使用ClickHouse分析Stack Overflow数据
该数据集包含在 Stack Overflow 上发生的所有 Posts、Users、Votes、Comments、Badges、PostHistory 和 PostLinks。
用户可以下载经过预处理的 Parquet 版本数据，这些数据包含截至 2024 年 4 月的每一篇帖子，或者下载最新的 XML 格式数据并加载。在历史上，Stack Overflow 定期提供这些数据更新——通常每 3 个月一次。
以下图表显示了假设 Parquet 格式的可用表的架构。
有关此数据架构的描述可以在 这里 找到。
预准备数据
我们提供这一数据的 Parquet 格式副本，最新版本截至 2024 年 4 月。尽管就行数（6000万条帖子）而言对于 ClickHouse 来说数据量较小，但该数据集包含大量文本和大的字符串列。
CREATE DATABASE stackoverflow
以下时间为位于 eu-west-2 的 96 GiB、24 vCPU ClickHouse Cloud 集群的时间。数据集位于 eu-west-3。
Posts
CREATE TABLE stackoverflow.posts
(
`Id` Int32 CODEC(Delta(4), ZSTD(1)),
`PostTypeId` Enum8('Question' = 1, 'Answer' = 2, 'Wiki' = 3, 'TagWikiExcerpt' = 4, 'TagWiki' = 5, 'ModeratorNomination' = 6, 'WikiPlaceholder' = 7, 'PrivilegeWiki' = 8),
`AcceptedAnswerId` UInt32,
`CreationDate` DateTime64(3, 'UTC'),
`Score` Int32,
`ViewCount` UInt32 CODEC(Delta(4), ZSTD(1)),
`Body` String,
`OwnerUserId` Int32,
`OwnerDisplayName` String,
`LastEditorUserId` Int32,
`LastEditorDisplayName` String,
`LastEditDate` DateTime64(3, 'UTC') CODEC(Delta(8), ZSTD(1)),
`LastActivityDate` DateTime64(3, 'UTC'),
`Title` String,
`Tags` String,
`AnswerCount` UInt16 CODEC(Delta(2), ZSTD(1)),
`CommentCount` UInt8,
`FavoriteCount` UInt8,
`ContentLicense` LowCardinality(String),
`ParentId` String,
`CommunityOwnedDate` DateTime64(3, 'UTC'),
`ClosedDate` DateTime64(3, 'UTC')
)
ENGINE = MergeTree
PARTITION BY toYear(CreationDate)
ORDER BY (PostTypeId, toDate(CreationDate), CreationDate)
INSERT INTO stackoverflow.posts SELECT * FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/stackoverflow/parquet/posts/*.parquet')
0 rows in set. Elapsed: 265.466 sec. Processed 59.82 million rows, 38.07 GB (225.34 thousand rows/s., 143.42 MB/s.)
帖子也可以按年份提供，例如 https://datasets-documentation.s3.eu-west-3.amazonaws.com/stackoverflow/parquet/posts/2020.parquet
Votes
CREATE TABLE stackoverflow.votes
(
`Id` UInt32,
`PostId` Int32,
`VoteTypeId` UInt8,
`CreationDate` DateTime64(3, 'UTC'),
`UserId` Int32,
`BountyAmount` UInt8
)
ENGINE = MergeTree
ORDER BY (VoteTypeId, CreationDate, PostId, UserId)
INSERT INTO stackoverflow.votes SELECT * FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/stackoverflow/parquet/votes/*.parquet')
0 rows in set. Elapsed: 21.605 sec. Processed 238.98 million rows, 2.13 GB (11.06 million rows/s., 98.46 MB/s.)
投票也可以按年份提供，例如 https://datasets-documentation.s3.eu-west-3.amazonaws.com/stackoverflow/parquet/votes/2020.parquet
CREATE TABLE stackoverflow.comments
(
`Id` UInt32,
`PostId` UInt32,
`Score` UInt16,
`Text` String,
`CreationDate` DateTime64(3, 'UTC'),
`UserId` Int32,
`UserDisplayName` LowCardinality(String)
)
ENGINE = MergeTree
ORDER BY CreationDate
INSERT INTO stackoverflow.comments SELECT * FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/stackoverflow/parquet/comments/*.parquet')
0 rows in set. Elapsed: 56.593 sec. Processed 90.38 million rows, 11.14 GB (1.60 million rows/s., 196.78 MB/s.)
