> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-detect-table-modification.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# How to query Parquet files
> Learn how to query Parquet files with chDB.
A lot of the world's data lives in Amazon S3 buckets.
In this guide, we'll learn how to query that data using chDB.
Setup
Let's first create a virtual environment:
```bash theme={null}
python -m venv .venv
source .venv/bin/activate
```
And now we'll install chDB.
Make sure you have version 2.0.2 or higher:
```bash theme={null}
pip install "chdb>=2.0.2"
```
And now we're going to install IPython:
```bash theme={null}
pip install ipython
```
We're going to use `ipython` to run the commands in the rest of the guide, which you can launch by running:
```bash theme={null}
ipython
```
You can also use the code in a Python script or in your favorite notebook.
We're going to explore a Parquet file from the [Amazon reviews](/get-started/sample-datasets/amazon-reviews) dataset.
But first, let's install `chDB`:
```python theme={null}
import chdb
```
When querying Parquet files, we can use the [`ParquetMetadata`](/reference/formats/Parquet/ParquetMetadata) input format to have it return Parquet metadata rather than the content of the file.
Let's use the `DESCRIBE` clause to see the fields returned when we use this format:
```python theme={null}
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')
```
```text theme={null}
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))))))
```
Let's have now have a look at the metadata for this file.
`columns` and `row_groups` both contain arrays of tuples containing many properties, so we'll exclude those for now.
```python theme={null}
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')
```
```text theme={null}
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
```
From this output, we learn that this Parquet file has over 40 million rows, split across 42 row groups, with 15 columns of data per row.
A row group is a logical horizontal partitioning of the data into rows.
Each row group has associated metadata and querying tools can make use of that metadata to efficiently query the file.
Let's take a look at one of the row groups:
```python theme={null}
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')
```
```text theme={null}
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 🚅 +🐧=💥 😀
```
Querying Parquet files
Next, let's query the contents of the file.
We can do this by adjusting the above query to remove `ParquetMetadata` and then, say, compute the most popular `star_rating` across all reviews:
```python theme={null}
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')
```
```text theme={null}
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
```
Interestingly, there are more 5 star reviews than all the other ratings combined!
It looks like people like the products on Amazon or, if they don't, they just don't submit a rating.