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Applet 数据分析应用"

使用 Pigsty Grafana & Echarts 工具箱进行数据分析与可视化

Applet的结构

Applet，是一种自包含的，运行于Pigsty基础设施中的数据小应用。

一个Pigsty应用通常包括以下内容中的至少一样或全部：

  • 图形界面（Grafana Dashboard定义） 放置于ui目录
  • 数据定义（PostgreSQL DDL File），放置于 sql 目录
  • 数据文件（各类资源，需要下载的文件），放置于data目录
  • 逻辑脚本（执行各类逻辑），放置于bin目录

Pigsty默认提供了几个样例应用：

  • pglog， 分析PostgreSQL CSV日志样本。
  • covid， 可视化WHO COVID-19数据，查阅各国疫情数据。
  • pglog， NOAA ISD，可以查询全球30000个地表气象站从1901年来的气象观测记录。

应用的结构

一个Pigsty应用会在应用根目录提供一个安装脚本：install或相关快捷方式。您需要使用管理用户在 管理节点 执行安装。安装脚本会检测当前的环境（获取 METADB_URL， PIGSTY_HOME，GRAFANA_ENDPOINT等信息以执行安装）

通常，带有APP标签的面板会被列入Pigsty Grafana首页导航中App下拉菜单中，带有APP和Overview标签的面板则会列入首页面板导航中。

您可以从 https://github.com/Vonng/pigsty/releases/download/v1.5.1/app.tgz 下载带有基础数据的应用进行安装。

1 - PGLOG：PG自带日志分析应用

Pigsty自带的，用于分析PostgreSQL CSV日志样本的一个样例Applet

PGLOG是Pigsty自带的一个样例应用，固定使用MetaDB中pglog.sample表作为数据来源。您只需要将日志灌入该表，然后访问相关Dashboard即可。

Pigsty提供了一些趁手的命令，用于拉取csv日志，并灌入样本表中。在元节点上，默认提供下列快捷命令：

catlog  [node=localhost]  [date=today]   # 打印CSV日志到标准输出
pglog                                    # 从标准输入灌入CSVLOG
pglog12                                  # 灌入PG12格式的CSVLOG
pglog13                                  # 灌入PG13格式的CSVLOG
pglog14                                  # 灌入PG14格式的CSVLOG (=pglog)

catlog | pglog                       # 分析当前节点当日的日志
catlog node-1 '2021-07-15' | pglog   # 分析node-1在2021-07-15的csvlog

接下来，您可以访问以下的连接，查看样例日志分析界面。

  • PGLOG Overview: 呈现整份CSV日志样本详情，按多种维度聚合。

  • PGLOG Session: 呈现日志样本中一条具体连接的详细信息。

catlog命令从特定节点拉取特定日期的CSV数据库日志，写入stdout

默认情况下，catlog会拉取当前节点当日的日志，您可以通过参数指定节点与日期。

组合使用pglog与catlog，即可快速拉取数据库CSV日志进行分析。

catlog | pglog                       # 分析当前节点当日的日志
catlog node-1 '2021-07-15' | pglog   # 分析node-1在2021-07-15的csvlog

2 - NOAA ISD 全球气象站历史数据查询

以ISD数据集为例，展现如何将数据导入数据库中

如果您拥有数据库后不知道干点什么，不妨参试试这个开源项目：Vonng/isd

您可以直接复用监控系统Grafana，以交互式的方式查阅近30000个地面气象站过去120年间的亚小时级气象数据。

这是一个功能完成的数据应用，可以查询全球30000个地表气象站从1901年来的气象观测记录。

项目地址：https://github.com/Vonng/isd

在线Demo地址：https://demo.pigsty.cc/d/isd-overview

isd-overview.jpg

快速上手

克隆本仓库

git clone https://github.com/Vonng/isd.git; cd isd;

准备一个 PostgreSQL 实例

该 PostgreSQL 实例应当启用了 PostGIS 扩展。使用 PGURL 环境变量传递数据库连接信息：

# Pigsty 默认使用的管理员账号是 dbuser_dba，密码是 DBUser.DBA
export PGURL=postgres://dbuser_dba:DBUser.DBA@127.0.0.1:5432/meta?sslmode=disable
psql "${PGURL}" -c 'SELECT 1'  # 检查连接是否可用

获取并导入ISD气象站元数据

这是一份每日更新的气象站元数据，包含了气象站的经纬度、海拔、名称、国家、省份等信息，使用以下命令下载并导入。

make reload-station   # 相当于先下载最新的Station数据再加载：get-station + load-station

获取并导入最新的 isd.daily 数据

isd.daily 是一个每日更新的数据集，包含了全球各气象站的日观测数据摘要，使用以下命令下载并导入。 请注意，直接从 NOAA 网站下载的原始数据需要经过解析方可入库，所以你需要下载或构建一个 ISD 数据 Parser。

make get-parser       # 从 Github 下载 Parser 二进制，当然你也可以用 make build 直接用 go 构建。
make reload-daily     # 下载本年度最新的 isd.daily 数据并导入数据库中

加载解析好的 CSV 数据集

ISD Daily 数据集有一些脏数据与重复数据，如果你不想手工解析处理清洗，这里也提供了一份解析好的稳定CSV数据集。

该数据集包含了截止到 2023-06-24 的 isd.daily 数据，你可以直接下载并导入 PostgreSQL 中，不需要 Parser，

make get-stable       # 从 Github 上获取稳定的 isd.daily 历史数据集。
make load-stable      # 将下载好的稳定历史数据集加载到 PostgreSQL 数据库中。

更多数据

ISD数据集有两个部分是每日更新的，气象站元数据，以及最新年份的 isd.daily （如 2023 年的 Tarball）。

你可以使用以下命令下载并刷新这两个部分。如果数据集没有更新，那么这些命令不会重新下载同样的数据包

make reload           # 实际上是：reload-station + reload-daily

你也可以使用以下命令下载并加载特定年份的 isd.daily 数据：

bin/get-daily  2022                   # 获取 2022 年的每日气象观测摘要 (1900-2023)
bin/load-daily "${PGURL}" 2022        # 加载 2022 年的每日气象观测摘要 (1900-2023)

除了每日摘要 isd.daily， ISD 还提供了一份更详细的亚小时级原始观测记录 isd.hourly，下载与加载的方式与前者类似：

bin/get-hourly  2022                  # 下载特定某一年的小时级观测记录（例如2022年，可选 1900-2023）
bin/load-hourly "${PGURL}" 2022       # 加载特定某一年的小时级观测记录

数据

数据集概要

ISD提供了四个数据集：亚小时级原始观测数据，每日统计摘要数据，月度统计摘要，年度统计摘要

数据集 备注
ISD Hourly 亚小时级观测记录
ISD Daily 每日统计摘要
ISD Monthly 没有用到，因为可以从 isd.daily 计算生成
ISD Yearly 没有用到，因为可以从 isd.daily 计算生成