评论也可以按年份提供，例如 https://datasets-documentation.s3.eu-west-3.amazonaws.com/stackoverflow/parquet/comments/2020.parquet
Users
CREATE TABLE stackoverflow.users
(
`Id` Int32,
`Reputation` LowCardinality(String),
`CreationDate` DateTime64(3, 'UTC') CODEC(Delta(8), ZSTD(1)),
`DisplayName` String,
`LastAccessDate` DateTime64(3, 'UTC'),
`AboutMe` String,
`Views` UInt32,
`UpVotes` UInt32,
`DownVotes` UInt32,
`WebsiteUrl` String,
`Location` LowCardinality(String),
`AccountId` Int32
)
ENGINE = MergeTree
ORDER BY (Id, CreationDate)
INSERT INTO stackoverflow.users SELECT * FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/stackoverflow/parquet/users.parquet')
0 rows in set. Elapsed: 10.988 sec. Processed 22.48 million rows, 1.36 GB (2.05 million rows/s., 124.10 MB/s.)
Badges
CREATE TABLE stackoverflow.badges
(
`Id` UInt32,
`UserId` Int32,
`Name` LowCardinality(String),
`Date` DateTime64(3, 'UTC'),
`Class` Enum8('Gold' = 1, 'Silver' = 2, 'Bronze' = 3),
`TagBased` Bool
)
ENGINE = MergeTree
ORDER BY UserId
INSERT INTO stackoverflow.badges SELECT * FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/stackoverflow/parquet/badges.parquet')
0 rows in set. Elapsed: 6.635 sec. Processed 51.29 million rows, 797.05 MB (7.73 million rows/s., 120.13 MB/s.)
PostLinks
CREATE TABLE stackoverflow.postlinks
(
`Id` UInt64,
`CreationDate` DateTime64(3, 'UTC'),
`PostId` Int32,
`RelatedPostId` Int32,
`LinkTypeId` Enum8('Linked' = 1, 'Duplicate' = 3)
)
ENGINE = MergeTree
ORDER BY (PostId, RelatedPostId)
INSERT INTO stackoverflow.postlinks SELECT * FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/stackoverflow/parquet/postlinks.parquet')
0 rows in set. Elapsed: 1.534 sec. Processed 6.55 million rows, 129.70 MB (4.27 million rows/s., 84.57 MB/s.)
PostHistory
CREATE TABLE stackoverflow.posthistory
(
`Id` UInt64,
`PostHistoryTypeId` UInt8,
`PostId` Int32,
`RevisionGUID` String,
`CreationDate` DateTime64(3, 'UTC'),
`UserId` Int32,
`Text` String,
`ContentLicense` LowCardinality(String),
`Comment` String,
`UserDisplayName` String
)
ENGINE = MergeTree
ORDER BY (CreationDate, PostId)
INSERT INTO stackoverflow.posthistory SELECT * FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/stackoverflow/parquet/posthistory/*.parquet')
0 rows in set. Elapsed: 422.795 sec. Processed 160.79 million rows, 67.08 GB (380.30 thousand rows/s., 158.67 MB/s.)
