Development Plan
Scenarios
Assume that table person of Hive stores the user consumption amount of the current day and table2 of HBase stores the history consumption data.
In table person, name=1 and account=100 indicates that the consumption amount of user 1 in the current day is 100 CNY.
In table2, key=1 and cf:cid=1000 indicate that the history comsuption amount of user 1 is 1000 CNY.
The Spark application shall achieve the following function:
Add the current consumption amount (100) to the history consumption amount (1000).
The running result is that the total consumption amount of user 1 (key=1) in table2 is 1100 CNY (cf:cid=1100).
Data Preparation
Before developing the application, create the Hive table person and insert data to it. Create HBase table2.
- Place the source log file to HDFS.
- Create a blank file log1.txt in the local and write the following content to the file:
1,100
- Create a directory /tmp/input in HDFS and copy the log1.txt file to the directory.
- On the Linux HDFS client, run the hadoop fs -mkdir /tmp/input command (or the hdfs dfs command) to create a directory.
- On the Linux HDFS client, run the hadoop fs -put log1.txt /tmp/input command to upload the data file.
- Create a blank file log1.txt in the local and write the following content to the file:
- Store the imported data to the Hive table.
Ensure that JDBCServer is started. Use the Beeline tool to create a Hive table and insert data to it.
- Run the following commands to create the Hive table person:
(
name STRING,
account INT
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' ESCAPED BY '\\' STORED AS TEXTFILE;
- Run the following command to insert data to the table:
- Run the following commands to create the Hive table person:
- Create an HBase table.
Ensure that JDBCServer is started. Use the Spark-beeline command tool to create an HBase table and insert data to it.
- Run the following commands to create the HBase table table2:
(
key string,
cid string
)
using org.apache.spark.sql.hbase.HBaseSource
options(
hbaseTableName "table2",
keyCols "key",
colsMapping "cid=cf.cid");
- Run the following command to insert data to the table:
- Run the following commands to create the HBase table table2:
Development Idea
- Query the data in Hive table person.
- Query the data in table2 using the key value of table person.
- Add the queried data.
- Write the results of the preceding step to table2.
Packaging the Project
- Use the Maven tool provided by IDEA to pack the project and generate a JAR file. For details, see Writing and Running the Spark Program in the Linux Environment.
- Upload the JAR file to any directory (for example, /opt/female/) on the server where the Spark client is located.
Before running the sample project, set the spark.yarn.security.credentials.hbase.enabled configuration item to true in the spark-defaults.conf configuration file of Spark client. (The default value is false. Changing the value to true does not affect existing services. If you want to uninstall the HBase service, change the value back to false first.)
Running Tasks
Go to the Spark client directory and run the following commands to invoke the bin/spark-submit script to run the code (The class name and file name must be the same as those in the actual code. The following is only an example):
- Run Java or Scala sample code.
- bin/spark-submit --class com.huawei.bigdata.spark.examples.SparkHivetoHbase --master yarn --deploy-mode client /opt/female/SparkHivetoHbase-1.0.jar
- Run the Python sample project
- PySpark does not provide HBase-related APIs. Therefore, Python is used to invoke Java code in this sample. Use Maven to pack the provided Java code into a JAR file and place it in the same directory. When running the Python program, use --jars to load the JAR file to classpath
- bin/spark-submit --master yarn --deploy-mode client --jars /opt/female/SparkHivetoHbasePythonExample/SparkHivetoHbase-1.0.jar /opt/female/SparkHivetoHbasePythonExample/SparkHivetoHbasePythonExample.py
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