每日摘要数据集

  • 压缩包大小 2.8GB (截止至 2023-06-24)
  • 表大小 24GB，索引大小 6GB，PostgreSQL 中总大小约为 30GB
  • 如果启用了 timescaledb 压缩，总大小可以压缩到 4.5 GB。

亚小时级观测数据级

  • 压缩包总大小 117GB
  • 灌入数据库后表大小 1TB+ ，索引大小 600GB+，总大小 1.6TB

数据库模式

气象站元数据表

CREATE TABLE isd.station
(
    station    VARCHAR(12) PRIMARY KEY,
    usaf       VARCHAR(6) GENERATED ALWAYS AS (substring(station, 1, 6)) STORED,
    wban       VARCHAR(5) GENERATED ALWAYS AS (substring(station, 7, 5)) STORED,
    name       VARCHAR(32),
    country    VARCHAR(2),
    province   VARCHAR(2),
    icao       VARCHAR(4),
    location   GEOMETRY(POINT),
    longitude  NUMERIC GENERATED ALWAYS AS (Round(ST_X(location)::NUMERIC, 6)) STORED,
    latitude   NUMERIC GENERATED ALWAYS AS (Round(ST_Y(location)::NUMERIC, 6)) STORED,
    elevation  NUMERIC,
    period     daterange,
    begin_date DATE GENERATED ALWAYS AS (lower(period)) STORED,
    end_date   DATE GENERATED ALWAYS AS (upper(period)) STORED
);

每日摘要表

CREATE TABLE IF NOT EXISTS isd.daily
(
    station     VARCHAR(12) NOT NULL, -- station number 6USAF+5WBAN
    ts          DATE        NOT NULL, -- observation date
    -- 气温 & 露点
    temp_mean   NUMERIC(3, 1),        -- mean temperature ℃
    temp_min    NUMERIC(3, 1),        -- min temperature ℃
    temp_max    NUMERIC(3, 1),        -- max temperature ℃
    dewp_mean   NUMERIC(3, 1),        -- mean dew point ℃
    -- 气压
    slp_mean    NUMERIC(5, 1),        -- sea level pressure (hPa)
    stp_mean    NUMERIC(5, 1),        -- station pressure (hPa)
    -- 可见距离
    vis_mean    NUMERIC(6),           -- visible distance (m)
    -- 风速
    wdsp_mean   NUMERIC(4, 1),        -- average wind speed (m/s)
    wdsp_max    NUMERIC(4, 1),        -- max wind speed (m/s)
    gust        NUMERIC(4, 1),        -- max wind gust (m/s) 
    -- 降水 / 雪深
    prcp_mean   NUMERIC(5, 1),        -- precipitation (mm)
    prcp        NUMERIC(5, 1),        -- rectified precipitation (mm)
    sndp        NuMERIC(5, 1),        -- snow depth (mm)
    -- FRSHTT (Fog/Rain/Snow/Hail/Thunder/Tornado) 雾/雨/雪/雹/雷/龙卷
    is_foggy    BOOLEAN,              -- (F)og
    is_rainy    BOOLEAN,              -- (R)ain or Drizzle
    is_snowy    BOOLEAN,              -- (S)now or pellets
    is_hail     BOOLEAN,              -- (H)ail
    is_thunder  BOOLEAN,              -- (T)hunder
    is_tornado  BOOLEAN,              -- (T)ornado or Funnel Cloud
    -- 统计聚合使用的记录数
    temp_count  SMALLINT,             -- record count for temp
    dewp_count  SMALLINT,             -- record count for dew point
    slp_count   SMALLINT,             -- record count for sea level pressure
    stp_count   SMALLINT,             -- record count for station pressure
    wdsp_count  SMALLINT,             -- record count for wind speed
    visib_count SMALLINT,             -- record count for visible distance
    -- 气温标记
    temp_min_f  BOOLEAN,              -- aggregate min temperature
    temp_max_f  BOOLEAN,              -- aggregate max temperature
    prcp_flag   CHAR,                 -- precipitation flag: ABCDEFGHI
    PRIMARY KEY (station, ts)
); -- PARTITION BY RANGE (ts);

亚小时级原始观测数据表

ISD Hourly 
CREATE TABLE IF NOT EXISTS isd.hourly
(
    station    VARCHAR(12) NOT NULL, -- station id
    ts         TIMESTAMP   NOT NULL, -- timestamp
    -- air
    temp       NUMERIC(3, 1),        -- [-93.2,+61.8]
    dewp       NUMERIC(3, 1),        -- [-98.2,+36.8]
    slp        NUMERIC(5, 1),        -- [8600,10900]
    stp        NUMERIC(5, 1),        -- [4500,10900]
    vis        NUMERIC(6),           -- [0,160000]
    -- wind
    wd_angle   NUMERIC(3),           -- [1,360]
    wd_speed   NUMERIC(4, 1),        -- [0,90]
    wd_gust    NUMERIC(4, 1),        -- [0,110]
    wd_code    VARCHAR(1),           -- code that denotes the character of the WIND-OBSERVATION.
    -- cloud
    cld_height NUMERIC(5),           -- [0,22000]
    cld_code   VARCHAR(2),           -- cloud code
    -- water
    sndp       NUMERIC(5, 1),        -- mm snow
    prcp       NUMERIC(5, 1),        -- mm precipitation
    prcp_hour  NUMERIC(2),           -- precipitation duration in hour
    prcp_code  VARCHAR(1),           -- precipitation type code
    -- sky
    mw_code    VARCHAR(2),           -- manual weather observation code
    aw_code    VARCHAR(2),           -- auto weather observation code
    pw_code    VARCHAR(1),           -- weather code of past period of time
    pw_hour    NUMERIC(2),           -- duration of pw_code period
    -- misc
    -- remark     TEXT,
    -- eqd        TEXT,
    data       JSONB                 -- extra data
) PARTITION BY RANGE (ts);

解析器

NOAA ISD 提供的原始数据是高度压缩的专有格式，需要通过解析器加工，才能转换为数据库表的格式。

针对 Daily 与 Hourly 两份数据集，这里提供了两个 Parser： isdd and isdh。 这两个解析器都以年度数据压缩包作为输入，产生 CSV 结果作为输出，以管道的方式工作，如下所示：

NAME
        isd -- Intergrated Surface Dataset Parser

SYNOPSIS
        isd daily   [-i <input|stdin>] [-o <output|stout>] [-v]
        isd hourly  [-i <input|stdin>] [-o <output|stout>] [-v] [-d raw|ts-first|hour-first]

