MaxCompute:Overview

Category

Description

Dataset name

Schema name

GitHub public event data

A large number of developers develop open source projects on GitHub and generate a large number of events during the development process. GitHub records the information about each event, including the event type, event details, developer, and code repository. GitHub also exposes public events, such as the events of starring repositories and submitting code.

GitHub public event dataset

github_events

National statistics data

Includes annual gross domestic product (GDP) data for countries around the world and all provinces in the Chinese mainland.

National statistics dataset

national_data

TPC performance data

TPC-DS

TPC-DS is a decision support benchmark that models several generally applicable aspects of a decision support system, including queries and data maintenance. TPC-DS enables emerging technologies, such as big data systems, to perform benchmark tests.

• TPC-DS 10-GB performance test dataset

• TPC-DS 100-GB performance test dataset

• TPC-DS 1-TB performance test dataset

• TPC-DS 10-TB performance test dataset

• tpcds_10g

• tpcds_100g

• tpcds_1t

• tpcds_10t

TPC-H

TPC-H is a decision support benchmark. It consists of a suite of business-oriented ad hoc queries and concurrent data modifications. TPC-H illustrates decision support systems that perform highly complex queries on large amounts of data and provide answers to critical business questions.

• TPC-H 10-GB performance test dataset

• TPC-H 100-GB performance test dataset

• TPC-H 1-TB performance test dataset

• TPC-H 10-TB performance test dataset

• tpch_10g

• tpch_100g

• tpch_1t

• tpch_10t

TPCx-BB

TPCx-BB is a TPC Express benchmark, which is designed to measure the performance of Hadoop-based big data systems. TPCx-BB measures the performance of both hardware and software components by executing 30 frequently performed analytical queries.

• TPCx-BB 10-GB performance test dataset

• TPCx-BB 100-GB performance test dataset

• TPCx-BB 1-TB performance test dataset

• TPCx-BB 10-TB performance test dataset

• tpcbb_10g

• tpcbb_100g

• tpcbb_1t

• tpcbb_10t

Digital business data

Includes Taobao advertising data, Taobao shopping data, and e-commerce data of Alibaba Group.

Digital business dataset

commerce

Life service data

Includes data of pre-owned houses, movies and box office, mobile phone number attribution, and administrative, urban, and rural division code information.

Life service dataset

life_service

Financial stock data

Includes stock information.

Financial stock dataset

finance

Project name

BIGDATA_PUBLIC_DATASET

Schema name

github_events

Supported regions

China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Ulanqab), China (Shenzhen), and China (Chengdu)

Table name and description

A large number of developers develop open source projects on GitHub and generate a large number of events during the development process. GitHub records the information about each event, including the event type, event details, developer, and code repository. GitHub also exposes public events, such as the events of starring repositories and submitting code. For more information about event types, see GitHub event types.

MaxCompute batch processes and develops large amounts of public event data that is provided by GH Archive and generates the following tables:

• dwd_github_events_odps: the fact table that stores GitHub public event data.

• dws_overview_by_repo_month: the aggregate table that stores monthly metric data of GitHub public events.

Update cycle

• dwd_github_events_odps: updated every T+1 hours.

• dws_overview_by_repo_month: updated every T+1 days.

Schema query

-- Enable session-level schema syntax. 
set odps.namespace.schema=true; 
-- Query the schema of the dwd_github_events_odps table. If you want to query the schema of another table, replace the schema name and table name in the following statement. 
desc bigdata_public_dataset.github_events.dwd_github_events_odps;

Query example

-- Enable session-level schema syntax. 
SET odps.namespace.schema=true; 
-- Obtain the rankings of starred repositories in the previous year. Note that the situations such as unstarring are not considered in this example.
SELECT
    repo_id,
    repo_name,
    COUNT(actor_login) total
FROM
    bigdata_public_dataset.github_events.dwd_github_events_odps
WHERE
    ds>=date_add(getdate(), -365)
    AND type = 'WatchEvent'
GROUP BY
    repo_id,
    repo_name
ORDER BY
    total DESC
LIMIT 10;

For more information about data and query examples, see GitHub public event data.

