使用Data Lake Analytics + OSS分析CSV格式的TPC-H数据集
0. Data Lake Analytics(DLA)简介
关于Data Lake的概念,更多阅读可以参考:
https://en.wikipedia.org/wiki/Data_lake
以及AWS和Azure关于Data Lake的解读:
https://amazonaws-china.com/big-data/datalakes-and-analytics/what-is-a-data-lake/
https://azure.microsoft.com/en-us/solutions/data-lake/
终于,阿里云现在也有了自己的数据湖分析产品:https://www.aliyun.com/product/datalakeanalytics
可以点击申请使用(目前公测阶段还属于邀测模式,我们会尽快审批申请),体验本教程的TPC-H CSV数据格式的数据分析之旅。
产品文档:https://help.aliyun.com/product/70174.html
1. 开通Data Lake Analytics与OSS服务
如果您已经开通,可以跳过该步骤。如果没有开通,可以参考:https://help.aliyun.com/document_detail/70386.html
进行产品开通服务申请。
2. 下载TPC-H测试数据集
可以从这下载TPC-H 100MB的数据集:
https://public-datasets-cn-hangzhou.oss-cn-hangzhou.aliyuncs.com/tpch_100m_data.zip
3. 上传数据文件到OSS
登录阿里云官网的OSS控制台:https://oss.console.aliyun.com/overview
规划您要使用的OSS bucket,创建或选择好后,点击“文件管理”,因为有8个数据文件,为每个数据文件创建对应的文件目录:
创建好8个目录如下:
点击进入目录,上传相应的数据文件,例如,customer目录,则上传customer.tbl文件。
上传好后,如下图。然后,依次把其他7个数据文件也上传到对应的目录下。
至此,8个数据文件都上传到了您的OSS bucket中:
oss://xxx/tpch_100m/customer/customer.tbl
oss://xxx/tpch_100m/lineitem/lineitem.tbl
oss://xxx/tpch_100m/nation/nation.tbl
oss://xxx/tpch_100m/orders/orders.tbl
oss://xxx/tpch_100m/part/part.tbl
oss://xxx/tpch_100m/partsupp/partsupp.tbl
oss://xxx/tpch_100m/region/region.tbl
oss://xxx/tpch_100m/supplier/supplier.tbl
4. 登录Data Lake Analytics控制台
https://openanalytics.console.aliyun.com/
点击“登录数据库”,输入开通服务时分配的用户名和密码,登录Data Lake Analytics控制台。
5. 创建Schema和Table
输入创建SCHEMA的语句,点击“同步执行”。
CREATE SCHEMA tpch_100m with DBPROPERTIES(
LOCATION = 'oss://test-bucket-julian-1/tpch_100m/',
catalog='oss'
);
(注意:目前在同一个阿里云region,Data Lake Analytics的schema名全局唯一,建议schema名尽量根据业务定义,已有重名schema,在创建时会提示报错,则请换一个schema名字。)
Schema创建好后,在“数据库”的下拉框中,选择刚刚创建的schema。然后在SQL文本框中输入建表语句,点击同步执行。
建表语句语法参考:https://help.aliyun.com/document_detail/72006.html
TPC-H对应的8个表的建表语句如下,分别贴入文档框中执行(LOCATION子句中的数据文件位置请根据您的实际OSS bucket目录相应修改)。(注意:目前控制台中还不支持多个SQL语句执行,请单条语句执行。)
CREATE EXTERNAL TABLE nation (
N_NATIONKEY INT,
N_NAME STRING,
N_ID 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';
CREATE EXTERNAL TABLE lineitem (
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
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION 'oss://test-bucket-julian-1/tpch_100m/lineitem';
CREATE EXTERNAL TABLE orders (
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
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION 'oss://test-bucket-julian-1/tpch_100m/orders';
CREATE EXTERNAL TABLE supplier (
S_SUPPKEY INT,
S_NAME STRING,
S_ADDRESS STRING,
S_NATIONKEY INT,
S_PHONE STRING,
S_ACCTBAL DOUBLE,
S_COMMENT STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION 'oss://test-bucket-julian-1/tpch_100m/supplier';
CREATE EXTERNAL TABLE partsupp (
PS_PARTKEY INT,
PS_SUPPKEY INT,
PS_AVAILQTY INT,
PS_SUPPLYCOST DOUBLE,
PS_COMMENT STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION 'oss://test-bucket-julian-1/tpch_100m/partsupp';
CREATE EXTERNAL TABLE customer (
C_CUSTKEY INT,
C_NAME STRING,
C_ADDRESS STRING,
C_NATIONKEY INT,
C_PHONE STRING,
C_ACCTBAL DOUBLE,
C_MKTSEGMENT STRING,