DESCRIPTION
        The isd program takes noaa isd daily/hourly raw tarball data as input.
        and generate parsed data in csv format as output. Works in pipe mode

        cat data/daily/2023.tar.gz | bin/isd daily -v | psql ${PGURL} -AXtwqc "COPY isd.daily FROM STDIN CSV;" 

        isd daily  -v -i data/daily/2023.tar.gz  | psql ${PGURL} -AXtwqc "COPY isd.daily FROM STDIN CSV;"
        isd hourly -v -i data/hourly/2023.tar.gz | psql ${PGURL} -AXtwqc "COPY isd.hourly FROM STDIN CSV;"

OPTIONS
        -i  <input>     input file, stdin by default
        -o  <output>    output file, stdout by default
        -p  <profpath>  pprof file path, enable if specified
        -d              de-duplicate rows for hourly dataset (raw, ts-first, hour-first)
        -v              verbose mode
        -h              print help

用户界面

这里提供了几个使用 Grafana 制作的 Dashboard，可以用于探索 ISD 数据集，查询气象站与历史气象数据。

ISD Overview

全局概览，总体指标与气象站导航。

isd-overview.jpg

ISD Country

展示单个国家/地区内所有的气象站。

isd-country.jpg

ISD Station

展示单个气象站的详细信息，元数据，天/月/年度汇总指标。

ISD Station Dashboard

isd-station.jpg

ISD Detail

展示一个气象站原始亚小时级观测指标数据，需要 isd.hourly 数据集。

ISD Station Dashboard

isd-detail.jpg




3 - WHO COVID-19 疫情大盘

Pigsty 自带的，用于展示世界卫生组织官方疫情数据的一个样例 Applet

Covid 是 Pigsty 自带的，用于展示世界卫生组织官方疫情数据大盘的一个样例 Applet。

您可以查阅每个国家与地区 COVID-19 的感染与死亡案例，以及全球的疫情趋势。

概览

GitHub 仓库地址：https://github.com/Vonng/pigsty-app/tree/master/covid

在线Demo地址：https://demo.pigsty.cc/d/covid

安装

在管理节点上进入应用目录，执行make以完成安装。

make            # 完成所有配置

其他一些子任务：

make reload     # download latest data and pour it again
make ui         # install grafana dashboards
make sql        # install database schemas
make download   # download latest data
make load       # load downloaded data into database
make reload     # download latest data and pour it into database

4 - AWS 阿里云 服务器价格

分析阿里云 / AWS 上算力与存储的价格 (ECS/ESSD)

概览

GitHub 仓库地址：https://github.com/Vonng/pigsty-app/tree/master/cloud

在线Demo地址：https://demo.pigsty.cc/d/ecs

文章地址：《剖析算力成本：阿里云真降价了吗？》

数据源

Aliyun ECS 价格可以在 价格计算器 - 定价详情 - 价格下载 中获取 CSV 原始数据。

模式

下载 阿里云 价格明细并导入分析

CREATE EXTENSION file_fdw;
CREATE SERVER fs FOREIGN DATA WRAPPER file_fdw;

DROP FOREIGN TABLE IF EXISTS aliyun_ecs CASCADE;
CREATE FOREIGN TABLE aliyun_ecs
    (
        "region" text,
        "system" text,
        "network" text,
        "isIO" bool,
        "instanceId" text,
        "hourlyPrice" numeric,
        "weeklyPrice" numeric,
        "standard" numeric,
        "monthlyPrice" numeric,
        "yearlyPrice" numeric,
        "2yearPrice" numeric,
        "3yearPrice" numeric,
        "4yearPrice" numeric,
        "5yearPrice" numeric,
        "id" text,
        "instanceLabel" text,
        "familyId" text,
        "serverType" text,
        "cpu" text,
        "localStorage" text,
        "NvmeSupport" text,
        "InstanceFamilyLevel" text,
        "EniTrunkSupported" text,
        "InstancePpsRx" text,
        "GPUSpec" text,
        "CpuTurboFrequency" text,
        "InstancePpsTx" text,
        "InstanceTypeId" text,
        "GPUAmount" text,
        "InstanceTypeFamily" text,
        "SecondaryEniQueueNumber" text,
        "EniQuantity" text,
        "EniPrivateIpAddressQuantity" text,
        "DiskQuantity" text,
        "EniIpv6AddressQuantity" text,
        "InstanceCategory" text,
        "CpuArchitecture" text,
        "EriQuantity" text,
        "MemorySize" numeric,
        "EniTotalQuantity" numeric,
        "PhysicalProcessorModel" text,
        "InstanceBandwidthRx" numeric,
        "CpuCoreCount" numeric,
        "Generation" text,
        "CpuSpeedFrequency" numeric,
        "PrimaryEniQueueNumber" text,
        "LocalStorageCategory" text,
        "InstanceBandwidthTx" text,
        "TotalEniQueueQuantity" text
        ) SERVER fs OPTIONS ( filename '/tmp/aliyun-ecs.csv', format 'csv',header 'true');

AWS EC2 同理，可以从 Vantage 下载价格清单：


DROP FOREIGN TABLE IF EXISTS aws_ec2 CASCADE;
CREATE FOREIGN TABLE aws_ec2
    (
        "name" TEXT,
        "id" TEXT,
        "Memory" TEXT,
        "vCPUs" TEXT,
        "GPUs" TEXT,
        "ClockSpeed" TEXT,
        "InstanceStorage" TEXT,
        "NetworkPerformance" TEXT,
        "ondemand" TEXT,
        "reserve" TEXT,
        "spot" TEXT
        ) SERVER fs OPTIONS ( filename '/tmp/aws-ec2.csv', format 'csv',header 'true');



DROP VIEW IF EXISTS ecs;
CREATE VIEW ecs AS
SELECT "region"                                       AS region,
       "id"                                           AS id,
       "instanceLabel"                                AS name,
       "familyId"                                     AS family,
       "CpuCoreCount"                                 AS cpu,
       "MemorySize"                                   AS mem,
       round("5yearPrice" / "CpuCoreCount" / 60, 2)   AS ycm5, -- ¥ / (core·month)
       round("4yearPrice" / "CpuCoreCount" / 48, 2)   AS ycm4, -- ¥ / (core·month)
       round("3yearPrice" / "CpuCoreCount" / 36, 2)   AS ycm3, -- ¥ / (core·month)
       round("2yearPrice" / "CpuCoreCount" / 24, 2)   AS ycm2, -- ¥ / (core·month)
       round("yearlyPrice" / "CpuCoreCount" / 12, 2)  AS ycm1, -- ¥ / (core·month)
       round("standard" / "CpuCoreCount", 2)          AS ycmm, -- ¥ / (core·month)
       round("hourlyPrice" / "CpuCoreCount" * 720, 2) AS ycmh, -- ¥ / (core·month)
       "CpuSpeedFrequency"::NUMERIC                   AS freq,
       "CpuTurboFrequency"::NUMERIC                   AS freq_turbo,
       "Generation"                                   AS generation
FROM aliyun_ecs
WHERE system = 'linux';

