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Data Lake Analytics + OSS数据文件格式处理大全

0. 前言

Data Lake Analytics是Serverless化的云上交互式查询分析服务。用户可以使用标准的SQL语句，对存储在OSS、TableStore上的数据无需移动，直接进行查询分析。

目前该产品已经正式登陆阿里云，欢迎大家申请试用，体验更便捷的数据分析服务。
请参考https://help.aliyun.com/document_detail/70386.html 进行产品开通服务申请。

在上一篇教程中，我们介绍了如何分析CSV格式的TPC-H数据集。除了纯文本文件(例如，CSV，TSV等)，用户存储在OSS上的其他格式的数据文件，也可以使用Data Lake Analytics进行查询分析，包括ORC, PARQUET, JSON, RCFILE, AVRO甚至ESRI规范的地理JSON数据，还可以用正则表达式匹配的文件等。

本文详细介绍如何根据存储在OSS上的文件格式使用Data Lake Analytics （下文简称 DLA）进行分析。DLA内置了各种处理文件数据的SerDe（Serialize/Deserilize的简称,目的是用于序列化和反序列化）实现，用户无需自己编写程序，基本上能选用DLA中的一款或多款SerDe来匹配您OSS上的数据文件格式。如果还不能满足您特殊文件格式的处理需求，请联系我们，尽快为您实现。

1. 存储格式与SerDe

用户可以依据存储在OSS上的数据文件进行建表，通过STORED AS 指定数据文件的格式。
例如，

CREATE EXTERNAL TABLE nation (
    N_NATIONKEY INT,
    N_NAME STRING,
    N_REGIONKEY INT,
    N_COMMENT STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION 'oss://test-bucket-julian-1/tpch_100m/nation';

建表成功后可以使用SHOW CREATE TABLE语句查看原始建表语句。

mysql> show create table nation;
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Result                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| CREATE EXTERNAL TABLE `nation`(
  `n_nationkey` int,
  `n_name` string,
  `n_regionkey` int,
  `n_comment` string)
ROW FORMAT DELIMITED
    FIELDS TERMINATED BY '|'
STORED AS `TEXTFILE`
LOCATION
  'oss://test-bucket-julian-1/tpch_100m/nation'|
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row in set (1.81 sec)

下表中列出了目前DLA已经支持的文件格式，当针对下列格式的文件建表时，可以直接使用STORED AS，DLA会选择合适的SERDE/INPUTFORMAT/OUTPUTFORMAT。

在指定了STORED AS 的同时，还可以根据具体文件的特点，指定SerDe (用于解析数据文件并映射到DLA表)，特殊的列分隔符等。
后面的部分会做进一步的讲解。

2. 示例

2.1 CSV文件

CSV文件，本质上还是纯文本文件，可以使用STORED AS TEXTFILE。
列与列之间以逗号分隔，可以通过ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' 表示。

普通CSV文件

例如，数据文件oss://bucket-for-testing/oss/text/cities/city.csv的内容为

Beijing,China,010
ShangHai,China,021
Tianjin,China,022

建表语句可以为

CREATE EXTERNAL TABLE city (
    city STRING,
    country STRING,
    code INT
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION 'oss://bucket-for-testing/oss/text/cities';

使用OpenCSVSerde__处理引号__引用的字段

OpenCSVSerde在使用时需要注意以下几点：

  1. 用户可以为行的字段指定字段分隔符、字段内容引用符号和转义字符，例如：WITH SERDEPROPERTIES ("separatorChar" = ",", "quoteChar" = "`", "escapeChar" = "\" )；
  2. 不支持字段内嵌入的行分割符；
  3. 所有字段定义STRING类型；
  4. 其他数据类型的处理，可以在SQL中使用函数进行转换。
    例如，
CREATE EXTERNAL TABLE test_csv_opencsvserde (
  id STRING,
  name STRING,
  location STRING,
  create_date STRING,
  create_timestamp STRING,
  longitude STRING,
  latitude STRING
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.OpenCSVSerde'
with serdeproperties(
'separatorChar'=',',
'quoteChar'='"',
'escapeChar'='\\'
)
STORED AS TEXTFILE LOCATION 'oss://test-bucket-julian-1/test_csv_serde_1';

自定义分隔符

需要自定义列分隔符(FIELDS TERMINATED BY)，转义字符(ESCAPED BY)，行结束符(LINES TERMINATED BY)。
需要在建表语句中指定

