设置
python -m venv .venv
source .venv/bin/activate
pip install "chdb>=2.0.2"
pip install ipython
ipython 运行本指南其余部分中的命令，你可以通过运行以下命令启动它：
ipython
探索 Parquet 元数据
chDB：
import chdb
ParquetMetadata 输入格式，让它返回 Parquet 元数据，而不是文件内容。
我们使用 DESCRIBE 子句来查看使用这种格式时会返回哪些字段：
query = """
DESCRIBE s3(
'https://datasets-documentation.s3.eu-west-3.amazonaws.com/amazon_reviews/amazon_reviews_2015.snappy.parquet',
ParquetMetadata
)
SETTINGS describe_compact_output=1
"""
chdb.query(query, 'TabSeparated')
num_columns UInt64
num_rows UInt64
num_row_groups UInt64
format_version String
metadata_size UInt64
total_uncompressed_size UInt64
total_compressed_size UInt64
columns Array(Tuple(name String, path String, max_definition_level UInt64, max_repetition_level UInt64, physical_type String, logical_type String, compression String, total_uncompressed_size UInt64, total_compressed_size UInt64, space_saved String, encodings Array(String)))
row_groups Array(Tuple(num_columns UInt64, num_rows UInt64, total_uncompressed_size UInt64, total_compressed_size UInt64, columns Array(Tuple(name String, path String, total_compressed_size UInt64, total_uncompressed_size UInt64, have_statistics Bool, statistics Tuple(num_values Nullable(UInt64), null_count Nullable(UInt64), distinct_count Nullable(UInt64), min Nullable(String), max Nullable(String))))))
columns 和 row_groups 都是包含许多属性的元组数组，因此暂时先不看它们。
query = """
SELECT * EXCEPT(columns, row_groups)
FROM s3(
'https://datasets-documentation.s3.eu-west-3.amazonaws.com/amazon_reviews/amazon_reviews_2015.snappy.parquet',
ParquetMetadata
)
"""
chdb.query(query, 'Vertical')
Row 1:
──────
num_columns: 15
num_rows: 41905631
num_row_groups: 42
format_version: 2.6
metadata_size: 79730
total_uncompressed_size: 14615827169
total_compressed_size: 9272262304
query = """
WITH rowGroups AS (
SELECT rg
FROM s3(
'https://datasets-documentation.s3.eu-west-3.amazonaws.com/amazon_reviews/amazon_reviews_2015.snappy.parquet',
ParquetMetadata
)
ARRAY JOIN row_groups AS rg
LIMIT 1
)
SELECT tupleElement(c, 'name') AS name, tupleElement(c, 'total_compressed_size') AS total_compressed_size,
tupleElement(c, 'total_uncompressed_size') AS total_uncompressed_size,
tupleElement(tupleElement(c, 'statistics'), 'min') AS min,
tupleElement(tupleElement(c, 'statistics'), 'max') AS max
FROM rowGroups
ARRAY JOIN tupleElement(rg, 'columns') AS c
"""
chdb.query(query, 'DataFrame')
name total_compressed_size total_uncompressed_size min max
0 review_date 493 646 16455 16472
1 marketplace 66 64 US US
2 customer_id 5207967 7997207 10049 53096413
3 review_id 14748425 17991290 R10004U8OQDOGE RZZZUTBAV1RYI
4 product_id 8003456 13969668 0000032050 BT00DDVMVQ
5 product_parent 5758251 7974737 645 999999730
6 product_title 41068525 63355320 ! Small S 1pc Black 1pc Navy (Blue) Replacemen... 🌴 Vacation On The Beach
7 product_category 1726 1815 Apparel Pet Products
8 star_rating 369036 374046 1 5
9 helpful_votes 538940 1022990 0 3440
10 total_votes 610902 1080520 0 3619
11 vine 11426 125999 0 1
12 verified_purchase 102634 125999 0 1
13 review_headline 16538189 27634740 🤹🏽♂️🎤Great product. Practice makes perfect. D...
14 review_body 145886383 232457911 🚅 +🐧=💥 😀
查询 Parquet 文件
ParquetMetadata，然后例如统计所有评论中最常见的 star_rating：
query = """
SELECT star_rating, count() AS count, formatReadableQuantity(count)
FROM s3(
'https://datasets-documentation.s3.eu-west-3.amazonaws.com/amazon_reviews/amazon_reviews_2015.snappy.parquet'
)
GROUP BY ALL
ORDER BY star_rating
"""
chdb.query(query, 'DataFrame')
star_rating count formatReadableQuantity(count())
0 1 3253070 3.25 million
1 2 1865322 1.87 million
2 3 3130345 3.13 million
3 4 6578230 6.58 million
4 5 27078664 27.08 million