DROP VIEW IF EXISTS ec2;
CREATE VIEW ec2 AS
SELECT id,
       name,
       split_part(id, '.', 1)                                                               as family,
       split_part(id, '.', 2)                                                               as spec,
       (regexp_match(split_part(id, '.', 1), '^[a-zA-Z]+(\d)[a-z0-9]*'))[1]                 as gen,
       regexp_substr("vCPUs", '^[0-9]+')::int                                               as cpu,
       regexp_substr("Memory", '^[0-9]+')::int                                              as mem,
       CASE spot
           WHEN 'unavailable' THEN NULL
           ELSE round((regexp_substr("spot", '([0-9]+.[0-9]+)')::NUMERIC * 7.2), 2) END     AS spot,
       CASE ondemand
           WHEN 'unavailable' THEN NULL
           ELSE round((regexp_substr("ondemand", '([0-9]+.[0-9]+)')::NUMERIC * 7.2), 2) END AS ondemand,
       CASE reserve
           WHEN 'unavailable' THEN NULL
           ELSE round((regexp_substr("reserve", '([0-9]+.[0-9]+)')::NUMERIC * 7.2), 2) END  AS reserve,
       "ClockSpeed"                                                                         AS freq
FROM aws_ec2;

可视化

5 - 使用一条 SQL 计算扑克24点

用一条SQL给出扑克牌24点的计算表达式，PostgreSQL 解法。

题目

题目如下： 《数据库编程大赛：一条SQL计算扑克牌24点》

有一张表 cards，id 是自增字段的数字主键，另外有4个字段 c1,c2,c3,c4 ，每个字段随机从 1~10 之间选择一个整数 要求选手使用一条 SQL 给出 24 点的计算公式，返回的内容示例如右图：

其中 result 字段是计算的表达式，只需返回1个解，如果没有解，result 返回null

  1. 24 点的计算规则：只能使用加减乘除四则运算，不能使用阶乘、指数等运算符，每个数字最少使用一次，且只能使用一次，可以使用小括号改变优先级

  2. 只能使用一条 SQL ，可以使用数据库内置函数，但是不能使用存储过程/自定义函数和代码块。

  3. SQL 正确性大家在 NineData 平台 demo 数据库自己验证，或在自己的数据库上验证，组委会评测服务器是 4 核 CPU ，32 GB 内存

  4. 选手个人诚信参赛，不允许提交别人的比赛代码，如果发现有类似代码，工作组以第一个提交的为有效参赛

  5. 每个选手最多提交 3 次比赛代码

  6. 提交的 SQL 不能超过 10 KB大小

作为 MySQL 老司机，NineData 搞的这个比赛暗吹 MySQL 的水平比姜高到不知道哪里去了 —— 为什么这么说呢？

因为 10KB 的大小限制非常猥琐 —— 最快的解法都是质数查表，而这种方式所有解的文本拼接大小大约是 10018 个字符。要想压缩这个表到 10KB 以内，必须要用到一些压缩技巧。

MySQL 是带有 COMPRESS 和 UNCOMPRESS 函数的，而 PostgreSQL 原生是没带的，需要用到 pgsql-gzip 扩展，而这个扩展在 NineData 比赛的平台上是不提供的。

下面是使用 PostgreSQL 的解法：

创建随机测试数据表

CREATE SCHEMA poker24;
DROP TABLE IF EXISTS poker24.cards;
CREATE TABLE poker24.cards AS
SELECT i                   AS id,
       ceil(random() * 10) AS c1,
       ceil(random() * 10) AS c2,
       ceil(random() * 10) AS c3,
       ceil(random() * 10) AS c4
FROM generate_series(1, 1000000) i;

ALTER TABLE poker24.cards ADD PRIMARY KEY (id);