原始数据集
原始数据集以压缩的 (7zip) XML 格式可在 https://archive.org/download/stackexchange 获取 - 文件前缀为 stackoverflow.com*。
wget https://archive.org/download/stackexchange/stackoverflow.com-Badges.7z
wget https://archive.org/download/stackexchange/stackoverflow.com-Comments.7z
wget https://archive.org/download/stackexchange/stackoverflow.com-PostHistory.7z
wget https://archive.org/download/stackexchange/stackoverflow.com-PostLinks.7z
wget https://archive.org/download/stackexchange/stackoverflow.com-Posts.7z
wget https://archive.org/download/stackexchange/stackoverflow.com-Users.7z
wget https://archive.org/download/stackexchange/stackoverflow.com-Votes.7z
这些文件最大可达 35GB，下载时间约为 30 分钟，具体取决于网络连接 - 下载服务器的速度限制在约 20MB/秒。
转换为 JSON
在撰写时，ClickHouse 还不支持 XML 作为输入格式。为了将数据加载到 ClickHouse 中，我们首先将其转换为 NDJSON。
要将 XML 转换为 JSON，我们推荐使用 xq Linux 工具，这是一个用于 XML 文档的简单 jq 封装。
安装 xq 和 jq：
sudo apt install jq
pip install yq
以下步骤适用于上述任何文件。我们以 stackoverflow.com-Posts.7z 文件为例，按需修改。
使用 p7zip 提取文件。这将生成一个 XML 文件 - 在此案例中为 Posts.xml。
文件压缩比例约为4.5x。在22GB压缩后，帖子文件解压后需要大约97G。
p7zip -d stackoverflow.com-Posts.7z
以下将 XML 文件拆分为每个包含 10000 行的文件。
mkdir posts
cd posts
# the following splits the input xml file into sub files of 10000 rows
tail +3 ../Posts.xml | head -n -1 | split -l 10000 --filter='{ printf "<rows>\n"; cat - ; printf "</rows>\n"; } > $FILE' -
运行上述命令后，用户将获得一组每个包含 10000 行的文件。这确保了下一个命令的内存开销不会过大（XML 到 JSON 的转换是在内存中进行的）。
find . -maxdepth 1 -type f -exec xq -c '.rows.row[]' {} \; | sed -e 's:"@:":g' > posts_v2.json
上述命令将生成一个名为 posts.json 的文件。
使用以下命令加载到 ClickHouse 中。请注意，为 posts.json 文件指定了架构。这需要根据数据类型进行调整，以便与目标表对齐。
clickhouse local --query "SELECT * FROM file('posts.json', JSONEachRow, 'Id Int32, PostTypeId UInt8, AcceptedAnswerId UInt32, CreationDate DateTime64(3, \'UTC\'), Score Int32, ViewCount UInt32, Body String, OwnerUserId Int32, OwnerDisplayName String, LastEditorUserId Int32, LastEditorDisplayName String, LastEditDate DateTime64(3, \'UTC\'), LastActivityDate DateTime64(3, \'UTC\'), Title String, Tags String, AnswerCount UInt16, CommentCount UInt8, FavoriteCount UInt8, ContentLicense String, ParentId String, CommunityOwnedDate DateTime64(3, \'UTC\'), ClosedDate DateTime64(3, \'UTC\')') FORMAT Native" | clickhouse client --host <host> --secure --password <password> --query "INSERT INTO stackoverflow.posts_v2 FORMAT Native"
示例查询
一些简单的问题供您入门。
SELECT
arrayJoin(arrayFilter(t -> (t != ''), splitByChar('|', Tags))) AS Tags,
count() AS c
FROM stackoverflow.posts
GROUP BY Tags
ORDER BY c DESC
LIMIT 10
┌─Tags───────┬───────c─┐
│ javascript │ 2527130 │
│ python │ 2189638 │
│ java │ 1916156 │
│ c# │ 1614236 │
│ php │ 1463901 │
│ android │ 1416442 │
│ html │ 1186567 │
│ jquery │ 1034621 │
│ c++ │ 806202 │
│ css │ 803755 │
└────────────┴─────────┘
10 rows in set. Elapsed: 1.013 sec. Processed 59.82 million rows, 1.21 GB (59.07 million rows/s., 1.19 GB/s.)
Peak memory usage: 224.03 MiB.
