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NEW QUESTION: 1
ソリューションアーキテクトがAWSで新しいウェブアプリケーションを開発しています。アーキテクトは、このアプリケーションが非常に人気が出ると予想しているため、負荷をサポートするためにアプリケーションを拡張する必要があります。アーキテクトは、インスタンスをプロビジョニングまたは管理せずに、ソフトウェア開発と新機能の展開に集中したいと考えています。
適切なソリューションは何ですか？
A. Elastic Load Balancing with Auto Scaling groups and Amazon EC2
B. Amazon CloudFront and AWS Lambda
C. Amazon API Gateway and AWS Lambda
D. Amazon API Gateway and Amazon EC2
Answer: C

NEW QUESTION: 2

A. Option C
B. Option B
C. Option A
D. Option D
Answer: A

NEW QUESTION: 3
CORRECT TEXT
Problem Scenario 74 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.orders
table=retail_db.order_items
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Columns of order table : (orderjd , order_date , ordercustomerid, order status}
Columns of orderjtems table : (order_item_td , order_item_order_id ,
order_item_product_id,
order_item_quantity,order_item_subtotal,order_item_product_price)
Please accomplish following activities.
1. Copy "retaildb.orders" and "retaildb.orderjtems" table to hdfs in respective directory p89_orders and p89_order_items .
2. Join these data using orderjd in Spark and Python
3. Now fetch selected columns from joined data Orderld, Order date and amount collected on this order.
4. Calculate total order placed for each date, and produced the output sorted by date.
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution:
Step 1 : Import Single table .
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=orders --target-dir=p89_orders - -m1 sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=order_items ~target-dir=p89_ order items -m 1
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the
MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Read the data from one of the partition, created using above command, hadoopfs
-cat p89_orders/part-m-00000 hadoop fs -cat p89_order_items/part-m-00000
Step 3 : Load these above two directory as RDD using Spark and Python (Open pyspark terminal and do following). orders = sc.textFile("p89_orders") orderitems = sc.textFile("p89_order_items")
Step 4 : Convert RDD into key value as (orderjd as a key and rest of the values as a value)
#First value is orderjd
ordersKeyValue = orders.map(lambda line: (int(line.split(",")[0]), line))
#Second value as an Orderjd
orderltemsKeyValue = orderltems.map(lambda line: (int(line.split(",")[1]), line))
Step 5 : Join both the RDD using orderjd
joinedData = orderltemsKeyValue.join(ordersKeyValue)
#print the joined data
tor line in joinedData.collect():
print(line)
Format of joinedData as below.
[Orderld, 'All columns from orderltemsKeyValue', 'All columns from orders Key Value']
Step 6 : Now fetch selected values Orderld, Order date and amount collected on this order.
revenuePerOrderPerDay = joinedData.map(lambda row: (row[0]( row[1][1].split(",")[1]( f!oat(row[1][0].split('\M}[4]}}}
#printthe result
for line in revenuePerOrderPerDay.collect():
print(line)
Step 7 : Select distinct order ids for each date.
#distinct(date,order_id)
distinctOrdersDate = joinedData.map(lambda row: row[1][1].split('\")[1] + "," + str(row[0])).distinct() for line in distinctOrdersDate.collect(): print(line)
Step 8 : Similar to word count, generate (date, 1) record for each row. newLineTuple = distinctOrdersDate.map(lambda line: (line.split(",")[0], 1))
Step 9 : Do the count for each key(date), to get total order per date. totalOrdersPerDate = newLineTuple.reduceByKey(lambda a, b: a + b}
#print results
for line in totalOrdersPerDate.collect():
print(line)
step 10 : Sort the results by date sortedData=totalOrdersPerDate.sortByKey().collect()
#print results
for line in sortedData:
print(line)

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