Project name

BIGDATA_PUBLIC_DATASET

Schema name

national_data

Supported regions

China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Ulanqab), China (Shenzhen), and China (Chengdu)

Table name and description

• annual_gdp_by_province: stores the annual GDP data of each province in the Chinese mainland.

• annual_gdp_by_country: stores the annual GDP data of each country around the world.

Update cycle

Fixed data is provided and is not updated.

Schema query

-- Enable session-level schema syntax. 
set odps.namespace.schema=true; 
-- Query the schema of the annual_gdp_by_province table. If you want to query the schema of another table, replace the schema name and table name in the following statement. 
desc bigdata_public_dataset.national_data.annual_gdp_by_province;

Query example

-- Enable session-level schema syntax. 
SET odps.namespace.schema=true; 
-- Query the GDP change trend of Beijing over the past 20 years. 
SELECT
    region,
    gdp,
    year
FROM
    bigdata_public_dataset.national_data.annual_gdp_by_province
WHERE
    region='Beijing'
ORDER BY
    year ASC
LIMIT 20;

Project name

BIGDATA_PUBLIC_DATASET

Schema name

tpcds_10g, tpcds_100g, tpcds_1t, and tpcds_10t

Supported regions

China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Ulanqab), China (Shenzhen), China (Chengdu), China (Hong Kong), Singapore, Germany (Frankfurt), Indonesia (Jakarta), US (Silicon Valley), US (Virginia), Malaysia (Kuala Lumpur), India (Mumbai), UK (London), UAE (Dubai), Australia (Sydney), and Japan (Tokyo)

Table name and description

The TPC-DS model simulates the sales system of a large-scale national chain retailer. The sales system involves three sales channels: store (brick-and-mortar store), web (online store), and catalog (telephone order). Each channel uses one table to store sales records, another table to store return records, and multiple dimension tables to store information such as product information, promotion information, and user information. Table details:

• call_center: stores the information about the customer service center.

• catalog_page: stores the information about catalogs.

• catalog_returns: stores the return records of products sold by using telephone orders.

• catalog_sales: stores the sales records of products sold by using telephone orders.

• customer: stores the customer information.

• customer_address: stores the customer address information.

• customer_demographics: stores the basic credit information about customers.

• date_dim: stores the time dimension information.

• household_demographics: stores the basic household credit information.

• income_band: stores the revenue information.

• inventory: stores the inventory information.

• item: stores the product information.

• promotion: stores the product promotion information.

• reason: stores the reasons for sales returns.

• ship_mode: stores the product shipment information.

• store: stores the merchant information.

• store_returns: stores the product return records for store sales.

• store_sales: stores the product sales records for store sales.

• time_dim: stores the time dimension information.

• warehouse: stores the warehouse information.

• web_page: stores the product web page information.

• web_returns: stores the product return records for web sales.

• web_sales: stores the product sales records for web sales.

• web_site: stores the basic information about the website.

Update cycle

Fixed data is provided and is not updated.

Schema query

-- Enable session-level schema syntax. 
SET odps.namespace.schema=TRUE; 
-- Query the schema of the call_center table in tpcds_10g. If you want to query a table schema from another dataset, replace the schema name and table name in the following statement. 
DESC bigdata_public_dataset.tpcds_10g.call_center;

Query example

SET odps.namespace.schema=TRUE; 
SELECT dt.d_year ,
       item.i_brand_id brand_id ,
       item.i_brand brand ,
       SUM(ss_sales_price) sum_agg
FROM bigdata_public_dataset.tpcds_10g.date_dim dt ,
     bigdata_public_dataset.tpcds_10g.store_sales ,
     bigdata_public_dataset.tpcds_10g.item
WHERE dt.d_date_sk = store_sales.ss_sold_date_sk
  AND store_sales.ss_item_sk = item.i_item_sk
  AND item.i_manufact_id = 190
  AND dt.d_moy = 12
GROUP BY dt.d_year ,
         item.i_brand ,
         item.i_brand_id
ORDER BY dt.d_year,
         sum_agg DESC,
         brand_id LIMIT 100;

For more query sample files of different data specifications, see TPC-DS data.