解法

基本思想是是使用质数编码，将所有可能的结果分配唯一主键编号，快速计算 24 点：

EXPLAIN ANALYZE
WITH a(i, result) AS (
    SELECT (split_part(kv, ':', 1))::INTEGER AS i, split_part(kv, ':', 2) AS result
    FROM regexp_split_to_table('152:((1+1)+1)*8,156:(6*2)*(1+1),204:(7+1)*(2+1),228:((1*1)+2)*8,276:(9-1)*(2+1),348:(10+2)*(1+1),140:(4*3)*(1+1),220:(5+1)*(3+1),260:((1+1)+6)*3,340:((1*1)+7)*3,380:(8*3)+(1-1),460:(9+3)*(1+1),580:(10-(1+1))*3,196:((1+1)+4)*4,308:((1*1)+5)*4,364:(6*4)+(1-1),476:(7-(1*1))*4,532:(8+4)*(1+1),644:(9-1)*(4-1),812:((1+1)*10)+4,484:(5*5)-(1*1),572:(5-(1*1))*6,748:(7+5)*(1+1),836:(5-(1+1))*8,676:(6+6)*(1+1),988:(8*6)/(1+1),1196:((1+1)*9)+6,1972:((1+1)*7)+10,1444:((1+1)*8)+8,126:(4*2)*(2+1),198:(2+2)*(5+1),234:(6+2)*(2+1),306:(2+2)*(7-1),342:((2-1)+2)*8,414:((2+1)+9)*2,522:(10-2)*(2+1),150:(3*2)*(3+1),210:((2+1)+3)*4,330:(5+3)*(2+1),390:((2-1)+3)*6,510:(7*3)+(2+1),570:(8*3)*(2-1),690:(9*3)-(2+1),870:(10-(2*1))*3,294:(4+4)*(2+1),462:((2-1)+5)*4,546:(6*4)*(2-1),714:(7-(2-1))*4,798:(4-(2-1))*8,966:(9-(2+1))*4,1218:((2*1)*10)+4,726:(5*5)-(2-1),858:(5-(2-1))*6,1122:(7+5)*(2*1),1254:(5-(2*1))*8,1518:((2+1)*5)+9,1914:(10*2)+(5-1),1014:((2+1)*6)+6,1326:(7-(2+1))*6,1482:(6-(2+1))*8,1794:((2*1)*9)+6,2262:((2+1)*10)-6,1734:((7*7)-1)/2,1938:(8*2)+(7+1),2346:(9*2)+(7-1),2958:((2*1)*7)+10,2166:((2*1)*8)+8,2622:(9*8)/(2+1),3306:((8-1)*2)+10,250:(3+3)*(3+1),350:((3+1)+4)*3,550:(5+3)*(3*1),650:((3-1)+6)*3,850:(7*3)+(3*1),950:((8+1)*3)-3,1150:(9-3)*(3+1),1450:(10-(3-1))*3,490:((3-1)+4)*4,770:(5*4)+(3+1),910:6/(1-(3/4)),1190:(7*4)-(3+1),1330:((3+1)*4)+8,1610:(9-(3*1))*4,2030:(10-4)*(3+1),1430:(6*3)+(5+1),1870:(7+5)*(3-1),2090:(5-(3-1))*8,2530:((3*1)*5)+9,3190:(10*3)-(5+1),1690:(6+6)*(3-1),2210:(7-(3*1))*6,2470:(8-(3+1))*6,2990:((3-1)*9)+6,3770:((3*1)*10)-6,2890:(7-3)*(7-1),3230:(7-(3+1))*8,3910:(9/3)*(7+1),4930:((3-1)*7)+10,3610:((3+1)*8)-8,4370:(9*8)/(3*1),5510:(8/3)*(10-1),5290:(9/3)*(9-1),6670:((10+1)*3)-9,8410:(10+10)+(3+1),686:((4+1)*4)+4,1078:(5*4)+(4*1),1274:((6+1)*4)-4,1666:(7*4)-(4*1),1862:((4*1)*4)+8,2254:(9-(4-1))*4,2842:(10-4)*(4*1),1694:(5*4)+(5-1),2002:6/((5/4)-1),2618:(7*4)-(5-1),2926:(8-4)*(5+1),3542:((4-1)*5)+9,4466:(10-4)*(5-1),2366:((4+1)*6)-6,3094:(7-(4-1))*6,3458:(6-(4-1))*8,4186:(9-(4+1))*6,5278:((4-1)*10)-6,4046:(7-4)*(7+1),4522:(7-(4*1))*8,5474:(7-4)*(9-1),5054:(8-(4+1))*8,6118:(9*8)/(4-1),9338:(10+9)+(4+1),11774:(10+10)+(4*1),2662:(5-(1/5))*5,3146:(6*5)-(5+1),5566:(9-5)*(5+1),7018:((10-5)*5)-1,3718:((5*1)*6)-6,4862:(6*5)-(7-1),5434:(8-(5-1))*6,6578:(9-(5*1))*6,8294:(10-6)*(5+1),7106:(7-(5-1))*8,8602:(9-5)*(7-1),10846:(7*5)-(10+1),7942:((5-1)*8)-8,9614:(9-(5+1))*8,12122:(10+8)+(5+1),11638:(9+9)+(5+1),14674:(10+9)+(5*1),18502:(10+10)+(5-1),4394:((6-1)*6)-6,6422:6/(1-(6/8)),7774:(9-(6-1))*6,9802:(10-6)*(6*1),10166:(9-6)*(7+1),12818:(10+7)+(6+1),9386:(8-(6-1))*8,11362:(9+8)+(6+1),14326:(10-(6+1))*8,13754:(9+9)+(6*1),17342:(10+9)+(6-1),13294:(9+7)+(7+1),16762:(10-7)*(7+1),12274:(8+8)+(7+1),14858:(9-(7-1))*8,18734:(10+8)+(7-1),17986:(9+9)+(7-1),22678:(10-7)*(9-1),13718:(8+8)+(8*1),16606:(9+8)+(8-1),20938:(10-(8-1))*8,135:(3*2)*(2+2),189:(4+2)*(2+2),297:((5*2)+2)*2,459:((7*2)-2)*2,513:(8-2)*(2+2),621:((9+2)*2)+2,783:(10*2)+(2+2),225:(3+3)*(2+2),315:((2+2)+4)*3,495:((5*2)-2)*3,585:((2/2)+3)*6,765:((2/2)+7)*3,855:(8*3)+(2-2),1035:(9-3)*(2+2),1305:((10+3)*2)-2,441:((4*2)-2)*4,693:(5*4)+(2+2),819:(6*4)+(2-2),1071:(7*4)-(2+2),1197:((2+2)*4)+8,1449:(9*2)+(4+2),1827:(10-4)*(2+2),1089:(5*5)-(2/2),1287:(5-(2/2))*6,1683:(7*2)+(5*2),1881:((8+5)*2)-2,2277:((5-2)+9)*2,2871:((5+2)*2)+10,1521:(6/2)*(6+2),1989:((7+2)*2)+6,2223:(8-(2+2))*6,2691:((6/2)+9)*2,3393:(10*2)+(6-2),2601:((7-2)+7)*2,2907:(7-(2+2))*8,4437:((10/2)+7)*2,3249:((2+2)*8)-8,3933:(9*2)+(8-2),4959:(10-2)+(8*2),6003:((9-2)*2)+10,7569:(10+10)+(2+2),375:((3+2)+3)*3,825:((5+2)*3)+3,975:((3-2)+3)*6,1275:((3-2)+7)*3,1425:(8*3)*(3-2),1725:((3+2)*3)+9,2175:(10*3)-(3*2),735:((3+2)*4)+4,1155:((3-2)+5)*4,1365:(6*4)*(3-2),1785:(7-(3-2))*4,1995:(4-(3-2))*8,2415:(9*4)/(3/2),3045:(10*3)-(4+2),1815:(5*5)-(3-2),2145:(5-(3-2))*6,2805:(7*3)+(5-2),3135:(5+3)+(8*2),3795:(9-5)*(3*2),4785:(5-3)*(10+2),2535:((3+2)*6)-6,3315:(7*3)+(6/2),3705:((8+2)*3)-6,4485:(9-(3+2))*6,5655:(10-6)*(3*2),4335:(7+3)+(7*2),4845:(8/3)*(7+2),5865:(9+7)*(3/2),7395:(7-3)+(10*2),5415:(8-(3+2))*8,6555:(9-(3*2))*8,8265:(10+8)+(3*2),7935:(9+9)+(3*2),10005:(10+9)+(3+2),12615:((10-3)*2)+10,1029:((4-2)+4)*4,1617:((5+2)*4)-4,1911:((4*2)-4)*6,2499:(7-4)*(4*2),2793:(8-4)*(4+2),3381:((9-2)*4)-4,4263:((4-2)*10)+4,2541:((5+5)*2)+4,3003:(6*5)-(4+2),3927:(7+5)*(4-2),4389:(5-(4-2))*8,5313:(9-5)*(4+2),6699:(10+4)+(5*2),3549:(6+6)*(4-2),4641:(7-4)*(6+2),5187:(8*6)/(4-2),6279:((4-2)*9)+6,7917:(10-6)*(4+2),6069:((7+7)*2)-4,6783:((7*2)-8)*4,8211:(9+7)+(4*2),10353:((4-2)*7)+10,7581:((4-2)*8)+8,9177:(9-(4+2))*8,11571:(10+8)+(4+2),11109:(9+9)+(4+2),14007:(10-4)+(9*2),17661:((4/10)+2)*10,6171:(5+5)+(7*2),6897:((5/5)+2)*8,8349:((5-2)*5)+9,10527:(5-(2/10))*5,5577:((5-2)*6)+6,7293:(7-(5-2))*6,8151:(6-(5-2))*8,9867:((5/2)*6)+9,12441:((5-2)*10)-6,9537:(7+7)+(5*2),10659:((5*2)-7)*8,12903:(7*5)-(9+2),16269:(10+7)+(5+2),11913:(8*5)-(8*2),14421:(9+8)+(5+2),18183:(10-(5+2))*8,22011:(9-5)+(10*2),27753:(10/5)*(10+2),6591:(6+6)+(6*2),8619:(7-(6/2))*6,9633:(8-(6-2))*6,11661:(9-6)*(6+2),14703:(10+6)+(6+2),12597:(7-(6-2))*8,15249:(9+7)+(6+2),19227:(10-7)*(6+2),14079:(8+8)+(6+2),17043:((6*2)-9)*8,21489:(10-8)*(6*2),20631:((9-6)+9)*2,26013:(9-6)*(10-2),32799:(10+10)+(6-2),16473:(8+7)+(7+2),25143:((10/7)+2)*7,18411:(8-(7-2))*8,22287