LOAD
如果您需要将外部存储上的数据导入MaxCompute的表或表的分区中，可以通过LOAD命令实现该操作。本文为您介绍如何使用LOAD命令向MaxCompute中导入位于外部存储上的CSV格式或其它开源格式数据。
执行load操作前需要具备项目空间创建表权限（CreateTable）及修改表权限（Alter）。授权操作请参见MaxCompute权限。
本文中的命令您可以在如下工具平台执行：
功能介绍
MaxCompute支持您使用load overwrite或load into命令将OSS、Amazon Redshift、BigQuery外部存储的CSV格式或其他开源格式数据导入MaxCompute的表或表的分区。其中：Amazon Redshift和BigQuery的数据需要先导入OSS，才可以通过OSS导入MaxCompute。
MaxCompute也支持将数据按照动态分区方式导入MaxCompute的分区表的分区。
load into命令会直接向表或分区中追加数据。load overwrite命令会先清空表或分区中的原有数据，再向表或分区中插入数据。
前提条件
在向MaxCompute导入外部存储数据前，您需要先对MaxCompute进行授权。load overwrite或load into命令的授权模式沿用了MaxCompute外部表的授权模式，您可以一键授权，具备高安全性，详情请参见STS模式授权。
完成授权后，您需要根据导入数据的格式类型，选择对应的导入方式：
使用限制
只支持将外部存储的数据导入至同区域的MaxCompute项目空间中。
将数据下载到分区表时，外部路径下数据的Schema不包含分区列。
通过内置Extractor（StorageHandler）导入数据
命令格式
{load overwrite|into} table <table_name> [partition (<pt_spec>)] from location <external_location> stored by <StorageHandler> [with serdeproperties (<Options>)];参数说明
table_name：必填。需要插入数据的目标表名称。目标表需要提前创建，目标表的Schema（除分区列）需要和外部数据格式一致。
pt_spec：可选。需要插入数据的目标表分区信息。格式为
(partition_col1 = partition_col_value1, partition_col2 = partition_col_value2, ...)。external_location：必填。指定读取外部存储数据的OSS目录，格式为
'oss://<oss_endpoint>/<object>'，详情请参见OSS访问域名使用规则，系统会默认读取该目录下所有的文件。StorageHandler：必填。指定内置的StorageHandler名称。
com.aliyun.odps.CsvStorageHandler是内置的处理CSV格式文件的StorageHandler，定义了如何读或写CSV文件。您只需要指定该参数，相关逻辑已经由系统实现。使用方法和MaxCompute外部表一致，详情请参见创建OSS外部表。Options：可选。指定外部表相关参数，SERDEPROPERTIES支持的属性和MaxCompute外部表一致，属性列表详情请参见创建OSS外部表。
使用示例
通过内置Extractor（StorageHandler）导入数据。假设MaxCompute和OSS的Owner是同一个账号，通过阿里云内网将vehicle.csv文件的数据导入MaxCompute。
将vehicle.csv文件保存至OSS Bucket目录下
mc-test/data_location/，地域为oss-cn-hangzhou，并组织OSS目录路径。创建OSS Bucket详情请参见创建存储空间。vehicle.csv文件数据如下：
1,1,51,1,46.81006,-92.08174,9/14/2014 0:00,S 1,2,13,1,46.81006,-92.08174,9/14/2014 0:00,NE 1,3,48,1,46.81006,-92.08174,9/14/2014 0:00,NE 1,4,30,1,46.81006,-92.08174,9/14/2014 0:00,W 1,5,47,1,46.81006,-92.08174,9/14/2014 0:00,S 1,6,9,1,46.81006,-92.08174,9/14/2014 0:00,S 1,7,53,1,46.81006,-92.08174,9/14/2014 0:00,N 1,8,63,1,46.81006,-92.08174,9/14/2014 0:00,SW 1,9,4,1,46.81006,-92.08174,9/14/2014 0:00,NE 1,10,31,1,46.81006,-92.08174,9/14/2014 0:00,N根据Bucket、地域、Endpoint信息组织OSS目录路径如下：
oss://oss-cn-hangzhou-internal.aliyuncs.com/mc-test/data_location/登录MaxCompute客户端创建目标表ambulance_data_csv_load。命令示例如下：
create table ambulance_data_csv_load ( vehicleId INT, recordId INT, patientId INT, calls INT, locationLatitute DOUBLE, locationLongtitue DOUBLE, recordTime STRING, direction STRING );执行
load overwrite命令，将OSS上的vehicle.csv文件导入目标表。命令示例如下：load overwrite table ambulance_data_csv_load from location 'oss://oss-cn-hangzhou-internal.aliyuncs.com/mc-test/data_location/' stored by 'com.aliyun.odps.CsvStorageHandler' with serdeproperties ( 'odps.properties.rolearn'='acs:ram::xxxxx:role/aliyunodpsdefaultrole', --AliyunODPSDefaultRole的ARN信息，可通过RAM角色管理页面获取。 'odps.text.option.delimiter'=',' );查看角色的ARN信息请参见查看RAM角色。
查看目标表ambulance_data_csv_load的导入结果。命令示例如下：
--开启全表扫描，仅此Session有效。 set odps.sql.allow.fullscan=true; select * from ambulance_data_csv_load;返回结果如下：
+------------+------------+------------+------------+------------------+-------------------+------------+------------+ | vehicleid | recordid | patientid | calls | locationlatitute | locationlongtitue | recordtime | direction | +------------+------------+------------+------------+------------------+-------------------+------------+------------+ | 1 | 1 | 51 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | S | | 1 | 2 | 13 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | NE | | 1 | 3 | 48 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | NE | | 1 | 4 | 30 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | W | | 1 | 5 | 47 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | S | | 1 | 6 | 9 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | S | | 1 | 7 | 53 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | N | | 1 | 8 | 63 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | SW | | 1 | 9 | 4 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | NE | | 1 | 10 | 31 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | N | +------------+------------+------------+------------+------------------+-------------------+------------+------------+
