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這期內容當中小編將會給大家帶來有關Data Lake Analytics + OSS數據文件的格式處理分別是怎樣的，文章內容豐富且以專業的角度為大家分析和敘述，閱讀完這篇文章希望大家可以有所收獲。
Data Lake Analytics是Serverless化的云上交互式查詢分析服務。用戶可以使用標準的SQL語句，對存儲在OSS、TableStore上的數據無需移動，直接進行查詢分析。
除了純文本文件(例如，CSV，TSV等)，用戶存儲在OSS上的其他格式的數據文件，也可以使用Data Lake Analytics進行查詢分析，包括ORC, PARQUET, JSON, RCFILE, AVRO甚至ESRI規范的地理JSON數據，還可以用正則表達式匹配的文件等。
下面詳細介紹如何根據存儲在OSS上的文件格式使用Data Lake Analytics （下文簡稱 DLA）進行分析。DLA內置了各種處理文件數據的SerDe（Serialize/Deserilize的簡稱,目的是用于序列化和反序列化）實現，用戶無需自己編寫程序，基本上能選用DLA中的一款或多款SerDe來匹配您OSS上的數據文件格式。
用戶可以依據存儲在OSS上的數據文件進行建表，通過STORED AS 指定數據文件的格式。
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語句查看原始建表語句。
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已經支持的文件格式，當針對下列格式的文件建表時，可以直接使用STORED AS，DLA會選擇合適的SERDE/INPUTFORMAT/OUTPUTFORMAT。
存儲格式 | 描述 |
STORED AS TEXTFILE | 數據文件的存儲格式為純文本文件。默認的文件類型。 文件中的每一行對應表中的一條記錄。 |
STORED AS ORC | 數據文件的存儲格式為ORC。 |
STORED AS PARQUET | 數據文件的存儲格式為PARQUET。 |
STORED AS RCFILE | 數據文件的存儲格式為RCFILE。 |
STORED AS AVRO | 數據文件的存儲格式為AVRO。 |
STORED AS JSON | 數據文件的存儲格式為JSON (Esri ArcGIS的地理JSON數據文件 除外)。 |
在指定了STORED AS 的同時，還可以根據具體文件的特點，指定SerDe (用于解析數據文件并映射到DLA表)，特殊的列分隔符等。
后面的部分會做進一步的講解。
CSV文件，本質上還是純文本文件，可以使用STORED AS TEXTFILE。
列與列之間以逗號分隔，可以通過ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' 表示。
例如，數據文件oss://bucket-for-testing/oss/text/cities/city.csv的內容為
Beijing,China,010 ShangHai,China,021 Tianjin,China,022
建表語句可以為
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在使用時需要注意以下幾點：
用戶可以為行的字段指定字段分隔符、字段內容引用符號和轉義字符，例如：WITH SERDEPROPERTIES ("separatorChar" = ",", "quoteChar" = "`", "escapeChar" = "\" )；
不支持字段內嵌入的行分割符；
所有字段定義STRING類型；
其他數據類型的處理，可以在SQL中使用函數進行轉換。
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';
需要自定義列分隔符(FIELDS TERMINATED BY)，轉義字符(ESCAPED BY)，行結束符(LINES TERMINATED BY)。
需要在建表語句中指定
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' ESCAPED BY '\\' LINES TERMINATED BY '\n'
在csv文件中，有時會帶有HEADER信息，需要在數據讀取時忽略掉這些內容。這時需要在建表語句中定義skip.header.line.count。
例如，數據文件oss://my-bucket/datasets/tpch/nation_csv/nation_header.tbl的內容如下：
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|
相應的建表語句為：
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和數據文件的實際行數n有如下關系:
當x<=0時，DLA在讀取文件時，不會過濾掉任何信息，即全部讀取；
當0
當x>=n時，DLA在讀取文件時，會過濾掉所有的文件內容。
與CSV文件類似，TSV格式的文件也是純文本文件，列與列之間的分隔符為Tab。
例如，數據文件oss://bucket-for-testing/oss/text/cities/city.tsv的內容為
Beijing China 010 ShangHai China 021 Tianjin China 022
建表語句可以為
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';
假設您的數據字段的分隔符包含多個字符，可采用如下示例建表語句，其中每行的數據字段分割符為“||”，可以替換為您具體的分割符字符串。
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可以處理的JSON文件通常以純文本的格式存儲，數據文件的編碼方式需要是UTF-8。
在JSON文件中，每行必須是一個完整的JSON對象。
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"}需要改寫成：
{"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"}建表語句可以寫
CREATE EXTERNAL TABLE t1 (id int, name string, c3 timestamp) STORED AS JSON LOCATION 'oss://path/to/t1/directory';
使用struct和array結構定義嵌套的JSON數據。
例如，用戶原始數據（注意：無論是否嵌套，一條完整的JSON數據都只能放在一行上，才能被Data Lake Analytics處理）：
{ "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" } ] } }使用在線JSON格式化工具格式化后，數據內容如下：
{
"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"
}
]
}
}則建表語句可以寫成如下（注意：LOCATION中指定的路徑必須是JSON數據文件所在的目錄，該目錄下的所有JSON文件都能被識別為該表的數據）：
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/';
