Esegui lo streaming di un argomento Kafka in Hive

Apache Kafka è una piattaforma di streaming distribuita open source per pipeline di dati e integrazione dei dati in tempo reale. Fornisce un sistema di streaming efficiente e scalabile per l'utilizzo in una varietà di applicazioni, tra cui:

  • Analisi in tempo reale
  • Elaborazione dei flussi
  • Aggregazione dei log
  • Messaggistica distribuita
  • Streaming di eventi

Obiettivi

  1. Installa Kafka su un cluster Dataproc HA con ZooKeeper (in questo tutorial indicato come "cluster Dataproc Kafka").

  2. Crea dati fittizi dei clienti, quindi pubblicali in un argomento Kafka.

  3. Crea tabelle Hive parquet e ORC in Cloud Storage per ricevere i dati degli argomenti Kafka in streaming.

  4. Invia un job PySpark per abbonarti e trasmettere in streaming l'argomento Kafka in Cloud Storage in formato Parquet e ORC.

  5. Esegui una query sui dati della tabella Hive in streaming per conteggiare i messaggi Kafka in streaming.

Costi

In questo documento vengono utilizzati i seguenti componenti fatturabili di Google Cloud:

Per generare una stima dei costi in base all'utilizzo previsto, utilizza il calcolatore prezzi.

I nuovi utenti di Google Cloud potrebbero avere diritto a una prova senza costi.

Al termine delle attività descritte in questo documento, puoi evitare l'addebito di ulteriori costi eliminando le risorse che hai creato. Per ulteriori informazioni, vedi Pulizia.

Prima di iniziare

Se non l'hai ancora fatto, crea un progetto Google Cloud .

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

  3. Verify that billing is enabled for your Google Cloud project.

  4. Enable the Dataproc, Compute Engine, and Cloud Storage APIs.

    Roles required to enable APIs

    To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

    Enable the APIs

  5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

  6. Verify that billing is enabled for your Google Cloud project.

  7. Enable the Dataproc, Compute Engine, and Cloud Storage APIs.

    Roles required to enable APIs

    To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

    Enable the APIs

    8.
  8. In the Google Cloud console, go to the Cloud Storage Buckets page.

    Go to Buckets

  9. Click Create.
  10. On the Create a bucket page, enter your bucket information. To go to the next step, click Continue.
    1. In the Get started section, do the following:
      • Enter a globally unique name that meets the bucket naming requirements.
      • To add a bucket label, expand the Labels section (), click Add label, and specify a key and a value for your label.
    2. In the Choose where to store your data section, do the following:
      1. Select a Location type.
      2. Choose a location where your bucket's data is permanently stored from the Location type drop-down menu.
      3. To set up cross-bucket replication, select Add cross-bucket replication via Storage Transfer Service and follow these steps:

        Set up cross-bucket replication

        1. In the Bucket menu, select a bucket.

        2. In the Replication settings section, click Configure to configure settings for the replication job.

          The Configure cross-bucket replication pane appears.

          • To filter objects to replicate by object name prefix, enter a prefix that you want to include or exclude objects from, then click Add a prefix.
          • To set a storage class for the replicated objects, select a storage class from the Storage class menu. If you skip this step, the replicated objects will use the destination bucket's storage class by default.
          • Click Done.
    3. In the Choose how to store your data section, do the following:
      1. Select a default storage class for the bucket or Autoclass for automatic storage class management of your bucket's data.
      2. To enable hierarchical namespace, in the Optimize storage for data-intensive workloads section, select Enable hierarchical namespace on this bucket.
    4. In the Choose how to control access to objects section, select whether or not your bucket enforces public access prevention, and select an access control method for your bucket's objects.
    5. In the Choose how to protect object data section, do the following:
      • Select any of the options under Data protection that you want to set for your bucket.
        • To enable soft delete, click the Soft delete policy (For data recovery) checkbox, and specify the number of days you want to retain objects after deletion.
        • To set Object Versioning, click the Object versioning (For version control) checkbox, and specify the maximum number of versions per object and the number of days after which the noncurrent versions expire.
        • To enable the retention policy on objects and buckets, click the Retention (For compliance) checkbox, and then do the following:
          • To enable Object Retention Lock, click the Enable object retention checkbox.
          • To enable Bucket Lock, click the Set bucket retention policy checkbox, and choose a unit of time and a length of time for your retention period.
      • To choose how your object data will be encrypted, expand the Data encryption section (), and select a Data encryption method.
  11. Click Create.

