft.Spark.Sql.Streaming.OutputMode.Append)
.Format("console")
.Start();
query.AwaitTermination();
}
return(Task.CompletedTask);
}
public void Run(string[] args)
{
if (args.Length != 3)
{
Console.Error.WriteLine(
"Usage: SentimentAnalysisStream <host> <port> <model path>");
Environment.Exit(1);
}
// Create Spark Session
SparkSession spark = SparkSession
.Builder()
.AppName("Streaming Sentiment Analysis")
.GetOrCreate();
// Setup stream connection info
string hostname = args[0];
string port = args[1];
// Read streaming data into DataFrame
DataFrame words = spark
.ReadStream()
.Format("socket")
.Option("host", hostname)
.Option("port", port)
.Load();
// Use ML.NET in a UDF to evaluate each incoming entry
spark.Udf().Register <string, bool>(
"MLudf",
input => Sentiment(input, args[2]));
// Use Spark SQL to call ML.NET UDF
// Display results of sentiment analysis on each entry
words.CreateOrReplaceTempView("WordsSentiment");
DataFrame sqlDf = spark
.Sql("SELECT WordsSentiment.value, MLudf(WordsSentiment.value) FROM WordsSentiment");
// Handle data continuously as it arrives
StreamingQuery query = sqlDf
.WriteStream()
.Format("console")
.Start();
query.AwaitTermination();
}
public static void Main(string[] args)
{
var spark = SparkSession.Builder()
.AppName("meuovo")
.GetOrCreate();
var input = spark.ReadStream()
.Format("kafka")
.Option("kafka.bootstrap.servers", "localhost:9092")
.Option("subscribe", "b7f45352-6abf-436b-9c4a-98141699728c")
.Load()
.SelectExpr("CAST(value AS STRING)");
StreamingQuery query = input.WriteStream()
.OutputMode(OutputMode.Append)
.Format("console")
.Start();
query.AwaitTermination();
}
public void Run(string[] args)
{
// Default to running on localhost:9999
string hostname = "localhost";
int port = 9999;
// User designated their own host and port
if (args.Length == 2)
{
hostname = args[0];
port = int.Parse(args[1]);
}
SparkSession spark = SparkSession
.Builder()
.AppName("Streaming example with a UDF")
.GetOrCreate();
DataFrame lines = spark
.ReadStream()
.Format("socket")
.Option("host", hostname)
.Option("port", port)
.Load();
// UDF to produce an array
// Array includes: 1) original string 2) original string + length of original string
Func <Column, Column> udfArray =
Udf <string, string[]>((str) => new string[] { str, $"{str} {str.Length}" });
DataFrame arrayDF = lines.Select(Explode(udfArray(lines["value"])));
// Process and display each incoming line
StreamingQuery query = arrayDF
.WriteStream()
.Format("console")
.Start();
query.AwaitTermination();
}
public void Run(string[] args)
{
string servidoresKafka = args[0];
string connectionString = args.Length > 1 ? args[1] : string.Empty;
// Obtém a referência ao contexto de execução do Spark
SparkSession spark = SparkSession
.Builder()
.AppName("Credit Card Category")
.GetOrCreate();
spark.Conf().Set("spark.sql.shuffle.partitions", "1"); // sem essa configuração, cada stage ficou com 200 tasks, o que levou uns 4 minutos pra cada batch executar
// Criando um dataframe pra receber dados do Kafka
DataFrame df = spark
.ReadStream()
.Format("kafka")
.Option("kafka.bootstrap.servers", servidoresKafka)
.Option("subscribe", "transactions")
.Load()
.SelectExpr("CAST(value AS STRING)");
/* Criando schema pra validar o JSON que virá nas mensagens do Kafka
* Exemplo do JSON:
* {
* "transaction":"431",
* "number":"0015-0000-0000-0000",
* "lat":-23.1618,
* "lng":-46.47201,
* "amount":91.01487,
* "category":"pets",
* "eventTime":"2021-01-05T19:07:19.3888"
* }
*/
var schema = new StructType(new[]
{
new StructField("transaction", new StringType()),
new StructField("number", new StringType()),
new StructField("lat", new DoubleType()),
new StructField("lng", new DoubleType()),
new StructField("amount", new DoubleType()),
new StructField("category", new StringType()),
new StructField("eventTime", new TimestampType())
});
// Fazendo o parse do JSON pra um array ...
