Amazon Customer Review

[Edit this page](https://github.com/ClickHouse/clickhouse-docs/blob/main/docs/getting-started/example-datasets/amazon-reviews.md)

This dataset contains over 150M customer reviews of Amazon products. The data is in snappy-compressed Parquet files in AWS S3 that total 49GB in size (compressed). Let's walk through the steps to insert it into ClickHouse.

Note

The queries below were executed on a **Production** instance of ClickHouse Cloud. For more information see
["Playground specifications"](/docs/getting-started/playground#specifications).

## Loading the dataset[​](#loading-the-dataset "Direct link to Loading the dataset")

1. Without inserting the data into ClickHouse, we can query it in place. Let's grab some rows, so we can see what they look like:

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

The rows look like:

```
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. Let's define a new `MergeTree` table named `amazon_reviews` to store this data in ClickHouse:

```
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. The following `INSERT` command uses the `s3Cluster` table function, which allows the processing of multiple S3 files in parallel using all the nodes of your cluster. We also use a wildcard to insert any file that starts with the name `https://datasets-documentation.s3.eu-west-3.amazonaws.com/amazon_reviews/amazon_reviews_*.snappy.parquet`:

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

Tip

In ClickHouse Cloud, the name of the cluster is `default`. Change `default` to the name of your cluster...or use the `s3` table function (instead of `s3Cluster`) if you don't have a cluster.

5. That query doesn't take long - averaging about 300,000 rows per second. Within 5 minutes or so you should see all the rows inserted:

```
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[​](#example-queries "Direct link to 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
```

Note

This query is using a [projection](/docs/data-modeling/projections) 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](https://datalemur.com/questions/sql-avg-review-ratings)!):

```
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;
```

- [Loading the dataset](#loading-the-dataset)- [Example queries](#example-queries)

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