    12. Passaggi del tutorial

    Per creare un cluster Dataproc Kafka per leggere un argomento Kafka in Cloud Storage in formato Parquet o ORC, segui questi passaggi.

    Copia lo script di installazione di Kafka in Cloud Storage

    Lo script dell'kafka.sh azione di inizializzazione installa Kafka su un cluster Dataproc.

    1. Sfoglia il codice. 

      #!/bin/bash
#    Copyright 2015 Google, Inc.
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#    See the License for the specific language governing permissions and
#    limitations under the License.
#
# This script installs Apache Kafka (http://kafka.apache.org) on a Google Cloud
# Dataproc cluster.

set -euxo pipefail

readonly ZOOKEEPER_HOME=/usr/lib/zookeeper
readonly KAFKA_HOME=/usr/lib/kafka
readonly KAFKA_PROP_FILE='/etc/kafka/conf/server.properties'
readonly ROLE="$(/usr/share/google/get_metadata_value attributes/dataproc-role)"
readonly RUN_ON_MASTER="$(/usr/share/google/get_metadata_value attributes/run-on-master || echo false)"
readonly KAFKA_ENABLE_JMX="$(/usr/share/google/get_metadata_value attributes/kafka-enable-jmx || echo false)"
readonly KAFKA_JMX_PORT="$(/usr/share/google/get_metadata_value attributes/kafka-jmx-port || echo 9999)"
readonly INSTALL_KAFKA_PYTHON="$(/usr/share/google/get_metadata_value attributes/install-kafka-python || echo false)"

# The first ZooKeeper server address, e.g., "cluster1-m-0:2181".
ZOOKEEPER_ADDRESS=''
# Integer broker ID of this node, e.g., 0
BROKER_ID=''

function retry_apt_command() {
  cmd="$1"
  for ((i = 0; i < 10; i++)); do
    if eval "$cmd"; then
      return 0
    fi
    sleep 5
  done
  return 1
}

function recv_keys() {
  retry_apt_command "apt-get install -y gnupg2 &&\
                     apt-key adv --keyserver keyserver.ubuntu.com --recv-keys B7B3B788A8D3785C"
}

function update_apt_get() {
  retry_apt_command "apt-get update"
}

function install_apt_get() {
  pkgs="$@"
  retry_apt_command "apt-get install -y $pkgs"
}

function err() {
  echo "[$(date +'%Y-%m-%dT%H:%M:%S%z')]: $@" >&2
  return 1
}

# Returns the list of broker IDs registered in ZooKeeper, e.g., " 0, 2, 1,".
function get_broker_list() {
  ${KAFKA_HOME}/bin/zookeeper-shell.sh "${ZOOKEEPER_ADDRESS}" \
    <<<"ls /brokers/ids" |
    grep '\[.*\]' |
    sed 's/\[/ /' |
    sed 's/\]/,/'
}

# Waits for zookeeper to be up or time out.
function wait_for_zookeeper() {
  for i in {1..20}; do
    if "${ZOOKEEPER_HOME}/bin/zkCli.sh" -server "${ZOOKEEPER_ADDRESS}" ls /; then
      return 0
    else
      echo "Failed to connect to ZooKeeper ${ZOOKEEPER_ADDRESS}, retry ${i}..."
      sleep 5
    fi
  done
  echo "Failed to connect to ZooKeeper ${ZOOKEEPER_ADDRESS}" >&2
  exit 1
}

