在Spark中消费TableStore数据_开源大数据平台 E-MapReduce(EMR)-阿里云帮助中心

本文介绍Spark如何消费Table Store的数据。

Spark接入Table Store

准备一张数据表pet，其中name为主键。

name	owner	species	sex	birth	death
Fluffy	Harold	cat	f	1993-02-04	-
Claws	Gwen	cat	m	1994-03-17	-
Buffy	Harold	dog	f	1989-05-13	-
Fang	Benny	dog	m	1990-08-27	-
Bowser	Diane	dog	m	1979-08-31	1995-07-29
Chirpy	Gwen	bird	f	1998-09-11	-
Whistler	Gwen	bird	-	1997-12-09	-
Slim	Benny	snake	m	1996-04-29	-
Puffball	Diane	hamster	f	1999-03-30	-

以下示例演示了如何在Spark中消费Table Store的数据。

private static RangeRowQueryCriteria fetchCriteria() {
    RangeRowQueryCriteria res = new RangeRowQueryCriteria("pet");
    res.setMaxVersions(1);
    List<PrimaryKeyColumn> lower = new ArrayList<PrimaryKeyColumn>();
    List<PrimaryKeyColumn> upper = new ArrayList<PrimaryKeyColumn>();
    lower.add(new PrimaryKeyColumn("name", PrimaryKeyValue.INF_MIN));
    upper.add(new PrimaryKeyColumn("name", PrimaryKeyValue.INF_MAX));
    res.setInclusiveStartPrimaryKey(new PrimaryKey(lower));
    res.setExclusiveEndPrimaryKey(new PrimaryKey(upper));
    return res;
}
public static void main(String[] args) {
    SparkConf sparkConf = new SparkConf().setAppName("RowCounter");
    JavaSparkContext sc = new JavaSparkContext(sparkConf);
    Configuration hadoopConf = new Configuration();
    JavaSparkContext sc = null;
    try {
        sc = new JavaSparkContext(sparkConf);
        Configuration hadoopConf = new Configuration();
        TableStore.setCredential(
                hadoopConf,
                new Credential(accessKeyId, accessKeySecret, securityToken));
        Endpoint ep = new Endpoint(endpoint, instance);
        TableStore.setEndpoint(hadoopConf, ep);
        TableStoreInputFormat.addCriteria(hadoopConf, fetchCriteria());
        JavaPairRDD<PrimaryKeyWritable, RowWritable> rdd = sc.newAPIHadoopRDD(
                hadoopConf, TableStoreInputFormat.class,
                PrimaryKeyWritable.class, RowWritable.class);
        System.out.println(
            new Formatter().format("TOTAL: %d", rdd.count()).toString());
    } finally {
        if (sc != null) {
            sc.close();
        }
    }
}

spark-sql访问Table Store

命令示例如下。

spark-sql --jars /opt/apps/SPARK-EXTENSION/spark-extension-current/spark3-emrsdk/* \
  --hiveconf accessKeyId=$ALIBABA_CLOUD_ACCESS_KEY_ID \
  --hiveconf accessKeySecret=$ALIBABA_CLOUD_ACCESS_KEY_SECRET

说明

运行代码示例前必须先配置环境变量。关于如何配置环境变量，请参见配置环境变量。
/opt/apps/SPARK-EXTENSION/spark-extension-current/spark3-emrsdk/*中包含TableStore DataSource类型，如果您EMR集群使用的是Spark2，则应该将上面命令中的spark3替换为spark2。
针对Spark3，如果查询Table Store时报错java.lang.ClassNotFoundException: org.apache.commons.net.util.Base64，则需要添加commons-net依赖，--jars参数值可以改为/opt/apps/SPARK-EXTENSION/spark-extension-current/spark3-emrsdk/*,/opt/apps/HADOOP-COMMON/hadoop-common-current/share/hadoop/common/lib/commons-net-3.6.jar。
如果您不想在每次执行命令时都添加--jars参数，可以在Spark服务的配置页签，修改配置项spark.driver.extraClassPath和spark.executor.extraClassPath，在配置项的值中添加/opt/apps/SPARK-EXTENSION/spark-extension-current/spark3-emrsdk/*:/opt/apps/HADOOP-COMMON/hadoop-common-current/share/hadoop/common/lib/commons-net-3.6.jar的内容。

建表和读取数据示例如下。

create table test_tableStore
using tablestore
  options(endpoint = 'https://test.cn-hangzhou.vpc.tablestore.aliyuncs.com',
  access.key.id = '${hiveconf:accessKeyId}',
  access.key.secret = '${hiveconf:accessKeySecret}',
  table.name = 'test_table',
  instance.name = 'test_instance',
  catalog = '{"columns":{"pk":{"col":"pk","type":"string"},"data":{"col":"data","type":"string"}}}'
);

select * from test_tableStore

Spark访问Table Store

Spark接入Table Store

spark-sql访问Table Store

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