# TiDB Cloud Serverless 入门 ## 概览 在本次练习中，您将学习 TiDB Cloud Serverless 的基本操作和一些高级功能。具体来说，我们将介绍如何创建集群并从 MySQL 客户端发出查询、通过 Python 脚本访问数据库以及使用 TiFlash 加速复杂查询。这是一个实用的研讨会，即使是初学者也可以一次性学习 TiDB Cloud Serverless 的基本操作和高级功能。 ### 实验时长 + 完成此练习大约需要 90 分钟。 ### 实验任务 + 连接到 TiDB Cloud Serverless 集群并从 MySQL Client 执行查询语句。 + 创建 Python 脚本来访问 TiDB。 + 通过使用列式存储 TiFlash 来加速复杂查询。 > [!TIP] > 点击 **开始** 启动实验环境。 > 环境准备就绪后，点击底部工具栏中的 **前进** 进入 **模块 1**。 > 然后，按照实验指南的步骤连接到实践环境并开始你的任务。 ## 模块 1: 连接到 TiDB Cloud Serverless ### 步骤 1. 在本地打开终端并登录远程 Linux + 点击这里 - [终端 1](ssh://monitor1-ssh#1) 打开一个新的终端，连接到为你的实践实验分配的虚拟机。 + 登录后显示的信息如下： ```output , #_ ~\_ ####_ Amazon Linux 2 ~~ \_#####\ ~~ \###| ~~ \#/ ___ ~~ V~' '-> ~~~ / A newer version of Amazon Linux is available! ~~._. _/ _/ _/ Amazon Linux 2023, GA and supported until 2028-03-15. _/m/' https://aws.amazon.com/linux/amazon-linux-2023/ ``` ### 步骤 2. 使用三种模式检查与 TiDB Cloud Serverless 集群的连通性 + 以下操作在远程 Linux 上执行。使用以下三种模式检查与 TiDB Cloud Serverless 集群的连通性。（当要退出 MySQL 客户端时，使用 `EXIT`。） + A. 使用 SSL 连接：使用以下命令进行连接。连接将会成功。 ```bash mysql \ --comments -u '${LAB:SERVERLESS_CLUSTER_USERNAME}' -p'${LAB:SERVERLESS_CLUSTER_PASSWORD}' \ -h ${LAB:SERVERLESS_CLUSTER_HOST} -P ${LAB:SERVERLESS_CLUSTER_PORT} \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt ``` ```output mysql> ``` ```sql EXIT; ``` + B. 连接（默认使用 SSL）：当不指定 `--ssl-mode` 选项时， 它的默认值是 `PREFERRED`，MySQL 客户端默认优先尝试 SSL 连接，因此连接将成功。 ```bash mysql \ --comments -u '${LAB:SERVERLESS_CLUSTER_USERNAME}' -p'${LAB:SERVERLESS_CLUSTER_PASSWORD}' \ -h ${LAB:SERVERLESS_CLUSTER_HOST} -P ${LAB:SERVERLESS_CLUSTER_PORT} ``` ```output mysql> ``` ```sql EXIT; ``` + C. 连接（不使用 SSL）：更改为 `--ssl-mode=DISABLED`。将发生连接错误。 ```bash mysql \ --comments -u '${LAB:SERVERLESS_CLUSTER_USERNAME}' -p'${LAB:SERVERLESS_CLUSTER_PASSWORD}' \ -h ${LAB:SERVERLESS_CLUSTER_HOST} -P ${LAB:SERVERLESS_CLUSTER_PORT} \ --ssl-mode=DISABLED ``` ```output mysql: [Warning] Using a password on the command line interface can be insecure. ERROR 1105 (HY000): Connections using insecure transport are prohibited. See https://docs.tidb.io/tidbcloud/secure-connections-to-serverless-tier-clusters ``` ### 步骤 3. 基本数据库操作 + 连接到 TiDB。 ```bash mysql \ --comments -u '${LAB:SERVERLESS_CLUSTER_USERNAME}' -p'${LAB:SERVERLESS_CLUSTER_PASSWORD}' \ -h ${LAB:SERVERLESS_CLUSTER_HOST} -P ${LAB:SERVERLESS_CLUSTER_PORT} \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt ``` ```output mysql> ``` + 创建一个新的数据库。 ```sql DROP DATABASE IF EXISTS ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}; CREATE DATABASE ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}; SHOW DATABASES\G ``` ```output *************************** 1. row *************************** Database: INFORMATION_SCHEMA *************************** 2. row *************************** Database: PERFORMANCE_SCHEMA *************************** 3. row *************************** Database: lab_demo_201_2370028 *************************** 4. row *************************** Database: mysql *************************** 5. row *************************** Database: test 5 rows in set (0.02 sec) ``` + 创建一个新表。 ```sql USE ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}; CREATE TABLE color (id INT AUTO_INCREMENT, name TEXT, Point INT, PRIMARY KEY (id)); INSERT INTO color (name, Point) VALUES ('red', 1); INSERT INTO color (name, Point) VALUES ('orange', 1); INSERT INTO color (name, Point) VALUES ('yellow', 2); SELECT * FROM color; ``` ```output Database changed Query OK, 0 rows affected (0.14 sec) Query OK, 1 row affected (0.04 sec) Query OK, 1 row affected (0.01 sec) Query OK, 1 row affected (0.01 sec) +----+--------+-------+ | id | name | Point | +----+--------+-------+ | 1 | red | 1 | | 2 | orange | 1 | | 3 | yellow | 2 | +----+--------+-------+ 3 rows in set (0.01 sec) ``` ### 步骤 4. 检查 MySQL 兼容性 + TiDB 与 MySQL 兼容但并非 100% 兼容。这里我们检查 MySQL 8.0 的功能，例如窗口函数和不支持的存储过程。您可以从以下 URL 检查兼容性：[https://docs.pingcap.com/tidb/stable/mysql-compatibility/](https://docs.pingcap.com/tidb/stable/mysql-compatibility/)。 + 检查默认字符集和排序规则是否不同。 ```sql SHOW CREATE TABLE color\G ``` ```output *************************** 1. row *************************** Table: color Create Table: CREATE TABLE `color` ( `id` int(11) NOT NULL AUTO_INCREMENT, `name` text DEFAULT NULL, `Point` int(11) DEFAULT NULL, PRIMARY KEY (`id`) /*T![clustered_index] CLUSTERED */ ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin AUTO_INCREMENT=30001 1 row in set (0.00 sec) ``` + 窗口函数示例。 ```sql SELECT id, name, Point, RANK() OVER w as 'rank' FROM color WINDOW w AS (ORDER BY Point); ``` ```output +----+--------+-------+------+ | id | name | Point | rank | +----+--------+-------+------+ | 1 | red | 1 | 1 | | 2 | orange | 1 | 1 | | 3 | yellow | 2 | 3 | +----+--------+-------+------+ 3 rows in set (0.01 sec) ``` + 存储过程。由于不受支持，此操作将会失败。 ```sql delimiter // CREATE PROCEDURE sample() BEGIN SELECT * FROM color; END // ``` ```output ERROR 8108 (HY000): Unsupported type *ast.ProcedureInfo ``` ```sql EXIT // ``` ```output Bye ``` ## 模块 2: 从 Python 访问 TiDB Cloud Serverless ### 步骤 1. 导入示例数据 + 链接到 TiDB。 ```bash mysql \ --comments -u '${LAB:SERVERLESS_CLUSTER_USERNAME}' -p'${LAB:SERVERLESS_CLUSTER_PASSWORD}' \ -h ${LAB:SERVERLESS_CLUSTER_HOST} -P ${LAB:SERVERLESS_CLUSTER_PORT} \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt ``` ```output mysql> ``` + 创建示例数据库，并导入示例 **标普 500** 数据。 > [!TIP] > 您可以安全地忽略此步骤中导入脚本中的 `LOCK TABLE` 命令导致的 `Access Denied` 消息。在像 TiDB 这样的分布式 SQL 数据库中，`LOCK TABLE` 不是必需的。 ```sql CREATE DATABASE ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}; USE ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}; SOURCE sp500insight.sql ``` ```output ... Query OK, 7947 rows affected (0.34 sec) Records: 7947 Duplicates: 0 Warnings: 0 Query OK, 7882 rows affected (0.34 sec) Records: 7882 Duplicates: 0 Warnings: 0 ... Query OK, 0 rows affected, 1 warning (0.01 sec) ``` ### 步骤 2. 