C_COMMENT STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION 'oss://test-bucket-julian-1/tpch_100m/customer';
CREATE EXTERNAL TABLE part (
P_PARTKEY INT,
P_NAME STRING,
P_MFGR STRING,
P_BRAND STRING,
P_TYPE STRING,
P_SIZE INT,
P_CONTAINER STRING,
P_RETAILPRICE DOUBLE,
P_COMMENT STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION 'oss://test-bucket-julian-1/tpch_100m/part';
CREATE EXTERNAL TABLE region (
R_REGIONKEY INT,
R_NAME STRING,
R_COMMENT STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION 'oss://test-bucket-julian-1/tpch_100m/region';
建表完毕后,刷新页面,在左边导航条中能看到schema下的8张表。
6. 执行TPC-H查询
TPC-H总共22条查询,如下:
Q1:
SELECT l_returnflag,
l_linestatus,
Sum(l_quantity) AS sum_qty,
Sum(l_extendedprice) AS sum_base_price,
Sum(l_extendedprice * (1 - l_discount)) AS sum_disc_price,
Sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) AS sum_charge,
Avg(l_quantity) AS avg_qty,
Avg(l_extendedprice) AS avg_price,
Avg(l_discount) AS avg_disc,
Count(*) AS count_order
FROM lineitem
WHERE l_shipdate <= date '1998-12-01' - INTERVAL '93' day
GROUP BY l_returnflag,
l_linestatus
ORDER BY l_returnflag,
l_linestatus
LIMIT 1;
Q2:
SELECT s_acctbal,
s_name,
n_name,
p_partkey,
p_mfgr,
s_address,
s_phone,
s_comment
FROM part,
supplier,
partsupp,
nation,
region
WHERE p_partkey = ps_partkey
AND s_suppkey = ps_suppkey
AND p_size = 35
AND p_type LIKE '%NICKEL'
AND s_nationkey = n_nationkey
AND n_regionkey = r_regionkey
AND r_name = 'MIDDLE EAST'
Q3:
SELECT l_orderkey,
Sum(l_extendedprice * (1 - l_discount)) AS revenue,
o_orderdate,
o_shippriority
FROM customer,
orders,
lineitem
WHERE c_mktsegment = 'AUTOMOBILE'
AND c_custkey = o_custkey
AND l_orderkey = o_orderkey
AND o_orderdate < date '1995-03-31'
AND l_shipdate > date '1995-03-31'
GROUP BY l_orderkey,
o_orderdate,
o_shippriority
ORDER BY revenue DESC,
o_orderdate
LIMIT 10;
Q4:
SELECT o_orderpriority,
Count(*) AS order_count
FROM orders,
lineitem
WHERE o_orderdate >= date '1997-10-01'
AND o_orderdate < date '1997-10-01' + INTERVAL '3' month
AND l_orderkey = o_orderkey
AND l_commitdate < l_receiptdate
GROUP BY o_orderpriority
ORDER BY o_orderpriority
LIMIT 1;
Q5:
SELECT n_name,
Sum(l_extendedprice * (1 - l_discount)) AS revenue
FROM customer,
orders,
lineitem,
supplier,
nation,
region
WHERE c_custkey = o_custkey
AND l_orderkey = o_orderkey
AND l_suppkey = s_suppkey
AND c_nationkey = s_nationkey
AND s_nationkey = n_nationkey
AND n_regionkey = r_regionkey
AND r_name = 'ASIA'
AND o_orderdate >= date '1995-01-01'
AND o_orderdate < date '1995-01-01' + INTERVAL '1' year
GROUP BY n_name
ORDER BY revenue DESC
LIMIT 1;
Q6:
SELECT sum(l_extendedprice * l_discount) AS revenue
FROM lineitem
WHERE l_shipdate >= date '1995-01-01'
AND l_shipdate < date '1995-01-01' + interval '1' year
AND l_discount between 0.04 - 0.01 AND 0.04 + 0.01
AND l_quantity < 24
LIMIT 1;
Q7:
SELECT supp_nation,
cust_nation,
l_year,
Sum(volume) AS revenue
FROM (
SELECT n1.n_name AS supp_nation,
n2.n_name AS cust_nation,
Extract(year FROM l_shipdate) AS l_year,
l_extendedprice * (1 - l_discount) AS volume