# Wait until the current broker is registered or time out.
function wait_for_kafka() {
  for i in {1..20}; do
    local broker_list=$(get_broker_list || true)
    if [[ "${broker_list}" == *" ${BROKER_ID},"* ]]; then
      return 0
    else
      echo "Kafka broker ${BROKER_ID} is not registered yet, retry ${i}..."
      sleep 5
    fi
  done
  echo "Failed to start Kafka broker ${BROKER_ID}." >&2
  exit 1
}

function install_and_configure_kafka_server() {
  # Find zookeeper list first, before attempting any installation.
  local zookeeper_client_port
  zookeeper_client_port=$(grep 'clientPort' /etc/zookeeper/conf/zoo.cfg |
    tail -n 1 |
    cut -d '=' -f 2)

  local zookeeper_list
  zookeeper_list=$(grep '^server\.' /etc/zookeeper/conf/zoo.cfg |
    cut -d '=' -f 2 |
    cut -d ':' -f 1 |
    sort |
    uniq |
    sed "s/$/:${zookeeper_client_port}/" |
    xargs echo |
    sed "s/ /,/g")

  if [[ -z "${zookeeper_list}" ]]; then
    # Didn't find zookeeper quorum in zoo.cfg, but possibly workers just didn't
    # bother to populate it. Check if YARN HA is configured.
    zookeeper_list=$(bdconfig get_property_value --configuration_file \
      /etc/hadoop/conf/yarn-site.xml \
      --name yarn.resourcemanager.zk-address 2>/dev/null)
  fi

  # If all attempts failed, error out.
  if [[ -z "${zookeeper_list}" ]]; then
    err 'Failed to find configured Zookeeper list; try "--num-masters=3" for HA'
  fi

  ZOOKEEPER_ADDRESS="${zookeeper_list%%,*}"

  # Install Kafka from Dataproc distro.
  install_apt_get kafka-server || dpkg -l kafka-server ||
    err 'Unable to install and find kafka-server.'

  mkdir -p /var/lib/kafka-logs
  chown kafka:kafka -R /var/lib/kafka-logs

  if [[ "${ROLE}" == "Master" ]]; then
    # For master nodes, broker ID starts from 10,000.
    if [[ "$(hostname)" == *-m ]]; then
      # non-HA
      BROKER_ID=10000
    else
      # HA
      BROKER_ID=$((10000 + $(hostname | sed 's/.*-m-\([0-9]*\)$/\1/g')))
    fi
  else
    # For worker nodes, broker ID is a random number generated less than 10000.
    # 10000 is choosen since the max broker ID allowed being set is 10000.
    BROKER_ID=$((RANDOM % 10000))
  fi
  sed -i 's|log.dirs=/tmp/kafka-logs|log.dirs=/var/lib/kafka-logs|' \
    "${KAFKA_PROP_FILE}"
  sed -i 's|^\(zookeeper\.connect=\).*|\1'${zookeeper_list}'|' \
    "${KAFKA_PROP_FILE}"
  sed -i 's,^\(broker\.id=\).*,\1'${BROKER_ID}',' \
    "${KAFKA_PROP_FILE}"
  echo -e '\nreserved.broker.max.id=100000' >>"${KAFKA_PROP_FILE}"
  echo -e '\ndelete.topic.enable=true' >>"${KAFKA_PROP_FILE}"

  if [[ "${KAFKA_ENABLE_JMX}" == "true" ]]; then
    sed -i '/kafka-run-class.sh/i export KAFKA_JMX_OPTS="-Dcom.sun.management.jmxremote=true -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false -Djava.rmi.server.hostname=localhost -Djava.net.preferIPv4Stack=true"' /usr/lib/kafka/bin/kafka-server-start.sh
    sed -i "/kafka-run-class.sh/i export JMX_PORT=${KAFKA_JMX_PORT}" /usr/lib/kafka/bin/kafka-server-start.sh
  fi

  wait_for_zookeeper

  # Start Kafka.
  service kafka-server restart

  wait_for_kafka
}

function install_kafka_python_package() {
  KAFKA_PYTHON_PACKAGE="kafka-python==2.0.2"
  if [[ "${INSTALL_KAFKA_PYTHON}" != "true" ]]; then
    return
  fi