ROW FORMAT DELIMITED
    FIELDS TERMINATED BY '\t'
    ESCAPED BY '\\'
    LINES TERMINATED BY '\n'

忽略CSV文件中的HEADER

在csv文件中，有时会带有HEADER信息，需要在数据读取时忽略掉这些内容。这时需要在建表语句中定义skip.header.line.count。

例如，数据文件oss://my-bucket/datasets/tpch/nation_csv/nation_header.tbl的内容如下：

N_NATIONKEY|N_NAME|N_REGIONKEY|N_COMMENT
0|ALGERIA|0| haggle. carefully final deposits detect slyly agai|
1|ARGENTINA|1|al foxes promise slyly according to the regular accounts. bold requests alon|
2|BRAZIL|1|y alongside of the pending deposits. carefully special packages are about the ironic forges. slyly special |
3|CANADA|1|eas hang ironic, silent packages. slyly regular packages are furiously over the tithes. fluffily bold|
4|EGYPT|4|y above the carefully unusual theodolites. final dugouts are quickly across the furiously regular d|
5|ETHIOPIA|0|ven packages wake quickly. regu|

相应的建表语句为：

CREATE EXTERNAL TABLE nation_header (
    N_NATIONKEY INT,
    N_NAME STRING,
    N_REGIONKEY INT,
    N_COMMENT STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION 'oss://my-bucket/datasets/tpch/nation_csv/nation_header.tbl'
TBLPROPERTIES ("skip.header.line.count"="1");

skip.header.line.count的取值x和数据文件的实际行数n有如下关系:

  • 当x<=0时，DLA在读取文件时，不会过滤掉任何信息，即全部读取；
  • 当0
  • 当x>=n时，DLA在读取文件时，会过滤掉所有的文件内容。

2.2 TSV文件

与CSV文件类似，TSV格式的文件也是纯文本文件，列与列之间的分隔符为Tab。

例如，数据文件oss://bucket-for-testing/oss/text/cities/city.tsv的内容为

Beijing    China    010
ShangHai    China    021
Tianjin    China    022

建表语句可以为

CREATE EXTERNAL TABLE city (
    city STRING,
    country STRING,
    code INT
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE
LOCATION 'oss://bucket-for-testing/oss/text/cities';

2.3 多字符数据字段分割符文件

假设您的数据字段的分隔符包含多个字符，可采用如下示例建表语句，其中每行的数据字段分割符为“||”，可以替换为您具体的分割符字符串。

ROW FORMAT SERDE 'org.apache.hadoop.hive.contrib.serde2.MultiDelimitSerDe'
with serdeproperties(
"field.delim"="||"
)

示例：

CREATE EXTERNAL TABLE test_csv_multidelimit (
  id STRING,
  name STRING,
  location STRING,
  create_date STRING,
  create_timestamp STRING,
  longitude STRING,
  latitude STRING
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.contrib.serde2.MultiDelimitSerDe'
with serdeproperties(
"field.delim"="||"
)
STORED AS TEXTFILE LOCATION 'oss://bucket-for-testing/oss/text/cities/';

2.4 JSON文件

DLA可以处理的JSON文件通常以纯文本的格式存储，数据文件的编码方式需要是UTF-8。
在JSON文件中，每行必须是一个完整的JSON对象。
例如，下面的文件格式是不被接受的

{"id": 123, "name": "jack",
"c3": "2001-02-03 12:34:56"}
{"id": 456, "name": "rose", "c3": "1906-04-18 05:12:00"}
{"id": 789, "name": "tom", "c3": "2001-02-03 12:34:56"}
{"id": 234, "name": "alice", "c3": "1906-04-18 05:12:00"}

需要改写成：

{"id": 123, "name": "jack", "c3": "2001-02-03 12:34:56"}
{"id": 456, "name": "rose", "c3": "1906-04-18 05:12:00"}
{"id": 789, "name": "tom", "c3": "2001-02-03 12:34:56"}
{"id": 234, "name": "alice", "c3": "1906-04-18 05:12:00"}

不含嵌套的JSON数据

建表语句可以写

CREATE EXTERNAL TABLE t1 (id int, name string, c3 timestamp)
STORED AS JSON
LOCATION 'oss://path/to/t1/directory';

含有嵌套的JSON文件

使用struct和array结构定义嵌套的JSON数据。
例如，用户原始数据（注意：无论是否嵌套，一条完整的JSON数据都只能放在一行上，才能被Data Lake Analytics处理）：