FROM supplier,
lineitem,
orders,
customer,
nation n1,
nation n2
WHERE s_suppkey = l_suppkey
AND o_orderkey = l_orderkey
AND c_custkey = o_custkey
AND s_nationkey = n1.n_nationkey
AND c_nationkey = n2.n_nationkey
AND ( (
n1.n_name = 'GERMANY'
AND n2.n_name = 'INDIA')
OR (
n1.n_name = 'INDIA'
AND n2.n_name = 'GERMANY') )
AND l_shipdate BETWEEN date '1995-01-01' AND date '1996-12-31' ) AS shipping
GROUP BY supp_nation,
cust_nation,
l_year
ORDER BY supp_nation,
cust_nation,
l_year
LIMIT 1;
Q8:
SELECT o_year,
Sum(
CASE
WHEN nation = 'INDIA' THEN volume
ELSE 0
end) / Sum(volume) AS mkt_share
FROM (
SELECT Extract(year FROM o_orderdate) AS o_year,
l_extendedprice * (1 - l_discount) AS volume,
n2.n_name AS nation
FROM part,
supplier,
lineitem,
orders,
customer,
nation n1,
nation n2,
region
WHERE p_partkey = l_partkey
AND s_suppkey = l_suppkey
AND l_orderkey = o_orderkey
AND o_custkey = c_custkey
AND c_nationkey = n1.n_nationkey
AND n1.n_regionkey = r_regionkey
AND r_name = 'ASIA'
AND s_nationkey = n2.n_nationkey
AND o_orderdate BETWEEN date '1995-01-01' AND date '1996-12-31'
AND p_type = 'STANDARD ANODIZED STEEL' ) AS all_nations
GROUP BY o_year
ORDER BY o_year
LIMIT 1;
Q9:
SELECT nation,
o_year,
Sum(amount) AS sum_profit
FROM (
SELECT n_name AS nation,
Extract(year FROM o_orderdate) AS o_year,
l_extendedprice * (1 - l_discount) - ps_supplycost * l_quantity AS amount
FROM part,
supplier,
lineitem,
partsupp,
orders,
nation
WHERE s_suppkey = l_suppkey
AND ps_suppkey = l_suppkey
AND ps_partkey = l_partkey
AND p_partkey = l_partkey
AND o_orderkey = l_orderkey
AND s_nationkey = n_nationkey
AND p_name LIKE '%aquamarine%' ) AS profit
GROUP BY nation,
o_year
ORDER BY nation,
o_year DESC
LIMIT 1;
Q10:
SELECT c_custkey,
c_name,
Sum(l_extendedprice * (1 - l_discount)) AS revenue,
c_acctbal,
n_name,
c_address,
c_phone,
c_comment
FROM customer,
orders,
lineitem,
nation
WHERE c_custkey = o_custkey
AND l_orderkey = o_orderkey
AND o_orderdate >= date '1994-08-01'
AND o_orderdate < date '1994-08-01' + INTERVAL '3' month
AND l_returnflag = 'R'
AND c_nationkey = n_nationkey
GROUP BY c_custkey,
c_name,
c_acctbal,
c_phone,
n_name,
c_address,
c_comment
ORDER BY revenue DESC
LIMIT 20;
Q11:
SELECT ps_partkey,
Sum(ps_supplycost * ps_availqty) AS value
FROM partsupp,
supplier,
nation
WHERE ps_suppkey = s_suppkey
AND s_nationkey = n_nationkey
AND n_name = 'PERU'
GROUP BY ps_partkey
HAVING Sum(ps_supplycost * ps_availqty) >
(
SELECT Sum(ps_supplycost * ps_availqty) * 0.0001000000 as sum_value
FROM partsupp,
supplier,
nation
WHERE ps_suppkey = s_suppkey
AND s_nationkey = n_nationkey
AND n_name = 'PERU'
)
ORDER BY value DESC
LIMIT 1;
Q12:
SELECT l_shipmode, sum(case when o_orderpriority = '1-URGENT' or o_orderpriority = '2-HIGH' then 1
else 0
end) AS high_line_count, sum(case when o_orderpriority <> '1-URGENT' and o_orderpriority <> '2-HIGH' then 1
else 0
end) AS low_line_count
FROM orders,
lineitem
WHERE o_orderkey = l_orderkey
AND l_shipmode in ('MAIL', 'TRUCK')
AND l_commitdate < l_receiptdate
AND l_shipdate < l_commitdate
