这期内容当中小编将会给大家带来有关Data Lake Analytics + OSS数据文件的格式处理分别是怎样的，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。
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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。
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。
下面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。
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。
存储格式 | 描述 |
STORED AS TEXTFILE | 数据文件的存储格式为纯文本文件。默认的文件类型。 文件中的每一行对应表中的一条记录。 |
STORED AS ORC | 数据文件的存储格式为ORC。 |
STORED AS PARQUET | 数据文件的存储格式为PARQUET。 |
STORED AS RCFILE | 数据文件的存储格式为RCFILE。 |
STORED AS AVRO | 数据文件的存储格式为AVRO。 |
STORED AS JSON | 数据文件的存储格式为JSON (Esri ArcGIS的地理JSON数据文件 除外)。 |
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。
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。
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';
OpenCSVSerde在使用时需要注意以下几点：
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" = "\" )；
不支持字段内嵌入的行分割符；
所有字段定义STRING类型；
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';
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'
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。
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';
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/';
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"}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';
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 | +---------+------+--------------------+-------+
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 | +-----------+
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';
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';
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'
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：
| Avro类型 | 对应DLA类型 |
|---|---|
| null | void |
| boolean | boolean |
| int | int |
| long | bigint |
| float | float |
| double | double |
| bytes | binary |
| string | string |
| record | struct |
| map | map |
| list | array |
| union | union |
| enum | string |
| fixed | binary |
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" | +-----------+----------+-------+------------------------------+---------------------------------------------+--------+------+---------+--------------------------------------------------------------------------------------------------------------------------+
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/'
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。
上述就是小编为大家分享的Data Lake Analytics + OSS数据文件的格式处理分别是怎样的了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注创新互联行业资讯频道。