{       "DocId": "Alibaba",         "User_1": {             "Id": 1234,             "Username": "bob1234",          "Name": "Bob",          "ShippingAddress": {                    "Address1": "969 Wenyi West St.",                     "Address2": null,                       "City": "Hangzhou",                      "Province": "Zhejiang"           },              "Orders": [{                            "ItemId": 6789,                                 "OrderDate": "11/11/2017"                       },                      {                               "ItemId": 4352,                                 "OrderDate": "12/12/2017"                       }               ]       } }

使用在线JSON格式化工具格式化后，数据内容如下：

{
    "DocId": "Alibaba",
    "User_1": {
        "Id": 1234,
        "Username": "bob1234",
        "Name": "Bob",
        "ShippingAddress": {
            "Address1": "969 Wenyi West St.",
            "Address2": null,
            "City": "Hangzhou",
            "Province": "Zhejiang"
        },
        "Orders": [
            {
                "ItemId": 6789,
                "OrderDate": "11/11/2017"
            },
            {
                "ItemId": 4352,
                "OrderDate": "12/12/2017"
            }
        ]
    }
}

则建表语句可以写成如下（注意：LOCATION中指定的路径必须是JSON数据文件所在的目录，该目录下的所有JSON文件都能被识别为该表的数据）：

CREATE EXTERNAL TABLE json_table_1 (
    docid string,
    user_1 struct<
            id:INT,
            username:string,
            name:string,
            shippingaddress:struct<
                            address1:string,
                            address2:string,
                            city:string,
                            province:string
                            >,
            orders:array<
                    struct<
                        itemid:INT,
                        orderdate:string
                    >
            >
    >
)
STORED AS JSON
LOCATION 'oss://xxx/test/json/hcatalog_serde/table_1/';

对该表进行查询：

select * from json_table_1;

+---------+----------------------------------------------------------------------------------------------------------------+
| docid   | user_1                                                                                                         |
+---------+----------------------------------------------------------------------------------------------------------------+
| Alibaba | [1234, bob1234, Bob, [969 Wenyi West St., null, Hangzhou, Zhejiang], [[6789, 11/11/2017], [4352, 12/12/2017]]] |
+---------+----------------------------------------------------------------------------------------------------------------+

对于struct定义的嵌套结构，可以通过“.”进行层次对象引用，对于array定义的数组结构，可以通过“[数组下标]”（注意：数组下标从1开始）进行对象引用。

select DocId,
       User_1.Id,
       User_1.ShippingAddress.Address1,
       User_1.Orders[1].ItemId
from json_table_1
where User_1.Username = 'bob1234'
  and User_1.Orders[2].OrderDate = '12/12/2017';

+---------+------+--------------------+-------+
| DocId   | id   | address1           | _col3 |
+---------+------+--------------------+-------+
| Alibaba | 1234 | 969 Wenyi West St. |  6789 |
+---------+------+--------------------+-------+

使用JSON函数处理数据

例如，把“value_string”的嵌套JSON值作为字符串存储：

{"data_key":"com.taobao.vipserver.domains.meta.biz.alibaba.com","ts":1524550275112,"value_string":"{\"appName\":\"\",\"apps\":[],\"checksum\":\"50fa0540b430904ee78dff07c7350e1c\",\"clusterMap\":{\"DEFAULT\":{\"defCkport\":80,\"defIPPort\":80,\"healthCheckTask\":null,\"healthChecker\":{\"checkCode\":200,\"curlHost\":\"\",\"curlPath\":\"/status.taobao\",\"type\":\"HTTP\"},\"name\":\"DEFAULT\",\"nodegroup\":\"\",\"sitegroup\":\"\",\"submask\":\"0.0.0.0/0\",\"syncConfig\":{\"appName\":\"trade-ma\",\"nodegroup\":\"tradema\",\"pubLevel\":\"publish\",\"role\":\"\",\"site\":\"\"},\"useIPPort4Check\":true}},\"disabledSites\":[],\"enableArmoryUnit\":false,\"enableClientBeat\":false,\"enableHealthCheck\":true,\"enabled\":true,\"envAndSites\":\"\",\"invalidThreshold\":0.6,\"ipDeleteTimeout\":1800000,\"lastModifiedMillis\":1524550275107,\"localSiteCall\":true,\"localSiteThreshold\":0.8,\"name\":\"biz.alibaba.com\",\"nodegroup\":\"\",\"owners\":[\"junlan.zx\",\"张三\",\"李四\",\"cui.yuanc\"],\"protectThreshold\":0,\"requireSameEnv\":false,\"resetWeight\":false,\"symmetricCallType\":null,\"symmetricType\":\"warehouse\",\"tagName\":\"ipGroup\",\"tenantId\":\"\",\"tenants\":[],\"token\":\"1cf0ec0c771321bb4177182757a67fb0\",\"useSpecifiedURL\":false}"}