AND l_receiptdate >= date '1996-01-01'
AND l_receiptdate < date '1996-01-01' + interval '1' year
GROUP BY l_shipmode
ORDER BY l_shipmode
LIMIT 1;
Q13:
SELECT c_count, count(*) AS custdist
FROM (
SELECT c_custkey, count(o_orderkey) AS c_count
FROM customer,
orders
WHERE c_custkey = o_custkey
AND o_comment NOT LIKE '%pending%accounts%'
GROUP BY c_custkey ) AS c_orders
GROUP BY c_count
ORDER BY custdist DESC, c_count DESC
LIMIT 1;
Q14:
SELECT 100.00 * sum(case when p_type like 'PROMO%' then l_extendedprice * (1 - l_discount)
else 0
end) / sum(l_extendedprice * (1 - l_discount)) AS promo_revenue
FROM lineitem,
part
WHERE l_partkey = p_partkey
AND l_shipdate >= date '1996-01-01'
AND l_shipdate < date '1996-01-01' + interval '1' month
LIMIT 1;
Q15:
WITH revenue0 AS
(
SELECT l_suppkey AS supplier_no, sum(l_extendedprice * (1 - l_discount)) AS total_revenue
FROM lineitem
WHERE l_shipdate >= date '1993-01-01'
AND l_shipdate < date '1993-01-01' + interval '3' month
GROUP BY l_suppkey
)
SELECT s_suppkey, s_name, s_address, s_phone, total_revenue
FROM supplier, revenue0
WHERE s_suppkey = supplier_no
AND total_revenue IN (
SELECT max(total_revenue)
FROM revenue0 )
ORDER BY s_suppkey;
Q16:
SELECT p_brand, p_type, p_size, count(distinct ps_suppkey) AS supplier_cnt
FROM partsupp,
part
WHERE p_partkey = ps_partkey
AND p_brand <> 'Brand#23'
AND p_type NOT LIKE 'PROMO BURNISHED%'
AND p_size IN (1, 13, 10, 28, 21, 35, 31, 11)
AND ps_suppkey NOT IN (
SELECT s_suppkey
FROM supplier
WHERE s_comment LIKE '%Customer%Complaints%' )
GROUP BY p_brand, p_type, p_size
ORDER BY supplier_cnt DESC, p_brand, p_type, p_size
LIMIT 1;
Q17:
SELECT
sum(l_extendedprice) / 7.0 AS avg_yearly
FROM
lineitem,
part
WHERE p_partkey = l_partkey
AND p_brand = 'Brand#44'
AND p_container = 'WRAP PKG'
AND l_quantity < (
SELECT
0.2 * avg(l_quantity)
FROM
lineitem, part
WHERE
l_partkey = p_partkey
);
Q18:
SELECT c_name, c_custkey, o_orderkey, o_orderdate, o_totalprice, sum(l_quantity)
FROM customer,
orders,
lineitem
WHERE o_orderkey IN (
SELECT l_orderkey
FROM lineitem
GROUP BY l_orderkey
HAVING sum(l_quantity) > 315 )
AND c_custkey = o_custkey
AND o_orderkey = l_orderkey
GROUP BY c_name, c_custkey, o_orderkey, o_orderdate, o_totalprice
ORDER BY o_totalprice DESC, o_orderdate
LIMIT 100;
Q19:
SELECT sum(l_extendedprice* (1 - l_discount)) AS revenue
FROM lineitem,
part
WHERE ( p_partkey = l_partkey and p_brand = 'Brand#12'
and p_container in ('SM CASE', 'SM BOX', 'SM PACK', 'SM PKG')
and l_quantity >= 6 and l_quantity <= 6 + 10
and p_size between 1 and 5
and l_shipmode in ('AIR', 'AIR REG')
and l_shipinstruct = 'DELIVER IN PERSON' )
or ( p_partkey = l_partkey and p_brand = 'Brand#13'
and p_container in ('MED BAG', 'MED BOX', 'MED PKG', 'MED PACK')
and l_quantity >= 10 and l_quantity <= 10 + 10
and p_size between 1 and 10
and l_shipmode in ('AIR', 'AIR REG')
and l_shipinstruct = 'DELIVER IN PERSON' )
or ( p_partkey = l_partkey and p_brand = 'Brand#24'
and p_container in ('LG CASE', 'LG BOX', 'LG PACK', 'LG PKG')
and l_quantity >= 21 and l_quantity <= 21 + 10