  if [[ "$(echo "${DATAPROC_IMAGE_VERSION} > 2.0" | bc)" -eq 1 ]]; then
    /opt/conda/default/bin/pip install "${KAFKA_PYTHON_PACKAGE}" || { sleep 10; /opt/conda/default/bin/pip install "${KAFKA_PYTHON_PACKAGE}"; }
  else
    OS=$(. /etc/os-release && echo "${ID}")
    if [[ "${OS}" == "rocky" ]]; then
      yum install -y python2-pip
    else
      apt-get install -y python-pip
    fi
    pip2 install "${KAFKA_PYTHON_PACKAGE}" || { sleep 10; pip2 install "${KAFKA_PYTHON_PACKAGE}"; } || { sleep 10; pip install "${KAFKA_PYTHON_PACKAGE}"; }
  fi
}

function remove_old_backports {
  # This script uses 'apt-get update' and is therefore potentially dependent on
  # backports repositories which have been archived.  In order to mitigate this
  # problem, we will remove any reference to backports repos older than oldstable

  # https://github.com/GoogleCloudDataproc/initialization-actions/issues/1157
  oldstable=$(curl -s https://deb.debian.org/debian/dists/oldstable/Release | awk '/^Codename/ {print $2}');
  stable=$(curl -s https://deb.debian.org/debian/dists/stable/Release | awk '/^Codename/ {print $2}');

  matched_files="$(grep -rsil '\-backports' /etc/apt/sources.list*)"
  if [[ -n "$matched_files" ]]; then
    for filename in "$matched_files"; do
      grep -e "$oldstable-backports" -e "$stable-backports" "$filename" || \
        sed -i -e 's/^.*-backports.*$//' "$filename"
    done
  fi
}

function main() {
  OS=$(. /etc/os-release && echo "${ID}")
  if [[ ${OS} == debian ]] && [[ $(echo "${DATAPROC_IMAGE_VERSION} <= 2.1" | bc -l) == 1 ]]; then
    remove_old_backports
  fi
  recv_keys || err 'Unable to receive keys.'
  update_apt_get || err 'Unable to update packages lists.'
  install_kafka_python_package

  # Only run the installation on workers; verify zookeeper on master(s).
  if [[ "${ROLE}" == 'Master' ]]; then
    service zookeeper-server status ||
      err 'Required zookeeper-server not running on master!'
    if [[ "${RUN_ON_MASTER}" == "true" ]]; then
      # Run installation on masters.
      install_and_configure_kafka_server
    else
      # On master nodes, just install kafka command-line tools and libs but not
      # kafka-server.
      install_apt_get kafka ||
        err 'Unable to install kafka libraries on master!'
    fi
  else
    # Run installation on workers.
    install_and_configure_kafka_server
  fi
}

main

    2. Copia lo script kafka.sh dell'azione di inizializzazione nel bucket Cloud Storage. Questo script installa Kafka su un cluster Dataproc.

      1. Apri Cloud Shell ed esegui questo comando: 

        gcloud storage cp gs://goog-dataproc-initialization-actions-REGION/kafka/kafka.sh gs://BUCKET_NAME/scripts/

        Effettua le seguenti sostituzioni:

        • REGION: kafka.sh è archiviato in bucket pubblici con tag regionali in Cloud Storage. Specifica una regione di Compute Engine geograficamente vicina (ad esempio us-central1).
        • BUCKET_NAME: il nome del bucket Cloud Storage.

    Crea un cluster Dataproc Kafka

    1. Apri Cloud Shell, quindi esegui il seguente comando gcloud dataproc clusters create per creare un cluster HA Dataproc che installa i componenti Kafka e ZooKeeper:

      gcloud dataproc clusters create KAFKA_CLUSTER \
    --project=PROJECT_ID \
    --region=REGION \
    --image-version=2.1-debian11 \
    --num-masters=3 \
    --enable-component-gateway \
    --initialization-actions=gs://BUCKET_NAME/scripts/kafka.sh

      Note:

