Lately, Parquet has grow to be an ordinary format for information storage in Large Knowledge ecosystems. Its column-oriented format gives a number of benefits:
- Sooner question execution when solely a subset of columns is being processed
- Fast calculation of statistics throughout all information
- Decreased storage quantity due to environment friendly compression
When mixed with storage frameworks like Delta Lake or Apache Iceberg, it seamlessly integrates with question engines (e.g., Trino) and information warehouse compute clusters (e.g., Snowflake, BigQuery). On this article, the content material of a Parquet file is dissected utilizing primarily customary Python instruments to higher perceive its construction and the way it contributes to such performances.
Writing Parquet file(s)
To provide Parquet recordsdata, we use PyArrow, a Python binding for Apache Arrow that shops dataframes in reminiscence in columnar format. PyArrow permits fine-grained parameter tuning when writing the file. This makes PyArrow superb for Parquet manipulation (one may also merely use Pandas).
# generator.py
import pyarrow as pa
import pyarrow.parquet as pq
from faker import Faker
faux = Faker()
Faker.seed(12345)
num_records = 100
# Generate faux information
names = [fake.name() for _ in range(num_records)]
addresses = [fake.address().replace("n", ", ") for _ in range(num_records)]
birth_dates = [
fake.date_of_birth(minimum_age=67, maximum_age=75) for _ in range(num_records)
]
cities = [addr.split(", ")[1] for addr in addresses]
birth_years = [date.year for date in birth_dates]
# Solid the info to the Arrow format
name_array = pa.array(names, kind=pa.string())
address_array = pa.array(addresses, kind=pa.string())
birth_date_array = pa.array(birth_dates, kind=pa.date32())
city_array = pa.array(cities, kind=pa.string())
birth_year_array = pa.array(birth_years, kind=pa.int32())
# Create schema with non-nullable fields
schema = pa.schema(
[
pa.field("name", pa.string(), nullable=False),
pa.field("address", pa.string(), nullable=False),
pa.field("date_of_birth", pa.date32(), nullable=False),
pa.field("city", pa.string(), nullable=False),
pa.field("birth_year", pa.int32(), nullable=False),
]
)
desk = pa.Desk.from_arrays(
[name_array, address_array, birth_date_array, city_array, birth_year_array],
schema=schema,
)
print(desk)pyarrow.Desk
identify: string not null
tackle: string not null
date_of_birth: date32[day] not null
metropolis: string not null
birth_year: int32 not null
----
identify: [["Adam Bryan","Jacob Lee","Candice Martinez","Justin Thompson","Heather Rubio"]]
tackle: [["822 Jennifer Field Suite 507, Anthonyhaven, UT 98088","292 Garcia Mall, Lake Belindafurt, IN 69129","31738 Jonathan Mews Apt. 024, East Tammiestad, ND 45323","00716 Kristina Trail Suite 381, Howelltown, SC 64961","351 Christopher Expressway Suite 332, West Edward, CO 68607"]]
date_of_birth: [[1955-06-03,1950-06-24,1955-01-29,1957-02-18,1956-09-04]]
metropolis: [["Anthonyhaven","Lake Belindafurt","East Tammiestad","Howelltown","West Edward"]]
birth_year: [[1955,1950,1955,1957,1956]]The output clearly displays a columns-oriented storage, in contrast to Pandas, which often shows a standard “row-wise” desk.
How is a Parquet file saved?
Parquet recordsdata are typically saved in low cost object storage databases like S3 (AWS) or GCS (GCP) to be simply accessible by information processing pipelines. These recordsdata are often organized with a partitioning technique by leveraging listing constructions:
# generator.py
num_records = 100
# ...
# Writing the parquet recordsdata to disk
pq.write_to_dataset(
desk,
root_path='dataset',
partition_cols=['birth_year', 'city']
)If birth_year and metropolis columns are outlined as partitioning keys, PyArrow creates such a tree construction within the listing dataset:
dataset/
├─ birth_year=1949/
├─ birth_year=1950/
│ ├─ metropolis=Aaronbury/
│ │ ├─ 828d313a915a43559f3111ee8d8e6c1a-0.parquet
│ │ ├─ 828d313a915a43559f3111ee8d8e6c1a-0.parquet
│ │ ├─ …
│ ├─ metropolis=Alicialand/
│ ├─ …
├─ birth_year=1951 ├─ ...
The technique allows partition pruning: when a question filters on these columns, the engine can use folder names to learn solely the required recordsdata. Because of this the partitioning technique is essential for limiting delay, I/O, and compute sources when dealing with massive volumes of information (as has been the case for many years with conventional relational databases).
