hadoop - Sqoop 导入 : composite primary key and textual primary key

Question

堆栈:使用 Ambari 2.1 安装 HDP-2.3.2.0-2950

源数据库模式位于 sql server 上，它包含几个表，它们的主键为:

一个varchar

Composite - 两个 varchar 列或一个 varchar + 一个 int 列或
两个 int 列。有一张大 table 吗？具有三个的行
PK 中的列 一个 int + 两个 varchar 列

根据 Sqoop 文档:

Sqoop cannot currently split on multi-column indices. If your table has no index column, or has a multi-column key, then you must also manually choose a splitting column.

第一个问题是:“手动选择拆分列”的预期是什么 - 我怎样才能牺牲 pk 而只使用一个列，或者我错过了一些概念？

SQL Server 表是(只有两列，它们形成一个复合主键):

ChassiNo    varchar(8)  Unchecked
ECU_Name    nvarchar(15)    Unchecked

我继续导入 源表有 7909097 条记录 :

sqoop import --connect 'jdbc:sqlserver://somedbserver;database=somedb' --username someusname --password somepass --as-textfile --fields-terminated-by '|&|'  --table ChassiECU --num-mappers 8  --warehouse-dir /dataload/tohdfs/reio/odpdw/may2016 --verbose

令人担忧的警告和不正确的映射器输入和记录:

16/05/13 10:59:04 WARN manager.CatalogQueryManager: The table ChassiECU contains a multi-column primary key. Sqoop will default to the column ChassiNo only for this job.
16/05/13 10:59:08 WARN db.TextSplitter: Generating splits for a textual index column.
16/05/13 10:59:08 WARN db.TextSplitter: If your database sorts in a case-insensitive order, this may result in a partial import or duplicate records.
16/05/13 10:59:08 WARN db.TextSplitter: You are strongly encouraged to choose an integral split column.
16/05/13 10:59:38 INFO mapreduce.Job: Counters: 30
        File System Counters
                FILE: Number of bytes read=0
                FILE: Number of bytes written=1168400
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=1128
                HDFS: Number of bytes written=209961941
                HDFS: Number of read operations=32
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=16
        Job Counters
                Launched map tasks=8
                Other local map tasks=8
                Total time spent by all maps in occupied slots (ms)=62785
                Total time spent by all reduces in occupied slots (ms)=0
                Total time spent by all map tasks (ms)=62785
                Total vcore-seconds taken by all map tasks=62785
                Total megabyte-seconds taken by all map tasks=128583680
        Map-Reduce Framework
                Map input records=15818167
                Map output records=15818167
                Input split bytes=1128
                Spilled Records=0
                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=780
                CPU time spent (ms)=45280
                Physical memory (bytes) snapshot=2219433984
                Virtual memory (bytes) snapshot=20014182400
                Total committed heap usage (bytes)=9394716672
        File Input Format Counters
                Bytes Read=0
        File Output Format Counters
                Bytes Written=209961941
16/05/13 10:59:38 INFO mapreduce.ImportJobBase: Transferred 200.2353 MB in 32.6994 seconds (6.1235 MB/sec)
16/05/13 10:59:38 INFO mapreduce.ImportJobBase: Retrieved 15818167 records.

创建表:

CREATE EXTERNAL TABLE IF NOT EXISTS ChassiECU(`ChassiNo` varchar(8),
`ECU_Name` varchar(15)) ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'  LOCATION '/dataload/tohdfs/reio/odpdw/may2016/ChassiECU';

糟糕的结果(没有错误)--问题:15818167 vs 7909097(sql server)记录:

 > select count(1) from ChassiECU;
Query ID = hive_20160513110313_8e294d83-78aa-4e52-b90f-b5640268b8ac
Total jobs = 1
Launching Job 1 out of 1
Tez session was closed. Reopening...
Session re-established.
Status: Running (Executing on YARN cluster with App id application_1446726117927_0059)
--------------------------------------------------------------------------------
        VERTICES      STATUS  TOTAL  COMPLETED  RUNNING  PENDING  FAILED  KILLED
--------------------------------------------------------------------------------
Map 1 ..........   SUCCEEDED     14         14        0        0       0       0
Reducer 2 ......   SUCCEEDED      1          1        0        0       0       0
--------------------------------------------------------------------------------
VERTICES: 02/02  [==========================>>] 100%  ELAPSED TIME: 6.12 s
--------------------------------------------------------------------------------
OK
_c0
15818167

令人惊讶的是，如果复合键由一个 int (用于拆分)组成，我得到的记录要么准确，要么不匹配少于 10 条，但我仍然对这些感到担忧!

我该如何进行？

Answer 1

手动指定拆分列。拆分列不一定等于PK。您可以拥有复杂的 PK 和一些 int Split 列。您可以指定任何整数列，甚至是简单的函数(一些简单的函数，如子字符串或强制转换，而不是聚合或分析)。 拆分列最好应该是均匀分布的整数 .
例如，如果您的拆分列包含值 -1 的几行和值 10000 - 10000000 和 num-mappers=8 的 10M 行，那么 sqoop 将不均匀地在映射器之间拆分数据集:

第一个映射器将获得几行 -1，

第 2-7 个映射器将获得 0 行，

第 8 个映射器将获得近 10M 行，

这将导致数据倾斜，并且第 8 个映射器将永远运行或
甚至失败。我也有 使用非整数时重复
使用 MS-SQL 拆分列 .因此，使用整数拆分列。在你的情况下
对于只有两个 varchar 列的表，您可以
(1) 添加代理 int PK 并将其也用作拆分或
(2) 使用带有 WHERE 的自定义查询手动拆分数据子句并使用 num-mappers=1 运行 sqoop 几次，或者
(3)申请一些 确定性整数非聚合 对您的 varchar 列起作用，例如 cast(substr(...) as int) 或 second(timestamp_col)或 datepart(second, date)等作为拆分列。
对于 Teradata，您可以使用 AMP 编号: HASHAMP (HASHBUCKET (HASHROW (string_column_list)))从非整数键列表中获取整数 AMP 编号并依赖 AMP 之间的 TD 分布。我直接使用简单函数作为拆分，而不将其作为派生列添加到查询中