      • KAFKA_CLUSTER: il nome del cluster, che deve essere univoco all'interno di un progetto. Il nome deve iniziare con una lettera minuscola e può contenere fino a 51 lettere minuscole, numeri e trattini. Non può terminare con un trattino. Il nome di un cluster eliminato può essere riutilizzato.
      • PROJECT_ID: il progetto da associare a questo cluster.
      • REGION: la regione di Compute Engine in cui si troverà il cluster, ad esempio us-central1.
        • Puoi aggiungere il flag facoltativo --zone=ZONE per specificare una zona all'interno della regione specificata, ad esempio us-central1-a. Se non specifichi una zona, la funzionalità di posizionamento automatico delle zone di Dataproc seleziona una zona con la regione specificata.
      • --image-version: per questo tutorial è consigliata la versione immagine di Dataproc 2.1-debian11. Nota: ogni versione dell'immagine contiene un insieme di componenti preinstallati, incluso il componente Hive utilizzato in questo tutorial (vedi Versioni delle immagini Dataproc supportate).
      • --num-master: i nodi master 3 creano un cluster HA. Il componente Zookeeper, richiesto da Kafka, è preinstallato su un cluster HA.
      • --enable-component-gateway: attiva il gateway dei componenti Dataproc.
      • BUCKET_NAME: il nome del bucket Cloud Storage che contiene lo /scripts/kafka.shscript di inizializzazione (vedi Copia lo script di installazione di Kafka in Cloud Storage).

    Crea un argomento Kafka custdata

    Per creare un argomento Kafka sul cluster Kafka Dataproc:

    1. Utilizza l'utilità SSH per aprire una finestra del terminale sulla VM master del cluster.

    2. Crea un argomento Kafka custdata.

      /usr/lib/kafka/bin/kafka-topics.sh \
    --bootstrap-server KAFKA_CLUSTER-w-0:9092 \
    --create --topic custdata

      Note:

      • KAFKA_CLUSTER: inserisci il nome del tuo cluster Kafka. -w-0:9092 indica il broker Kafka in esecuzione sulla porta 9092 sul nodo worker-0.

      • Dopo aver creato l'argomento custdata, puoi eseguire i seguenti comandi:

        # List all topics.
/usr/lib/kafka/bin/kafka-topics.sh \
    --bootstrap-server KAFKA_CLUSTER-w-0:9092 \
    --list

        
# Consume then display topic data.
/usr/lib/kafka/bin/kafka-console-consumer.sh \
    --bootstrap-server KAFKA_CLUSTER-w-0:9092 \
    --topic custdata

        
# Count the number of messages in the topic.
/usr/lib/kafka/bin/kafka-run-class.sh kafka.tools.GetOffsetShell \
    --broker-list KAFKA_CLUSTER-w-0:9092 \
    --topic custdata

        
# Delete topic.
/usr/lib/kafka/bin/kafka-topics.sh \
    --bootstrap-server KAFKA_CLUSTER-w-0:9092 \
    --delete --topic custdata

    Pubblica contenuti nell'argomento Kafka custdata

    Il seguente script utilizza lo strumento kafka-console-producer.sh Kafka per generare dati dei clienti fittizi in formato CSV.

    1. Copia e incolla lo script nel terminale SSH sul nodo master del cluster Kafka. Premi <return> per eseguire lo script.

      for i in {1..10000}; do \
custname="cust name${i}"
uuid=$(dbus-uuidgen)
age=$((45 + $RANDOM % 45))
amount=$(echo "$(( $RANDOM % 99999 )).$(( $RANDOM % 99 ))")
message="${uuid}:${custname},${age},${amount}"
echo ${message}
done | /usr/lib/kafka/bin/kafka-console-producer.sh \
--broker-list KAFKA_CLUSTER-w-0:9092 \
--topic custdata \
--property "parse.key=true" \
--property "key.separator=:"

      Note:

      • KAFKA_CLUSTER: il nome del cluster Kafka.

    2. Esegui questo comando Kafka per verificare che l'argomento custdata contenga 10.000 messaggi.

      /usr/lib/kafka/bin/kafka-run-class.sh kafka.tools.GetOffsetShell \
--broker-list KAFKA_CLUSTER-w-0:9092 \
--topic custdata

      Note:

      • KAFKA_CLUSTER: il nome del cluster Kafka.

      Output previsto: 

      custdata:0:10000

    Crea tabelle Hive in Cloud Storage

    Crea tabelle Hive per ricevere i dati degli argomenti Kafka in streaming. Esegui i seguenti passaggi per creare tabelle Hive cust_parquet (parquet) e cust_orc (ORC) nel bucket Cloud Storage.