使用在线JSON格式化工具格式化后，数据内容如下：

{
    "data_key": "com.taobao.vipserver.domains.meta.biz.alibaba.com",
    "ts": 1524550275112,
    "value_string": "{\"appName\":\"\",\"apps\":[],\"checksum\":\"50fa0540b430904ee78dff07c7350e1c\",\"clusterMap\":{\"DEFAULT\":{\"defCkport\":80,\"defIPPort\":80,\"healthCheckTask\":null,\"healthChecker\":{\"checkCode\":200,\"curlHost\":\"\",\"curlPath\":\"/status.taobao\",\"type\":\"HTTP\"},\"name\":\"DEFAULT\",\"nodegroup\":\"\",\"sitegroup\":\"\",\"submask\":\"0.0.0.0/0\",\"syncConfig\":{\"appName\":\"trade-ma\",\"nodegroup\":\"tradema\",\"pubLevel\":\"publish\",\"role\":\"\",\"site\":\"\"},\"useIPPort4Check\":true}},\"disabledSites\":[],\"enableArmoryUnit\":false,\"enableClientBeat\":false,\"enableHealthCheck\":true,\"enabled\":true,\"envAndSites\":\"\",\"invalidThreshold\":0.6,\"ipDeleteTimeout\":1800000,\"lastModifiedMillis\":1524550275107,\"localSiteCall\":true,\"localSiteThreshold\":0.8,\"name\":\"biz.alibaba.com\",\"nodegroup\":\"\",\"owners\":[\"junlan.zx\",\"张三\",\"李四\",\"cui.yuanc\"],\"protectThreshold\":0,\"requireSameEnv\":false,\"resetWeight\":false,\"symmetricCallType\":null,\"symmetricType\":\"warehouse\",\"tagName\":\"ipGroup\",\"tenantId\":\"\",\"tenants\":[],\"token\":\"1cf0ec0c771321bb4177182757a67fb0\",\"useSpecifiedURL\":false}"
}

建表语句为

CREATE external TABLE json_table_2 (
   data_key string,
   ts bigint,
   value_string string
)
STORED AS JSON
LOCATION 'oss://xxx/test/json/hcatalog_serde/table_2/';

表建好后，可进行查询：

select * from json_table_2;

+---------------------------------------------------+---------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| data_key                                          | ts            | value_string                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+---------------------------------------------------+---------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| com.taobao.vipserver.domains.meta.biz.alibaba.com | 1524550275112 | {"appName":"","apps":[],"checksum":"50fa0540b430904ee78dff07c7350e1c","clusterMap":{"DEFAULT":{"defCkport":80,"defIPPort":80,"healthCheckTask":null,"healthChecker":{"checkCode":200,"curlHost":"","curlPath":"/status.taobao","type":"HTTP"},"name":"DEFAULT","nodegroup":"","sitegroup":"","submask":"0.0.0.0/0","syncConfig":{"appName":"trade-ma","nodegroup":"tradema","pubLevel":"publish","role":"","site":""},"useIPPort4Check":true}},"disabledSites":[],"enableArmoryUnit":false,"enableClientBeat":false,"enableHealthCheck":true,"enabled":true,"envAndSites":"","invalidThreshold":0.6,"ipDeleteTimeout":1800000,"lastModifiedMillis":1524550275107,"localSiteCall":true,"localSiteThreshold":0.8,"name":"biz.alibaba.com","nodegroup":"","owners":["junlan.zx","张三","李四","cui.yuanc"],"protectThreshold":0,"requireSameEnv":false,"resetWeight":false,"symmetricCallType":null,"symmetricType":"warehouse","tagName":"ipGroup","tenantId":"","tenants":[],"token":"1cf0ec0c771321bb4177182757a67fb0","useSpecifiedURL":false}       |
+---------------------------------------------------+---------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

下面SQL示例json_parse，json_extract_scalar，json_extract等常用JSON函数的使用方式：

mysql> select json_extract_scalar(json_parse(value), '$.owners[1]') from json_table_2;