對該表進行查詢：
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]]] | +---------+----------------------------------------------------------------------------------------------------------------+
對于struct定義的嵌套結構，可以通過“.”進行層次對象引用，對于array定義的數組結構，可以通過“[數組下標]”（注意：數組下標從1開始）進行對象引用。
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值作為字符串存儲：
{"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\",\"張三\",\"李四\",\"cui.yuanc\"],\"protectThreshold\":0,\"requireSameEnv\":false,\"resetWeight\":false,\"symmetricCallType\":null,\"symmetricType\":\"warehouse\",\"tagName\":\"ipGroup\",\"tenantId\":\"\",\"tenants\":[],\"token\":\"1cf0ec0c771321bb4177182757a67fb0\",\"useSpecifiedURL\":false}"}使用在線JSON格式化工具格式化后，數據內容如下：
{
"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\",\"張三\",\"李四\",\"cui.yuanc\"],\"protectThreshold\":0,\"requireSameEnv\":false,\"resetWeight\":false,\"symmetricCallType\":null,\"symmetricType\":\"warehouse\",\"tagName\":\"ipGroup\",\"tenantId\":\"\",\"tenants\":[],\"token\":\"1cf0ec0c771321bb4177182757a67fb0\",\"useSpecifiedURL\":false}"
}建表語句為
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，可進行查詢：
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","張三","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函數的使用方式：
mysql> select json_extract_scalar(json_parse(value), '$.owners[1]') from json_table_2; +--------+ | _col0 | +--------+ | 張三 | +--------+ mysql> select json_extract_scalar(json_obj.json_col, '$.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開源項目Hive支持的一種優化的列存儲文件格式。與CSV文件相比，不僅可以節省存儲空間，還可以得到更好的查詢性能。
對于ORC文件，只需要在建表時指定 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開源項目Hadoop支持的一種列存儲的文件格式。
使用DLA建表時，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), 列存儲文件，可以有效地將關系型表結構存儲在分布式系統中，并且可以被高效地讀取和處理。
DLA在建表時，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針對AVRO文件建表時，需要指定STORED AS AVRO，并且定義的字段需要符合AVRO文件的schema。
如果不確定可以通過使用Avro提供的工具，獲得schema，并根據schema建表。
在Apache Avro官網下載avro-tools-.jar到本地，執行下面的命令獲得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"
} ]
}建表語句如下，其中fields中的name對應表中的列名，type需要參考本文檔中的表格轉成DLA支持的類型
CREATE EXTERNAL TABLE doctors( number int, first_name string, last_name string) STORED AS AVRO LOCATION 'oss://mybucket-for-testing/directory/to/doctors';
大多數情況下，Avro的類型可以直接轉換成DLA中對應的類型。如果該類型在DLA不支持，則會轉換成接近的類型。具體請參照下表：
| Avro類型 | 對應DLA類型 |
|---|---|
| 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 |
通常此類型的文件是以純文本格式存儲在OSS上的，每一行代表表中的一條記錄，并且每行可以用正則表達式匹配。
例如，Apache WebServer日志文件就是這種類型的文件。
某日志文件的內容為：
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則表達式表示，列之間使用空格分隔：
([^ ]*) ([^ ]*) ([^ ]*) (-|\\[[^\\]]*\\]) ([^ \"]*|\"[^\"]*\") (-|[0-9]*) (-|[0-9]*)(?: ([^ \"]*|\"[^\"]*\") ([^ \"]*|\"[^\"]*\"))?
針對上面的文件格式，建表語句可以表示為：
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';
查詢結果
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數據文件的SerDe處理，關于這種地理JSON數據格式說明，可以參考：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/'
通過以上例子可以看出，DLA可以支持大部分開源存儲格式的文件。對于同一份數據，使用不同的存儲格式，在OSS中存儲文件的大小，DLA的查詢分析速度上會有較大的差別。推薦使用ORC格式進行文件的存儲和查詢。
為了獲得更快的查詢速度，DLA還在不斷的優化中，后續也會支持更多的數據源，為用戶帶來更好的大數據分析體驗。
上述就是小編為大家分享的Data Lake Analytics + OSS數據文件的格式處理分別是怎樣的了，如果剛好有類似的疑惑，不妨參照上述分析進行理解。如果想知道更多相關知識，歡迎關注億速云行業資訊頻道。
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