回答最多的用户（活跃账户）
账户需要 UserId。
SELECT
any(OwnerUserId) UserId,
OwnerDisplayName,
count() AS c
FROM stackoverflow.posts WHERE OwnerDisplayName != '' AND PostTypeId='Answer' AND OwnerUserId != 0
GROUP BY OwnerDisplayName
ORDER BY c DESC
LIMIT 5
┌─UserId─┬─OwnerDisplayName─┬────c─┐
│ 22656 │ Jon Skeet │ 2727 │
│ 23354 │ Marc Gravell │ 2150 │
│ 12950 │ tvanfosson │ 1530 │
│ 3043 │ Joel Coehoorn │ 1438 │
│ 10661 │ S.Lott │ 1087 │
└────────┴──────────────────┴──────┘
5 rows in set. Elapsed: 0.154 sec. Processed 35.83 million rows, 193.39 MB (232.33 million rows/s., 1.25 GB/s.)
Peak memory usage: 206.45 MiB.
点击屋相关帖子中浏览量最多的
SELECT
Id,
Title,
ViewCount,
AnswerCount
FROM stackoverflow.posts
WHERE Title ILIKE '%ClickHouse%'
ORDER BY ViewCount DESC
LIMIT 10
┌───────Id─┬─Title────────────────────────────────────────────────────────────────────────────┬─ViewCount─┬─AnswerCount─┐
│ 52355143 │ Is it possible to delete old records from clickhouse table? │ 41462 │ 3 │
│ 37954203 │ Clickhouse Data Import │ 38735 │ 3 │
│ 37901642 │ Updating data in Clickhouse │ 36236 │ 6 │
│ 58422110 │ Pandas: How to insert dataframe into Clickhouse │ 29731 │ 4 │
│ 63621318 │ DBeaver - Clickhouse - SQL Error [159] .. Read timed out │ 27350 │ 1 │
│ 47591813 │ How to filter clickhouse table by array column contents? │ 27078 │ 2 │
│ 58728436 │ How to search the string in query with case insensitive on Clickhouse database? │ 26567 │ 3 │
│ 65316905 │ Clickhouse: DB::Exception: Memory limit (for query) exceeded │ 24899 │ 2 │
│ 49944865 │ How to add a column in clickhouse │ 24424 │ 1 │
│ 59712399 │ How to cast date Strings to DateTime format with extended parsing in ClickHouse? │ 22620 │ 1 │
└──────────┴──────────────────────────────────────────────────────────────────────────────────┴───────────┴─────────────┘
10 rows in set. Elapsed: 0.472 sec. Processed 59.82 million rows, 1.91 GB (126.63 million rows/s., 4.03 GB/s.)
Peak memory usage: 240.01 MiB.
最具争议的帖子
SELECT
Id,
Title,
UpVotes,
DownVotes,
abs(UpVotes - DownVotes) AS Controversial_ratio
FROM stackoverflow.posts
INNER JOIN
(
SELECT
PostId,
countIf(VoteTypeId = 2) AS UpVotes,
countIf(VoteTypeId = 3) AS DownVotes
FROM stackoverflow.votes
GROUP BY PostId
HAVING (UpVotes > 10) AND (DownVotes > 10)
) AS votes ON posts.Id = votes.PostId
WHERE Title != ''
ORDER BY Controversial_ratio ASC
LIMIT 3
┌───────Id─┬─Title─────────────────────────────────────────────┬─UpVotes─┬─DownVotes─┬─Controversial_ratio─┐
│ 583177 │ VB.NET Infinite For Loop │ 12 │ 12 │ 0 │
│ 9756797 │ Read console input as enumerable - one statement? │ 16 │ 16 │ 0 │
│ 13329132 │ What's the point of ARGV in Ruby? │ 22 │ 22 │ 0 │
└──────────┴───────────────────────────────────────────────────┴─────────┴───────────┴─────────────────────┘
3 rows in set. Elapsed: 4.779 sec. Processed 298.80 million rows, 3.16 GB (62.52 million rows/s., 661.05 MB/s.)
Peak memory usage: 6.05 GiB.
我们感谢 Stack Overflow 在 cc-by-sa 4.0 许可下提供此数据，承认他们的努力以及数据的原始来源，链接为 https://archive.org/details/stackexchange。