导入其他开源格式数据
命令格式
{load overwrite|into} table <table_name> [partition (<pt_spec>)] from location <external_location> [row format serde '<serde_class>' [with serdeproperties (<Options>)] ] stored as <file_format>;参数说明
table_name：必填。需要插入数据的目标表名称。目标表需要提前创建，目标表的Schema（除分区列）需要和外部数据格式一致。
pt_spec：可选。需要插入数据的目标表分区信息。格式为
(partition_col1 = partition_col_value1, partition_col2 = partition_col_value2, ...)。external_location：必填。指定读取外部存储数据的OSS目录，格式为
'oss://<oss_endpoint>/<object>'，详情请参见OSS访问域名使用规则，系统会默认读取该目录下所有的文件。serde_class：可选。使用方法和MaxCompute外部表一致，详情请参见创建OSS外部表。
Options：必填。指定外部表相关参数，SERDEPROPERTIES支持的属性和MaxCompute外部表一致，属性列表详情请参见创建OSS外部表。
file_format：必填。指定导入数据文件格式。例如ORC、PARQUET、RCFILE、SEQUENCEFILE和TEXTFILE。使用方法和MaxCompute外部表一致，详情请参见创建OSS外部表。
说明serde_class和Options使用默认值时，可以省略不写。导入的单个文件大小不能超过3 GB，如果文件过大，建议拆分。
使用示例
示例1：导入其他开源格式数据。假设MaxCompute和OSS的Owner是同一个账号，通过阿里云内网将vehicle.textfile文件的数据导入MaxCompute。
将vehicle.txtfile文件保存至OSS Bucket目录下
mc-test/data_location/，地域为oss-cn-hangzhou，并组织OSS目录路径。创建OSS Bucket详情请参见创建存储空间。vehicle.textfile文件数据如下：
1,1,51,1,46.81006,-92.08174,9/14/2014 0:00,S 1,2,13,1,46.81006,-92.08174,9/14/2014 0:00,NE 1,3,48,1,46.81006,-92.08174,9/14/2014 0:00,NE 1,4,30,1,46.81006,-92.08174,9/14/2014 0:00,W 1,5,47,1,46.81006,-92.08174,9/14/2014 0:00,S 1,6,9,1,46.81006,-92.08174,9/14/2014 0:00,S 1,7,53,1,46.81006,-92.08174,9/14/2014 0:00,N 1,8,63,1,46.81006,-92.08174,9/14/2014 0:00,SW 1,9,4,1,46.81006,-92.08174,9/14/2014 0:00,NE 1,10,31,1,46.81006,-92.08174,9/14/2014 0:00,N根据Bucket、地域、Endpoint信息组织OSS目录路径如下：
oss://oss-cn-hangzhou-internal.aliyuncs.com/mc-test/data_location/登录MaxCompute客户端创建目标表ambulance_data_textfile_load_pt。命令示例如下：
create table ambulance_data_textfile_load_pt ( vehicleId STRING, recordId STRING, patientId STRING, calls STRING, locationLatitute STRING, locationLongtitue STRING, recordTime STRING, direction STRING) partitioned by (ds STRING);执行
load overwrite命令，将OSS上的vehicle.textfile文件导入目标表。命令示例如下：load overwrite table ambulance_data_textfile_load_pt partition(ds='20200910') from location 'oss://oss-cn-hangzhou-internal.aliyuncs.com/mc-test/data_location/' row format serde 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' stored as textfile;查看目标表ambulance_data_textfile_load_pt的导入结果。命令示例如下：
--开启全表扫描，仅此Session有效。 set odps.sql.allow.fullscan=true; select * from ambulance_data_textfile_load_pt;返回结果如下：
+------------+------------+------------+------------+------------------+-------------------+------------+------------+------------+ | vehicleid | recordid | patientid | calls | locationlatitute | locationlongtitue | recordtime | direction | ds | +------------+------------+------------+------------+------------------+-------------------+------------+------------+------------+ | 1,1,51,1,46.81006,-92.08174,9/14/2014 0:00,S | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 20200910 | | 1,2,13,1,46.81006,-92.08174,9/14/2014 0:00,NE | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 20200910 | | 1,3,48,1,46.81006,-92.08174,9/14/2014 0:00,NE | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 20200910 | | 1,4,30,1,46.81006,-92.08174,9/14/2014 0:00,W | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 20200910 | | 1,5,47,1,46.81006,-92.08174,9/14/2014 0:00,S | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 20200910 | | 1,6,9,1,46.81006,-92.08174,9/14/2014 0:00,S | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 20200910 | | 1,7,53,1,46.81006,-92.08174,9/14/2014 0:00,N | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 20200910 | | 1,8,63,1,46.81006,-92.08174,9/14/2014 0:00,SW | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 20200910 | | 1,9,4,1,46.81006,-92.08174,9/14/2014 0:00,NE | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 20200910 | | 1,10,31,1,46.81006,-92.08174,9/14/2014 0:00,N | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 20200910 | +------------+------------+------------+------------+------------------+-------------------+------------+------------+------------+
示例2：将数据按动态分区方式导入目标表。
说明如果OSS目录下的子目录是以分区名方式组织的，则可以将数据按动态分区的方式导入到分区表。
将vehicle1.csv文件和vehicle2.csv文件分别保存至OSS Bucket目录
mc-test/data_location/ds=20200909/和mc-test/data_location/ds=20200910/，地域为oss-cn-hangzhou，并组织OSS目录路径。创建OSS Bucket详情请参见创建存储空间。vehicle1.csv文件和vehicle2.csv文件数据如下：
--vehicle1.csv 1,1,51,1,46.81006,-92.08174,9/14/2014 0:00,S 1,2,13,1,46.81006,-92.08174,9/14/2014 0:00,NE 1,3,48,1,46.81006,-92.08174,9/14/2014 0:00,NE 1,4,30,1,46.81006,-92.08174,9/14/2014 0:00,W 1,5,47,1,46.81006,-92.08174,9/14/2014 0:00,S 1,6,9,1,46.81006,-92.08174,9/14/2014 0:00,S --vehicle2.csv 1,7,53,1,46.81006,-92.08174,9/14/2014 0:00,N 1,8,63,1,46.81006,-92.08174,9/14/2014 0:00,SW 1,9,4,1,46.81006,-92.08174,9/14/2014 0:00,NE 1,10,31,1,46.81006,-92.08174,9/14/2014 0:00,N根据Bucket、地域、Endpoint信息组织OSS目录路径如下：
oss://oss-cn-hangzhou-internal.aliyuncs.com/mc-test/data_location/ds=20200909/' oss://oss-cn-hangzhou-internal.aliyuncs.com/mc-test/data_location/ds=20200910/'登录MaxCompute客户端创建目标表ambulance_data_csv_load_dynpt。命令示例如下：
create table ambulance_data_csv_load_dynpt ( vehicleId STRING, recordId STRING, patientId STRING, calls STRING, locationLatitute STRING, locationLongtitue STRING, recordTime STRING, direction STRING) partitioned by (ds STRING);执行
load overwrite命令，将OSS上的文件导入目标表。命令示例如下：load overwrite table ambulance_data_csv_load_dynpt partition(ds) from location 'oss://oss-cn-hangzhou-internal.aliyuncs.com/mc-test/data_location/' row format serde 'org.apache.hadoop.hive.serde2.OpenCSVSerde' stored as textfile;查看目标表ambulance_data_csv_load_dynpt的导入结果。命令示例如下：
--开启全表扫描，仅此Session有效。 set odps.sql.allow.fullscan=true; select * from ambulance_data_csv_load_dynpt;返回结果如下：
+------------+------------+------------+------------+------------------+-------------------+------------+------------+------------+ | vehicleid | recordid | patientid | calls | locationlatitute | locationlongtitue | recordtime | direction | ds | +------------+------------+------------+------------+------------------+-------------------+------------+------------+------------+ | 1 | 7 | 53 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | N | 20200909 | | 1 | 8 | 63 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | SW | 20200909 | | 1 | 9 | 4 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | NE | 20200909 | | 1 | 10 | 31 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | N | 20200909 | | 1 | 1 | 51 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | S | 20200910 | | 1 | 2 | 13 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | NE | 20200910 | | 1 | 3 | 48 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | NE | 20200910 | | 1 | 4 | 30 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | W | 20200910 | | 1 | 5 | 47 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | S | 20200910 | | 1 | 6 | 9 | 1 | 46.81006 | -92.08174 | 9/14/2014 0:00 | S | 20200910 | +------------+------------+------------+------------+------------------+-------------------+------------+------------+------------+