访问示例数据 ```sql USE ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}; SELECT sector, industry, COUNT(*) AS companies FROM companies c WHERE c.stock_symbol IN (SELECT stock_symbol FROM index_compositions WHERE index_symbol = 'SP500') GROUP BY sector, industry ORDER BY sector, companies DESC; ``` ```output Database changed +------------------------+------------------------------------------+-----------+ | sector | industry | companies | +------------------------+------------------------------------------+-----------+ | Basic Materials | Specialty Chemicals | 10 | | Basic Materials | Agricultural Inputs | 4 | | Basic Materials | Building Materials | 2 | | Basic Materials | Chemicals | 2 | | Basic Materials | Steel | 2 | | Basic Materials | Gold | 1 | | Basic Materials | Copper | 1 | | Communication Services | Entertainment | 7 | ... | Utilities | Utilities—Regulated Electric | 23 | | Utilities | Utilities—Diversified | 2 | | Utilities | Utilities—Regulated Gas | 2 | | Utilities | Utilities—Regulated Water | 1 | | Utilities | Utilities—Independent Power Producers | 1 | +------------------------+------------------------------------------+-----------+ 113 rows in set, 1 warning (0.19 sec) ``` ### 步骤 3. 创建用于执行复杂分析查询的脚本 + 退出到操作系统提示符。 ```sql EXIT; ``` + 创建一个 Python 脚本。 ```bash cat > test01.py <<'EOF' import mysql.connector import datetime from prettytable import PrettyTable config = { 'user': '${LAB:SERVERLESS_CLUSTER_USERNAME}', 'password': '${LAB:SERVERLESS_CLUSTER_PASSWORD}', 'host': '${LAB:SERVERLESS_CLUSTER_HOST}', 'database': '${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}', 'raise_on_warnings': False, 'port': ${LAB:SERVERLESS_CLUSTER_PORT}, } def execQuery(query, queryName): print(queryName) print("Query execution start time: ", datetime.datetime.now().strftime("%H:%M:%S.%f")) # Executing the query cursor.execute(query) print("Query execution end time: ", datetime.datetime.now().strftime("%H:%M:%S.%f")) # Fetching all rows from the last executed query results = cursor.fetchall() # Creating a PrettyTable object my_table = PrettyTable() # Adding field names (column names) to the table field_names = [i[0] for i in cursor.description] my_table.field_names = field_names # Adding rows to the table for row in results: my_table.add_row(row) # Printing the table print(my_table) print("==========================") try: # Establishing the connection conn = mysql.connector.connect(**config) # Creating a cursor object cursor = conn.cursor() # Your SQL query queryName= "■■■Query 1: Market capitalization by industry■■■" query = """ SELECT `sector`, SUM(`market_cap`) AS `total_market_cap` FROM `companies` GROUP BY `sector`; """ execQuery(query, queryName) queryName= "■■■Query 2: Market capitalization ranking in the energy industry■■■" query = """ WITH index_companies AS ( SELECT c.stock_symbol, c.short_name, c.exchange_symbol, ic.weight, c.market_cap AS market_cap, c.revenue_growth AS revenue_growth FROM companies c JOIN index_compositions ic ON c.stock_symbol = ic.stock_symbol WHERE index_symbol = 'SP500' AND sector = 'Energy' ), companies_latest_price AS ( SELECT stock_symbol, DATE(last_day) AS last_change_day, last_close_price, last_2nd_close_price, last_close_price - last_2nd_close_price AS last_change, (last_close_price - last_2nd_close_price) / last_2nd_close_price AS last_change_percentage FROM ( SELECT LAST_VALUE(stock_symbol) OVER (PARTITION BY stock_symbol ORDER BY record_date) AS stock_symbol, LAST_VALUE(record_date) OVER (PARTITION BY stock_symbol ORDER BY record_date) AS last_day, NTH_VALUE(close, 1) OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS last_close_price, NTH_VALUE(close, 2) OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS last_2nd_close_price, ROW_NUMBER() OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS row_num FROM stock_price_history sph WHERE record_date > DATE_SUB((SELECT MAX(record_date) FROM stock_price_history), INTERVAL 2 DAY) AND sph.stock_symbol IN (SELECT stock_symbol FROM index_companies ic) ) sub WHERE row_num = 2 ) SELECT ic.stock_symbol, ic.exchange_symbol, ic.short_name, ic.weight, ic.market_cap, ic.revenue_growth, clp.last_change, clp.last_change_day, clp.last_close_price, clp.last_2nd_close_price, clp.last_change_percentage FROM index_companies ic LEFT JOIN companies_latest_price clp ON ic.stock_symbol = clp.stock_symbol ORDER BY ic.weight DESC; """ execQuery(query, queryName) except mysql.connector.Error as err: print(f"Error: {err}") finally: # Closing the connection if conn.is_connected(): cursor.close() conn.close() print("MySQL connection is closed") EOF ls -l test01.py ``` ```output -rw-r--r--. 1 rocky rocky 3636 Apr 28 02:22 test01.py ``` ### 步骤 4. 运行脚本执行分析查询 + 运行 Python 脚本从样本数据中获取两份报告。请注意执行时间。 ```bash python test01.py ``` ```output ■■■Query 1: Market capitalization by industry■■■ Query execution start time: 02:22:15.881417 Query execution end time: 02:22:15.902559 +------------------------+------------------+ | sector | total_market_cap | +------------------------+------------------+ | Real Estate | 927820086272 | | Utilities | 1023797270016 | | Healthcare | 5122903213056 | | Technology | 7906855563776 | | Financial Services | 4661122020864 | | Industrials | 3199253085184 | | Consumer Cyclical | 3238477340160 | | Consumer Defensive | 2872367655424 | | Energy | 1788549832704 | | Basic Materials | 889871172608 | | Communication Services | 3669841019392 | +------------------------+------------------+ ========================== ■■■Query 2: Market capitalization ranking in the energy industry■■■ Query execution start time: 02:22:15.904362 Query execution end time: 02:22:18.595003 +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ | stock_symbol | exchange_symbol | short_name | weight | market_cap | revenue_growth | last_change | last_change_day | last_close_price | last_2nd_close_price | last_change_percentage | +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ | XOM | NYQ | Exxon Mobil Corporation | 0.013441937585551282 | 474511933440 | 0.689 | 1.2800064086914100 | 2023-01-10 | 111.3700027465820300 | 110.0899963378906200 | 0.01162690935843785832 | | CVX | NYQ | Chevron Corporation | 0.009567226003564158 | 337731289088 | 0.81 | -0.8399963378906000 | 2023-01-10 | 175.1999969482422000 | 176.0399932861328000 | -0.00477162218772232275 | | COP | NYQ | ConocoPhillips | 0.004417814517419704 | 155952644096 | 1.237 | 0.1599960327148400 | 2023-01-10 | 118.2399978637695300 | 118.0800018310546900 | 0.00135497993084178221 | | SLB | NYQ | Schlumberger N.V. | 0.0022377960040345308 | 78996119552 | 0.279 | None | None | None | None | None | | EOG | NYQ | EOG Resources, Inc. | 0.0021024604924476343 | 74218659840 | 0.924 | -0.7600021362304600 | 2023-01-10 | 126.8499984741211000 | 127.6100006103515600 | -0.00595566282105957140 | | MPC | NYQ | Marathon Petroleum Corporation | 0.002050747487098508 | 72393146368 | 0.818 | 1.0999984741211000 | 2023-01-10 | 117.5999984741211000 | 116.5000000000000000 | 0.00944204698816394850 | | OXY | NYQ | Occidental Petroleum Corporatio | 0.001695676548826279 | 59858837504 | 0.792 | None | None | None | None | None | | PXD | NYQ | Pioneer Natural Resources Compa | 0.0016017013932190817 | 56541433856 | 0.641 | None | None | None | None | None | | VLO | NYQ | Valero Energy Corporation | 0.0015472850935959574 | 54620491776 | 0.593 | None | None | None | None | None | | HES | NYQ | Hess Corporation | 0.0012744122628788822 | 44987846656 | 0.776 | 1.6300048828125000 | 2023-01-10 | 145.2500000000000000 | 143.6199951171875000 | 0.01134942861878312162 | | PSX | NYQ | Phillips 66 | 0.0012589621320069535 | 44442443776 | 0.799 | None | None | None | None | None | | KMI | NYQ | Kinder Morgan, Inc. | 0.0012062691273686955 | 42582335488 | 0.354 | 0.0400009155273440 | 2023-01-10 | 18.7800006866455080 | 18.7399997711181640 | 0.00213452059850037319 | | DVN | NYQ | Devon Energy Corporation | 0.0012053375130788024 | 42549448704 | 0.789 | -0.2800025939941400 | 2023-01-10 | 62.8499984741210940 | 63.1300010681152340 | -0.00443533326875800678 | | WMB | NYQ | Williams Companies, Inc. (The) | 0.0011162172177908748 | 39403425792 | 0.091 | None | None | None | None | None | | HAL | NYQ | Halliburton Company | 0.0010393744130051477 | 36690808832 | 0.388 | 0.0500030517578160 | 2023-01-10 | 40.9900016784668000 | 40.9399986267089840 | 0.00122137404580161222 | | OKE | NYQ | ONEOK, Inc. | 0.0008854096904464799 | 31255721984 | 0.769 | None | None | None | None | None | | BKR | NMS | Baker Hughes Company | 0.0008807471517968449 | 31091130368 | 0.054 | -0.0400009155273450 | 2023-01-10 | 30.8299999237060550 | 30.8700008392334000 | -0.00129578602008027511 | | FANG | NMS | Diamondback Energy, Inc. | 0.000730020047064916 | 25770334208 | 0.565 | 0.2900085449218600 | 2023-01-10 | 142.2400054931640600 | 141.9499969482422000 | 0.00204303311839874783 | | MRO | NYQ | Marathon Oil Corporation | 0.0005965698099807201 | 21059426304 | 0.731 | 0.0399990081787110 | 2023-01-10 | 27.0499992370605470 | 27.0100002288818360 | 0.00148089625471161593 | | CTRA | NYQ | Coterra Energy Inc. | 0.0005690298110137097 | 20087240704 | 5.204 | -0.3899993896484380 | 2023-01-10 | 24.6900005340576170 | 25.0799999237060550 | -0.01555021494556719473 | | TRGP | NYQ | Targa Resources, Inc. | 0.0004842024080652878 | 17092760576 | 0.773 | None | None | None | None | None | | APA | NMS | APA Corporation | 0.00039910236086756015 | 14088655872 | 0.374 | 0.5799980163574200 | 2023-01-10 | 43.8199996948242200 | 43.2400016784668000 | 0.01341345961709929139 | | EQT | NYQ | EQT Corporation | 0.0003576031445812937 | 12623697920 | 1.092 | -0.6399993896484380 | 2023-01-10 | 33.4000015258789060 | 34.0400009155273440 | -0.01880139166966068805 | +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ ========================== MySQL connection is closed ``` ## 模块 3: 使用列式存储加速复杂查询 ### 步骤 1. 执行复杂查询并审查执行计划 + 连接到 TiDB 并运行分析查询。 ```bash mysql \ --comments -u '${LAB:SERVERLESS_CLUSTER_USERNAME}' -p'${LAB:SERVERLESS_CLUSTER_PASSWORD}' \ -h ${LAB:SERVERLESS_CLUSTER_HOST} -P ${LAB:SERVERLESS_CLUSTER_PORT} \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt ``` ```sql USE ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}; WITH index_companies AS ( SELECT c.stock_symbol, c.short_name, c.exchange_symbol, ic.weight, c.market_cap AS market_cap, c.revenue_growth AS revenue_growth FROM companies c JOIN index_compositions ic ON c.stock_symbol = ic.stock_symbol WHERE index_symbol = 'SP500' AND sector = 'Energy' ), companies_latest_price AS ( SELECT stock_symbol, DATE(last_day) AS last_change_day, last_close_price, last_2nd_close_price, last_close_price - last_2nd_close_price AS last_change, (last_close_price - last_2nd_close_price) / last_2nd_close_price AS last_change_percentage FROM ( SELECT LAST_VALUE(stock_symbol) OVER (PARTITION BY stock_symbol ORDER BY record_date) AS stock_symbol, LAST_VALUE(record_date) OVER (PARTITION BY stock_symbol ORDER BY record_date) AS last_day, NTH_VALUE(close, 1) OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS last_close_price, NTH_VALUE(close, 2) OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS last_2nd_close_price, ROW_NUMBER() OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS row_num FROM stock_price_history sph WHERE record_date > DATE_SUB((SELECT MAX(record_date) FROM stock_price_history), INTERVAL 2 DAY) AND sph.stock_symbol IN (SELECT stock_symbol FROM index_companies ic) ) sub WHERE row_num = 2 ) SELECT ic.stock_symbol, ic.exchange_symbol, ic.short_name, ic.weight, ic.market_cap, ic.revenue_growth, clp.last_change, clp.last_change_day, clp.last_close_price, clp.last_2nd_close_price, clp.last_change_percentage FROM index_companies ic LEFT JOIN companies_latest_price clp ON ic.stock_symbol = clp.stock_symbol ORDER BY ic.weight DESC; ``` ```output +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ | stock_symbol | exchange_symbol | short_name | weight | market_cap | revenue_growth | last_change | last_change_day | last_close_price | last_2nd_close_price | last_change_percentage | +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ | XOM | NYQ | Exxon Mobil Corporation | 0.013441937585551282 | 474511933440 | 0.689 | 1.2800064086914100 | 2023-01-10 | 111.3700027465820300 | 110.0899963378906200 | 0.01162690935843785832 | | CVX | NYQ | Chevron Corporation | 0.009567226003564158 | 337731289088 | 0.81 | -0.8399963378906000 | 2023-01-10 | 175.1999969482422000 | 176.0399932861328000 | -0.00477162218772232275 | | COP | NYQ | ConocoPhillips | 0.004417814517419704 | 155952644096 | 1.237 | 0.1599960327148400 | 2023-01-10 | 118.2399978637695300 | 118.0800018310546900 | 0.00135497993084178221 | | SLB | NYQ | Schlumberger N.V. | 0.0022377960040345308 | 78996119552 | 0.279 | NULL | NULL | NULL | NULL | NULL | | EOG | NYQ | EOG Resources, Inc. | 0.0021024604924476343 | 74218659840 | 0.924 | -0.7600021362304600 | 2023-01-10 | 126.8499984741211000 | 127.6100006103515600 | -0.00595566282105957140 | | MPC | NYQ | Marathon Petroleum Corporation | 0.002050747487098508 | 72393146368 | 0.818 | 