Amazon 客户评论

该数据集包含超过 1.5 亿条 Amazon 商品的客户评论。数据以存储在 AWS S3 中的 snappy 压缩 Parquet 文件形式提供，压缩后总大小为 49GB。下面我们逐步演示如何将其导入 ClickHouse。

注意

下面的查询是在 Production 环境的 ClickHouse Cloud 实例上执行的。更多信息请参阅 "Playground 规格说明"。

加载数据集

1. 在不将数据插入 ClickHouse 的情况下，我们可以直接在原处对其进行查询。先取出几行数据，看看它们的样子：

SELECT *
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/amazon_reviews/amazon_reviews_2015.snappy.parquet')
LIMIT 3

这些行如下所示：

Row 1:
──────
review_date:       16462
marketplace:       US
customer_id:       25444946 -- 25.44 million
review_id:         R146L9MMZYG0WA
product_id:        B00NV85102
product_parent:    908181913 -- 908.18 million
product_title:     XIKEZAN iPhone 6 Plus 5.5 inch Waterproof Case, Shockproof Dirtproof Snowproof Full Body Skin Case Protective Cover with Hand Strap & Headphone Adapter & Kickstand
product_category:  Wireless
star_rating:       4
helpful_votes:     0
total_votes:       0
vine:              false
verified_purchase: true
review_headline:   case is sturdy and protects as I want
review_body:       I won't count on the waterproof part (I took off the rubber seals at the bottom because the got on my nerves). But the case is sturdy and protects as I want.

Row 2:
──────
review_date:       16462
marketplace:       US
customer_id:       1974568 -- 1.97 million
review_id:         R2LXDXT293LG1T
product_id:        B00OTFZ23M
product_parent:    951208259 -- 951.21 million
product_title:     Season.C Chicago Bulls Marilyn Monroe No.1 Hard Back Case Cover for Samsung Galaxy S5 i9600
product_category:  Wireless
star_rating:       1
helpful_votes:     0
total_votes:       0
vine:              false
verified_purchase: true
review_headline:   One Star
review_body:       Cant use the case because its big for the phone. Waist of money!

Row 3:
──────
review_date:       16462
marketplace:       US
customer_id:       24803564 -- 24.80 million
review_id:         R7K9U5OEIRJWR
product_id:        B00LB8C4U4
product_parent:    524588109 -- 524.59 million
product_title:     iPhone 5s Case, BUDDIBOX [Shield] Slim Dual Layer Protective Case with Kickstand for Apple iPhone 5 and 5s
product_category:  Wireless
star_rating:       4
helpful_votes:     0
total_votes:       0
vine:              false
verified_purchase: true
review_headline:   but overall this case is pretty sturdy and provides good protection for the phone
review_body:       The front piece was a little difficult to secure to the phone at first, but overall this case is pretty sturdy and provides good protection for the phone, which is what I need. I would buy this case again.

2. 让我们在 ClickHouse 中定义一个名为 amazon_reviews 的新 MergeTree 表来存储这些数据：

CREATE DATABASE amazon

CREATE TABLE amazon.amazon_reviews
(
    `review_date` Date,
    `marketplace` LowCardinality(String),
    `customer_id` UInt64,
    `review_id` String,
    `product_id` String,
    `product_parent` UInt64,
    `product_title` String,
    `product_category` LowCardinality(String),
    `star_rating` UInt8,
    `helpful_votes` UInt32,
    `total_votes` UInt32,
    `vine` Bool,
    `verified_purchase` Bool,
    `review_headline` String,
    `review_body` String,
    PROJECTION helpful_votes
    (
        SELECT *
        ORDER BY helpful_votes
    )
)
ENGINE = MergeTree
ORDER BY (review_date, product_category)

3. 下面的 INSERT 命令使用了 s3Cluster 表函数，它可以利用集群中所有节点并行处理多个 S3 文件。我们还使用通配符来插入所有名称以 https://datasets-documentation.s3.eu-west-3.amazonaws.com/amazon_reviews/amazon_reviews_*.snappy.parquet 开头的文件：

INSERT INTO amazon.amazon_reviews SELECT *
FROM s3Cluster('default', 
'https://datasets-documentation.s3.eu-west-3.amazonaws.com/amazon_reviews/amazon_reviews_*.snappy.parquet')

提示

在 ClickHouse Cloud 中，集群名称为 default。请将 default 更改为你的集群名称……或者如果你没有集群，可以使用 s3 表函数（而不是 s3Cluster）。

5. 该查询执行时间很短——平均每秒大约处理 300,000 行数据。大约 5 分钟内你就应该能看到所有行都已插入：

SELECT formatReadableQuantity(count())
FROM amazon.amazon_reviews

6. Let's see how much space our data is using:

SELECT
    disk_name,
    formatReadableSize(sum(data_compressed_bytes) AS size) AS compressed,
    formatReadableSize(sum(data_uncompressed_bytes) AS usize) AS uncompressed,
    round(usize / size, 2) AS compr_rate,
    sum(rows) AS rows,
    count() AS part_count
FROM system.parts
WHERE (active = 1) AND (table = 'amazon_reviews')
GROUP BY disk_name
ORDER BY size DESC

The original data was about 70G, but compressed in ClickHouse it takes up about 30G.

Example queries

7. Let's run some queries. Here are the top 10 most-helpful reviews in the dataset:

SELECT
    product_title,
    review_headline
FROM amazon.amazon_reviews
ORDER BY helpful_votes DESC
LIMIT 10

注意

This query is using a projection to speed up performance.

8. Here are the top 10 products in Amazon with the most reviews:

SELECT
    any(product_title),
    count()
FROM amazon.amazon_reviews
GROUP BY product_id
ORDER BY 2 DESC
LIMIT 10;

9. Here are the average review ratings per month for each product (an actual Amazon job interview question!):

SELECT
    toStartOfMonth(review_date) AS month,
    any(product_title),
    avg(star_rating) AS avg_stars
FROM amazon.amazon_reviews
GROUP BY
    month,
    product_id
ORDER BY
    month DESC,
    product_id ASC
LIMIT 20;

10. Here are the total number of votes per product category. This query is fast because product_category is in the primary key:

SELECT
    sum(total_votes),
    product_category
FROM amazon.amazon_reviews
GROUP BY product_category
ORDER BY 1 DESC

11. Let's find the products with the word "awful" occurring most frequently in the review. This is a big task - over 151M strings have to be parsed looking for a single word:

SELECT
    product_id,
    any(product_title),
    avg(star_rating),
    count() AS count
FROM amazon.amazon_reviews
WHERE position(review_body, 'awful') > 0
GROUP BY product_id
ORDER BY count DESC
LIMIT 50;

Notice the query time for such a large amount of data. The results are also a fun read!

12. We can run the same query again, except this time we search for awesome in the reviews:

SELECT 
    product_id,
    any(product_title),
    avg(star_rating),
    count() AS count
FROM amazon.amazon_reviews
WHERE position(review_body, 'awesome') > 0
GROUP BY product_id
ORDER BY count DESC
LIMIT 50;