and p_size between 1 and 15
and l_shipmode in ('AIR', 'AIR REG')
and l_shipinstruct = 'DELIVER IN PERSON' )
LIMIT 1;
Q20:
with temp_table as
(
select 0.5 * sum(l_quantity) as col1
from lineitem,
partsupp
where l_partkey = ps_partkey and l_suppkey = ps_suppkey
and l_shipdate >= date '1993-01-01'
and l_shipdate < date '1993-01-01' + interval '1' year
)
select s_name, s_address
from supplier,
nation
where s_suppkey in (
select ps_suppkey
from partsupp,
temp_table
where ps_partkey in (
select p_partkey
from part
where p_name like 'dark%' )
and ps_availqty > temp_table.col1 )
and s_nationkey = n_nationkey and n_name = 'JORDAN'
order by s_name
limit 1;
Q21:
select
s_name,
count(*) as numwait
from
supplier,
lineitem l1,
orders,
nation
where
s_suppkey = l1.l_suppkey
and o_orderkey = l1.l_orderkey
and o_orderstatus = 'F'
and l1.l_receiptdate > l1.l_commitdate
and exists (
select
*
from
lineitem l2
where
l2.l_orderkey = l1.l_orderkey
and l2.l_suppkey <> l1.l_suppkey
)
and not exists (
select
*
from
lineitem l3
where
l3.l_orderkey = l1.l_orderkey
and l3.l_suppkey <> l1.l_suppkey
and l3.l_receiptdate > l3.l_commitdate
)
and s_nationkey = n_nationkey
and n_name = 'SAUDI ARABIA'
group by
s_name
order by
numwait desc,
s_name
limit 100;
Q22:
with temp_table_1 as
(
select avg(c_acctbal) as avg_value
from customer
where c_acctbal > 0.00 and substring(c_phone from 1 for 2)
in ('33', '29', '37', '35', '25', '27', '43')
),
temp_table_2 as
(
select count(*) as count1
from orders, customer
where o_custkey = c_custkey
)
select cntrycode, count(*) as numcust, sum(c_acctbal) as totacctbal
from (
select substring(c_phone from 1 for 2) as cntrycode, c_acctbal
from customer, temp_table_1, temp_table_2
where substring(c_phone
from 1
for 2) in ('33', '29', '37', '35', '25', '27', '43')
and c_acctbal > temp_table_1.avg_value
and temp_table_2.count1 = 0) as custsale
group by cntrycode
order by cntrycode
limit 1;
7. 异步执行查询
Data Lake Analytics支持“同步执行”模式和“异步执行”模式。“同步执行”模式下,控制台界面等待执行结果返回;“异步执行”模式下,立刻返回查询任务的ID。
点击“执行状态”,可以看到该异步查询任务的执行状态,主要分为:“RUNNING”,“SUCCESS”,“FAILURE”。
点击“刷新”,当STATUS变为“SUCCESS”时,表示查询成功,同时可查看查询耗时“ELAPSE_TIME”和查询扫描的数据字节数“SCANNED_DATA_BYTES”。
8. 查看查询历史
点击“执行历史”,可以看到您执行的查询的历史详细信息,包括:
1)查询语句;
2)查询耗时与执行具体时间;
3)查询结果返回行数;
4)查询状态;
5)查询扫描的字节数;
6)结果集回写到的目标OSS文件(Data Lake Analytics会将查询结果集保存用户的bucket中)。
查询结果文件自动上传到用户同region的OSS bucket中,其中包括结果数据文件和结果集元数据描述文件。
{QueryLocation}/{query_name}|Unsaved}/{yyyy}/{mm}/{dd}/{query_id}/xxx.csv
{QueryLocation}/{query_name}|Unsaved}/{yyyy}/{mm}/{dd}/{query_id}/xxx.csv.metadata
其中QueryLocation为:
aliyun-oa-query-results-<your_account_id>-<oss_region>
9. 后续
至此,本教程一步一步教您如何利用Data Lake Analytics云产品分析您OSS上的CSV格式的数据文件。除了CSV文件外,Data Lake Analytics还支持Parquet、ORC、json、RCFile、AVRO等多种格式文件的数据分析能力。特别是Parquet、ORC,相比CSV文件,有极大的性能和成本优势(同样内容的数据集,拥有更小的存储空间、更快的查询性能,这也意味着更低的分析成本)。
后续陆续会有更多教程和文章,手把手教您轻松使用Data Lake Analytics进行数据湖上数据分析和探索,开启您的云上低成本、即存即用的数据分析和探索之旅。
原文链接
更多技术干货 请关注阿里云云栖社区微信号 :yunqiinsight
使用Data Lake Analytics + OSS分析CSV格式的TPC-H数据集的更多相关文章
- 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 ...