For more information about data, see TPC Benchmark DS Standard Specification.

Project name

BIGDATA_PUBLIC_DATASET

Schema name

tpch_10g, tpch_100g, tpch_1t, and tpch_10t

Supported regions

China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Ulanqab), China (Shenzhen), China (Chengdu), China (Hong Kong), Singapore, Germany (Frankfurt), Indonesia (Jakarta), US (Silicon Valley), US (Virginia), Malaysia (Kuala Lumpur), India (Mumbai), UK (London), UAE (Dubai), Australia (Sydney), and Japan (Tokyo)

Table name and description

TPC-H is a benchmark that is used to evaluate online analysis and processing. TPC-H data simulates business behavior between a provider and a buyer. TPC-H data contains information such as order information, product information, and user information. Table details:

• customer: stores the consumer information.

• lineitem: stores the online product information.

• nation: stores the country information.

• orders: stores the order information.

• part: stores the part information.

• partsupp: stores the supplier part information.

• region: stores the region information.

• supplier: stores the supplier information.

Update cycle

Fixed data is provided and is not updated.

Schema query

-- Enable session-level schema syntax. 
SET odps.namespace.schema=TRUE; 
-- Query the schema of the lineitem table in tpch_10g. If you want to query a table schema from another dataset, replace the schema name and table name in the following statement. 
DESC bigdata_public_dataset.tpch_10g.lineitem;

Query example

SET odps.namespace.schema=TRUE; 
SET odps.sql.validate.orderby.limit=FALSE;
SET odps.sql.hive.compatible=TRUE;
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 bigdata_public_dataset.tpch_10g.lineitem
WHERE l_shipdate <= date'1998-12-01' - interval '90' DAY
GROUP BY l_returnflag,
         l_linestatus
ORDER BY l_returnflag,
         l_linestatus;

For more information about data and sample queries, see TPC Benchmark H Standard Specification.

Project name

BIGDATA_PUBLIC_DATASET

Schema name

tpcxbb_10g, tpcxbb_100g, tpcxbb_1t, and tpcxbb_10t

Supported regions

China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Ulanqab), China (Shenzhen), China (Chengdu), China (Hong Kong), Singapore, Germany (Frankfurt), Indonesia (Jakarta), US (Silicon Valley), US (Virginia), Malaysia (Kuala Lumpur), India (Mumbai), UK (London), UAE (Dubai), Australia (Sydney), and Japan (Tokyo)

Table name and description

TPCx-BB is a big data benchmark test tool that simulates an online retail scenario. TPCx-BB data includes sales records, return records, product information, and promotion information. Table details:

• customer: stores the customer information.

• customer_address: stores the customer address information.

• customer_demographics: stores the basic credit information about customers.

• date_dim: stores the time dimension information.

• household_demographics: stores the basic household credit information.

• income_band: stores the revenue information.

• inventory: stores the inventory information.

• item: stores the product information.

• item_marketprices: stores the product price information of competitors.

• product_reviews: stores the product review information.

• promotion: stores the product promotion information.

• reason: stores the reasons for sales returns.

• ship_mode: stores the product shipment information.

• store: stores the store information.

• store_returns: stores the product return records for store sales.

• store_sales: stores the product sales records for store sales.

• time_dim: stores the time dimension information.

• warehouse: stores the warehouse information.

• web_clickstreams: stores the web page click information.

• web_page: stores the product web page information.

• web_returns: stores the product return records for web sales.

• web_sales: stores the product sales records for web sales.

• web_site: stores the product website information.

Update cycle

Fixed data is provided and is not updated.

Schema query

-- Enable session-level schema syntax. 
SET odps.namespace.schema=TRUE; 
-- Query the schema of the web_sales table in tpcxbb_10g. If you want to query a table schema from another dataset, replace the schema name and table name in the following statement. 
DESC bigdata_public_dataset.tpcxbb_10g.web_sales;

Query example

SET odps.namespace.schema=TRUE; 
select * FROM bigdata_public_dataset.tpcxbb_10g.web_sales limit 100;

For more information about data and query examples, see TPCx-BB Standard Specification.