    1. Inserisci il tuo BUCKET_NAME nel seguente script, quindi copia e incolla lo script nel terminale SSH sul nodo master del cluster Kafka, poi premi <return> per creare uno script ~/hivetables.hql (Hive Query Language).

      Nel passaggio successivo eseguirai lo script ~/hivetables.hql per creare tabelle Hive parquet e ORC nel bucket Cloud Storage.

      cat > ~/hivetables.hql <<EOF
drop table if exists cust_parquet;
create external table if not exists cust_parquet
(uuid string, custname string, age string, amount string)
row format delimited fields terminated by ','
stored as parquet
location "gs://BUCKET_NAME/tables/cust_parquet";

      
drop table if exists cust_orc;
create external table if not exists cust_orc
(uuid string, custname string, age string, amount string)
row format delimited fields terminated by ','
stored as orc
location "gs://BUCKET_NAME/tables/cust_orc";
EOF

    2. Nel terminale SSH del nodo master del cluster Kafka, invia il job Hive ~/hivetables.hql per creare tabelle Hive cust_parquet (parquet) e cust_orc (ORC) nel bucket Cloud Storage.

      gcloud dataproc jobs submit hive \
    --cluster=KAFKA_CLUSTER \
    --region=REGION \
    -f ~/hivetables.hql

      Note:

      • Il componente Hive è preinstallato sul cluster Dataproc Kafka. Consulta le versioni release 2.1.x per un elenco delle versioni dei componenti Hive incluse nelle immagini 2.1 rilasciate di recente.
      • KAFKA_CLUSTER: il nome del cluster Kafka.
      • REGION: la regione in cui si trova il cluster Kafka.

    Trasmettere in streaming Kafka custdata alle tabelle Hive

    1. Esegui questo comando nel terminale SSH sul nodo master del cluster Kafka per installare la libreria kafka-python. Per trasmettere i dati degli argomenti Kafka a Cloud Storage è necessario un client Kafka. 
      pip install kafka-python

    2. Inserisci il tuo BUCKET_NAME, quindi copia e incolla il seguente codice PySpark nel terminale SSH sul nodo master del cluster Kafka e premi <return> per creare un file streamdata.py.

      Lo script si iscrive all'argomento Kafka custdata, quindi trasmette i dati alle tabelle Hive in Cloud Storage. Il formato di output, che può essere Parquet o ORC, viene passato allo script come parametro.

      cat > streamdata.py <<EOF
#!/bin/python

import sys
from pyspark.sql.functions import *
from pyspark.sql.types import *
from pyspark.sql import SparkSession
from kafka import KafkaConsumer

def getNameFn (data): return data.split(",")[0]
def getAgeFn  (data): return data.split(",")[1]
def getAmtFn  (data): return data.split(",")[2]

def main(cluster, outputfmt):
    spark = SparkSession.builder.appName("APP").getOrCreate()
    spark.sparkContext.setLogLevel("WARN")
    Logger = spark._jvm.org.apache.log4j.Logger
    logger = Logger.getLogger(__name__)

    rows = spark.readStream.format("kafka") \
    .option("kafka.bootstrap.servers", cluster+"-w-0:9092").option("subscribe", "custdata") \
    .option("startingOffsets", "earliest")\
    .load()

    getNameUDF = udf(getNameFn, StringType())
    getAgeUDF  = udf(getAgeFn,  StringType())
    getAmtUDF  = udf(getAmtFn,  StringType())

    logger.warn("Params passed in are cluster name: " + cluster + "  output format(sink): " + outputfmt)

    query = rows.select (col("key").cast("string").alias("uuid"),\
        getNameUDF      (col("value").cast("string")).alias("custname"),\
        getAgeUDF       (col("value").cast("string")).alias("age"),\
        getAmtUDF       (col("value").cast("string")).alias("amount"))

    writer = query.writeStream.format(outputfmt)\
            .option("path","gs://BUCKET_NAME/tables/cust_"+outputfmt)\
            .option("checkpointLocation", "gs://BUCKET_NAME/chkpt/"+outputfmt+"wr") \
        .outputMode("append")\
        .start()

    writer.awaitTermination()

if __name__=="__main__":
    if len(sys.argv) < 2:
        print ("Invalid number of arguments passed ", len(sys.argv))
        print ("Usage: ", sys.argv[0], " cluster  format")
        print ("e.g.:  ", sys.argv[0], " <cluster_name>  orc")
        print ("e.g.:  ", sys.argv[0], " <cluster_name>  parquet")
    main(sys.argv[1], sys.argv[2])

EOF

    3. Nel terminale SSH sul nodo master del cluster Kafka, esegui spark-submit per trasmettere i dati in streaming alle tabelle Hive in Cloud Storage.