The pruning impact may be simply verified by counting the recordsdata opened by a Python script that filters the beginning yr:
# question.py
import duckdb
duckdb.sql(
"""
SELECT *
FROM read_parquet('dataset/*/*/*.parquet', hive_partitioning = true)
the place birth_year = 1949
"""
).present()> strace -e hint=open,openat,learn -f python question.py 2>&1 | grep "dataset/.*.parquet"
[pid 37] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Boxpercent201306/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 3
[pid 37] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Boxpercent201306/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 3
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Boxpercent201306/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 4
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Boxpercent203487/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 5
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Boxpercent203487/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 3
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Clarkemouth/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 4
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Clarkemouth/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 5
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=DPOpercent20APpercent2020198/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 3
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=DPOpercent20APpercent2020198/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 4
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Eastpercent20Morgan/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 5
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Eastpercent20Morgan/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 3
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=FPOpercent20AApercent2006122/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 4
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=FPOpercent20AApercent2006122/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 5
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Newpercent20Michelleport/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 3
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Newpercent20Michelleport/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 4
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Northpercent20Danielchester/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 5
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Northpercent20Danielchester/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 3
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Portpercent20Chase/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 4
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Portpercent20Chase/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 5
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Richardmouth/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 3
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Richardmouth/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 4
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Robbinsshire/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 5
[pid 39] openat(AT_FDCWD, "dataset/birth_year=1949/metropolis=Robbinsshire/e1ad1666a2144fbc94892d4ac1234c64-0.parquet", O_RDONLY) = 3Solely 23 recordsdata are learn out of 100.
Studying a uncooked Parquet file
Let’s decode a uncooked Parquet file with out specialised libraries. For simplicity, the dataset is dumped right into a single file with out compression or encoding.
# generator.py
# ...
pq.write_table(
desk,
"dataset.parquet",
use_dictionary=False,
compression="NONE",
write_statistics=True,
column_encoding=None,
)The very first thing to know is that the binary file is framed by 4 bytes whose ASCII illustration is “PAR1”. The file is corrupted if this isn’t the case.
# reader.py
with open("dataset.parquet", "rb") as file:
parquet_data = file.learn()
assert parquet_data[:4] == b"PAR1", "Not a sound parquet file"
assert parquet_data[-4:] == b"PAR1", "File footer is corrupted"As indicated within the documentation, the file is split into two components: the “row teams” containing precise information, and the footer containing metadata (schema under).
The footer
The scale of the footer is indicated within the 4 bytes previous the top marker as an unsigned integer written in “little endian” format (famous “unpack perform).
# reader.py
import struct
# ...
footer_length = struct.unpack("Footer dimension in bytes: 1088The footer data is encoded in a cross-language serialization format referred to as Apache Thrift. Utilizing a human-readable however verbose format like JSON after which translating it into binary could be much less environment friendly when it comes to reminiscence utilization. With Thrift, one can declare information constructions as follows:
struct Buyer {
1: required string identify,
2: non-obligatory i16 birthYear,
3: non-obligatory checklist pursuits
} On the premise of this declaration, Thrift can generate Python code to decode byte strings with such information construction (it additionally generates code to carry out the encoding half). The thrift file containing all the info constructions applied in a Parquet file may be downloaded right here. After having put in the thrift binary, let’s run:
thrift -r --gen py parquet.thriftThe generated Python code is positioned within the “gen-py” folder. The footer’s information construction is represented by the FileMetaData class – a Python class routinely generated from the Thrift schema. Utilizing Thrift’s Python utilities, binary information is parsed and populated into an occasion of this FileMetaData class.
# reader.py
import sys
# ...
# Add the generated lessons to the python path
sys.path.append("gen-py")
from parquet.ttypes import FileMetaData, PageHeader
from thrift.transport import TTransport
from thrift.protocol import TCompactProtocol
def read_thrift(information, thrift_instance):
"""
Learn a Thrift object from a binary buffer.
Returns the Thrift object and the variety of bytes learn.
"""
transport = TTransport.TMemoryBuffer(information)
protocol = TCompactProtocol.TCompactProtocol(transport)
thrift_instance.learn(protocol)
return thrift_instance, transport._buffer.inform()
# The variety of bytes learn is just not used for now
file_metadata_thrift, _ = read_thrift(footer_data, FileMetaData())
print(f"Variety of rows in the entire file: {file_metadata_thrift.num_rows}")
print(f"Variety of row teams: {len(file_metadata_thrift.row_groups)}")
Variety of rows in the entire file: 100
Variety of row teams: 1The footer incorporates in depth details about the file’s construction and content material. As an illustration, it precisely tracks the variety of rows within the generated dataframe. These rows are all contained inside a single “row group.” However what’s a “row group?”
Row teams
Not like purely column-oriented codecs, Parquet employs a hybrid method. Earlier than writing column blocks, the dataframe is first partitioned vertically into row teams (the parquet file we generated is simply too small to be cut up in a number of row teams).
This hybrid construction gives a number of benefits:
Parquet calculates statistics (akin to min/max values) for every column inside every row group. These statistics are essential for question optimization, permitting question engines to skip complete row teams that don’t match filtering standards. For instance, if a question filters for birth_year > 1955 and a row group’s most beginning yr is 1954, the engine can effectively skip that complete information part. This optimisation is known as “predicate pushdown”. Parquet additionally shops different helpful statistics like distinct worth counts and null counts.