:((9+7)*2)-8,34017:(10+9)+(7-2),42891:(10-7)*(10-2),20577:((8/2)*8)-8,24909:(9-(8-2))*8,31407:(10+8)+(8-2),30153:(9+9)+(8-2),38019:(10-(9-2))*8,47937:(10+10)+(8/2),58029:(10+9)+(10/2),625:((3*3)*3)-3,875:((3*3)-3)*4,1375:(5*3)+(3*3),1625:(6*3)+(3+3),2125:(7-3)*(3+3),2375:((3+3)-3)*8,2875:(9-(3/3))*3,3625:(10*3)-(3+3),1225:(4*3)+(4*3),1925:((3/3)+5)*4,2275:(6*4)+(3-3),2975:(7-(3/3))*4,3325:(8-4)*(3+3),4025:(9-(4-3))*3,3025:(5*5)-(3/3),3575:(6*5)-(3+3),4675:((5*3)-7)*3,6325:(9-5)*(3+3),7975:(10+5)+(3*3),4225:((6/3)+6)*3,5525:(7*3)+(6-3),6175:((3*3)-6)*8,7475:(9+6)+(3*3),9425:(10-6)*(3+3),7225:((3/7)+3)*7,8075:(8+7)+(3*3),9775:(9-3)*(7-3),9025:8/(3-(8/3)),10925:(9-(3+3))*8,13775:(10+8)+(3+3),13225:(9+9)+(3+3),16675:(10*3)-(9-3),1715:((4+3)*4)-4,2695:((4-3)+5)*4,3185:(6*4)*(4-3),4165:(7-(4-3))*4,4655:((4+3)-4)*8,5635:(9*4)-(4*3),7105:((10-3)*4)-4,4235:(5*5)-(4-3),5005:(5-(4-3))*6,6545:(7+5)+(4*3),7315:(8*4)-(5+3),8855:((5*3)-9)*4,11165:(10/5)*(4*3),5915:(6+6)+(4*3),8645:(8-6)*(4*3),10465:(9-(6-3))*4,13195:(10-4)+(6*3),10115:(7*4)-(7-3),11305:((7-3)*4)+8,13685:(9-7)*(4*3),17255:(10+7)+(4+3),15295:(9+8)+(4+3),19285:(10-(4+3))*8,18515:(9+9)*(4/3),29435:(10*3)-(10-4),7865:((5+5)*3)-6,10285:(7+5)*(5-3),11495:(8-5)*(5+3),13915:(9-(5/5))*3,9295:(6+6)*(5-3),12155:(7+5)*(6/3),13585:(8*6)/(5-3),16445:(9-6)*(5+3),20735:(10+6)+(5+3),17765:(8-5)+(7*3),21505:(9+7)+(5+3),27115:(10-7)*(5+3),19855:(8+8)+(5+3),24035:(9*3)-(8-5),29095:((5/3)*9)+9,36685:(10/5)*(9+3),46255:(10-(10/5))*3,10985:((6-3)*6)+6,14365:(7-(6-3))*6,16055:((6+3)-6)*8,19435:(9+6)+(6+3),24505:((6-3)*10)-6,18785:((7-6)+7)*3,20995:(8+7)+(6+3),25415:(9-6)+(7*3),32045:((6/3)*7)+10,23465:((6/3)*8)+8,28405:(9*8)/(6-3),35815:(10-(8-6))*3,34385:(9*3)-(9-6),43355:(10-6)*(9-3),54665:(3-(6/10))*10,24565:(7+7)+(7+3),27455:((7+3)-7)*8,33235:(9-(7/7))*3,41905:(10-7)+(7*3),30685:((7-3)*8)-8,37145:(9-(8-7))*3,44965:(9-7)*(9+3),56695:(9*3)-(10-7),71485:(10+10)+(7-3),34295:((8+3)-8)*8,41515:(9-8)*(8*3),52345:((10*8)-8)/3,50255:(9-9)+(8*3),63365:(10+9)+(8-3),79895:(10-10)+(8*3),60835:(9+9)+(9-3),76705:((9+9)-10)*3,96715:(9-(10/10))*3,2401:(4*4)+(4+4),3773:((4/4)+5)*4,4459:((4+4)-4)*6,5831:(7-4)*(4+4),6517:(8*4)-(4+4),7889:((9-4)*4)+4,9947:(10*4)-(4*4),5929:(5*5)-(4/4),7007:(5-(4/4))*6,9163:(7-(5-4))*4,10241:(8-5)*(4+4),15631:((10-5)*4)+4,12103:(8+4)*(6-4),14651:(9-6)*(4+4),18473:(10+6)+(4+4),14161:(4-(4/7))*7,15827:(7*4)-(8-4),19159:(9+7)+(4+4),24157:(10-7)*(4+4),17689:(8+8)+(4+4),21413:(9*4)-(8+4),26999:(10-4)*(8-4),41209:((10*10)-4)/4,9317:(5*5)-(5-4),11011:((5+4)-5)*6,14399:(7-(5/5))*4,16093:(4-(5/5))*8,19481:(9-5)+(5*4),24563:(10+5)+(5+4),13013:(6-5)*(6*4),17017:(7+5)*(6-4),19019:((5+4)-6)*8,23023:(9+6)+(5+4),29029:(10-6)+(5*4),22253:(7*5)-(7+4),24871:(8+7)+(5+4),30107:((7-4)*5)+9,37961:((7-5)*10)+4,27797:(5-(8/4))*8,33649:(9-(8-5))*4,42427:(10/5)*(8+4),40733:((9/9)+5)*4,51359:(9-5)*(10-4),64757:((10/5)*10)+4,15379:((6+4)-6)*6,20111:(7-6)*(6*4),22477:(8+6)+(6+4),27209:((6-4)*9)+6,34307:(10+6)*(6/4),26299:(7+7)+(6+4),29393:((6+4)-7)*8,35581:(9+7)*(6/4),44863:((6-4)*7)+10,32851:((6-4)*8)+8,39767:(9-8)*(6*4),50141:((8-6)*10)+4,48139:(9-9)+(6*4),60697:(10-9)*(6*4),76531:(10-10)+(6*4),34391:(7-(7/7))*4,38437:((7+7)-8)*4,42959:((7+4)-8)*8,52003:(9*8)/(7-4),65569:((7/4)*8)+10,62951:(7-(9/9))*4,79373:(10*4)-(9+7),100079:(7-(10/10))*4,48013:((8-4)*8)-8,58121:((8+4)-9)*8,73283:(10-8)*(8+4),70357:(4-(9/9))*8,88711:((9+4)-10)*8,111853:(10+10)+(8-4),107387:(10+9)+(9-4),14641:(5*5)-(5/5),17303:(5*5)-(6-5),30613:(9+5)+(5+5),20449:((5+5)-6)*6,26741:(5*5)-(7-6),29887:(8+6)+(5+5),34969:(7+7)+(5+5),39083:((5+5)-7)*8,59653:(10/5)*(7+5),43681:(5*5)-(8/8),52877:(5*5)-(9-8),66671:(10+5)*(8/5),64009:(5*5)-(9/9),80707:(5*5)-(10-9),101761:(5*5)-(10/10),24167:(5-(6/6))*6,31603:(7+6)+(6+5),35321:((8-5)*6)+6,42757:(9*6)-(6*5),53911:((10-5)*6)-6,41327:(5-(7/7))*6,46189:(8-6)*(7+5),55913:((7-5)*9)+6,51623:((6+5)-8)*8,62491:((8+5)-9)*6,78793:(6*5)/(10/8),75647:((9-6)*5)+9,95381:((9+5)-10)*6,120263:(10+10)*(6/5),73117:(9-7)*(7+5),92191:((7-5)*7)+10,67507:((7-5)*8)+8,81719:((7+5)-9)*8,103037:(10-8)*(7+5),124729:((10-7)*5)+9,157267:((7/5)*10)+10,75449:(8*5)-(8+8),91333:(9*8)/(8-5),115159:((8+5)-10)*8,212773:(10+10)+(9-5),28561:(6+6)+(6+6),41743:(8-6)*(6+6),50531:(6*6)/(9/6),63713:(10*6)-(6*6),66079:(9-7)*(6+6),83317:((10-7)*6)+6,61009:(8*6)/(8-6),73853:((6+6)-9)*8,93119:(10-8)*(6+6),112723:((9-6)*10)-6,108953:((7+7)-10)*6,96577:(8*6)/(9-7),121771:((7+6)-10)*8,116909:(7*6)-(9+9),185861:((10-7)*10)-6,89167:((8-6)*8)+8,107939:(9*8)-(8*6),136097:(8*6)/(10-8),130663:(9+9)*(8/6),164749:((10-8)*9)+6,199433:((9/6)*10)+9,317057:(10+10)+(10-6),192763:((9-7)*7)+10,141151:((9-7)*8)+8,177973:(10*8)-(8*7),215441:(9*8)/(10-7),271643:((10-8)*7)+10,198911:((10-8)*8)+8', ',') AS kv
)
SELECT c.id, c1, c2, c3, c4, result
FROM poker24.cards c LEFT JOIN a a ON a.i =
( CASE c1 WHEN 1 THEN 2 WHEN 2 THEN 3 WHEN 3 THEN 5 WHEN 4 THEN 7 WHEN 5 THEN 11 WHEN 6 THEN 13 WHEN 7 THEN 17 WHEN 8 THEN 19 WHEN 9 THEN 23 WHEN 10 THEN 29 END
* CASE c2 WHEN 1 THEN 2 WHEN 2 THEN 3 WHEN 3 THEN 5 WHEN 4 THEN 7 WHEN 5 THEN 11 WHEN 6 THEN 13 WHEN 7 THEN 17 WHEN 8 THEN 19 WHEN 9 THEN 23 WHEN 10 THEN 29 END
* CASE c3 WHEN 1 THEN 2 WHEN 2 THEN 3 WHEN 3 THEN 5 WHEN 4 THEN 7 WHEN 5 THEN 11 WHEN 6 THEN 13 WHEN 7 THEN 17 WHEN 8 THEN 19 WHEN 9 THEN 23 WHEN 10 THEN 29 END
* CASE c4 WHEN 1 THEN 2 WHEN 2 THEN 3 WHEN 3 THEN 5 WHEN 4 THEN 7 WHEN 5 THEN 11 WHEN 6 THEN 13 WHEN 7 THEN 17 WHEN 8 THEN 19 WHEN 9 THEN 23 WHEN 10 THEN 29 END);