1.0999984741211000 | 2023-01-10 | 117.5999984741211000 | 116.5000000000000000 | 0.00944204698816394850 | | OXY | NYQ | Occidental Petroleum Corporatio | 0.001695676548826279 | 59858837504 | 0.792 | NULL | NULL | NULL | NULL | NULL | | PXD | NYQ | Pioneer Natural Resources Compa | 0.0016017013932190817 | 56541433856 | 0.641 | NULL | NULL | NULL | NULL | NULL | | VLO | NYQ | Valero Energy Corporation | 0.0015472850935959574 | 54620491776 | 0.593 | NULL | NULL | NULL | NULL | NULL | | HES | NYQ | Hess Corporation | 0.0012744122628788822 | 44987846656 | 0.776 | 1.6300048828125000 | 2023-01-10 | 145.2500000000000000 | 143.6199951171875000 | 0.01134942861878312162 | | PSX | NYQ | Phillips 66 | 0.0012589621320069535 | 44442443776 | 0.799 | NULL | NULL | NULL | NULL | NULL | | KMI | NYQ | Kinder Morgan, Inc. | 0.0012062691273686955 | 42582335488 | 0.354 | 0.0400009155273440 | 2023-01-10 | 18.7800006866455080 | 18.7399997711181640 | 0.00213452059850037319 | | DVN | NYQ | Devon Energy Corporation | 0.0012053375130788024 | 42549448704 | 0.789 | -0.2800025939941400 | 2023-01-10 | 62.8499984741210940 | 63.1300010681152340 | -0.00443533326875800678 | | WMB | NYQ | Williams Companies, Inc. (The) | 0.0011162172177908748 | 39403425792 | 0.091 | NULL | NULL | NULL | NULL | NULL | | HAL | NYQ | Halliburton Company | 0.0010393744130051477 | 36690808832 | 0.388 | 0.0500030517578160 | 2023-01-10 | 40.9900016784668000 | 40.9399986267089840 | 0.00122137404580161222 | | OKE | NYQ | ONEOK, Inc. | 0.0008854096904464799 | 31255721984 | 0.769 | NULL | NULL | NULL | NULL | NULL | | BKR | NMS | Baker Hughes Company | 0.0008807471517968449 | 31091130368 | 0.054 | -0.0400009155273450 | 2023-01-10 | 30.8299999237060550 | 30.8700008392334000 | -0.00129578602008027511 | | FANG | NMS | Diamondback Energy, Inc. | 0.000730020047064916 | 25770334208 | 0.565 | 0.2900085449218600 | 2023-01-10 | 142.2400054931640600 | 141.9499969482422000 | 0.00204303311839874783 | | MRO | NYQ | Marathon Oil Corporation | 0.0005965698099807201 | 21059426304 | 0.731 | 0.0399990081787110 | 2023-01-10 | 27.0499992370605470 | 27.0100002288818360 | 0.00148089625471161593 | | CTRA | NYQ | Coterra Energy Inc. | 0.0005690298110137097 | 20087240704 | 5.204 | -0.3899993896484380 | 2023-01-10 | 24.6900005340576170 | 25.0799999237060550 | -0.01555021494556719473 | | TRGP | NYQ | Targa Resources, Inc. | 0.0004842024080652878 | 17092760576 | 0.773 | NULL | NULL | NULL | NULL | NULL | | APA | NMS | APA Corporation | 0.00039910236086756015 | 14088655872 | 0.374 | 0.5799980163574200 | 2023-01-10 | 43.8199996948242200 | 43.2400016784668000 | 0.01341345961709929139 | | EQT | NYQ | EQT Corporation | 0.0003576031445812937 | 12623697920 | 1.092 | -0.6399993896484380 | 2023-01-10 | 33.4000015258789060 | 34.0400009155273440 | -0.01880139166966068805 | +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ 23 rows in set, 1 warning (4.50 sec) ``` + 在查询开头添加 `EXPLAIN` 并检查复杂查询的执行计划。（查询结果很宽，所以先运行 `PAGER less -S`。要切换回来，运行 `NOPAGER`。） ```sql PAGER less -S ``` ```sql EXPLAIN WITH index_companies AS ( SELECT c.stock_symbol, c.short_name, c.exchange_symbol, ic.weight, c.market_cap AS market_cap, c.revenue_growth AS revenue_growth FROM companies c JOIN index_compositions ic ON c.stock_symbol = ic.stock_symbol WHERE index_symbol = 'SP500' AND sector = 'Energy' ), companies_latest_price AS ( SELECT stock_symbol, DATE(last_day) AS last_change_day, last_close_price, last_2nd_close_price, last_close_price - last_2nd_close_price AS last_change, (last_close_price - last_2nd_close_price) / last_2nd_close_price AS last_change_percentage FROM ( SELECT LAST_VALUE(stock_symbol) OVER (PARTITION BY stock_symbol ORDER BY record_date) AS stock_symbol, LAST_VALUE(record_date) OVER (PARTITION BY stock_symbol ORDER BY record_date) AS last_day, NTH_VALUE(close, 1) OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS last_close_price, NTH_VALUE(close, 2) OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS last_2nd_close_price, ROW_NUMBER() OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS row_num FROM stock_price_history sph WHERE record_date > DATE_SUB((SELECT MAX(record_date) FROM stock_price_history), INTERVAL 2 DAY) AND sph.stock_symbol IN (SELECT stock_symbol FROM index_companies ic) ) sub WHERE row_num = 2 ) SELECT ic.stock_symbol, ic.exchange_symbol, ic.short_name, ic.weight, ic.market_cap, ic.revenue_growth, clp.last_change, clp.last_change_day, clp.last_close_price, clp.last_2nd_close_price, clp.last_change_percentage FROM index_companies ic LEFT JOIN companies_latest_price clp ON ic.stock_symbol = clp.stock_symbol ORDER BY ic.weight DESC; ``` ```output | id | estRows | task | access object | operator info | +------------------------------------------------------+------------+-----------+---------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Sort_125 | 23.00 | root | | ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.weight:desc | | └─Projection_127 | 23.00 | root | | ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.exchange_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.short_name, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.weight, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.market_cap, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.revenue_growth, Column#118, Column#117, Column#109, Column#108, Column#119 | | └─HashJoin_129 | 23.00 | root | | left outer join, equal:[eq(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, Column#111)] | | ├─CTEFullScan_156(Build) | 23.00 | root | CTE:index_companies AS ic | data:CTE_0 | | └─Projection_131(Probe) | 1572.00 | root | | Column#111, date(Column#110)->Column#117, Column#109, Column#108, minus(Column#109, Column#108)->Column#118, div(minus(Column#109, Column#108), Column#108)->Column#119 | | └─Selection_132 | 1572.00 | root | | eq(Column#107, 2), not(isnull(Column#111)) | | └─Window_133 | 1965.00 | root | | last_value(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol)->Column#111 over(partition by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol order by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date range between unbounded preceding and current row) | | └─Window_134 | 1965.00 | root | | last_value(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date)->Column#110 