Project name

BIGDATA_PUBLIC_DATASET

Schema name

commerce

Supported regions

China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Ulanqab), China (Shenzhen), and China (Chengdu)

Table name and description

• adv_raw_sample: stores the raw data that is randomly sampled from the logs for advertisement clicks of more than 1 million users within 8 days.

• adv_ad_feature: stores the basic information about some advertisements in the raw_sample table.

• user_profile: stores the basic information about all users in the raw_sample table.

• behavior_log: stores the shopping behavior of all users in the raw_sample table within 22 days. The behavior includes browsing, adding to the shopping cart, adding to favorites, and purchasing.

Update cycle

Fixed data is provided and is no longer incrementally updated.

Schema query

-- Enable session-level schema syntax. 
SET odps.namespace.schema=TRUE; 
-- Query the schema of the behavior_log table. If you want to query the schema of another table, replace the table name in the following statement. 
DESC bigdata_public_dataset.commerce.behavior_log;

Query example

-- Enable session-level schema syntax. 
SET odps.namespace.schema=TRUE; 
-- Query the IDs of the top 3 product categories in the behavior_log table within 22 days based on product sales. 
SELECT cate,
       count(btag) sales
FROM behavior_log
WHERE btag='buy'
GROUP BY cate
ORDER BY sales DESC LIMIT 3;

Project name

BIGDATA_PUBLIC_DATASET

Schema name

life_service

Supported regions

China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Ulanqab), China (Shenzhen), and China (Chengdu)

Table name and description

• movie_basic_info: stores the basic information about movies.

• movie_box: stores the basic information about box office.

• areacode_basic_info_2020: stores the basic information about administrative, urban, and rural division codes in 2020.

• phoneno_basic_info_2020: stores the basic information about mobile phone number attribution in 2020.

Update cycle

• movie_basic_info and movie_box: Data in date-specific partitions is provided and is no longer incrementally updated.

• areacode_basic_info_2020 and phoneno_basic_info_2020: Fixed data is provided and is no longer incrementally updated.

Schema query

-- Enable session-level schema syntax. 
SET odps.namespace.schema=TRUE; 
-- Query the schema of the movie_box table. If you want to query the schema of another table, replace the table name in the following statement. 
DESC bigdata_public_dataset.life_service.movie_box;

Query example

-- Enable session-level schema syntax. 
SET odps.namespace.schema=TRUE;
-- Query the names of the top 10 most popular movies on January 14, 2017 based on the box office. 
SELECT moviename
FROM bigdata_public_dataset.life_service.movie_box
WHERE ds ='20170114'
ORDER BY rank ASC LIMIT 10;

Project name

BIGDATA_PUBLIC_DATASET

Schema name

finance

Supported regions

China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Ulanqab), China (Shenzhen), and China (Chengdu)

Table name and description

• ods_enterprise_share_basic: stores the basic stock information.

• ods_enterprise_share_quarter_cashflow: stores the quarterly cash flow information.

• ods_enterprise_share_quarter_growth: stores the quarterly business growth data.

• ods_enterprise_share_quarter_operation: stores the quarterly financial turnover data.

• ods_enterprise_share_quarter_profit: stores the quarterly profit information.

• ods_enterprise_share_quarter_report: stores the quarterly report information.

• ods_enterprise_share_trade_h: stores the stock price information.

Update cycle

Data in date-specific partitions is provided and is no longer incrementally updated.

Schema query

-- Enable session-level schema syntax. 
SET odps.namespace.schema=TRUE; 
-- Query the schema of the ods_enterprise_share_basic table. If you want to query the schema of another table, replace the table name in the following statement. 
DESC bigdata_public_dataset.finance.ods_enterprise_share_basic;

Query example

-- Enable session-level schema syntax. 
SET odps.namespace.schema=TRUE;
-- Query the basic stock information on January 14, 2017. 
SELECT *
FROM bigdata_public_dataset.finance.ods_enterprise_share_basic
WHERE ds ='20170114' LIMIT 10;