当然，这里的字符串长度超过了 10000： 10896 个。我们可以用一些手段来压缩，比如把这个巨长的 CASE 弄成一个 inline 函数，然后再把主键从十进制数字字面值换成十六进制，其实长度就在 10KB 以内了。 不过规则禁止我们使用存储过程，这就要想其他办法了。主要就是如何压缩中间那个长字符串。

压缩优化

当然，这里的字符串长度超过了 10000： 10896 个。所以需要用到额外的压缩功能，来满足题目要求。 Pigsty 原生提供了 pgsql-gzip 扩展：

CREATE EXTENSION IF NOT EXISTS gzip;

然后我们把上面的结果表压缩一下，10018个字符压缩到 7796 个，总长度 8796，满足题目要求

WITH a(i, result) AS (SELECT (split_part(kv, ':', 1))::INTEGER AS i, split_part(kv, ':', 2) AS result
FROM regexp_split_to_table(encode(gunzip('\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escape'), ',') AS kv)
SELECT c.id, c1, c2, c3, c4, result FROM poker24.cards c LEFT JOIN a a ON a.i =
(CASE c1 WHEN 1 THEN 2 WHEN 2 THEN 3 WHEN 3 THEN 5 WHEN 4 THEN 7 WHEN 5 THEN 11 WHEN 6 THEN 13 WHEN 7 THEN 17 WHEN 8 THEN 19 WHEN 9 THEN 23 WHEN 10 THEN 29 END
*CASE c2 WHEN 1 THEN 2 WHEN 2 THEN 3 WHEN 3 THEN 5 WHEN 4 THEN 7 WHEN 5 THEN 11 WHEN 6 THEN 13 WHEN 7 THEN 17 WHEN 8 THEN 19 WHEN 9 THEN 23 WHEN 10 THEN 29 END
*CASE c3 WHEN 1 THEN 2 WHEN 2 THEN 3 WHEN 3 THEN 5 WHEN 4 THEN 7 WHEN 5 THEN 11 WHEN 6 THEN 13 WHEN 7 THEN 17 WHEN 8 THEN 19 WHEN 9 THEN 23 WHEN 10 THEN 29 END
*CASE c4 WHEN 1 THEN 2 WHEN 2 THEN 3 WHEN 3 THEN 5 WHEN 4 THEN 7 WHEN 5 THEN 11 WHEN 6 THEN 13 WHEN 7 THEN 17 WHEN 8 THEN 19 WHEN 9 THEN 23 WHEN 10 THEN 29 END);