over(partition by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol order by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date range between unbounded preceding and current row) | | └─Sort_154 | 1965.00 | root | | ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date | | └─Window_136 | 1965.00 | root | | nth_value(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.close, 1)->Column#109 over(partition by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol order by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date desc range between unbounded preceding and current row) | | └─Window_137 | 1965.00 | root | | nth_value(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.close, 2)->Column#108 over(partition by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol order by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date desc range between unbounded preceding and current row) | | └─Window_139 | 1965.00 | root | | row_number()->Column#107 over(partition by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol order by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date desc rows between current row and current row) | | └─Sort_151 | 1965.00 | root | | ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date:desc | | └─HashJoin_144 | 1965.00 | root | | inner join, equal:[eq(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol)] | | ├─HashAgg_145(Build) | 18.40 | root | | group by:${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, funcs:firstrow(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol)->${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol | | │ └─Selection_146 | 18.40 | root | | not(isnull(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol)) | | │ └─CTEFullScan_147 | 23.00 | root | CTE:index_companies AS ic | data:CTE_0 | | └─TableReader_150(Probe) | 1965.00 | root | | data:Selection_149 | | └─Selection_149 | 1965.00 | cop[tikv] | | gt(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date, 2023-01-09), not(isnull(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol)) | | └─TableFullScan_148 | 1157000.00 | cop[tikv] | table:sph | keep order:false | | CTE_0 | 23.00 | root | | Non-Recursive CTE | | └─Projection_113(Seed Part) | 23.00 | root | | ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.short_name, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.exchange_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.weight, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.market_cap, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.revenue_growth | | └─HashJoin_115 | 23.00 | root | | inner join, equal:[eq(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.stock_symbol)] | | ├─TableReader_118(Build) | 23.00 | root | | data:Selection_117 | | │ └─Selection_117 | 23.00 | cop[tikv] | | eq(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.sector, "Energy"), not(isnull(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol)), or(1, not(isnull(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol))) | | │ └─TableFullScan_116 | 493.00 | cop[tikv] | table:c | keep order:false | | └─TableReader_121(Probe) | 493.00 | root | | data:Selection_120 | | └─Selection_120 | 493.00 | cop[tikv] | | eq(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.index_symbol, "SP500"), not(isnull(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.stock_symbol)) | | └─TableFullScan_119 | 493.00 | cop[tikv] | table:ic | keep order:false | +------------------------------------------------------+------------+-----------+---------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 29 rows in set (1.08 sec) ``` + 按 `Q` 退出 `PAGER` 视图。 ```output mysql> ``` + 关闭 `PAGER`。 ```sql NOPAGER ``` ```output PAGER set to stdout mysql> ``` ### 步骤 2. 设置 TiFlash 同步 + 将以下三个表设置为与 TiFlash 同步：`stock_price_history`、`index_companies` 和 `companies`。 ```sql ALTER TABLE ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies SET TIFLASH REPLICA 2; ALTER TABLE ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions SET TIFLASH REPLICA 2; ALTER TABLE ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history SET TIFLASH REPLICA 2; ``` ```output ... Query OK, 0 rows affected, 1 warning (0.20 sec) ``` + 您可以使用以下查询检查同步状态。（当 `PROGRESS` 为 1 且 `AVAILABLE` 为 1 时，同步完成。） ```sql SELECT * FROM information_schema.tiflash_replica WHERE TABLE_SCHEMA = '${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}'; ``` + 同步进行中示例。 ```output +----------------------+---------------------+----------+---------------+-----------------+-----------+----------+ | TABLE_SCHEMA | TABLE_NAME | TABLE_ID | REPLICA_COUNT | LOCATION_LABELS | AVAILABLE | PROGRESS | +----------------------+---------------------+----------+---------------+-----------------+-----------+----------+ | lab_demo_201_2370038 | companies | 114 | 2 | | 0 | 0.5 | | lab_demo_201_2370038 | index_compositions | 116 | 2 | | 0 | 0 | | lab_demo_201_2370038 | stock_price_history | 122 | 2 | | 0 | 0 | +----------------------+---------------------+----------+---------------+-----------------+-----------+----------+ 3 rows in set (0.05 sec) ``` + 同步已完成示例。 ```output +----------------------+---------------------+----------+---------------+-----------------+-----------+----------+ | TABLE_SCHEMA | TABLE_NAME | TABLE_ID | REPLICA_COUNT | LOCATION_LABELS | AVAILABLE | PROGRESS | +----------------------+---------------------+----------+---------------+-----------------+-----------+----------+ | lab_demo_201_2370038 | companies | 114 | 2 | | 1 | 1 | | lab_demo_201_2370038 | index_compositions | 116 | 2 | | 1 | 1 | | lab_demo_201_2370038 | stock_price_history | 122 | 2 | | 1 | 1 | +----------------------+---------------------+----------+---------------+-----------------+-----------+----------+ 3 rows in set (0.05 sec) ``` ### 步骤 3. 再次运行复杂查询并检查执行计划 + 再次运行相同的查询并检查时间和执行计划。 ```sql WITH index_companies AS ( SELECT c.stock_symbol, c.short_name, c.exchange_symbol, ic.weight, c.market_cap AS market_cap, c.revenue_growth AS revenue_growth FROM companies c JOIN index_compositions ic ON c.stock_symbol = ic.stock_symbol WHERE index_symbol = 'SP500' AND sector = 'Energy' ), companies_latest_price AS ( SELECT stock_symbol, DATE(last_day) AS last_change_day, last_close_price, last_2nd_close_price, last_close_price - last_2nd_close_price AS last_change, (last_close_price - last_2nd_close_price) / last_2nd_close_price AS last_change_percentage FROM ( SELECT LAST_VALUE(stock_symbol) OVER (PARTITION BY stock_symbol ORDER BY record_date) AS stock_symbol, LAST_VALUE(record_date) OVER (PARTITION BY stock_symbol ORDER BY record_date) AS last_day, NTH_VALUE(close, 1) OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS last_close_price, NTH_VALUE(close, 2) OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS last_2nd_close_price, ROW_NUMBER() OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS row_num FROM stock_price_history sph WHERE record_date > DATE_SUB((SELECT MAX(record_date) FROM stock_price_history), INTERVAL 2 DAY) AND sph.stock_symbol IN (SELECT stock_symbol FROM index_companies ic) ) sub WHERE row_num = 2 ) SELECT ic.stock_symbol, ic.exchange_symbol, ic.short_name, ic.weight, ic.market_cap, ic.revenue_growth, clp.last_change, clp.last_change_day, clp.last_close_price, clp.last_2nd_close_price, clp.last_change_percentage FROM index_companies ic LEFT JOIN companies_latest_price clp ON ic.stock_symbol = clp.stock_symbol ORDER BY ic.weight DESC; ``` + 结果返回比之前快。 ```output +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ | stock_symbol | exchange_symbol | short_name | weight | market_cap | revenue_growth | last_change | last_change_day | last_close_price | last_2nd_close_price | last_change_percentage | +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ | XOM | NYQ | Exxon Mobil Corporation | 0.013441937585551282 | 474511933440 | 0.689 | 1.2800064086914100 | 2023-01-10 | 111.3700027465820300 | 110.0899963378906200 | 0.01162690935843785832 | | CVX | NYQ | Chevron Corporation | 0.009567226003564158 | 337731289088 | 0.81 | -0.8399963378906000 | 2023-01-10 | 175.1999969482422000 | 176.0399932861328000 | -0.00477162218772232275 | | COP | NYQ | ConocoPhillips | 0.004417814517419704 | 155952644096 | 1.237 | 0.1599960327148400 | 2023-01-10 | 118.2399978637695300 | 118.0800018310546900 | 0.00135497993084178221 | | SLB | NYQ | Schlumberger N.V. | 0.0022377960040345308 | 78996119552 | 0.279 | NULL | NULL | NULL | NULL | NULL | | EOG | NYQ | EOG Resources, Inc. | 0.0021024604924476343 | 74218659840 | 0.924 | -0.7600021362304600 | 2023-01-10 | 126.8499984741211000 | 127.6100006103515600 | -0.00595566282105957140 | | MPC | NYQ | Marathon Petroleum Corporation | 0.002050747487098508 | 72393146368 | 0.818 | 1.0999984741211000 | 2023-01-10 | 117.5999984741211000 | 116.5000000000000000 | 0.00944204698816394850 | | OXY | NYQ | Occidental Petroleum Corporatio | 0.001695676548826279 | 59858837504 | 0.792 | NULL | NULL | NULL | NULL | NULL | | PXD | NYQ | Pioneer Natural Resources Compa | 0.0016017013932190817 | 56541433856 | 0.641 | NULL | NULL | NULL | NULL | NULL | | VLO | NYQ | Valero Energy Corporation | 0.0015472850935959574 | 54620491776 | 0.593 | NULL | NULL | NULL | NULL | NULL | | HES | NYQ | Hess Corporation | 0.0012744122628788822 | 44987846656 | 0.776 | 1.6300048828125000 | 2023-01-10 | 145.2500000000000000 | 143.6199951171875000 | 0.01134942861878312162 | | PSX | NYQ | Phillips 66 | 0.0012589621320069535 | 44442443776 | 0.799 | NULL | NULL | NULL | NULL | NULL | | KMI | NYQ | Kinder Morgan, Inc. | 0.0012062691273686955 | 42582335488 | 0.354 | 0.0400009155273440 | 2023-01-10 | 18.7800006866455080 | 18.7399997711181640 | 0.00213452059850037319 | | DVN | NYQ | Devon Energy Corporation | 0.0012053375130788024 | 42549448704 | 0.789 | -0.2800025939941400 | 2023-01-10 | 62.8499984741210940 | 63.1300010681152340 | -0.00443533326875800678 | | WMB | NYQ | Williams Companies, Inc. (The) | 0.0011162172177908748 | 39403425792 | 0.091 | NULL | NULL | NULL | NULL | NULL | | HAL | NYQ | Halliburton Company | 0.0010393744130051477 | 36690808832 | 0.388 | 0.0500030517578160 | 2023-01-10 | 40.9900016784668000 | 40.9399986267089840 | 0.00122137404580161222 | | OKE | NYQ | ONEOK, Inc. | 0.0008854096904464799 | 31255721984 | 0.769 | NULL | NULL | NULL | NULL | NULL | | BKR | NMS | Baker Hughes Company | 0.0008807471517968449 | 31091130368 | 0.054 | -0.0400009155273450 | 2023-01-10 | 30.8299999237060550 | 30.8700008392334000 | -0.00129578602008027511 | | FANG | NMS | Diamondback Energy, Inc. | 0.000730020047064916 | 25770334208 | 0.565 | 0.2900085449218600 | 2023-01-10 | 142.2400054931640600 | 141.9499969482422000 | 0.00204303311839874783 | | MRO | NYQ | Marathon Oil Corporation | 0.0005965698099807201 | 21059426304 | 0.731 | 0.0399990081787110 | 2023-01-10 | 27.0499992370605470 | 27.0100002288818360 | 0.00148089625471161593 | | CTRA | NYQ | Coterra Energy Inc. | 0.0005690298110137097 | 20087240704 | 5.204 | -0.3899993896484380 | 2023-01-10 | 24.6900005340576170 | 25.0799999237060550 | -0.01555021494556719473 | | TRGP | NYQ | Targa Resources, Inc. | 0.0004842024080652878 | 17092760576 | 0.773 | NULL | NULL | NULL | NULL | NULL | | APA | NMS | APA Corporation | 0.00039910236086756015 | 14088655872 | 0.374 | 0.5799980163574200 | 2023-01-10 | 43.8199996948242200 | 43.2400016784668000 | 0.01341345961709929139 | | EQT | NYQ | EQT Corporation | 0.0003576031445812937 | 12623697920 | 1.092 | -0.6399993896484380 | 2023-01-10 | 33.4000015258789060 | 34.0400009155273440 | -0.01880139166966068805 | +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ 23 rows in set (0.62 sec) ``` + 可以看到执行计划使用了Tiflash（`mpp[tiflash]`）。 ```sql PAGER less -S EXPLAIN WITH index_companies AS ( SELECT c.stock_symbol, c.short_name, c.exchange_symbol, ic.weight, c.market_cap AS market_cap, c.revenue_growth AS revenue_growth FROM companies c JOIN index_compositions ic ON c.stock_symbol = ic.stock_symbol WHERE index_symbol = 'SP500' AND sector = 'Energy' ), companies_latest_price AS ( SELECT stock_symbol, DATE(last_day) AS last_change_day, last_close_price, last_2nd_close_price, last_close_price - last_2nd_close_price AS last_change, (last_close_price - last_2nd_close_price) / last_2nd_close_price AS last_change_percentage FROM ( SELECT LAST_VALUE(stock_symbol) OVER (PARTITION BY stock_symbol ORDER BY record_date) AS stock_symbol, LAST_VALUE(record_date) OVER (PARTITION BY stock_symbol ORDER BY record_date) AS last_day, NTH_VALUE(close, 1) OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS last_close_price, NTH_VALUE(close, 2) OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS last_2nd_close_price, ROW_NUMBER() OVER (PARTITION BY stock_symbol ORDER BY record_date DESC) AS row_num FROM stock_price_history sph WHERE record_date > DATE_SUB((SELECT MAX(record_date) FROM stock_price_history), INTERVAL 2 DAY) AND sph.stock_symbol IN (SELECT stock_symbol FROM index_companies ic) ) sub WHERE row_num = 2 ) SELECT ic.stock_symbol, ic.exchange_symbol, ic.short_name, ic.weight, ic.market_cap, ic.revenue_growth, clp.last_change, clp.last_change_day, clp.last_close_price, clp.last_2nd_close_price, clp.last_change_percentage FROM index_companies ic LEFT JOIN companies_latest_price clp ON ic.stock_symbol = clp.stock_symbol ORDER BY ic.weight DESC; ``` ```output +-------------------------------------------------------+---------+--------------+---------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | id | estRows | task | access object | operator info | +-------------------------------------------------------+---------+--------------+---------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Sort_164 | 23.00 | root | | ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.weight:desc | | └─Projection_166 | 23.00 | root | | ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.exchange_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.short_name, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.weight, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.market_cap, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.revenue_growth, Column#118, Column#117, Column#109, Column#108, Column#119 | | └─HashJoin_168 | 23.00 | root | | left outer join, equal:[eq(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, Column#111)] | | ├─CTEFullScan_199(Build) | 23.00 | root | CTE:index_companies AS ic | data:CTE_0 | | └─Projection_170(Probe) | 1572.00 | root | | Column#111, date(Column#110)->Column#117, Column#109, Column#108, minus(Column#109, Column#108)->Column#118, div(minus(Column#109, Column#108), Column#108)->Column#119 | | └─Selection_171 | 1572.00 | root | | eq(Column#107, 2), not(isnull(Column#111)) | | └─Window_172 | 1965.00 | root | | last_value(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol)->Column#111 over(partition by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol order by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date range between unbounded preceding and current row) | | └─Window_173 | 1965.00 | root | | last_value(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date)->Column#110 over(partition by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol order by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date range between unbounded preceding and current row) | | └─Sort_197 | 1965.00 | root | | ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date | | └─Window_175 | 1965.00 | root | | nth_value(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.close, 1)->Column#109 over(partition by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol order by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date desc range between unbounded preceding and current row) | | └─Window_176 | 1965.00 | root | | nth_value(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.close, 2)->Column#108 over(partition by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol order by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date desc range between unbounded preceding and current row) | | └─Window_178 | 1965.00 | root | | row_number()->Column#107 over(partition by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol order by ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date desc rows between current row and current row) | | └─Sort_194 | 1965.00 | root | | ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date:desc | | └─HashJoin_183 | 1965.00 | root | | inner join, equal:[eq(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol)] | | ├─HashAgg_184(Build) | 18.40 | root | | group by:${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, funcs:firstrow(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol)->${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol | | │ └─Selection_185 | 18.40 | root | | not(isnull(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol)) | | │ └─CTEFullScan_186 | 23.00 | root | CTE:index_companies AS ic | data:CTE_0 | | └─TableReader_193(Probe) | 1965.00 | root | | MppVersion: 1, data:ExchangeSender_192 | | └─ExchangeSender_192 | 1965.00 | mpp[tiflash] | | ExchangeType: PassThrough | | └─Selection_191 | 1965.00 | mpp[tiflash] | | not(isnull(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.stock_symbol)) | | └─TableFullScan_190 | 1965.00 | mpp[tiflash] | table:sph | pushed down filter:gt(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.stock_price_history.record_date, 2023-01-09), keep order:false | | CTE_0 | 23.00 | root | | Non-Recursive CTE | | └─Projection_131(Seed Part) | 23.00 | root | | ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.short_name, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.exchange_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.weight, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.market_cap, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.revenue_growth | | └─HashJoin_135 | 23.00 | root | | inner join, equal:[eq(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol, ${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.stock_symbol)] | | ├─TableReader_146(Build) | 23.00 | root | | data:Selection_145 | | │ └─Selection_145 | 23.00 | cop[tikv] | | eq(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.sector, "Energy"), not(isnull(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol)), or(1, not(isnull(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.companies.stock_symbol))) | | │ └─TableFullScan_144 | 493.00 | cop[tikv] | table:c | keep order:false | | └─TableReader_153(Probe) | 493.00 | root | | data:Selection_152 | | └─Selection_152 | 493.00 | cop[tikv] | | eq(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.index_symbol, "SP500"), not(isnull(${LAB:SERVERLESS_CLUSTER_DATABASE_NAME}.index_compositions.stock_symbol)) | | └─TableFullScan_151 | 493.00 | cop[tikv] | table:ic | keep order:false | +-------------------------------------------------------+---------+--------------+---------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 30 rows in set (0.52 sec) ``` ### 步骤 4. 