# reader.py
# ...
first_row_group = file_metadata_thrift.row_groups[0]
birth_year_column = first_row_group.columns[4]
min_stat_bytes = birth_year_column.meta_data.statistics.min
max_stat_bytes = birth_year_column.meta_data.statistics.max
min_year = struct.unpack("The beginning yr vary is between 1949 and 1958- Row teams allow parallel processing of information (notably helpful for frameworks like Apache Spark). The scale of those row teams may be configured based mostly on the computing sources obtainable (utilizing the
row_group_sizeproperty in performwrite_tablewhen utilizing PyArrow).
# generator.py
# ...
pq.write_table(
desk,
"dataset.parquet",
row_group_size=100,
)
# /! Maintain the default worth of "row_group_size" for the following components- Even when this isn’t the first goal of a column format, Parquet’s hybrid construction maintains cheap efficiency when reconstructing full rows. With out row teams, rebuilding a complete row may require scanning everything of every column which might be extraordinarily inefficient for big recordsdata.
Knowledge Pages
The smallest substructure of a Parquet file is the web page. It incorporates a sequence of values from the identical column and, subsequently, of the identical kind. The selection of web page dimension is the results of a trade-off:
- Bigger pages imply much less metadata to retailer and browse, which is perfect for queries with minimal filtering.
- Smaller pages cut back the quantity of pointless information learn, which is healthier when queries goal small, scattered information ranges.
Now let’s decode the contents of the primary web page of the column devoted to addresses whose location may be discovered within the footer (given by the data_page_offset attribute of the fitting ColumnMetaData) . Every web page is preceded by a Thrift PageHeader object containing some metadata. The offset truly factors to a Thrift binary illustration of the web page metadata that precedes the web page itself. The Thrift class is known as a PageHeader and can be discovered within the gen-py listing.
💡 Between the PageHeader and the precise values contained throughout the web page, there could also be just a few bytes devoted to implementing the Dremel format, which permits encoding nested information constructions. Since our information has a daily tabular format and the values aren’t nullable, these bytes are skipped when writing the file (https://parquet.apache.org/docs/file-format/data-pages/).
# reader.py
# ...
address_column = first_row_group.columns[1]
column_start = address_column.meta_data.data_page_offset
column_end = column_start + address_column.meta_data.total_compressed_size
column_content = parquet_data[column_start:column_end]
page_thrift, page_header_size = read_thrift(column_content, PageHeader())
page_content = column_content[
page_header_size : (page_header_size + page_thrift.compressed_page_size)
]
print(column_content[:100])b'6x00x00x00481 Mata Squares Suite 260, Lake Rachelville, KY 874642x00x00x00671 Barker Crossing Suite 390, Mooreto'The generated values lastly seem, in plain textual content and never encoded (as specified when writing the Parquet file). Nonetheless, to optimize the columnar format, it is strongly recommended to make use of one of many following encoding algorithms: dictionary encoding, run size encoding (RLE), or delta encoding (the latter being reserved for int32 and int64 varieties), adopted by compression utilizing gzip or snappy (obtainable codecs are listed right here). Since encoded pages comprise comparable values (all addresses, all decimal numbers, and many others.), compression ratios may be notably advantageous.
As documented within the specification, when character strings (BYTE_ARRAY) aren’t encoded, every worth is preceded by its dimension represented as a 4-byte integer. This may be noticed within the earlier output:
To learn all of the values (for instance, the primary 10), the loop is quite easy:
idx = 0
for _ in vary(10):
str_size = struct.unpack("481 Mata Squares Suite 260, Lake Rachelville, KY 87464
671 Barker Crossing Suite 390, Mooretown, MI 21488
62459 Jordan Knoll Apt. 970, Emilyfort, DC 80068
948 Victor Sq. Apt. 753, Braybury, RI 67113
365 Edward Place Apt. 162, Calebborough, AL 13037
894 Reed Lock, New Davidmouth, NV 84612
24082 Allison Squares Suite 345, North Sharonberg, WY 97642
00266 Johnson Drives, South Lori, MI 98513
15255 Kelly Plains, Richardmouth, GA 33438
260 Thomas Glens, Port Gabriela, OH 96758And there we have now it! We’ve got efficiently recreated, in a quite simple means, how a specialised library would learn a Parquet file. By understanding its constructing blocks together with headers, footers, row teams, and information pages, we will higher recognize how options like predicate pushdown and partition pruning ship such spectacular efficiency advantages in data-intensive environments. I’m satisfied understanding how Parquet works underneath the hood helps making higher selections about storage methods, compression decisions, and efficiency optimization.
All of the code used on this article is obtainable on my GitHub repository at https://github.com/kili-mandjaro/anatomy-parquet, the place you may discover extra examples and experiment with completely different Parquet file configurations.
Whether or not you might be constructing information pipelines, optimizing question efficiency, or just inquisitive about information storage codecs, I hope this deep dive into Parquet’s internal constructions has offered helpful insights to your Knowledge Engineering journey.
All photos are by the creator.