+--------+
| _col0  |
+--------+
| 张三    |
+--------+

mysql> select json_extract_scalar(json_obj.json_col, '$.DEFAULT.submask')
from (
  select json_extract(json_parse(value), '$.clusterMap') as json_col from json_table_2
) json_obj
where json_extract_scalar(json_obj.json_col, '$.DEFAULT.healthChecker.curlPath') = '/status.taobao';

+-----------+
| _col0     |
+-----------+
| 0.0.0.0/0 |
+-----------+

mysql> with json_obj as (select json_extract(json_parse(value), '$.clusterMap') as json_col from json_table_2)
select json_extract_scalar(json_obj.json_col, '$.DEFAULT.submask')
from json_obj
where json_extract_scalar(json_obj.json_col, '$.DEFAULT.healthChecker.curlPath') = '/status.taobao';

+-----------+
| _col0     |
+-----------+
| 0.0.0.0/0 |
+-----------+

2.5 ORC文件

Optimized Row Columnar(ORC)是Apache开源项目Hive支持的一种优化的列存储文件格式。与CSV文件相比，不仅可以节省存储空间，还可以得到更好的查询性能。

对于ORC文件，只需要在建表时指定 STORED AS ORC。
例如，

CREATE EXTERNAL TABLE orders_orc_date (
    O_ORDERKEY INT,
    O_CUSTKEY INT,
    O_ORDERSTATUS STRING,
    O_TOTALPRICE DOUBLE,
    O_ORDERDATE DATE,
    O_ORDERPRIORITY STRING,
    O_CLERK STRING,
    O_SHIPPRIORITY INT,
    O_COMMENT STRING
)
STORED AS ORC
LOCATION 'oss://bucket-for-testing/datasets/tpch/1x/orc_date/orders_orc';

2.6 PARQUET文件

Parquet是Apache开源项目Hadoop支持的一种列存储的文件格式。
使用DLA建表时，需要指定STORED AS PARQUET即可。
例如，

CREATE EXTERNAL TABLE orders_parquet_date (
    O_ORDERKEY INT,
    O_CUSTKEY INT,
    O_ORDERSTATUS STRING,
    O_TOTALPRICE DOUBLE,
    O_ORDERDATE DATE,
    O_ORDERPRIORITY STRING,
    O_CLERK STRING,
    O_SHIPPRIORITY INT,
    O_COMMENT STRING
)
STORED AS PARQUET
LOCATION 'oss://bucket-for-testing/datasets/tpch/1x/parquet_date/orders_parquet';

2.7 RCFILE文件

Record Columnar File (RCFile), 列存储文件，可以有效地将关系型表结构存储在分布式系统中，并且可以被高效地读取和处理。
DLA在建表时，需要指定STORED AS RCFILE。
例如，

CREATE EXTERNAL TABLE lineitem_rcfile_date (
    L_ORDERKEY INT,
    L_PARTKEY INT,
    L_SUPPKEY INT,
    L_LINENUMBER INT,
    L_QUANTITY DOUBLE,
    L_EXTENDEDPRICE DOUBLE,
    L_DISCOUNT DOUBLE,
    L_TAX DOUBLE,
    L_RETURNFLAG STRING,
    L_LINESTATUS STRING,
    L_SHIPDATE DATE,
    L_COMMITDATE DATE,
    L_RECEIPTDATE DATE,
    L_SHIPINSTRUCT STRING,
    L_SHIPMODE STRING,
    L_COMMENT STRING
)
STORED AS RCFILE
LOCATION 'oss://bucke-for-testing/datasets/tpch/1x/rcfile_date/lineitem_rcfile'

2.8 AVRO文件

DLA针对AVRO文件建表时，需要指定STORED AS AVRO，并且定义的字段需要符合AVRO文件的schema。

如果不确定可以通过使用Avro提供的工具，获得schema，并根据schema建表。
在Apache Avro官网下载avro-tools-.jar到本地，执行下面的命令获得Avro文件的schema：

java -jar avro-tools-1.8.2.jar getschema /path/to/your/doctors.avro
{
  "type" : "record",
  "name" : "doctors",
  "namespace" : "testing.hive.avro.serde",
  "fields" : [ {
    "name" : "number",
    "type" : "int",
    "doc" : "Order of playing the role"
  }, {
    "name" : "first_name",
    "type" : "string",
    "doc" : "first name of actor playing role"
  }, {
    "name" : "last_name",
    "type" : "string",
    "doc" : "last name of actor playing role"
  } ]
}