运行相同的脚本检查结果，确认性能已经提升 + 退出 MySQL 客户端并切换到 [终端 1](ssh://monitor1-ssh#1)。 ```sql EXIT ``` + 上面使用的查询实际上是演示 2 中创建的 `test01.py` 中的第二个查询。在演示 2 中，大约花费了 2 秒，但下面的结果表明它更快。 ```bash python test01.py ``` ```output ■■■Query 1: Market capitalization by industry■■■ Query execution start time: 02:25:59.895391 Query execution end time: 02:25:59.910081 +------------------------+------------------+ | sector | total_market_cap | +------------------------+------------------+ | Real Estate | 927820086272 | | Technology | 7906855563776 | | Financial Services | 4661122020864 | | Healthcare | 5122903213056 | | Utilities | 1023797270016 | | Consumer Defensive | 2872367655424 | | Communication Services | 3669841019392 | | Basic Materials | 889871172608 | | Energy | 1788549832704 | | Consumer Cyclical | 3238477340160 | | Industrials | 3199253085184 | +------------------------+------------------+ ========================== ■■■Query 2: Market capitalization ranking in the energy industry■■■ Query execution start time: 02:25:59.911859 Query execution end time: 02:26:00.290869 +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ | stock_symbol | exchange_symbol | short_name | weight | market_cap | revenue_growth | last_change | last_change_day | last_close_price | last_2nd_close_price | last_change_percentage | +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ | XOM | NYQ | Exxon Mobil Corporation | 0.013441937585551282 | 474511933440 | 0.689 | 1.2800064086914100 | 2023-01-10 | 111.3700027465820300 | 110.0899963378906200 | 0.01162690935843785832 | | CVX | NYQ | Chevron Corporation | 0.009567226003564158 | 337731289088 | 0.81 | -0.8399963378906000 | 2023-01-10 | 175.1999969482422000 | 176.0399932861328000 | -0.00477162218772232275 | | COP | NYQ | ConocoPhillips | 0.004417814517419704 | 155952644096 | 1.237 | 0.1599960327148400 | 2023-01-10 | 118.2399978637695300 | 118.0800018310546900 | 0.00135497993084178221 | | SLB | NYQ | Schlumberger N.V. | 0.0022377960040345308 | 78996119552 | 0.279 | None | None | None | None | None | | EOG | NYQ | EOG Resources, Inc. | 0.0021024604924476343 | 74218659840 | 0.924 | -0.7600021362304600 | 2023-01-10 | 126.8499984741211000 | 127.6100006103515600 | -0.00595566282105957140 | | MPC | NYQ | Marathon Petroleum Corporation | 0.002050747487098508 | 72393146368 | 0.818 | 1.0999984741211000 | 2023-01-10 | 117.5999984741211000 | 116.5000000000000000 | 0.00944204698816394850 | | OXY | NYQ | Occidental Petroleum Corporatio | 0.001695676548826279 | 59858837504 | 0.792 | None | None | None | None | None | | PXD | NYQ | Pioneer Natural Resources Compa | 0.0016017013932190817 | 56541433856 | 0.641 | None | None | None | None | None | | VLO | NYQ | Valero Energy Corporation | 0.0015472850935959574 | 54620491776 | 0.593 | None | None | None | None | None | | HES | NYQ | Hess Corporation | 0.0012744122628788822 | 44987846656 | 0.776 | 1.6300048828125000 | 2023-01-10 | 145.2500000000000000 | 143.6199951171875000 | 0.01134942861878312162 | | PSX | NYQ | Phillips 66 | 0.0012589621320069535 | 44442443776 | 0.799 | None | None | None | None | None | | KMI | NYQ | Kinder Morgan, Inc. | 0.0012062691273686955 | 42582335488 | 0.354 | 0.0400009155273440 | 2023-01-10 | 18.7800006866455080 | 18.7399997711181640 | 0.00213452059850037319 | | DVN | NYQ | Devon Energy Corporation | 0.0012053375130788024 | 42549448704 | 0.789 | -0.2800025939941400 | 2023-01-10 | 62.8499984741210940 | 63.1300010681152340 | -0.00443533326875800678 | | WMB | NYQ | Williams Companies, Inc. (The) | 0.0011162172177908748 | 39403425792 | 0.091 | None | None | None | None | None | | HAL | NYQ | Halliburton Company | 0.0010393744130051477 | 36690808832 | 0.388 | 0.0500030517578160 | 2023-01-10 | 40.9900016784668000 | 40.9399986267089840 | 0.00122137404580161222 | | OKE | NYQ | ONEOK, Inc. | 0.0008854096904464799 | 31255721984 | 0.769 | None | None | None | None | None | | BKR | NMS | Baker Hughes Company | 0.0008807471517968449 | 31091130368 | 0.054 | -0.0400009155273450 | 2023-01-10 | 30.8299999237060550 | 30.8700008392334000 | -0.00129578602008027511 | | FANG | NMS | Diamondback Energy, Inc. | 0.000730020047064916 | 25770334208 | 0.565 | 0.2900085449218600 | 2023-01-10 | 142.2400054931640600 | 141.9499969482422000 | 0.00204303311839874783 | | MRO | NYQ | Marathon Oil Corporation | 0.0005965698099807201 | 21059426304 | 0.731 | 0.0399990081787110 | 2023-01-10 | 27.0499992370605470 | 27.0100002288818360 | 0.00148089625471161593 | | CTRA | NYQ | Coterra Energy Inc. | 0.0005690298110137097 | 20087240704 | 5.204 | -0.3899993896484380 | 2023-01-10 | 24.6900005340576170 | 25.0799999237060550 | -0.01555021494556719473 | | TRGP | NYQ | Targa Resources, Inc. | 0.0004842024080652878 | 17092760576 | 0.773 | None | None | None | None | None | | APA | NMS | APA Corporation | 0.00039910236086756015 | 14088655872 | 0.374 | 0.5799980163574200 | 2023-01-10 | 43.8199996948242200 | 43.2400016784668000 | 0.01341345961709929139 | | EQT | NYQ | EQT Corporation | 0.0003576031445812937 | 12623697920 | 1.092 | -0.6399993896484380 | 2023-01-10 | 33.4000015258789060 | 34.0400009155273440 | -0.01880139166966068805 | +--------------+-----------------+---------------------------------+------------------------+--------------+----------------+---------------------+-----------------+----------------------+----------------------+-------------------------+ ========================== MySQL connection is closed ``` ### 步骤 5. 前往 TiDB Cloud 控制台开始您自己的旅程 + 您现在可以在[这里](https://tidbcloud.com/clusters)开始您的 TiDB Cloud 旅程。如果您在本实验中使用了自己的免费层级集群，您会在同一页面上找到它。我们下一个实验再见！ ## 总结 TiDB Cloud Serverless 通过提供一个轻松的自动扩展、MySQL兼容的数据库，进一步提升了 TiDB Cloud 的能力。这是一项完全托管的多租户服务，让用户无需担心其支持应用程序的基础设施，只需专注于应用程序。 无服务器层提供两种主要服务计划：一个适合初学者的免费集群计划和一个适合需要业务同步增长的应用程序的可扩展集群计划。在免费计划下，您可以享受到每月最多 5 GiB 的行存储和列存储以及 5000 万请求单位，全部无需费用，创造一个无风险的测试环境。随着应用需求的增加，可扩展计划提供增强的功能，具有自动扩展和基于实际使用的可预测定价。 此外，TiDB Cloud Serverless 的多租户意味着所有用户都会受益于相同的核心高质量功能，无论他们选择何种计划，并且能够轻松地从免费计划过渡到付费的可扩展计划。通过其 HTAP 能力无缝融合事务处理和分析处理，TiDB Cloud Serverless 确保能够高效地处理各种工作负载，同时保持高可用性。 对于寻求强大且高效的数据库管理解决方案的组织，TiDB Cloud 提供实时分析、企业级安全的高可用性，以及简单的定价计划，使其对最注重预算的团队也能够负担得起。要深入了解 TiDB Cloud 并亲身体验这些功能，请访问 [https://tidbcloud.com](https://tidbcloud.com)，获取免费试用并开启您与这个前沿平台的旅程。