      1. Inserisci il nome del tuo KAFKA_CLUSTER e dell'output FORMAT, quindi copia e incolla il seguente codice nel terminale SSH sul nodo master del cluster Kafka, quindi premi <return> per eseguire il codice e trasmettere in streaming i dati custdata di Kafka in formato Parquet alle tue tabelle Hive in Cloud Storage. 

        spark-submit --packages \
org.apache.spark:spark-streaming-kafka-0-10_2.12:3.1.3,org.apache.spark:spark-sql-kafka-0-10_2.12:3.1.3 \
    --conf spark.history.fs.gs.outputstream.type=FLUSHABLE_COMPOSITE \
    --conf spark.driver.memory=4096m \
    --conf spark.executor.cores=2 \
    --conf spark.executor.instances=2 \
    --conf spark.executor.memory=6144m \
    streamdata.py KAFKA_CLUSTER FORMAT

        Note:

        • KAFKA_CLUSTER: inserisci il nome del tuo cluster Kafka.
        • FORMAT: specifica parquet o orc come formato di output. Puoi eseguire il comando in successione per trasmettere in streaming entrambi i formati alle tabelle Hive: ad esempio, nella prima chiamata, specifica parquet per trasmettere in streaming l'argomento Kafka custdata alla tabella parquet Hive; poi, nella seconda chiamata, specifica il formato orc per trasmettere in streaming custdata alla tabella Hive ORC.

    4. Quando l'output standard si interrompe nel terminale SSH, il che indica che tutto il custdata è stato trasmesso in streaming, premi <control-c> nel terminale SSH per interrompere il processo.

    5. Elenca le tabelle Hive in Cloud Storage.

      gcloud storage ls gs://BUCKET_NAME/tables/* --recursive

      Note:

      • BUCKET_NAME: inserisci il nome del bucket Cloud Storage che contiene le tabelle Hive (vedi Creare tabelle Hive).

    Eseguire query sui dati in streaming

    1. Nel terminale SSH sul nodo master del cluster Kafka, esegui il seguente comando hive per conteggiare i messaggi custdata Kafka trasmessi in streaming nelle tabelle Hive in Cloud Storage.

      hive -e "select count(1) from TABLE_NAME"

      Note:

      • TABLE_NAME: specifica cust_parquet o cust_orc come nome della tabella Hive.

      Snippet di output previsto:

    ...
Status: Running (Executing on YARN cluster with App id application_....)

----------------------------------------------------------------------------------------------
        VERTICES      MODE        STATUS  TOTAL  COMPLETED  RUNNING  PENDING  FAILED  KILLED
----------------------------------------------------------------------------------------------
Map 1 .......... container     SUCCEEDED      1          1        0        0       0       0
Reducer 2 ...... container     SUCCEEDED      1          1        0        0       0       0
----------------------------------------------------------------------------------------------
VERTICES: 02/02  [==========================>>] 100%  ELAPSED TIME: 9.89 s
----------------------------------------------------------------------------------------------
OK
10000
Time taken: 21.394 seconds, Fetched: 1 row(s)

    Esegui la pulizia

    Elimina il progetto

    1. In the Google Cloud console, go to the Manage resources page.

      Go to Manage resources

    2. In the project list, select the project that you want to delete, and then click Delete.
    3. In the dialog, type the project ID, and then click Shut down to delete the project.

    Elimina risorse

    • In the Google Cloud console, go to the Cloud Storage Buckets page.

      Go to Buckets

    • Click the checkbox for the bucket that you want to delete.
    • To delete the bucket, click Delete, and then follow the instructions.
    • Elimina il cluster Kafka: 
      gcloud dataproc clusters delete KAFKA_CLUSTER \
    --region=${REGION}