- Data Lake Analytics: 使用DataWorks来调度DLA任务
DataWorks作为阿里云上广受欢迎的大数据开发调度服务,最近加入了对于Data Lake Analytics的支持,意味着所有Data Lake Analytics的客户可以获得任务开发.任务依赖 ...
- Data Lake Analytics中OSS LOCATION的使用说明
前言 Data Lake Analytic(后文简称 DLA)可以帮助用户通过标准的SQL语句直接对存储在OSS.TableStore上的数据进行查询分析. 在查询前,用户需要根据数据文件的格式和内容 ...
- 如何在Data Lake Analytics中使用临时表
前言 Data Lake Analytics (后文简称DLA)是阿里云重磅推出的一款用于大数据分析的产品,可以对存储在OSS,OTS上的数据进行查询分析.相较于传统的数据分析产品,用户无需将数据重新 ...
- 使用Data Lake Analytics从OSS清洗数据到AnalyticDB
前提 必须是同一阿里云region的Data Lake Analytics(DLA)到AnalyticDB的才能进行清洗操作: 开通并初始化了该region的DLA服务: 开通并购买了Analytic ...
- Data Lake Analytics,大数据的ETL神器!
0. Data Lake Analytics(简称DLA)介绍 数据湖(Data Lake)是时下大数据行业热门的概念:https://en.wikipedia.org/wiki/Data_lake. ...
- Data Lake Analytics的Geospatial分析函数
0. 简介 为满足部分客户在云上做Geometry数据的分析需求,阿里云Data Lake Analytics(以下简称:DLA)支持多种格式的地理空间数据处理函数,符合Open Geospatial ...
- Data Lake Analytics账号和权限体系详细介绍
一.Data Lake Analytics介绍 数据湖(Data Lake)是时下大数据行业热门的概念:https://en.wikipedia.org/wiki/Data_lake.基于数据湖做分析 ...
- 如何使用Data Lake Analytics创建分区表
前言 Data Lake Analytics(后文简称DLA)提供了无服务化的大数据分析服务,帮助用户通过标准的SQL语句直接对存储在OSS.TableStore上的数据进行查询分析. 在关系型数据库 ...
随机推荐
- SpringBoot 03_利用FastJson返回Json数据
自上一节:SpringBoot 02_返回json数据,可以返回json数据之后,由于有些人习惯于不同的Json框架,比如fastjson,这里介绍一下如何在SpringBoot中集成fastjson ...
- 《DSP using MATLAB》Problem 7.36
代码: %% ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ %% Output In ...
- C# GDI+编程(二)
常用的绘图函数 DrawArc绘制一个弧形 示例:graphics.DrawArc(pen,,,,,,) 倒数第二个参数,表示起始度数,最后一个参数是弧形的跨越度数.比如起始度数是90,跨越度数是12 ...
- DROOLS通过URL访问changset
package droolsRule; import java.net.Authenticator; import java.net.PasswordAuthentication; import ka ...
- 不小心使用vcpkg之后再使用conan,一直报链接错误
原来是使用vcpkg的时候,不小心使用了.\vcpkg integrate install命令,把vcpkg到所有的vs项目(这个不需要什么其他的引用,但是容易起冲突) 然后卸载掉就好了,这篇文章真是 ...
- 在HBase之上构建SQL引擎
- Luogu P3496 [POI2010]GIL-Guilds(贪心+搜索)
P3496 [POI2010]GIL-Guilds 题意 给一张无向图,要求你用黑(\(K\))白(\(S\))灰(\(N\))给点染色,且满足对于任意一个黑点,至少有一个白点和他相邻:对于任意一个白 ...
- [转][Prism]Composite Application Guidance for WPF(6)——服务
[Prism]Composite Application Guidance for WPF(6)——服务 周银辉 在Ioc和DI中,最熟悉的 ...
- [转]用DateTime.ToString(string format)输出不同格式的日期
DateTime.ToString()函数有四个重载.一般用得多的就是不带参数的那个了.殊不知,DateTime.ToString(string format)功能更强大,能输出不同格式的日期.以下把 ...
- final,finally和finalize之间的区别
(1)final用于声明属性,方法和类,分别表示属性不可变,方法不可覆盖,类不可继承.内部类要访问局部变量,局部变量必须定义成final类型,比如一段代码 (2)finally是异常处理语句结构的一部 ...