df = df.WithColumn("json", FromJson(
Col("value"),
schema.SimpleString)
)
.Select("json.*"); // ... e retornando todas as colunas do array como um novo dataframe
// Colocando um limite de 7 minutos para receber os eventos atrasados
df = df.WithWatermark("eventTime", "7 minutes");
// Somando os valores gastos, agrupando por categoria e por janelas de 2 minutos que se iniciam a cada 1 minuto
df = df.GroupBy(Window(Col("eventTime"), "2 minutes", "1 minutes"), Col("category"))
.Sum("amount").WithColumnRenamed("sum(amount)", "total")
.Select(Col("window.start"), Col("window.end"), Col("category"), Col("total"));
// Colocando o streaming pra funcionar e gravando os dados retornados
StreamingQuery query = df
.WriteStream()
.Format("console")
.OutputMode(OutputMode.Update)
//.Foreach(new MySQLForeachWriter(connectionString)) // Descomentar pra gravar em banco de dados
.Start();
query.AwaitTermination();
}
public void Run(string[] args)
{
string kafkaBrokers = args[0];
double maxSpeed = double.Parse(args[1]);
// Obtém a referência ao contexto de execução do Spark
SparkSession spark = SparkSession
.Builder()
.AppName("Credit Card Fraud")
.GetOrCreate();
spark.Conf().Set("spark.sql.shuffle.partitions", "1"); // sem essa configuração, cada stage ficou com 200 tasks, o que levou uns 4 minutos pra cada batch executar
// Criando um dataframe pra receber dados do Kafka
DataFrame df = spark
.ReadStream()
.Format("kafka")
.Option("kafka.bootstrap.servers", kafkaBrokers)
.Option("subscribe", "transactions")
.Load()
.SelectExpr("CAST(value AS STRING)");
/* Criando schema pra validar o JSON que virá nas mensagens do Kafka
* Exemplo do JSON:
* {
* "transaction":"431",
* "number":"0015-0000-0000-0000",
* "lat":-23.1618,
* "lng":-46.47201,
* "amount":91.01487,
* "category":"pets",
* "eventTime":"2021-01-05T19:07:19.3888"
* }
*/
var schema = new StructType(new[]
{
new StructField("transaction", new StringType()),
new StructField("number", new StringType()),
new StructField("lat", new DoubleType()),
new StructField("lng", new DoubleType()),
new StructField("amount", new DoubleType()),
new StructField("category", new StringType()),
new StructField("eventTime", new TimestampType())
});
// Fazendo o parse do JSON pra um array ...
df = df.WithColumn("json", FromJson(
df.Col("value"),
schema.SimpleString)
)
.Select("json.*"); // ... e retornando todas as colunas do array como um novo dataframe
// Gerando dois dataframes distintos para poder fazer o join e analisar a correção entre as transações
DataFrame df1 = df
.WithWatermark("eventTime", "7 minutes");
DataFrame df2 = df
.WithColumnRenamed("transaction", "transaction2")
.WithColumnRenamed("lat", "lat2")
.WithColumnRenamed("lng", "lng2")
.WithColumnRenamed("eventTime", "eventTime2")
.WithWatermark("eventTime2", "7 minutes");
// Efetuando o join para verificar a correlação de transações dos cartões de crédito
DataFrame dfJoin = df1.Join(df2,
df1.Col("number").EqualTo(df2.Col("number"))
.And(Col("transaction").NotEqual(Col("transaction2")))
.And(Col("eventTime2").Between(Col("eventTime"), Col("eventTime") + Expr("interval 5 minutes")))
);
//Registrando uma função personalizada pra ser usada no dataframe
spark.Udf().Register <double, double, double, double, double>("CalculateDistance", (lat1, lng1, lat2, lng2) => CalculateDistance(lat1, lng1, lat2, lng2));
spark.Udf().Register <double, Timestamp, Timestamp, double>("CalculateSpeed", (dist, eventTime, eventTime2) => CalculateSpeed(dist, eventTime, eventTime2));
// Criando novas colunas para armazenar a execução do código da UDF
dfJoin = dfJoin.WithColumn("dist", CallUDF("CalculateDistance", Col("lat"), Col("lng"), Col("lat2"), Col("lng2")));
dfJoin = dfJoin.WithColumn("speed", CallUDF("CalculateSpeed", Col("dist"), Col("eventTime"), Col("eventTime2")));
// Filtrando as transações que tiverem a velocidade acima do esperado (parâmetro "maxSpeed")
dfJoin = dfJoin.Where(Col("speed").Gt(maxSpeed));
// Colocando o streaming pra funcionar
StreamingQuery query = dfJoin
.WriteStream()
.Format("console")
.Option("truncate", "false")
.OutputMode(OutputMode.Append)
.Start();
query.AwaitTermination();
}