spark sql怎么連接和使用mysql數(shù)據(jù)源

這篇文章主要介紹“spark sql怎么連接和使用mysql數(shù)據(jù)源”，在日常操作中，相信很多人在spark sql怎么連接和使用mysql數(shù)據(jù)源問題上存在疑惑，小編查閱了各式資料，整理出簡單好用的操作方法，希望對大家解答”spark sql怎么連接和使用mysql數(shù)據(jù)源”的疑惑有所幫助！接下來，請跟著小編一起來學(xué)習(xí)吧！

spark sql 可以通過標(biāo)準(zhǔn)的jdbc連接數(shù)據(jù)庫，獲得數(shù)據(jù)源

public class SparkSql {public static SimpleDateFormat sdf = new SimpleDateFormat("_yyyyMMdd_HH_mm_ss");    private static final String appName = "spark sql test";    private static final String master = "spark://192.168.1.21:7077";    private static final String JDBCURL = "jdbc:mysql://192.168.1.18:3306/lng?user=root&password=123456";    public static void main(String[] avgs){

        SparkContext context = new SparkContext(master, appName);        SQLContext sqlContext = new SQLContext(context);        // Creates a DataFrame based on a table named "people"        // stored in a MySQL database.        DataFrame df = sqlContext
                .read()
                .format("jdbc")
                .option("url", JDBCURL)
                .option("dbtable", "tsys_user")
                .load();        // Looks the schema of this DataFrame.        df.printSchema();        // Counts people by age        DataFrame countsByAge = df.groupBy("customStyle").count();        countsByAge.show();        // Saves countsByAge to S3 in the JSON format.        countsByAge.write().format("json").save("hdfs://192.168.1.17:9000/administrator/sql-result" + sdf.format(new Date()));    }

}

如果沒有包含mysql的驅(qū)動程序，需要參考http://stackoverflow.com/questions/34764505/no-suitable-driver-found-for-jdbc-in-spark

1. You might want to assembly you application with your build manager (Maven,SBT) thus you'll not need to add the dependecies in your spark-submit cli. （意思就是把mysql的驅(qū)動程序打包到提交到spark的jar包里）

2. You can use the following option in your spark-submit cli :(改成下面，經(jīng)測試，可行，或者加入export SPARK_CLASSPATH=$SPARK_CLASSPATH:/usr/local/spark-1.6.1-bin-hadoop2.6/conf/driverLib/mysql-connector-java-5.1.36.jar 到conf/spark-env.sh）

   spark-submit --driver-class-path /usr/local/spark-1.6.1-bin-hadoop2.6/conf/driverLib/mysql-connector-java-5.1.36.jar --class com.xxx.SparkSql  /usr/local/spark.jar

   
   

   Explanation : Supposing that you have all your jars in a lib directory in your project root, this will read all the libraries and add them to the application submit.

3. You can also try to configure these 2 variables : spark.driver.extraClassPath and spark.executor.extraClassPath in SPARK_HOME/conf/spark-default.conf file and specify the value of these variables as the path of the jar file. Ensure that the same path exists on workernodes.（經(jīng)測，不行）

到此，關(guān)于“spark sql怎么連接和使用mysql數(shù)據(jù)源”的學(xué)習(xí)就結(jié)束了，希望能夠解決大家的疑惑。理論與實踐的搭配能更好的幫助大家學(xué)習(xí)，快去試試吧！若想繼續(xù)學(xué)習(xí)更多相關(guān)知識，請繼續(xù)關(guān)注億速云網(wǎng)站，小編會繼續(xù)努力為大家?guī)砀鄬嵱玫奈恼拢?/p>