结果

在本地 M1 Macbook Pro 上单核执行时间大约是 0.58 秒，比第一名 0.67s 稍微快一点。

当然，因为 NineData 上面那个 PostgreSQL 没有 gzip 扩展，所以我也没用他们的平台（4c 32G）去提交成绩。

 Merge Right Join  (cost=118104.17..768224.17 rows=5000000 width=68) (actual time=457.485..555.265 rows=1000000 loops=1)
   Merge Cond: (((split_part(kv.kv, ':'::text, 1))::integer) = ((((CASE c.c1 WHEN '1'::double precision THEN 2 WHEN '2'::double precision THEN 3 WHEN '3'::double precision THEN 5 WHEN '4'::double precision THEN 7 WHEN '5'::double precision THEN 11 WHEN '6'::double precision THEN 13 WHEN '7'::double precision THEN 17 WHEN '8'::double precision THEN 19 WHEN '9'::double precision THEN 23 WHEN '10'::double precision THEN 29 ELSE NULL::integer END * CASE c.c2 WHEN '1'::double precision THEN 2 WHEN '2'::double precision THEN 3 WHEN '3'::double precision THEN 5 WHEN '4'::double precision THEN 7 WHEN '5'::double precision THEN 11 WHEN '6'::double precision THEN 13 WHEN '7'::double precision THEN 17 WHEN '8'::double precision THEN 19 WHEN '9'::double precision THEN 23 WHEN '10'::double precision THEN 29 ELSE NULL::integer END) * CASE c.c3 WHEN '1'::double precision THEN 2 WHEN '2'::double precision THEN 3 WHEN '3'::double precision THEN 5 WHEN '4'::double precision THEN 7 WHEN '5'::double precision THEN 11 WHEN '6'::double precision THEN 13 WHEN '7'::double precision THEN 17 WHEN '8'::double precision THEN 19 WHEN '9'::double precision THEN 23 WHEN '10'::double precision THEN 29 ELSE NULL::integer END) * CASE c.c4 WHEN '1'::double precision THEN 2 WHEN '2'::double precision THEN 3 WHEN '3'::double precision THEN 5 WHEN '4'::double precision THEN 7 WHEN '5'::double precision THEN 11 WHEN '6'::double precision THEN 13 WHEN '7'::double precision THEN 17 WHEN '8'::double precision THEN 19 WHEN '9'::double precision THEN 23 WHEN '10'::double precision THEN 29 ELSE NULL::integer END)))
   ->  Sort  (cost=62.33..64.83 rows=1000 width=64) (actual time=0.851..0.872 rows=566 loops=1)
         Sort Key: ((split_part(kv.kv, ':'::text, 1))::integer)
         Sort Method: quicksort  Memory: 59kB
         ->  Function Scan on regexp_split_to_table kv  (cost=0.00..12.50 rows=1000 width=64) (actual time=0.491..0.654 rows=566 loops=1)
   ->  Sort  (cost=118041.84..120541.84 rows=1000000 width=36) (actual time=456.629..494.693 rows=1000000 loops=1)
         Sort Key: ((((CASE c.c1 WHEN '1'::double precision THEN 2 WHEN '2'::double precision THEN 3 WHEN '3'::double precision THEN 5 WHEN '4'::double precision THEN 7 WHEN '5'::double precision THEN 11 WHEN '6'::double precision THEN 13 WHEN '7'::double precision THEN 17 WHEN '8'::double precision THEN 19 WHEN '9'::double precision THEN 23 WHEN '10'::double precision THEN 29 ELSE NULL::integer END * CASE c.c2 WHEN '1'::double precision THEN 2 WHEN '2'::double precision THEN 3 WHEN '3'::double precision THEN 5 WHEN '4'::double precision THEN 7 WHEN '5'::double precision THEN 11 WHEN '6'::double precision THEN 13 WHEN '7'::double precision THEN 17 WHEN '8'::double precision THEN 19 WHEN '9'::double precision THEN 23 WHEN '10'::double precision THEN 29 ELSE NULL::integer END) * CASE c.c3 WHEN '1'::double precision THEN 2 WHEN '2'::double precision THEN 3 WHEN '3'::double precision THEN 5 WHEN '4'::double precision THEN 7 WHEN '5'::double precision THEN 11 WHEN '6'::double precision THEN 13 WHEN '7'::double precision THEN 17 WHEN '8'::double precision THEN 19 WHEN '9'::double precision THEN 23 WHEN '10'::double precision THEN 29 ELSE NULL::integer END) * CASE c.c4 WHEN '1'::double precision THEN 2 WHEN '2'::double precision THEN 3 WHEN '3'::double precision THEN 5 WHEN '4'::double precision THEN 7 WHEN '5'::double precision THEN 11 WHEN '6'::double precision THEN 13 WHEN '7'::double precision THEN 17 WHEN '8'::double precision THEN 19 WHEN '9'::double precision THEN 23 WHEN '10'::double precision THEN 29 ELSE NULL::integer END))
         Sort Method: external sort  Disk: 56760kB
         ->  Seq Scan on cards c  (cost=0.00..18384.00 rows=1000000 width=36) (actual time=0.028..213.760 rows=1000000 loops=1)
 Planning Time: 0.363 ms
 Execution Time: 581.782 ms

以上就是使用 PostgreSQL 一条SQL计算扑克牌24点的解法。

其实，如果在用上并行优化也许还能再快点，然后 PostgreSQL 还有一种其他数据库做不到的解法。那就是直接把这个查表动作封装成一个扩展，然后用C语言直接暴露存储过程给 SQL 调用。这样就能把这个计算过程优化到极致了。当然，这种我们也懒得折腾了。

6 - DB-Engine 数据库热度趋势分析

分析 DB-Engine 上的数据库管理系统，查阅其流行度变迁。

概览

GitHub 仓库地址：https://github.com/Vonng/pigsty-app/tree/master/db

在线Demo地址：https://demo.pigsty.cc/d/db-engine

7 - StackOverflow 全球开发者调研

分析 StackOverflow 最近七年全球开发者调研数据中关于数据库的部分

概览

GitHub 仓库地址：https://github.com/Vonng/pigsty-app/tree/master/db

在线Demo地址：https://demo.pigsty.cc/d/sf-survey