建表语句如下，其中fields中的name对应表中的列名，type需要参考本文档中的表格转成DLA支持的类型

CREATE EXTERNAL TABLE doctors(
number int,
first_name string,
last_name string)
STORED AS AVRO
LOCATION 'oss://mybucket-for-testing/directory/to/doctors';

大多数情况下，Avro的类型可以直接转换成DLA中对应的类型。如果该类型在DLA不支持，则会转换成接近的类型。具体请参照下表：

2.9 可以用正则表达式匹配的文件

通常此类型的文件是以纯文本格式存储在OSS上的，每一行代表表中的一条记录，并且每行可以用正则表达式匹配。
例如，Apache WebServer日志文件就是这种类型的文件。

某日志文件的内容为：

127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326
127.0.0.1 - - [26/May/2009:00:00:00 +0000] "GET /someurl/?track=Blabla(Main) HTTP/1.1" 200 5864 - "Mozilla/5.0 (Windows; U; Windows NT 6.0; en-US) AppleWebKit/525.19 (KHTML, like Gecko) Chrome/1.0.154.65 Safari/525.19"

每行文件可以用下面的正则表达式表示，列之间使用空格分隔：

([^ ]*) ([^ ]*) ([^ ]*) (-|\\[[^\\]]*\\]) ([^ \"]*|\"[^\"]*\") (-|[0-9]*) (-|[0-9]*)(?: ([^ \"]*|\"[^\"]*\") ([^ \"]*|\"[^\"]*\"))?

针对上面的文件格式，建表语句可以表示为：

CREATE EXTERNAL TABLE serde_regex(
  host STRING,
  identity STRING,
  userName STRING,
  time STRING,
  request STRING,
  status STRING,
  size INT,
  referer STRING,
  agent STRING)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
  "input.regex" = "([^ ]*) ([^ ]*) ([^ ]*) (-|\\[[^\\]]*\\]) ([^ \"]*|\"[^\"]*\") (-|[0-9]*) (-|[0-9]*)(?: ([^ \"]*|\"[^\"]*\") ([^ \"]*|\"[^\"]*\"))?"
)
STORED AS TEXTFILE
LOCATION 'oss://bucket-for-testing/datasets/serde/regex';

查询结果

mysql> select * from serde_regex;
+-----------+----------+-------+------------------------------+---------------------------------------------+--------+------+---------+--------------------------------------------------------------------------------------------------------------------------+
| host      | identity | userName | time                         | request                                     | status | size | referer | agent                                                                                                                    |
+-----------+----------+-------+------------------------------+---------------------------------------------+--------+------+---------+--------------------------------------------------------------------------------------------------------------------------+
| 127.0.0.1 | -        | frank | [10/Oct/2000:13:55:36 -0700] | "GET /apache_pb.gif HTTP/1.0"               | 200    | 2326 | NULL    | NULL                                                                                                                     |
| 127.0.0.1 | -        | -     | [26/May/2009:00:00:00 +0000] | "GET /someurl/?track=Blabla(Main) HTTP/1.1" | 200    | 5864 | -       | "Mozilla/5.0 (Windows; U; Windows NT 6.0; en-US) AppleWebKit/525.19 (KHTML, like Gecko) Chrome/1.0.154.65 Safari/525.19" |
+-----------+----------+-------+------------------------------+---------------------------------------------+--------+------+---------+--------------------------------------------------------------------------------------------------------------------------+

2.10 Esri ArcGIS的地理JSON数据文件

DLA支持Esri ArcGIS的地理JSON数据文件的SerDe处理，关于这种地理JSON数据格式说明，可以参考：https://github.com/Esri/spatial-framework-for-hadoop/wiki/JSON-Formats

示例：

CREATE EXTERNAL TABLE IF NOT EXISTS california_counties
(
    Name string,
    BoundaryShape binary
)
ROW FORMAT SERDE 'com.esri.hadoop.hive.serde.JsonSerde'
STORED AS INPUTFORMAT 'com.esri.json.hadoop.EnclosedJsonInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION 'oss://test_bucket/datasets/geospatial/california-counties/'

3. 总结

通过以上例子可以看出，DLA可以支持大部分开源存储格式的文件。对于同一份数据，使用不同的存储格式，在OSS中存储文件的大小，DLA的查询分析速度上会有较大的差别。推荐使用ORC格式进行文件的存储和查询。

为了获得更快的查询速度，DLA还在不断的优化中，后续也会支持更多的数据源，为用户带来更好的大数据分析体验。

 

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02-28 22:47
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